diff --git a/.github/workflows/verify.yaml b/.github/workflows/verify.yaml new file mode 100644 index 0000000000000000000000000000000000000000..099a42e202559fc53b4c4e51cc5007e51ff2d0eb --- /dev/null +++ b/.github/workflows/verify.yaml @@ -0,0 +1,20 @@ +name: Verify + +on: + push: + branches: [ "*" ] + +jobs: + build-backend: + runs-on: ubuntu-latest + steps: + - name: Check out the repository + uses: actions/checkout@v5 + - name: Set up Python 3.10 + uses: actions/setup-python@v6 + with: + python-version: '3.10' + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt diff --git a/.gitignore b/.gitignore index 3c84df6e0271059ef711718a475470a1150b4b50..b5bb5502ece0a489653f06a0823065dc5bf15ae1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ **/.idea **/.venv -**/venv \ No newline at end of file +**/venv +**/local-run-data \ No newline at end of file diff --git a/app.py b/app.py index ff3d724f3c25540ee9622d6f87332eb337e5bad7..8660e30b70671ba2cf38d1725f433b973a0a5531 100644 --- a/app.py +++ b/app.py @@ -1,21 +1,39 @@ import datetime -import random +import os.path +import sys import uuid from os import PathLike import gradio as gr import pandas as pd +import torch from config import APP_CONFIG from data_repository import REPOSITORY_INSTANCE, ModelScoringResult from designs_submission_validations import validate_github_link, validate_user_designs +from domain_constants import SCORE_NAMES_MAP, USER_GEN_DESIGNS_COLUMNS + +sys.path.append(os.path.join(os.path.dirname(__file__), "bike_bench_internal/src/")) +from bikebench.benchmarking import benchmarking_utils + def compute_scores(user_gen_designs: pd.DataFrame) -> ModelScoringResult: + user_gen_designs = pd.DataFrame(user_gen_designs, columns=USER_GEN_DESIGNS_COLUMNS) + designs_length = len(user_gen_designs) + if designs_length < 10_000: + raise Exception(f"Too few designs to evaluate. Expected > 10,000, got {designs_length}") + data_tens = torch.tensor(user_gen_designs.values, dtype=torch.float32) + main_scores, detailed_scores, all_evaluation_scores = benchmarking_utils.evaluate(data_tens, device="cpu", + evaluate_as_aggregate=False) return ModelScoringResult( - score=random.randint(50, 5000), - scoring_time=datetime.datetime.now(), + uuid=str(uuid.uuid4()), submission_time=datetime.datetime.now(), - uuid=str(uuid.uuid4()) + design_quality=main_scores[SCORE_NAMES_MAP["design_quality"]], + diversity_dpp=main_scores[SCORE_NAMES_MAP["diversity_dpp"]], + mean_novelty=main_scores[SCORE_NAMES_MAP["mean_novelty"]], + sim_to_data_mmd=main_scores[SCORE_NAMES_MAP["sim_to_data_mmd"]], + mean_violations=main_scores[SCORE_NAMES_MAP["mean_violations"]], + binary_validity=main_scores[SCORE_NAMES_MAP["binary_validity"]], ) @@ -29,21 +47,28 @@ def process_generated_designs(github_link: str, file: PathLike[str]): return f"File uploaded successfully, uuid {scores.uuid}" -with gr.Blocks() as gradio_app: - with gr.Tab("Bike Bench Leaderboard"): - gr.Markdown("Hello beautiful people!") - gr.Dataframe(REPOSITORY_INSTANCE.get_data_to_display, label="Scores of Previous Files") - - with gr.Tab("Upload File"): - gr.Interface( - fn=process_generated_designs, - inputs=[ - gr.Textbox(label="Github Link"), - gr.File(label="Upload a file"), - ], - outputs="text", - title="Bike Bench Leaderboard", - description="Upload a file to see the result." - ) - -gradio_app.launch(debug=(not APP_CONFIG.production)) +def build_approval_app(): + pass + + +def build_app(): + with gr.Blocks() as gradio_app: + with gr.Tab("Bike Bench Leaderboard"): + gr.Markdown("Hello beautiful people!") + gr.Dataframe(REPOSITORY_INSTANCE.get_data_to_display, label="Scores of Previous Files") + + with gr.Tab("Upload File"): + gr.Interface( + fn=process_generated_designs, + inputs=[ + gr.Textbox(label="Github Link"), + gr.File(label="Upload a file"), + ], + outputs="text", + title="Bike Bench Leaderboard", + description="Upload a file to see the result." + ) + return gradio_app + + +build_app().launch(debug=(not APP_CONFIG.production)) diff --git a/bike_bench_internal/.gitignore b/bike_bench_internal/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..a04807cc03b03450661d72b6588f484f70dd0e4a --- /dev/null +++ b/bike_bench_internal/.gitignore @@ -0,0 +1,9 @@ +**/__pycache__/ +__pycache__/ +src/resources/datasets/Generative_Modeling_Datasets/ +src/resources/datasets/Predictive_Modeling_Datasets/ +src/resources/datasets/Original_BIKED_Data/ +src/resources/datasets/Real_Extended_Data/ +src/resources/datasets/Synthetic_Extended_Data/ +**/BikeCAD_17.1_configuration/ +src/bikebench/introductory_notebooks/frames diff --git a/bike_bench_internal/README.md b/bike_bench_internal/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e6a7c8a8ab04875e027e73adbe0b8405332dc291 --- /dev/null +++ b/bike_bench_internal/README.md @@ -0,0 +1 @@ +# Bike-Bench-Internal diff --git a/bike_bench_internal/benchmark_models/__init__.py b/bike_bench_internal/benchmark_models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/baseline_dataset.ipynb b/bike_bench_internal/benchmark_models/baseline_dataset.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3e853e509a873970b9d2ca71145481e8daef537c --- /dev/null +++ b/bike_bench_internal/benchmark_models/baseline_dataset.ipynb @@ -0,0 +1,193 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4505aaf9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mileva/mambaforge/envs/bike-bench-cuda/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import sys\n", + "import torch\n", + "\n", + "sys.path.append(\"../../\")\n", + "from bikebench.data_loading import data_loading\n", + "from bikebench.benchmarking import benchmarking_utils" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a0514b52", + "metadata": {}, + "outputs": [], + "source": [ + "data = data_loading.load_bike_bench_train()\n", + "device = \"cpu\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "730f34c9", + "metadata": {}, + "outputs": [], + "source": [ + "sampled_data = data.sample(n=10000, replace=True, random_state=1)\n", + "data_tens = torch.tensor(sampled_data.values, dtype=torch.float32)\n", + "torch.save(data_tens, \"results/designs/dataset.pt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b41d06a3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:08<00:00, 11.81it/s]\n" + ] + } + ], + "source": [ + "data_tens = data_tens.to(device)\n", + "main_scores, detailed_scores, all_evaluation_scores = benchmarking_utils.evaluate(data_tens, device=device, evaluate_as_aggregate=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b99d7dea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Individual Min Objective Score ↓: Usability Score 0.547352\n", + "Individual Min Objective Score ↓: Drag Force (N) 19.556438\n", + "Individual Min Objective Score ↓: Knee Angle Error (deg.) 48.090554\n", + "Individual Min Objective Score ↓: Hip Angle Error (deg.) 12.293375\n", + "Individual Min Objective Score ↓: Arm Angle Error (deg.) 12.226366\n", + " ... \n", + "Individual Mean Constraint Violation Magnitude ↓: Top tube improperly joins head tube 0.314635\n", + "Individual Mean Constraint Violation Magnitude ↓: Top tube improperly joins seat tube 1.222789\n", + "Individual Mean Constraint Violation Magnitude ↓: Down tube intersects front wheel 0.044977\n", + "Individual Mean Constraint Violation Magnitude ↓: Saddle hits top tube 0.007300\n", + "Individual Mean Constraint Violation Magnitude ↓: Saddle hits head tube 0.000000\n", + "Length: 100, dtype: float64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "detailed_scores" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "97311500", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([100, 100, 50])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_evaluation_scores.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3cc9418b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mileva/Documents/Lyle/Bike-Bench-Internal/src/bikebench/benchmarking/score_report.py:147: UserWarning: No model_colors provided; using Matplotlib cycle.\n", + " warnings.warn(\"No model_colors provided; using Matplotlib cycle.\")\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bikebench.benchmarking import score_report\n", + "\n", + "all_evaluation_scores_dict = {f\"Train Set Sampling\": all_evaluation_scores}\n", + "main_scores_dict = {f\"Train Set Sampling\": main_scores}\n", + "dashboard = score_report.ScoreReportDashboard(\n", + " requirement_scores=all_evaluation_scores_dict,\n", + " overall_scores=main_scores_dict,\n", + ")\n", + "\n", + "for m in all_evaluation_scores_dict.keys():\n", + " dashboard.show_model(m)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "220bba6d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bike-bench-cuda", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/bike_bench_internal/benchmark_models/benchmarking_utils.py b/bike_bench_internal/benchmark_models/benchmarking_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..131f9caeaf69f58e22b0aca8b72a198d06aa0219 --- /dev/null +++ b/bike_bench_internal/benchmark_models/benchmarking_utils.py @@ -0,0 +1,201 @@ +import os +import torch +import pandas as pd +from bikebench.design_evaluation.scoring import construct_scorer, MainScores, DetailedScores +from bikebench.design_evaluation.design_evaluation import get_standard_evaluations +from bikebench.conditioning import conditioning +from tqdm import trange, tqdm + + + +def get_condition_by_idx(idx=0): + rider_condition = conditioning.sample_riders(10, split="test") + use_case_condition = conditioning.sample_use_case(10, split="test") + image_embeddings = conditioning.sample_image_embedding(10, split="test") + condition = {"Rider": rider_condition[idx], "Use Case": use_case_condition[idx], "Embedding": image_embeddings[idx]} + return condition + +def get_conditions_10k(): + rider_condition = conditioning.sample_riders(10000, split="test") + use_case_condition = conditioning.sample_use_case(10000, split="test") + image_embeddings = conditioning.sample_image_embedding(10000, split="test") + conditions = {"Rider": rider_condition, "Use Case": use_case_condition, "Embedding": image_embeddings} + return conditions + +def evaluate_uncond(result_tens, name, cond_idx, data_columns, device, save=True): + + condition = get_condition_by_idx(cond_idx) + + result_dir = os.path.join("results", "unconditional", f"cond_{cond_idx}", name) + os.makedirs(result_dir, exist_ok=True) + + main_scorer = construct_scorer(MainScores, get_standard_evaluations(device), data_columns) + detailed_scorer = construct_scorer(DetailedScores, get_standard_evaluations(device), data_columns) + + main_scores = main_scorer(result_tens, condition) + + detailed_scores = detailed_scorer(result_tens, condition) + + if save: + result_tens = result_tens.cpu() + torch.save(result_tens, os.path.join(result_dir, "result_tens.pt")) + main_scores.to_csv(os.path.join(result_dir, "main_scores.csv"), index_label=False, header=False) + detailed_scores.to_csv(os.path.join(result_dir, "detailed_scores.csv"), index_label=False, header=False) + return main_scores, detailed_scores + +def evaluate_cond(result_tens, name, data_columns, device, save=True): + condition = get_conditions_10k() + + condition = {"Rider": condition["Rider"], "Use Case": condition["Use Case"], "Embedding": condition["Embedding"]} + + result_dir = os.path.join("results", "conditional", name) + os.makedirs(result_dir, exist_ok=True) + + main_scorer = construct_scorer(MainScores, get_standard_evaluations(device), data_columns, device) + detailed_scorer = construct_scorer(DetailedScores, get_standard_evaluations(device), data_columns, device) + + main_scores = main_scorer(result_tens, condition) + detailed_scores = detailed_scorer(result_tens, condition) + + if save: + result_tens = result_tens.cpu() + torch.save(result_tens, os.path.join(result_dir, "result_tens.pt")) + main_scores.to_csv(os.path.join(result_dir, "main_scores.csv"), index_label=False, header=False) + detailed_scores.to_csv(os.path.join(result_dir, "detailed_scores.csv"), index_label=False, header=False) + + return main_scores, detailed_scores + + +def create_score_report_conditional(): + """ + Looks through the results folder and creates a score report for each conditional result. + """ + all_scores = [] + result_dir = os.path.join("results", "conditional") + for name in os.listdir(result_dir): + if os.path.isdir(os.path.join(result_dir, name)): + main_scores = pd.read_csv(os.path.join(result_dir, name, "main_scores.csv"), header=None) + main_scores.columns = ["Metric", "Score"] + main_scores["Model"] = name + all_scores.append(main_scores) + all_scores = pd.concat(all_scores, axis=0) + #make metric names the three columns, make models the rows + all_scores = all_scores.pivot(index="Model", columns="Metric", values="Score") + #drop the index name and the column name + all_scores.columns.name = None + all_scores.index.name = None + + return all_scores + +def create_score_report_unconditional(): + """ + Looks through the results folder and creates a score report for each unconditional result. + """ + all_scores = [] + result_dir = os.path.join("results", "unconditional") + for i in range(10): + c_dir = os.path.join(result_dir, f"cond_{i}") + for name in os.listdir(c_dir): + dirname = os.path.join(c_dir, name) + if os.path.isdir(dirname): + main_scores = pd.read_csv(os.path.join(dirname, "main_scores.csv"), header=None) + main_scores.columns = ["Metric", "Score"] + main_scores["Model"] = name + main_scores["Condition"] = i + all_scores.append(main_scores) + all_scores = pd.concat(all_scores, axis=0) + #average over condition + all_scores = all_scores.groupby(["Model", "Metric"]).mean().reset_index() + #make metric names the three columns, make models the rows + all_scores = all_scores.pivot(index="Model", columns="Metric", values="Score") + #drop the index name and the column name + all_scores.columns.name = None + all_scores.index.name = None + return all_scores + + +def rescore_unconditional(data_columns, device, cond_idxs = None, model_names = None, results_root="results/unconditional"): + """ + Recompute main and detailed scores for all unconditional results. + Overwrites only the CSV score files, leaves result_tens.pt untouched. + """ + + evals = get_standard_evaluations(device) + main_scorer = construct_scorer(MainScores, evals, data_columns, device) + detailed_scorer = construct_scorer(DetailedScores, evals, data_columns, device) + device = torch.device(device) + if cond_idxs is None: + cond_idxs = range(10) + for cond_idx in tqdm(cond_idxs): + cond_dir = os.path.join(results_root, f"cond_{cond_idx}") + if not os.path.isdir(cond_dir): + continue + # fetch the one shared condition for this index + condition = get_condition_by_idx(cond_idx) + + if model_names is not None: + models = model_names + else: + models = os.listdir(cond_dir) + + for model_name in models: + model_dir = os.path.join(cond_dir, model_name) + tensor_path = os.path.join(model_dir, "result_tens.pt") + if not os.path.isdir(model_dir) or not os.path.isfile(tensor_path): + continue + + # load results + result_tens = torch.load(tensor_path, map_location=device) + + # rescore + main_scores = main_scorer(result_tens, condition) + detailed_scores = detailed_scorer(result_tens, condition) + + # overwrite only the CSVs + main_scores.to_csv( + os.path.join(model_dir, "main_scores.csv"), header=False + ) + detailed_scores.to_csv( + os.path.join(model_dir, "detailed_scores.csv"), header=False + ) + + +def rescore_conditional(data_columns, device, model_names, results_root="results/conditional"): + """ + Recompute main and detailed scores for all conditional results. + Overwrites only the CSV score files, leaves result_tens.pt untouched. + """ + device = torch.device(device) + # fetch the full 10k‐point condition set once + condition = get_conditions_10k() + + # build scorers + evals = get_standard_evaluations(device) + main_scorer = construct_scorer(MainScores, evals, data_columns, device) + detailed_scorer = construct_scorer(DetailedScores, evals, data_columns, device) + + + if model_names is not None: + models = model_names + else: + models = os.listdir(results_root) + for model_name in models: + model_dir = os.path.join(results_root, model_name) + tensor_path = os.path.join(model_dir, "result_tens.pt") + if not os.path.isdir(model_dir) or not os.path.isfile(tensor_path): + continue + + # load results + result_tens = torch.load(tensor_path, map_location=device) + + # rescore + main_scores = main_scorer(result_tens, condition) + detailed_scores = detailed_scorer(result_tens, condition) + + # overwrite only the CSVs + main_scores.to_csv( + os.path.join(model_dir, "main_scores.csv"), header=False + ) + detailed_scores.to_csv( + os.path.join(model_dir, "detailed_scores.csv"), header=False + ) \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/generative_modeling_utils.py b/bike_bench_internal/benchmark_models/generative_modeling_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..503f739d9d3ca68835551a9d60352eefe9cf3fa9 --- /dev/null +++ b/bike_bench_internal/benchmark_models/generative_modeling_utils.py @@ -0,0 +1,641 @@ +from tqdm import tqdm, trange +import torch +import torch.nn as nn +from torch.utils.data import DataLoader, Dataset, TensorDataset +import numpy as np +from torch.autograd import grad +from diffusers import DDPMScheduler +from torch.nn import MSELoss + + +from bikebench.conditioning import conditioning +from bikebench.design_evaluation.design_evaluation import * +from bikebench.conditioning import conditioning +from bikebench.design_evaluation import scoring + +class TorchScaler: + def __init__(self, data): + self.data = data + self.mean = torch.mean(data, dim=0) + self.std = torch.std(data, dim=0) + + def scale(self, x): + return (x - self.mean) / self.std + + def unscale(self, x): + return x * self.std + self.mean + +def sample_continuous(num_samples, split="test", randomize = False): + emb = conditioning.sample_image_embedding(num_samples, split, randomize) + rider = conditioning.sample_riders(num_samples, split, randomize) + use_case = conditioning.sample_use_case(num_samples, split, randomize) + all = torch.cat((emb, rider, use_case), dim=1) + return all + +def sample_continuous_text(text_strings, device, split="test", randomize = False): + embedding_model = clip_embedding_calculator.ClipEmbeddingCalculator( + device=device, batch_size=64 + ) + text_embedding = embedding_model.embed_texts(text_strings) + num_samples = text_embedding.shape[0] + rider = conditioning.sample_riders(num_samples, split, randomize) + use_case = conditioning.sample_use_case(num_samples, split, randomize) + all = torch.cat((text_embedding, rider, use_case), dim=1) + return all + +def sample_standard(num_samples, split="test", randomize = False): + emb = conditioning.sample_image_embedding(num_samples, split, randomize) + rider = conditioning.sample_riders(num_samples, split, randomize) + use_case = conditioning.sample_use_case(num_samples, split, randomize) + condition = {"Rider": rider, "Use Case": use_case, "Embedding": emb} + return condition + +def parse_continuous_condition(condition): + image_embeddings = condition[:, :512] + use_case_condition = condition[:, -3:] + rider_condition = condition[:, 512:-3] + condition = {"Rider": rider_condition, "Use Case": use_case_condition, "Embedding": image_embeddings} + return condition + +def piecewise_constraint_score(constraint_scores, constraint_falloff = 10): + constraint_scores_safexp = torch.clamp(constraint_scores, max=0.0) + piece1 = torch.exp(constraint_scores_safexp * constraint_falloff)/constraint_falloff + piece2 = constraint_scores + 1/constraint_falloff + mask = constraint_scores < 0.0 + mask = mask.float() + result = piece1 * mask + piece2 * (1 - mask) + return result + +def get_composite_score_fn(scaler, columns, evaluations, constrant_vs_objective_weight = 10.0, constraint_falloff=10.0, device="cpu"): + evaluator, requirement_names, requirement_types = construct_tensor_evaluator(evaluations, columns) + + isobjective = torch.tensor(requirement_types) == 1 + + weights = scoring.get_ref_point(evaluator, requirement_names, requirement_names, reduction="meanabs", device=device) + weights = torch.tensor(weights, dtype=torch.float32, device=device) + + assert weights.min() > 0, "Ref point should be greater than 0" + + def composite_score_fn(x, continuous_condition, evaluator = evaluator, scaler = scaler): + #print if there are any NaN values in x + if torch.isnan(x).any(): + print("NaN values in x") + x = scaler.unscale(x) + condition = parse_continuous_condition(continuous_condition) + eval_scores = evaluator(x, condition) + scaled_scores = eval_scores / weights + objective_scores = scaled_scores[:, isobjective] + constraint_scores_raw = scaled_scores[:, ~isobjective] + constraint_scores = piecewise_constraint_score(constraint_scores_raw, constraint_falloff) + total_scores = torch.sum(objective_scores, dim=1) + torch.sum(constraint_scores, dim=1) * constrant_vs_objective_weight + composite_scores = total_scores / constrant_vs_objective_weight + + quality_scores = 1/composite_scores + # print("Quality scores: ", quality_scores) + if torch.isnan(quality_scores).any(): + print("NaN values in quality scores") + + mean_comp_scores = torch.mean(composite_scores) + constraint_satisfaction_rate = torch.mean(torch.all(constraint_scores_raw <= 0, dim=1).float()) + report = {"CSR": constraint_satisfaction_rate, "MCS": mean_comp_scores} + return quality_scores, report + + return composite_score_fn + +def get_uneven_batch_sizes(total_data_points, batch_size): + """ + Given the total number of data points and a target batch size, + returns a list of batch sizes that sum up to the total number of data points. + The batch sizes are distributed as evenly as possible but may be uneven. + + :param total_data_points: Total number of data points (int). + :param batch_size: Target batch size (int). + :return: A list of batch sizes (list of int). + """ + # Calculate the number of batches needed + num_batches = total_data_points // batch_size + remainder = total_data_points % batch_size + + # Initialize the batch sizes + batch_sizes = [batch_size] * num_batches + + # Distribute the remainder across the batches + for i in range(remainder): + batch_sizes[i%num_batches] += 1 + + return batch_sizes + + +def get_diversity_loss_fn(scaler:TorchScaler, columns, evaluations, diversity_weight=0.1, score_weight=0.1, constraint_vs_objective_weight=10.0, constraint_falloff=10.0, dpp_batch=16, device="cpu"): + composite_score_fn = get_composite_score_fn(scaler, columns, evaluations, constrant_vs_objective_weight = constraint_vs_objective_weight, constraint_falloff = constraint_falloff, device=device) + + def diversity_loss_fn(x, condition, diversity_weight=diversity_weight, score_weight=score_weight): + if diversity_weight == 0: + return torch.tensor(0.0), {"DIV-OFF": 0.0} + scores, report= composite_score_fn(x, condition) + + # Initialize the total loss + total_loss = 0.0 + + # Get uneven batch sizes based on the total number of data points + batch_sizes = get_uneven_batch_sizes(x.size(0), dpp_batch) + + # Split the data into uneven batches + start_idx = 0 + for batch_size in batch_sizes: + # Get the current batch + end_idx = start_idx + batch_size + x_batch = x[start_idx:end_idx] + scores_batch = scores[start_idx:end_idx] + # Compute pairwise squared Euclidean distances for the batch + r = torch.sum(x_batch ** 2, dim=1, keepdim=True) + D = r - 2 * torch.matmul(x_batch, x_batch.T) + r.T + D_norm = D / x_batch.size(1) # Normalize by the number of features + # Compute the similarity matrix using RBF for the batch + S = torch.exp(-0.5 * D_norm ** 2) / 2 + + # Compute the quality matrix for the batch + Q = torch.matmul(scores_batch, scores_batch.T) + Q = torch.pow(Q, score_weight) + L = S * Q + + L = (L + L.T) / 2.0 + + L_stable = L + 1e-6 * torch.eye(L.size(0), device=L.device) + + # Compute the eigenvalues of the similarity matrix for the batch + try: + eig_val = torch.linalg.eigvalsh(L_stable) + except: + print(f"Eigenvalue computation failed for batch with size {batch_size}") + eig_val = torch.ones(x_batch.size(0), device=x.device) + if torch.isnan(eig_val).any(): + print("NaNs detected in eig_val") + # if (eig_val <= 0).any(): + # print("Nonpositive eigenvalues:", eig_val) + # Compute the loss for the batch as the negative mean log of the eigenvalues + if torch.isinf(torch.log(eig_val)).any(): + print("Log produced inf! Min/max eig_val:", eig_val.min().item(), eig_val.max().item()) + + batch_loss = -torch.mean(torch.log(torch.clamp(eig_val, min=1e-6, max=1e6))) + + total_loss += batch_loss + + # Update the start index for the next batch + start_idx = end_idx + # Compute the final loss by averaging across batches + loss = total_loss / len(batch_sizes)* diversity_weight + return loss, report + return diversity_loss_fn + +class Down_Model(nn.Module): + def __init__(self, in_dim, out_dim, hidden_dim=400, num_hidden_layers=2): + super(Down_Model, self).__init__() + + self.layers = nn.ModuleList([nn.Linear(in_dim, hidden_dim), nn.LeakyReLU()]) + + for _ in range(num_hidden_layers - 1): + self.layers.append(nn.Linear(hidden_dim, hidden_dim)) + self.layers.append(nn.LeakyReLU()) + + self.layers.append(nn.Linear(hidden_dim, out_dim)) + + def forward(self, inputs): + x = inputs + for layer in self.layers: + x = layer(x) + return x + +class Up_Model(nn.Module): + def __init__(self, in_dim, out_dim, hidden_dim=400, num_hidden_layers=2): + super(Up_Model, self).__init__() + + self.layers = nn.ModuleList([nn.Linear(in_dim, hidden_dim), nn.LeakyReLU()]) + + for _ in range(num_hidden_layers - 1): + self.layers.append(nn.Linear(hidden_dim, hidden_dim)) + self.layers.append(nn.LeakyReLU()) + + self.layers.append(nn.Linear(hidden_dim, out_dim)) + + def forward(self, inputs): + x = inputs + for layer in self.layers: + x = layer(x) + return x + + + +def GAN_step(D, G, D_opt, G_opt, data_batch, cond_batch, noise_batch, batch_size, device, auxiliary_loss_fn): + criterion = nn.BCEWithLogitsLoss() + D.zero_grad() + real_label = torch.full((batch_size,), 1, dtype=torch.float, device=device) + fake_label = torch.full((batch_size,), 0, dtype=torch.float, device=device) + + data_and_condition = torch.cat([data_batch, cond_batch], dim=1) + noise_and_condition = torch.cat([noise_batch, cond_batch], dim=1) + + output = D(data_and_condition).view(-1) + L_D_real = criterion(output, real_label) + + fake_data = G(noise_and_condition) + fake_data_and_condition = torch.cat([fake_data, cond_batch], dim=1) + output = D(fake_data_and_condition.detach()).view(-1) + L_D_fake = criterion(output, fake_label) + + L_D_tot = L_D_real + L_D_fake + L_D_tot.backward() + D_opt.step() + + G.zero_grad() + fake_data = G(noise_and_condition) + fake_data_and_condition = torch.cat([fake_data, cond_batch], dim=1) + output = D(fake_data_and_condition).view(-1) + L_G = criterion(output, real_label) + + if auxiliary_loss_fn is not None: + L_aux, rep= auxiliary_loss_fn(fake_data, cond_batch) + L_G_tot = L_G + L_aux + + report = {"L_D_real": L_D_real.item(), "L_D_fake": L_D_fake.item(), "L_G": L_G.item(), "L_aux": L_aux.item()} + report.update(rep) + else: + L_G_tot = L_G + report = {"L_D_real": L_D_real.item(), "L_D_fake": L_D_fake.item(), "L_G": L_G.item()} + + L_G_tot.backward() + + torch.nn.utils.clip_grad_norm_(G.parameters(), max_norm=20) + + G_opt.step() + + return report + + +def VAE_step(D, G, D_opt, G_opt, data_batch, cond_batch, noise_batch, batch_size, device, auxiliary_loss_fn): + + D.zero_grad() + G.zero_grad() + + alpha = 0.2 + + data_and_condition = torch.cat([data_batch, cond_batch], dim=1) + + encoded = D(data_and_condition) + latent_dim = encoded.shape[1] // 2 + mu = encoded[:, :latent_dim] + logvar = encoded[:, latent_dim:] + + std = torch.exp(0.5 * logvar) + eps = torch.randn_like(std) + z = mu + eps * std # z = mu + sigma * epsilon + + # Forward pass through decoder (G) + noise_and_condition = torch.cat([z, cond_batch], dim=1) + + reconstructed = G(noise_and_condition) + + # Compute losses + L_R = nn.MSELoss()(reconstructed, data_batch) + L_KL = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp()) / data_batch.size(0) + + if auxiliary_loss_fn is not None: + L_aux, rep = auxiliary_loss_fn(reconstructed, cond_batch) + + L_tot = alpha * L_KL + L_R + L_aux + + report = {"L_KL": L_KL.item(), "L_R": L_R.item(), "L_tot": L_tot.item(), "L_aux": L_aux.item()} + report.update(rep) + else: + L_tot = alpha * L_KL + L_R + + report = {"L_KL": L_KL.item(), "L_R": L_R.item(), "L_tot": L_tot.item()} + + L_tot.backward() + + # total_norm_D = torch.sqrt(sum(p.grad.norm()**2 for p in D.parameters() if p.grad is not None)) + # total_norm_G = torch.sqrt(sum(p.grad.norm()**2 for p in G.parameters() if p.grad is not None)) + # print(f"Gradient norm for D: {total_norm_D.item():.4f}") + # print(f"Gradient norm for G: {total_norm_G.item():.4f}") + + torch.nn.utils.clip_grad_norm_(D.parameters(), max_norm=20) + torch.nn.utils.clip_grad_norm_(G.parameters(), max_norm=20) + + D_opt.step() + G_opt.step() + + return report + + +def DDPM_step_wrapper(scheduler: DDPMScheduler): + def DDPM_step(D, G, D_opt, G_opt, data_batch, cond_batch, noise_batch, batch_size, device, auxiliary_loss_fn): + # sample random t + t = torch.randint(0, scheduler.config.num_train_timesteps, (data_batch.size(0),), device=device).long() + + # compute x_t using q_sample + noise = torch.randn_like(data_batch, device=device) + x_t = scheduler.add_noise(data_batch, noise, t) + + # embed timestep and concat with cond + t_embedded = t.unsqueeze(-1).float() / scheduler.config.num_train_timesteps + x_input = torch.cat([x_t, t_embedded], dim=-1) + + # predict noise + noise_pred = D(x_input) + + # MSE loss with optional weighting (scheduler does not expose beta_t directly) + mse = MSELoss(reduction="none")(noise_pred, noise) + base_loss = mse.mean() + + # reconstruct x0 and compute auxiliary loss + alpha_cumprod = scheduler.alphas_cumprod.to(device) + sqrt_alpha_cumprod_t = alpha_cumprod[t].sqrt().unsqueeze(-1) + sqrt_one_minus_alpha_cumprod_t = (1 - alpha_cumprod[t]).sqrt().unsqueeze(-1) + x0_pred = (x_t - sqrt_one_minus_alpha_cumprod_t * noise_pred) / sqrt_alpha_cumprod_t + + if auxiliary_loss_fn is not None: + thresh = int(0.9 * scheduler.config.num_train_timesteps) + valid = (t < thresh) + + total_loss = base_loss + report = { + "loss": base_loss.item(), + } + else: + total_loss = base_loss + report = {"loss": base_loss.item()} + + D.zero_grad() + total_loss.backward() + D_opt.step() + + return report + + return DDPM_step + + +def DDPM_step_cond_wrapper(scheduler: DDPMScheduler): + def DDPM_step_cond(D, G, D_opt, G_opt, data_batch, cond_batch, noise_batch, batch_size, device, auxiliary_loss_fn): + # sample random t + t = torch.randint(0, scheduler.config.num_train_timesteps, (data_batch.size(0),), device=device).long() + + # compute x_t using q_sample + noise = torch.randn_like(data_batch, device=device) + x_t = scheduler.add_noise(data_batch, noise, t) + + # embed timestep and concat with cond + t_embedded = t.unsqueeze(-1).float() / scheduler.config.num_train_timesteps + x_input = torch.cat([x_t, cond_batch, t_embedded], dim=-1) + + # predict noise + noise_pred = D(x_input) + + # MSE loss with optional weighting (scheduler does not expose beta_t directly) + mse = MSELoss(reduction="none")(noise_pred, noise) + base_loss = mse.mean() + + # reconstruct x0 and compute auxiliary loss + alpha_cumprod = scheduler.alphas_cumprod.to(device) + sqrt_alpha_cumprod_t = alpha_cumprod[t].sqrt().unsqueeze(-1) + sqrt_one_minus_alpha_cumprod_t = (1 - alpha_cumprod[t]).sqrt().unsqueeze(-1) + x0_pred = (x_t - sqrt_one_minus_alpha_cumprod_t * noise_pred) / sqrt_alpha_cumprod_t + + if auxiliary_loss_fn is not None: + thresh = int(0.5 * scheduler.config.num_train_timesteps) + valid = (t < thresh) + + if valid.any(): + x0_sub, cond_sub = x0_pred[valid], cond_batch[valid] + L_aux, rep = auxiliary_loss_fn(x0_sub, cond_sub) + else: + L_aux = torch.tensor(0.0, device=device) + rep = {} + + total_loss = base_loss + L_aux + report = { + "loss": base_loss.item(), + "L_aux": L_aux.item(), + **rep + } + else: + total_loss = base_loss + report = {"loss": base_loss.item()} + + D.zero_grad() + total_loss.backward() + D_opt.step() + + return report + + return DDPM_step_cond + + +class ReusableDataLoader: + def __init__(self, dataset, batch_size, shuffle=True): + self.dataset = dataset + self.batch_size = batch_size + self.shuffle = shuffle + self.indices = list(range(len(self.dataset))) + self.previous_indices = [] + + def _shuffle_indices(self): + self.indices = torch.randperm(len(self.dataset)).tolist() + + def get_batch(self): + queued = self.previous_indices + while len(queued) < self.batch_size: + if self.shuffle: + self._shuffle_indices() + queued.extend(self.indices) # Add individual elements to queued list + + self.previous_indices = queued[self.batch_size:] # Store remaining indices for the next batch + batch_indices = queued[:self.batch_size] # Get the batch of the correct size + return torch.stack([self.dataset[i][0] for i in batch_indices]) + +def train(D, G, D_opt, G_opt, loader, num_steps, batch_size, noise_dim, train_step_fn, device, auxiliary_loss_fn, condition_sampler): + # Loss function + D.train() + G.train() + steps_range = trange(num_steps, position=0, leave=True) + + for step in steps_range: + data_batch = loader.get_batch().to(device) + noise_batch = torch.randn(batch_size, noise_dim).to(device) + cond_batch = condition_sampler(batch_size).to(device) + effective_auxiliary_loss_fn = auxiliary_loss_fn + report = train_step_fn(D, G, D_opt, G_opt, data_batch, cond_batch, noise_batch, batch_size, device, effective_auxiliary_loss_fn) + postfix = {key: "{:.4f}".format(value) for key, value in report.items()} + steps_range.set_postfix(postfix) + return D, G + + +def get_DDPM_generate_cond(scheduler: DDPMScheduler, data_dim, batch_size=64): + def DDPM_generate_cond(D, G, cond_batch, latent_dim, device, batch_size=batch_size): + with torch.no_grad(): + results = [] + numgen = cond_batch.shape[0] + for start_idx in range(0, numgen, batch_size): + end_idx = min(start_idx + batch_size, numgen) + current_batch_size = end_idx - start_idx + + x = torch.randn(current_batch_size, data_dim).to(device) + + for t in reversed(range(scheduler.config.num_train_timesteps)): + t_tensor = torch.full((current_batch_size,), t, dtype=torch.long, device=device) + t_embedded = t_tensor.unsqueeze(-1).float() / scheduler.config.num_train_timesteps + x_input = torch.cat([x, cond_batch, t_embedded], dim=-1) + + with torch.no_grad(): + noise_pred = D(x_input) + + x = scheduler.step(model_output=noise_pred, timestep=t, sample=x).prev_sample + results.append(x) + + return torch.cat(results, dim=0) + return DDPM_generate_cond + +def get_DDPM_generate_guided(scheduler: DDPMScheduler, data_dim, auxiliary_loss_fn, batch_size=64): + def DDPM_generate_guided(D, G, cond_batch, latent_dim, device, auxiliary_loss_fn=auxiliary_loss_fn, batch_size=batch_size): + results = [] + numgen = cond_batch.shape[0] + + num_guided_timesteps = int(0.5 * scheduler.config.num_train_timesteps) + + for start_idx in range(0, numgen, batch_size): + end_idx = min(start_idx + batch_size, numgen) + current_batch_size = end_idx - start_idx + + # Start with pure noise + x = torch.randn(current_batch_size, data_dim, device=device) + + for t in tqdm(reversed(range(scheduler.config.num_train_timesteps))): + t_tensor = torch.full((current_batch_size,), t, dtype=torch.long, device=device) + t_embedded = t_tensor.unsqueeze(-1).float() / scheduler.config.num_train_timesteps + x_input = torch.cat([x, t_embedded], dim=-1) + + # Model prediction + noise_pred = D(x_input) + + # Apply auxiliary loss only if t < threshold (later timesteps) + threshold = int(0.5 * scheduler.config.num_train_timesteps) + if t < threshold: + alpha_cumprod = scheduler.alphas_cumprod.to(device) + sqrt_alpha_cumprod_t = alpha_cumprod[t].sqrt().unsqueeze(-1) + sqrt_one_minus_alpha_cumprod_t = (1 - alpha_cumprod[t]).sqrt().unsqueeze(-1) + x0_pred = (x - sqrt_one_minus_alpha_cumprod_t * noise_pred) / sqrt_alpha_cumprod_t + + aux_loss, _ = auxiliary_loss_fn(x0_pred, cond_batch) + aux_loss.backward(retain_graph=True) + grad = x.grad / num_guided_timesteps + + # Update x based on this local gradient only + x = (x - grad).detach().requires_grad_(True) + x.retain_grad() + + # Always apply scheduler step + x = scheduler.step(model_output=noise_pred, timestep=t, sample=x).prev_sample + x.retain_grad() + + results.append(x) + + return torch.cat(results, dim=0) + + return DDPM_generate_guided + + + + + + +def VAE_generate(D, G, cond_batch, latent_dim, device): + with torch.no_grad(): + numgen = cond_batch.shape[0] + z = torch.randn(numgen, latent_dim).to(device) + z_and_condition = torch.cat([z, cond_batch], dim=1) + generated_data = G(z_and_condition) + return generated_data + +# def VAE_generate_cond(D, G, cond_batch, latent_dim, device): +# numgen = cond_batch.shape[0] +# z = torch.randn(numgen, latent_dim).to(device) +# labels = torch.ones(numgen, 1).to(device) +# z = torch.cat([z, labels], dim=1) +# generated_data = G(z) +# return generated_data + +def GAN_generate(D, G, cond_batch, noise_dim, device): + with torch.no_grad(): + numgen = cond_batch.shape[0] + noise = torch.randn(numgen, noise_dim).to(device) + noise_and_condition = torch.cat([noise, cond_batch], dim=1) + labels = torch.ones(numgen, 1).to(device) + generated_data = G(noise_and_condition) + return generated_data + +def train_model(data, model_type, train_params, auxiliary_loss_fn, condition_sampler, device): + batch_size, disc_lr, gen_lr, noise_dim, num_epochs, n_hidden, layer_size= train_params + + + + data = torch.tensor(data).float() + sample_condition = sample_continuous(data.shape[0], randomize=True).to(device) + + loader = ReusableDataLoader(TensorDataset(data), batch_size) + + data_dim = data.shape[1] + cond_dim = sample_condition.shape[1] + + if model_type in ["GAN"]: + train_step = GAN_step + generate_fn = GAN_generate + D_in = data_dim + cond_dim + D_out = 1 + G_in = noise_dim +cond_dim + G_out = data_dim + elif model_type in ["VAE"]: + train_step = VAE_step + generate_fn = VAE_generate + D_in = data_dim + cond_dim + D_out = 2*noise_dim + G_in = noise_dim + cond_dim + G_out = data_dim + elif model_type in ["DDPM_guided"]: + scheduler = DDPMScheduler(num_train_timesteps=100) + train_step = DDPM_step_wrapper(scheduler) + generate_fn = get_DDPM_generate_guided(scheduler, data_dim, auxiliary_loss_fn, batch_size=batch_size) + D_in = data_dim + 1 + D_out = data_dim + G_in = 1 #unused + G_out = 1 #unused + elif model_type in ["DDPM_conditional"]: + scheduler = DDPMScheduler(num_train_timesteps=1000) + train_step = DDPM_step_cond_wrapper(scheduler) + generate_fn = get_DDPM_generate_cond(scheduler, data_dim, batch_size=batch_size) + D_in = data_dim + cond_dim + 1 + D_out = data_dim + G_in = 1 #unused + G_out = 1 #unused + # else: + # raise ValueError("Invalid model_type") + + + D = Down_Model(D_in, D_out, layer_size, n_hidden) + G = Up_Model(G_in, G_out, layer_size, n_hidden) + + + D.to(device) + G.to(device) + D_opt = torch.optim.Adam(D.parameters(), lr=disc_lr, betas=(0.5,0.999)) + G_opt = torch.optim.Adam(G.parameters(), lr=gen_lr, betas=(0.5,0.999)) + + + + if num_epochs>0: + num_steps = num_epochs*len(data)//batch_size + else: + num_steps = -num_epochs #hacky way to specify fixed number of steps rather than epochs + + + train(D, G, D_opt, G_opt, loader, num_steps, batch_size, noise_dim, train_step, device, auxiliary_loss_fn, condition_sampler) + + return D, G, generate_fn diff --git a/bike_bench_internal/benchmark_models/libmoon/__init__.py b/bike_bench_internal/benchmark_models/libmoon/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/example.py b/bike_bench_internal/benchmark_models/libmoon/example.py new file mode 100644 index 0000000000000000000000000000000000000000..5109459191df1e2d08af6c8df8f412e40f39cc08 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/example.py @@ -0,0 +1,4 @@ +def add_one(number): + return number + 1 + + diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/__init__.py b/bike_bench_internal/benchmark_models/libmoon/problem/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/mop.py b/bike_bench_internal/benchmark_models/libmoon/problem/mop.py new file mode 100644 index 0000000000000000000000000000000000000000..d129598fd924e69003cd7460ae3f74617b3d9669 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/mop.py @@ -0,0 +1,93 @@ +import numpy as np +import torch + +class mop(): + def __init__(self, + n_var: int, + n_obj: int, + lbound: np.ndarray, + ubound: np.ndarray, + n_cons: int=0, + ) -> None: + + self.n_var = n_var + self.n_obj = n_obj + self.n_cons = n_cons + + self.lbound=lbound + self.ubound=ubound + + + @property + def get_number_variable(self) -> int: + return self.n_var + + @property + def get_number_objective(self) -> int: + return self.n_obj + + @property + def get_lower_bound(self) -> np.ndarray: + return self.lbound + + @property + def get_upper_bound(self) -> np.ndarray: + return self.ubound + + @property + def has_constraint(self) -> bool: + return self.n_cons > 0 + + def evaluate(self, x): + raise NotImplementedError("Subclasses should implement this method.") + + def __call__(self, x): + return self.evaluate(x) + + def evaluate(self, x: any) -> any: + """ + Evaluate the objectives for x + Parameters + ---------- + x : any + Tensor or ndarray + Returns + ------- + any + Tensor or ndarray correspondingly + Raises + ------ + ValueError + wrong type of x + """ + + if type(x) == torch.Tensor: + return self._evaluate_torch(torch.atleast_2d(x)) + elif isinstance(x, np.ndarray): + return self._evaluate_numpy(np.atleast_2d(x)) + else: + raise ValueError("Input has to be in the form of Tensor or ndarray!") + + def get_pf(self, n_points: int=100) -> np.ndarray: + """ + Get Pareto front + Parameters + ---------- + num_points : int, optional + _description_, by default 100 + Returns + ------- + np.ndarray + _description_ + """ + # TODO + # if method=='uniform': + if hasattr(self, "_get_pf"): return self._get_pf(n_points) + else: raise NotImplementedError("Subclasses should implement this method.") + + + +class mop_noCons(mop): + + def __init__(self, n_var: int, n_obj: int, lbound: np.ndarray, ubound: np.ndarray, n_cons: int = 0) -> None: + super().__init__(n_var, n_obj, lbound, ubound, n_cons) \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/mtl/__init__.py b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/mtl/fair_classify.py b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/fair_classify.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/__init__.py b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ac106a9c18082e83dc6372fb19551867f87ab75d --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/__init__.py @@ -0,0 +1 @@ +from .multimnist_loader import MultiMNISTData \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/adult_loader.py b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/adult_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..a6ff45cb789539aa3905061f2b403f3dad920c3f --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/adult_loader.py @@ -0,0 +1,124 @@ +import os +import pandas as pd +import torch + +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from torch.utils import data +from libmoon.util_global.constant import root_name + + + + +def load_dataset(path, s_label): + # print() + data = pd.read_csv(path) + # Preprocessing taken from https://www.kaggle.com/islomjon/income-prediction-with-ensembles-of-decision-trees + # replace missing values with majority class + data['workclass'] = data['workclass'].replace('?','Private') + data['occupation'] = data['occupation'].replace('?','Prof-specialty') + data['native-country'] = data['native-country'].replace('?','United-States') + + # education category + data.education = data.education.replace(['Preschool','1st-4th','5th-6th','7th-8th','9th','10th','11th','12th'],'left') + data.education = data.education.replace('HS-grad','school') + data.education = data.education.replace(['Assoc-voc','Assoc-acdm','Prof-school','Some-college'],'higher') + data.education = data.education.replace('Bachelors','undergrad') + data.education = data.education.replace('Masters','grad') + data.education = data.education.replace('Doctorate','doc') + + # marital status + data['marital-status'] = data['marital-status'].replace(['Married-civ-spouse','Married-AF-spouse'],'married') + data['marital-status'] = data['marital-status'].replace(['Never-married','Divorced','Separated','Widowed', 'Married-spouse-absent'], 'not-married') + + # income + data.income = data.income.replace('<=50K', 0) + data.income = data.income.replace('>50K', 1) + + # sex + data.gender = data.gender.replace('Male', 0) + data.gender = data.gender.replace('Female', 1) + + # mtldata.race = mtldata.race.replace('White', 0) + # mtldata.race = mtldata.race.replace('Black', 1) + # mtldata.race = mtldata.race.astype(int) + + # encode categorical values + data1 = data.copy() + data1 = pd.get_dummies(data1) + data1 = data1.drop(['income', s_label], axis=1) + # data1 = data1.drop(['income', s_label], axis=1) + + X = StandardScaler().fit(data1).transform(data1) + y = data['income'].values + s1 = data[s_label].values + # s2 = mtldata['race'].values + + return X, y, s1 + + + + +class ADULT(data.Dataset): + + + def __init__(self, split="train", sensible_attribute="gender"): + assert split in ["train", "val", "test"] + + # folder_name = os.path.dirname( os.path.dirname(__file__) ) + + + + + path = os.path.join(root_name, 'mtldata', "adult.csv") + + x, y, s1 = load_dataset(path, sensible_attribute) + + + x = torch.from_numpy(x).float() + y = torch.from_numpy(y).long() + s1 = torch.from_numpy(s1).long() + # s2 = torch.from_numpy(s2).long() + + # train/val/test split: 70/10/20 % + x_train, x_test, y_train, y_test, s1_train, s1_test = train_test_split(x, y, s1,test_size=.2, random_state=1) + x_train, x_val, y_train, y_val, s1_train, s1_val= train_test_split(x_train, y_train, s1_train, test_size=.125, random_state=1) + + if split == 'train': + self.x = x_train + self.y = y_train + self.s1 = s1_train + # self.s2 = s2_train + + elif split == 'val': + self.x = x_val + self.y = y_val + self.s1 = s1_val + # self.s2 = s2_val + elif split == 'test': + self.x = x_test + self.y = y_test + self.s1 = s1_test + # self.s2 = s2_test + + print("loaded {} instances for split {}. y positives={}, {} positives={}".format( + len(self.y), split, sum(self.y), sensible_attribute, sum(self.s1))) + + def __len__(self): + """__len__""" + return len(self.x) + + def __getitem__(self, index): + return dict(data=self.x[index], labels=self.y[index], sensible_attribute=self.s1[index]) + + def task_names(self): + return None + + + +if __name__ == "__main__": + dataset = ADULT(split="train") + trainloader = data.DataLoader(dataset, batch_size=256, num_workers=0) + + for i, data in enumerate(trainloader): + break \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/compas_loader.py b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/compas_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..846eb4a8d4371c4ffcbb009c6d7cce6e46a0e51e --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/compas_loader.py @@ -0,0 +1,101 @@ +import torch +import os +import pandas as pd + +from datetime import datetime +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler + + +def load_dataset(path, s_label): + + # following the preprocessing on + # https://github.com/propublica/compas-analysis/blob/master/Compas%20Analysis.ipynb + raw_data = pd.read_csv(path) + + data = raw_data[( + (raw_data['days_b_screening_arrest'] <= 30) & + (raw_data['days_b_screening_arrest'] >= -30) & + (raw_data['is_recid'] != -1) & + (raw_data['c_charge_degree'] != 'O') & + (raw_data['score_text'] != 'N/A') + )] + + # Only some columns are relevant + data = data[['age', 'c_charge_degree', 'race', 'age_cat', 'score_text', 'sex', 'priors_count', + 'days_b_screening_arrest', 'decile_score', 'is_recid', 'two_year_recid', 'c_jail_in', 'c_jail_out']] + + # convert c_jail_in and c_jail_out to time_in_jail, mesured in hours + def date_from_str(s): + return datetime.strptime(s, '%Y-%m-%d %H:%M:%S') + + data['c_jail_in'] = data['c_jail_in'].apply(date_from_str) + data['c_jail_out'] = data['c_jail_out'].apply(date_from_str) + + data['length_of_stay'] = data['c_jail_out'] - data['c_jail_in'] + + # data['length_of_stay'] = data['length_of_stay'].astype('timedelta64[h]') # modified by xz, 11.6 + data['length_of_stay'] = data['length_of_stay'].dt.days + + + data = data.drop(['c_jail_in', 'c_jail_out'], axis=1) + + # encode sex + data['sex'] = data['sex'].replace('Male', 0) + data['sex'] = data['sex'].replace('Female', 1) + + # one-hot encode categorical variables + data1 = data.copy() + data1 = data1.drop(['two_year_recid', 'sex'], axis=1) + data1 = pd.get_dummies(data1) + + x = StandardScaler().fit(data1).transform(data1) + y = data['two_year_recid'].values + s = data['sex'].values + + return x, y, s + + +class Compas(torch.utils.data.Dataset): + + def __init__(self, split, sensible_attribute='sex'): + assert split in ['train', 'val', 'test'] + + folder_name = os.path.dirname(os.path.dirname(__file__)) + path = os.path.join(folder_name, 'mtldata', "compas.csv") + + x, y, s = load_dataset(path, sensible_attribute) + + x = torch.from_numpy(x).float() + y = torch.from_numpy(y).long() + s = torch.from_numpy(s).long() + + # train/val/test split: 70/10/20 % + x_train, x_test, y_train, y_test, s_train, s_test = train_test_split(x, y, s, test_size=.2, random_state=1) + x_train, x_val, y_train, y_val, s_train, s_val = train_test_split(x_train, y_train, s_train, test_size=.125, random_state=1) + + if split == 'train': + self.x = x_train + self.y = y_train + self.s = s_train + elif split == 'val': + self.x = x_val + self.y = y_val + self.s = s_val + elif split == 'test': + self.x = x_test + self.y = y_test + self.s = s_test + + print("loaded {} instances for split {}. y positives={}, {} positives={}".format( + len(self.y), split, sum(self.y), sensible_attribute, sum(self.s))) + + + def __len__(self): + return len(self.y) + + def __getitem__(self, index): + return dict(data=self.x[index], labels=self.y[index], sensible_attribute=self.s[index]) + + def task_names(self): + return None \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/credit_loader.py b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/credit_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..667038970db9b59647151cb7fcbdf9874c99353d --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/credit_loader.py @@ -0,0 +1,80 @@ +import torch +import os +import pandas as pd + +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler + + + +def load_dataset(path, s_label): + data = pd.read_csv( path ) + + # convert categorical columns + to_categorical = [ + 'EDUCATION', 'MARRIAGE', 'PAY_0', 'PAY_2', + 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6' + ] + for column in to_categorical: + data[column] = data[column].astype('category') + + data.SEX = data.SEX.replace(1, 0) # male + data.SEX = data.SEX.replace(2, 1) # female + + # Scale and split + data1 = data.copy() + data1 = data1.drop(['default.payment.next.month', s_label], axis=1) + data1 = pd.get_dummies(data1) + + x = StandardScaler().fit(data1).transform(data1) + y = data['default.payment.next.month'].values + s = data[s_label].values + + return x, y, s + + +class Credit(torch.utils.data.Dataset): + + def __init__(self, split, sensible_attribute='SEX'): + assert split in ['train', 'val', 'test'] + + folder_name = os.path.dirname(os.path.dirname(__file__)) + path = os.path.join(folder_name, 'mtldata', "credit.csv") + + x, y, s = load_dataset(path, sensible_attribute) + + x = torch.from_numpy(x).float() + y = torch.from_numpy(y).long() + s = torch.from_numpy(s).long() + + # train/val/test split: 70/10/20 % + x_train, x_test, y_train, y_test, s_train, s_test = train_test_split(x, y, s, test_size=.2, random_state=1) + x_train, x_val, y_train, y_val, s_train, s_val = train_test_split(x_train, y_train, s_train, test_size=.125, random_state=1) + + if split == 'train': + self.x = x_train + self.y = y_train + self.s = s_train + elif split == 'val': + self.x = x_val + self.y = y_val + self.s = s_val + elif split == 'test': + self.x = x_test + self.y = y_test + self.s = s_test + + print("loaded {} instances for split {}. y positives={}, {} positives={}".format( + len(self.y), split, sum(self.y), sensible_attribute, sum(self.s))) + + + def __len__(self): + return len(self.y) + + def __getitem__(self, index): + return dict(data=self.x[index], labels=self.y[index], sensible_attribute=self.s[index]) + + def task_names(self): + return None + + diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/multimnist_loader.py b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/multimnist_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..68ca46087d875e263f4a3b010f71469767de3201 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/loaders/multimnist_loader.py @@ -0,0 +1,101 @@ +import torch +import pickle +from sklearn.model_selection import train_test_split +# taken from https://github.com/Xi-L/ParetoMTL and adapted +import os + +from libmoon.util_global.constant import root_name + + +class MultiMNISTData(torch.utils.data.Dataset): + """ + The datasets from ParetoMTL + """ + def __init__(self, dataset, split, root='data/multi', **kwargs): + assert dataset in ['mnist', 'fashion', 'fmnist'] + assert split in ['train', 'val', 'test'] + + # equal size of val and test split + train_split = .9 + + if dataset == 'mnist': + self.path = os.path.join(root_name, 'problem', 'mtl', 'data', 'multimnist', 'mnist.pickle') + + elif dataset == 'fashion': + self.path = os.path.join(root_name, 'problem', 'mtl', 'data', 'multimnist', 'fashion.pickle') + + elif dataset == 'fmnist': + self.path = os.path.join(root_name, 'problem', 'mtl', 'data', 'multimnist', 'fmnist.pickle') + + + self.val_size = .1 + with open(self.path, 'rb') as f: + trainX, trainLabel, testX, testLabel = pickle.load(f) + + n_train = len(trainX) + if self.val_size > 0: + trainX, valX, trainLabel, valLabel = train_test_split( + trainX, trainLabel, test_size=self.val_size, random_state=42 + ) + n_train = len(trainX) + n_val = len(valX) + + trainX = torch.from_numpy(trainX.reshape(n_train, 1, 36, 36)).float() + trainLabel = torch.from_numpy(trainLabel).long() + testX = torch.from_numpy(testX.reshape(20000, 1, 36, 36)).float() + testLabel = torch.from_numpy(testLabel).long() + + if self.val_size > 0: + valX = torch.from_numpy(valX.reshape(n_val, 1, 36, 36)).float() + valLabel = torch.from_numpy(valLabel).long() + + if split in ['train', 'val']: + n = int(len(trainX) * train_split) + if split == 'val': + self.X = valX + self.y = valLabel + elif split == 'train': + self.X = trainX + self.y = trainLabel + elif split == 'test': + self.X = testX + self.y = testLabel + + def __getitem__(self, index): + return dict(data=self.X[index], labels_l=self.y[index, 0], labels_r=self.y[index, 1]) + + def __len__(self): + return len(self.X) + + def task_names(self): + return ['l', 'r'] + + + + + + + +if __name__ == '__main__': + import matplotlib.pyplot as plt + dst = MultiMNISTData(dataset='mnist', split='val') + loader = torch.utils.data.DataLoader(dst, batch_size=10, shuffle=True, num_workers=0) + + for dat in loader: + ims = dat['data'].view(10, 36, 36).numpy() + labs_l = dat['labels_l'] + labs_r = dat['labels_r'] + + f, axarr = plt.subplots(2, 5) + for j in range(5): + for i in range(2): + axarr[i][j].imshow(ims[j * 2 + i, :, :], cmap='gray') + axarr[i][j].set_title('{}_{}'.format(labs_l[j * 2 + i], labs_r[j * 2 + i])) + plt.show() + a = input() + + if a == 'ex': + break + else: + plt.close() + diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/mtl/mnist.py b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/mnist.py new file mode 100644 index 0000000000000000000000000000000000000000..dcf081c93307dd6563dd0ef96ccab8a1d057c85c --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/mnist.py @@ -0,0 +1,189 @@ +import matplotlib.pyplot as plt +from libmoon.util_global.constant import root_name +from libmoon.problem.mtl.loaders.multimnist_loader import MultiMNISTData +import torch + +from libmoon.problem.mtl.objectives import CrossEntropyLoss +from libmoon.problem.mtl.model.simple import MultiLeNet +from libmoon.util_global.weight_factor.funs import uniform_pref +from libmoon.util_global.constant import FONT_SIZE + +loss_1 = CrossEntropyLoss(label_name='labels_l', logits_name='logits_l') +loss_2 = CrossEntropyLoss(label_name='labels_r', logits_name='logits_r') + +from tqdm import tqdm +import numpy as np +from numpy import array +import os +from solver.gradient import get_core_solver +from solver.gradient.utils.util import get_grads_from_model, numel_params +from libmoon.util_global.constant import is_pref_based +import itertools + +class MultiMnistProblem: + + # How to train at the same time. + def __init__(self, args, prefs): + self.dataset = MultiMNISTData('mnist', 'train') + self.args = args + self.loader = torch.utils.data.DataLoader(self.dataset, batch_size=self.args.batch_size, shuffle=True, + num_workers=0) + self.dataset_test = MultiMNISTData('mnist', 'test') + self.loader_test = torch.utils.data.DataLoader(self.dataset_test, batch_size=args.batch_size, shuffle=True, + num_workers=0) + + self.lr = args.lr + self.prefs = prefs + self.n_prob = len(prefs) + + self.model_arr = [MultiLeNet([1, 36, 36]) for _ in range(self.n_prob)] + + num_params = numel_params(self.model_arr[0]) + print('num_params: ', num_params) + + for model in self.model_arr: + model.to(args.device) + + self.is_pref_flag = is_pref_based(args.mtd) + + if self.is_pref_flag: + self.core_solver_arr = [get_core_solver(args, pref) for pref in prefs] + self.optimizer_arr = [torch.optim.Adam(self.model_arr[idx].parameters(), lr=self.lr) for idx in + range(self.n_prob)] + else: + self.set_core_solver = get_core_solver(args) + + params = [model.parameters() for model in self.model_arr] + self.set_optimizer = torch.optim.Adam(itertools.chain(*params), lr=0.01) + + + def optimize(self): + loss_all = [] + for _ in tqdm(range(self.args.num_epoch)): + if self.is_pref_flag: + loss_hostory = [[] for i in range(self.n_prob)] + else: + loss_hostory = [] + + for data in self.loader: + data_ = {k: v.to(self.args.device) for k, v in data.items()} + + # pref based mtd + if self.is_pref_flag: + for pref_idx, (pref, model, optimizer) in enumerate( + zip(self.prefs, self.model_arr, self.optimizer_arr)): + logits_dict = self.model_arr[pref_idx](data_) + logits_dict['labels_l'] = data_['labels_l'] + logits_dict['labels_r'] = data_['labels_r'] + l1 = loss_1(**logits_dict) + l2 = loss_2(**logits_dict) + + l_contains_grad = [l1, l2] + G = get_grads_from_model(l_contains_grad, model) + + l1_np = np.array(l1.cpu().detach().numpy(), copy=True) + l2_np = np.array(l2.cpu().detach().numpy(), copy=True) + losses = array([l1_np, l2_np]) + alpha = self.core_solver_arr[pref_idx].get_alpha(G = G, losses=losses) + self.optimizer_arr[pref_idx].zero_grad() + (alpha[0] * l1 + alpha[1] * l2).backward() + self.optimizer_arr[pref_idx].step() + l1_np = np.array(l1.cpu().detach().numpy(), copy=True) + l2_np = np.array(l2.cpu().detach().numpy(), copy=True) + loss_hostory[pref_idx].append([l1_np, l2_np]) + else: + # set based method is more complicated. + losses = [0,] * self.n_prob + losses_ts = [0] * self.n_prob + + for model_idx, model in enumerate(self.model_arr): + logits_dict = self.model_arr[model_idx](data_) + logits_dict['labels_l'] = data_['labels_l'] + logits_dict['labels_r'] = data_['labels_r'] + l1 = loss_1(**logits_dict) + l2 = loss_2(**logits_dict) + + losses_ts[model_idx] = torch.stack([l1, l2]) + + l1_np, l2_np = np.array(l1.cpu().detach().numpy(), copy=True), np.array(l2.cpu().detach().numpy(), copy=True) + losses[model_idx] = [l1_np, l2_np] + + losses_ts = torch.stack(losses_ts) + losses = np.array(losses) + alpha = self.set_core_solver.get_alpha(losses).to(self.args.device) + self.set_optimizer.zero_grad() + torch.sum(alpha * losses_ts).backward() + self.set_optimizer.step() + loss_hostory.append(losses) + loss_hostory = np.array(loss_hostory) + if args.is_pref_based: + loss_history_mean = np.mean(loss_hostory, axis=1) + else: + loss_history_mean = np.mean(loss_hostory, axis=0) + loss_all.append(loss_history_mean) + return loss_all + + + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser() + + parser.add_argument('--problem', default='mnist', type=str) # For attribute in args, we all call problem. + parser.add_argument('--split', default='train', type=str) + parser.add_argument('--batch_size', default=512, type=int) + parser.add_argument('--shuffle', default=True, type=bool) + parser.add_argument('--lr', default=1e-2, type=float) + parser.add_argument('--num_epoch', default=10, type=int) + parser.add_argument('--use-cuda', default=True, type=bool) + + parser.add_argument('--mtd', default='hvgrad', type=str) + parser.add_argument('--agg-mtd', default='ls', type=str) # This att is only valid when args.mtd=agg. + parser.add_argument('--n-obj', default=2, type=int) # This att is only valid when args.mtd=agg. + + args = parser.parse_args() + args.is_pref_based = is_pref_based(args.mtd) + if torch.cuda.is_available() and args.use_cuda: + args.device = torch.device("cuda") # Use the GPU + print('cuda is available') + else: + args.device = torch.device("cpu") # Use the CPU + print('cuda is not available') + + prefs = uniform_pref(n_partition=10, n_obj=2, clip_eps=0.1) + args.n_prob = len(prefs) + + problem = MultiMnistProblem(args, prefs) + # args.n_obj = problem.n_obj + + loss_history = problem.optimize() + loss_history = np.array(loss_history) + + final_solution = loss_history[-1,:,:] + # plt.scatter(final_solution[:,0], final_solution[:,1], label='final solution') + for idx in range(loss_history.shape[1]): + plt.plot(loss_history[:,idx,0], loss_history[:,idx,1], 'o-', label='pref {}'.format(idx)) + + plt.plot(final_solution[:,0], final_solution[:,1], color='k', linewidth=3) + + plt.legend(fontsize=FONT_SIZE) + # draw pref + solution_norm = np.linalg.norm(final_solution, axis=1, keepdims=True) + prefs_norm = prefs / np.linalg.norm(prefs, axis=1, keepdims=True) * solution_norm + + if args.is_pref_based: + for pref in prefs_norm: + plt.plot([0, pref[0]], [0, pref[1]], color='k') + + + plt.xlabel('$L_1$', fontsize=FONT_SIZE) + plt.ylabel('$L_2$', fontsize=FONT_SIZE) + + + folder_name = os.path.join( root_name, 'output', args.problem, args.mtd) + os.makedirs(folder_name, exist_ok=True) + fig_name = os.path.join(folder_name, 'final_solution.svg') + plt.savefig(fig_name) + print('saved in ', fig_name) + + plt.show() diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/mtl/model/__init__.py b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/mtl/model/simple.py b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/model/simple.py new file mode 100644 index 0000000000000000000000000000000000000000..62de14925f2547613568ad8d4b5efdd96cdcf3db --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/model/simple.py @@ -0,0 +1,77 @@ +import torch.nn as nn + +''' + MTL problems need . +''' + + + +class MultiLeNet(nn.Module): + + def __init__(self, dim, **kwargs): + + ''' + :param dim: a 3d-array. [chanel, height, width] + :param kwargs: + ''' + super().__init__() + self.shared = nn.Sequential( + nn.Conv2d(dim[0], 10, kernel_size=5), + nn.MaxPool2d(kernel_size=2), + nn.ReLU(), + nn.Conv2d(10, 20, kernel_size=5), + nn.MaxPool2d(kernel_size=2), + nn.ReLU(), + nn.Flatten(), + nn.Linear(720, 50), + nn.ReLU(), + ) + self.private_left = nn.Linear(50, 10) + self.private_right = nn.Linear(50, 10) + + + def forward(self, batch): + x = batch['data'] + x = self.shared(x) + return dict(logits_l=self.private_left(x), logits_r=self.private_right(x)) + + def private_params(self): + return ['private_left.weight', 'private_left.bias', 'private_right.weight', 'private_right.bias'] + + + + + +class FullyConnected(nn.Module): + def __init__(self, dim, **kwargs): + super().__init__() + self.f = nn.Sequential( + nn.Linear(dim[0], 60), + nn.ReLU(), + nn.Linear(60, 25), + nn.ReLU(), + nn.Linear(25, 1), + ) + + def forward(self, batch): + x = batch['data'] + return dict(logits=self.f(x)) + + + + + +if __name__ == '__main__': + from libmoon.util_global.constant import root_name + import os + import pickle + + + + + pickle_name = os.path.join(root_name, 'problem', 'mtl', 'data', 'multimnist', 'mnist.pickle') + with open(pickle_name, 'rb') as f: + data = pickle.load(f) + + model = MultiLeNet([3, 32, 32]) + print('hello world') \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/mtl/objectives.py b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/objectives.py new file mode 100644 index 0000000000000000000000000000000000000000..15e6598c47eac44fd87c4b23c382009b0fdb4cd1 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/mtl/objectives.py @@ -0,0 +1,179 @@ +import torch + +def from_name(names, task_names): + objectives = { + 'CrossEntropyLoss': CrossEntropyLoss, + 'BinaryCrossEntropyLoss': BinaryCrossEntropyLoss, + 'L1Regularization': L1Regularization, + 'L2Regularization': L2Regularization, + 'ddp': DDPHyperbolicTangentRelaxation, + 'deo': DEOHyperbolicTangentRelaxation, + } + + if task_names is not None: + return [objectives[n]("labels_{}".format(t), "logits_{}".format(t)) for n, t in zip(names, task_names)] + else: + return [ objectives[n]() for n in names ] + + +class CrossEntropyLoss(torch.nn.CrossEntropyLoss): + def __init__(self, label_name='labels', logits_name='logits'): + super().__init__(reduction='mean') + self.label_name = label_name + self.logits_name = logits_name + + def __call__(self, **kwargs): + logits = kwargs[self.logits_name] + labels = kwargs[self.label_name] + return super().__call__(logits, labels) + + + +class BinaryCrossEntropyLoss(torch.nn.BCEWithLogitsLoss): + + def __init__(self, label_name='labels', logits_name='logits', pos_weight=None): + super().__init__(reduction='mean', pos_weight=torch.Tensor([pos_weight]).cuda() if pos_weight else None) + self.label_name = label_name + self.logits_name = logits_name + + def __call__(self, **kwargs): + logits = kwargs[self.logits_name] + labels = kwargs[self.label_name] + if logits.ndim == 2: + logits = torch.squeeze(logits) + if labels.dtype != torch.float: + labels = labels.float() + return super().__call__(logits, labels) + + + + + +class MSELoss(torch.nn.MSELoss): + + def __init__(self, label_name='labels'): + super().__init__() + self.label_name = label_name + + def __call__(self, **kwargs): + logits = kwargs['logits'] + labels = kwargs[self.label_name] + if logits.ndim == 2: + logits = torch.squeeze(logits) + return super().__call__(logits, labels) + + +class L1Regularization(): + + def __call__(self, **kwargs): + model = kwargs['model'] + return torch.linalg.norm(torch.cat([p.view(-1) for p in model.parameters()]), ord=1) + + +class L2Regularization(): + + def __call__(self, **kwargs): + model = kwargs['model'] + return torch.linalg.norm(torch.cat([p.view(-1) for p in model.parameters()]), ord=2) + + +class DDPHyperbolicTangentRelaxation(): + + def __init__(self, label_name='labels', logits_name='logits', s_name='sensible_attribute', c=1): + self.label_name = label_name + self.logits_name = logits_name + self.s_name = s_name + self.c = c + + def __call__(self, **kwargs): + logits = kwargs[self.logits_name] + labels = kwargs[self.label_name] + sensible_attribute = kwargs[self.s_name] + + n = logits.shape[0] + logits = torch.sigmoid(logits) + s_negative = logits[sensible_attribute.bool()] + s_positive = logits[~sensible_attribute.bool()] + + return 1 / n * torch.abs(torch.sum(torch.tanh(self.c * torch.relu(s_positive))) - torch.sum( + torch.tanh(self.c * torch.relu(s_negative)))) + + +class DEOHyperbolicTangentRelaxation(): + + def __init__(self, label_name='labels', logits_name='logits', s_name='sensible_attribute', c=1): + self.label_name = label_name + self.logits_name = logits_name + self.s_name = s_name + self.c = c + + def __call__(self, **kwargs): + logits = kwargs[self.logits_name] + labels = kwargs[self.label_name] + sensible_attribute = kwargs[self.s_name] + + n = logits.shape[0] + logits = torch.sigmoid(logits) + s_negative = logits[(sensible_attribute.bool()) & (labels == 1)] + s_positive = logits[(~sensible_attribute.bool()) & (labels == 1)] + + return 1 / n * torch.abs(torch.sum(torch.tanh(self.c * torch.relu(s_positive))) - torch.sum( + torch.tanh(self.c * torch.relu(s_negative)))) + + +""" +Popular problem proposed by + + Carlos Manuel Mira da Fonseca. Multiobjective genetic algorithms with + application to controlengineering problems.PhD thesis, University of Sheffield, 1995. + +with a concave pareto front. + +$ \mathcal{L}_1(\theta) = 1 - \exp{ - || \theta - 1 / \sqrt{d} || $ +$ \mathcal{L}_1(\theta) = 1 - \exp{ - || \theta + 1 / \sqrt{d} || $ + +with $\theta \in R^d$ and $ d = 100$ +""" + + +# +# class Fonseca1(): +# +# def f1(theta): +# d = len(theta) +# sum1 = autograd.numpy.sum([(theta[i] - 1.0 / autograd.numpy.sqrt(d)) ** 2 for i in range(d)]) +# f1 = 1 - autograd.numpy.exp(- sum1) +# return f1 +# +# f1_dx = autograd.grad(f1) +# +# def __call__(self, **kwargs): +# return f1(kwargs['parameters']) +# +# def gradient(self, **kwargs): +# return f1_dx(kwargs['parameters']) +# +# +# class Fonseca2(): +# +# def f2(theta): +# d = len(theta) +# sum1 = autograd.numpy.sum([(theta[i] + 1.0 / autograd.numpy.sqrt(d)) ** 2 for i in range(d)]) +# f1 = 1 - autograd.numpy.exp(- sum1) +# return f1 +# +# f2_dx = autograd.grad(f2) +# +# def __call__(self, **kwargs): +# return f2(kwargs['parameters']) +# +# def gradient(self, **kwargs): +# return f2_dx(kwargs['parameters']) + + + + + +# +# if __name__ == '__main__': +# problem = Fonseca1() diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/__init__.py b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f826955655d0df26a3470abbd49b52585c1f0454 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/__init__.py @@ -0,0 +1,4 @@ +from .vlmop import VLMOP1, VLMOP2 +from .zdt import ZDT1, ZDT2, ZDT3, ZDT4, ZDT6 +from .maf import MAF1 + diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/dtlz.py b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/dtlz.py new file mode 100644 index 0000000000000000000000000000000000000000..7dacd33bf1e1ee5b8724f1b4cb7c8bba61789897 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/dtlz.py @@ -0,0 +1,150 @@ + +import numpy as np +import torch +from ..mop import mop + + + + +class DTLZ1(mop): + + def __init__(self, n_var=30, n_obj=3, lbound=np.zeros(30), + ubound=np.ones(30)): + + super().__init__(n_var=n_var, + n_obj=n_obj, + lbound=lbound, + ubound=ubound) + self.problem_name= 'DTLZ1' + + def _evaluate_torch(self, x: torch.Tensor): + x1 = x[:,0] + x2 = x[:,1] + xm = x[:, 2:] + + g = 100 * (self.n_var - 2 + torch.sum(torch.pow(xm - 0.5, 2) - + torch.cos(20 * np.pi * (xm - 0.5)), dim=1)) + + f1 = 0.5 * x1 * x2 * (1+g) + f2 = 0.5 * x1 * (1 - x2) * (1+g) + f3 = 0.5 * (1 - x1) * (1+g) + return torch.stack((f1, f2, f3), dim=1) + + def _evaluate_numpy(self, x: np.ndarray): + x1 = x[:, 0] + x2 = x[:, 1] + xm = x[:, 2:] + + g = 100 * (self.n_var - 2 + np.sum(np.power(xm - 0.5, 2) - + np.cos(20 * np.pi * (xm - 0.5)), axis=1)) + + f1 = 0.5 * x1 * x2 * (1+g) + f2 = 0.5 * x1 * (1 - x2) * (1+g) + f3 = 0.5 * (1 - x1) * (1+g) + return np.stack((f1, f2, f3), axis=1) + + + +class DTLZ2(mop): + def __init__(self, n_var=30, n_obj=3, lbound=np.zeros(30), + ubound=np.ones(30)): + super().__init__(n_var=n_var, + n_obj=n_obj, + lbound=lbound, + ubound=ubound, ) + self.problem_name = 'DTLZ2' + + def _evaluate_torch(self, x): + xm = x[:, 2:] + g = torch.sum(torch.pow(xm - 0.5, 2), dim=1) + f1 = torch.cos(x[:, 0] * np.pi / 2) * torch.cos(x[:, 1] * np.pi / 2) * (1 + g) + f2 = torch.cos(x[:, 0] * np.pi / 2) * torch.sin(x[:, 1] * np.pi / 2) * (1 + g) + f3 = torch.sin(x[:, 0] * np.pi / 2) * (1 + g) + return torch.stack((f1, f2, f3), dim=1) + + def _evaluate_numpy(self, x): + xm = x[:, 2:] + g = np.sum(np.power(xm - 0.5, 2), axis=1) + f1 = np.cos(x[:, 0] * np.pi / 2) * np.cos(x[:, 1] * np.pi / 2) * (1 + g) + f2 = np.cos(x[:, 0] * np.pi / 2) * np.sin(x[:, 1] * np.pi / 2) * (1 + g) + f3 = np.sin(x[:, 0] * np.pi / 2) * (1 + g) + return np.stack((f1, f2, f3), axis=1) + + +class DTLZ3(mop): + def __init__(self, n_var=30, n_obj=3, lbound=np.zeros(30), + ubound=np.ones(30)): + super().__init__(n_var=n_var, + n_obj=n_obj, + lbound=lbound, + ubound=ubound, ) + self.problem_name = 'DTLZ3' + + + def _evaluate_torch(self, x): + xm = x[:, 2:] + g = 100 * (self.n_var - 2 + torch.sum(torch.pow(xm - 0.5, 2) - + torch.cos(20 * np.pi * (xm - 0.5)), dim=1)) + f1 = torch.cos(x[:, 0] * np.pi / 2) * torch.cos(x[:, 1] * np.pi / 2) * (1 + g) + f2 = torch.cos(x[:, 0] * np.pi / 2) * torch.sin(x[:, 1] * np.pi / 2) * (1 + g) + f3 = torch.sin(x[:, 0] * np.pi / 2) * (1 + g) + return torch.stack((f1, f2, f3), dim=1) + + def _evaluate_numpy(self, x): + xm = x[:, 2:] + + g = 100 * (self.n_var - 2 + np.sum(np.power(xm - 0.5, 2) - + np.cos(20 * np.pi * (xm - 0.5)), axis=1)) + + f1 = np.cos(x[:, 0] * np.pi / 2) * np.cos(x[:, 1] * np.pi / 2) * (1 + g) + f2 = np.cos(x[:, 0] * np.pi / 2) * np.sin(x[:, 1] * np.pi / 2) * (1 + g) + f3 = np.sin(x[:, 0] * np.pi / 2) * (1 + g) + return np.stack((f1, f2, f3), axis=1) + + +class DTLZ4(mop): + def __init__(self, n_var=30, n_obj=3, lbound=np.zeros(30), + ubound=np.ones(30)): + super().__init__(n_var=n_var, + n_obj=n_obj, + lbound=lbound, + ubound=ubound, ) + self.problem_name = 'DTLZ4' + self.alpha = 20 + + def _evaluate_torch(self, x): + xm = x[:, 2:] + g = torch.sum(torch.pow(xm - 0.5, 2), dim=1) + # alpha = 1 + + f1 = torch.cos(x[:, 0] ** self.alpha * np.pi / 2) * torch.cos(x[:, 1] ** self.alpha * np.pi / 2) * (1 + g) + f2 = torch.cos(x[:, 0] ** self.alpha * np.pi / 2) * torch.sin(x[:, 1] ** self.alpha * np.pi / 2) * (1 + g) + f3 = torch.sin(x[:, 0] ** self.alpha * np.pi / 2) * (1 + g) + return torch.stack((f1, f2, f3), dim=1) + + def _evaluate_numpy(self, x): + xm = x[:, 2:] + g = np.sum(np.power(xm - 0.5, 2), axis=1) + + f1 = np.cos(x[:, 0] ** self.alpha * np.pi / 2) * np.cos(x[:, 1] ** self.alpha * np.pi / 2) * (1 + g) + f2 = np.cos(x[:, 0] ** self.alpha * np.pi / 2) * np.sin(x[:, 1] ** self.alpha * np.pi / 2) * (1 + g) + f3 = np.sin(x[:, 0] ** self.alpha * np.pi / 2) * (1 + g) + return np.stack((f1, f2, f3), axis=1 ) + +# DTLZ5, DTLZ6. +# degenerated. + + +# DTLZ7 has disjoint Pareto front. + + + +if __name__ == '__main__': + x = torch.rand(100, 30) + problem = DTLZ4() + + y = problem.evaluate(x) + print( y ) + + + diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/maf.py b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/maf.py new file mode 100644 index 0000000000000000000000000000000000000000..0af3052e294a71427253d2046342c0344e817523 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/maf.py @@ -0,0 +1,44 @@ + +from numpy import array +import torch + + + +class MAF1: + def __init__(self): + ''' + n_obj can be set as any number. For simlicity, we set it as 3. + ''' + self.n_obj = 3 + self.n_var = 30 + self.lb = 0 + self.ub = 1 + + def evaluate(self, x): + if type(x) == torch.Tensor: + + g = torch.sum( torch.pow(x[:, 2:] - 0.5, 2), dim=1 ) + + f1 = (1 - x[:,0] * x[:,1]) * (1 + g) + f2 = (1 - x[:,0] * (1 - x[:,1]) ) * (1 + g) + f3 = x[:,0] * (1 + g) + + return torch.stack((f1, f2, f3), dim=1) + + else: + assert False + + + + def get_pf(self): + return array([[0.0, 0.0, 0.0]]) + + + +if __name__ == '__main__': + x = torch.rand(100, 30) + problem = MAF1() + + y = problem.evaluate(x) + print() + diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/re.py b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/re.py new file mode 100644 index 0000000000000000000000000000000000000000..da701cf8514ff85947b9df02e8fc57235321c65e --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/re.py @@ -0,0 +1,619 @@ + +import numpy as np +import torch +from ..mop import mop + +from numpy import array + + +class RE21(mop): + def __init__(self, n_var=4, n_obj=2, lbound=np.zeros(4), ubound=np.ones(4)): + self.problem_name = 'RE21' + self.n_var = n_var + self.n_obj = n_obj + self.n_cons = 0 + + self.n_original_constraints = 0 + + self.ideal = array([1237.8414230005742, 0.002761423749158419]) + # self.nadir = array([2086.36956042, 0.00341421356237]) + self.nadir = np.array([2886.3695604236013, 0.039999999999998245]) + + F = 10.0 + sigma = 10.0 + tmp_val = F / sigma + self.ubound = np.full(self.n_var, 3 * tmp_val) + self.lbound = np.zeros(self.n_var) + self.lbound[0] = tmp_val + self.lbound[1] = np.sqrt(2.0) * tmp_val + self.lbound[2] = np.sqrt(2.0) * tmp_val + self.lbound[3] = tmp_val + + + def _evaluate_numpy(self, x): + n_sub = len(x) + + x1 = x[:,0] + x2 = x[:,1] + x3 = x[:,2] + x4 = x[:,3] + f = np.zeros((n_sub, self.n_obj) ) + + F = 10.0 + sigma = 10.0 + E = 2.0 * 1e5 + L = 200.0 + + f[:,0] = L * ((2 * x1) + np.sqrt(2.0) * x2 + np.sqrt(x3) + x4) + f[:,1] = ((F * L) / E) * ((2.0 / x1) + (2.0 * np.sqrt(2.0) / x2) - (2.0 * np.sqrt(2.0) / x3) + (2.0 / x4)) + + # f_arr = np.stack((f1,f2), axis=1) + + f_arr_norm = (f - self.ideal) / (self.nadir - self.ideal) + # f_arr_norm = + f_arr_norm[:, 0] = 0.5 * f_arr_norm[:, 0] + + return f_arr_norm + + + def _evaluate_torch(self, x): + x1 = x[:, 0] + x2 = x[:, 1] + x3 = x[:, 2] + x4 = x[:, 3] + + F = 10.0 + sigma = 10.0 + E = 2.0 * 1e5 + L = 200.0 + + f1 = L * ( (2 * x1) + np.sqrt(2.0) * x2 + torch.sqrt(x3) + x4 ) + f2 = ((F * L) / E) * ((2.0 / x1) + (2.0 * np.sqrt(2.0) / x2) - (2.0 * np.sqrt(2.0) / x3) + (2.0 / x4)) + f_arr = torch.stack((f1, f2), dim=1) + f_arr_norm = (f_arr - self.ideal) / (self.nadir - self.ideal) + f_arr_norm[:, 0] = 0.5 * f_arr_norm[:, 0] + return f_arr_norm + + +class RE22(mop): + def __init__(self, n_var=3, n_obj=2, lbound=np.zeros(30), + ubound=np.ones(30)): + + + self.n_var=n_var + self.n_obj=n_obj + self.problem_name = 'RE22' + self.n_cons = 0 + self.n_original_constraints = 2 + + self.ideal = np.array([5.88, 0.0]) + self.nadir = np.array([361.262944647, 180.01547]) + + + self.ubound = np.zeros(self.n_var) + self.lbound = np.zeros(self.n_var) + + self.lbound[0] = 0.2 + self.lbound[1] = 0.0 + self.lbound[2] = 0.0 + self.ubound[0] = 15 + self.ubound[1] = 20 + self.ubound[2] = 40 + + self.n_var = n_var + self.n_obj = n_obj + + self.feasible_vals = np.array( + [0.20, 0.31, 0.40, 0.44, 0.60, 0.62, 0.79, 0.80, 0.88, 0.93, 1.0, 1.20, 1.24, 1.32, 1.40, 1.55, 1.58, 1.60, + 1.76, 1.80, 1.86, 2.0, 2.17, 2.20, 2.37, 2.40, 2.48, 2.60, 2.64, 2.79, 2.80, 3.0, 3.08, 3, 10, 3.16, 3.41, + 3.52, 3.60, 3.72, 3.95, 3.96, 4.0, 4.03, 4.20, 4.34, 4.40, 4.65, 4.74, 4.80, 4.84, 5.0, 5.28, 5.40, 5.53, + 5.72, 6.0, 6.16, 6.32, 6.60, 7.11, 7.20, 7.80, 7.90, 8.0, 8.40, 8.69, 9.0, 9.48, 10.27, 11.0, 11.06, 11.85, + 12.0, 13.0, 14.0, 15.0]) + + + def _evaluate_numpy(self, x): + n_sub = len(x) + + f = np.zeros( (n_sub, self.n_obj) ) + g = np.zeros( (n_sub, self.n_original_constraints) ) + # Reference: getNearestValue_sample2.py (https://gist.github.com/icchi-h/1d0bb1c52ebfdd31f14b3e811328390a) + idx_arr = [np.abs(np.asarray(self.feasible_vals) - x0).argmin() for x0 in x[:,0]] + x1 = array([self.feasible_vals[idx] for idx in idx_arr]) + x2 = x[:,1] + x3 = x[:,2] + + # First original objective function + f[:,0] = (29.4 * x1) + (0.6 * x2 * x3) + # Original constraint functions + g[:,0] = (x1 * x3) - 7.735 * ((x1 * x1) / x2) - 180.0 + g[:,1] = 4.0 - (x3 / x2) + g = np.where(g < 0, -g, 0) + f[:,1] = g[:,0] + g[:,1] + f_norm = (f - self.ideal) / (self.nadir - self.ideal) + f_norm[:, 0] = 0.5 * f_norm[:, 0] + + return f_norm + + def _evaluate_torch(self, x): + pass + + +class RE23(mop): + def __init__(self, n_var=4, n_obj=2, lbound=np.zeros(2), + ubound=np.ones(2)): + self.problem_name = 'RE23' + self.n_obj = n_obj + self.n_var = n_var + self.n_cons = 0 + self.n_original_constraints = 3 + + self.ideal = array([15.9018007813, 0.0]) + self.nadir = array([481.608088535, 44.2819047619]) + + self.ubound = np.zeros(self.n_var) + self.lbound = np.zeros(self.n_var) + self.lbound[0] = 1 + self.lbound[1] = 1 + self.lbound[2] = 10 + self.lbound[3] = 10 + self.ubound[0] = 100 + self.ubound[1] = 100 + self.ubound[2] = 200 + self.ubound[3] = 240 + + def _evaluate_numpy(self, x): + + f = np.zeros( (len(x), self.n_obj) ) + g = np.zeros( (len(x), self.n_original_constraints)) + + x1 = 0.0625 * np.round(x[:,0]).astype(np.int32) + x2 = 0.0625 * np.round(x[:,1]).astype(np.int32) + + x3 = x[:,2] + x4 = x[:,3] + + # First original objective function + f[:,0] = (0.6224 * x1 * x3 * x4) + (1.7781 * x2 * x3 * x3) + (3.1661 * x1 * x1 * x4) + (19.84 * x1 * x1 * x3) + + # Original constraint functions + g[:,0] = x1 - (0.0193 * x3) + g[:,1] = x2 - (0.00954 * x3) + g[:,2] = (np.pi * x3 * x3 * x4) + ((4.0 / 3.0) * (np.pi * x3 * x3 * x3)) - 1296000 + g = np.where(g < 0, -g, 0) + f[:,1] = g[:,0] + g[:,1] + g[:,2] + + f_norm = (f - self.ideal) / (self.nadir - self.ideal) + + return f_norm + + + + + +class RE24(mop): + def __init__(self, n_var=2, n_obj=2, lbound=np.zeros(2), + ubound=np.ones(2)): + super().__init__(n_var=n_var, + n_obj=n_obj, + lbound=lbound, + ubound=ubound, ) + + self.problem_name = 'RE24' + self.n_obj = 2 + self.n_var = 2 + + self.n_cons = 0 + self.n_original_constraints = 4 + + self.ubound = np.zeros(self.n_var) + self.lbound = np.zeros(self.n_var) + + self.lbound[0] = 0.5 + self.lbound[1] = 0.5 + + self.ubound[0] = 4 + self.ubound[1] = 50 + + self.ideal = np.array([60.5, 0.0]) + self.nadir = np.array([481.608088535, 44.2819047619]) + + + + + def _evaluate_numpy(self, x): + n_sub = len(x) + # f = np.zeros(self.n_objectives) + g = np.zeros( (n_sub, self.n_original_constraints) ) + + x1 = x[:,0] + x2 = x[:,1] + + # First original objective function + f1 = x1 + (120 * x2) + + E = 700000 + sigma_b_max = 700 + tau_max = 450 + delta_max = 1.5 + sigma_k = (E * x1 * x1) / 100 + sigma_b = 4500 / (x1 * x2) + tau = 1800 / x2 + delta = (56.2 * 10000) / (E * x1 * x2 * x2) + + g[:,0] = 1 - (sigma_b / sigma_b_max) + g[:,1] = 1 - (tau / tau_max) + g[:,2] = 1 - (delta / delta_max) + g[:,3] = 1 - (sigma_b / sigma_k) + g = np.where(g < 0, -g, 0) + f2 = g[:,0] + g[:,1] + g[:,2] + g[:,3] + + f_arr = np.stack((f1, f2), axis=1) + f_norm = (f_arr - self.ideal) / (self.nadir - self.ideal) + + return f_norm + + + def _evaluate_torch(self, x): + pass + + +class RE25(mop): + def __init__(self, n_var=3, n_obj=2): + self.problem_name = 'RE25' + self.n_obj = n_obj + self.n_var = n_var + + self.n_cons = 0 + self.n_original_constraints = 6 + + self.ideal = array([0.037591349242869145, 0.0]) + self.nadir = array([0.40397042546, 2224669.22419]) + + self.ubound = np.zeros( self.n_var ) + self.lbound = np.zeros( self.n_var ) + self.lbound[0] = 1 + self.lbound[1] = 0.6 + self.lbound[2] = 0.09 + self.ubound[0] = 70 + self.ubound[1] = 3 + self.ubound[2] = 0.5 + + self.feasible_vals = np.array( + [0.009, 0.0095, 0.0104, 0.0118, 0.0128, 0.0132, 0.014, 0.015, 0.0162, 0.0173, 0.018, 0.02, 0.023, 0.025, + 0.028, 0.032, 0.035, 0.041, 0.047, 0.054, 0.063, 0.072, 0.08, 0.092, 0.105, 0.12, 0.135, 0.148, 0.162, + 0.177, 0.192, 0.207, 0.225, 0.244, 0.263, 0.283, 0.307, 0.331, 0.362, 0.394, 0.4375, 0.5]) + + def _evaluate_numpy(self, x): + n_sub = len(x) + f = np.zeros( (n_sub, self.n_obj) ) + g = np.zeros( (n_sub, self.n_original_constraints) ) + x1 = np.round(x[:,0]) + x2 = x[:,1] + + # Reference: getNearestValue_sample2.py (https://gist.github.com/icchi-h/1d0bb1c52ebfdd31f14b3e811328390a) + idx_array = array([np.abs(np.asarray(self.feasible_vals) - x2).argmin() for x2 in x[:,2]]) + + x3 = array( [self.feasible_vals[idx] for idx in idx_array] ) + + # first original objective function + f[:,0] = (np.pi * np.pi * x2 * x3 * x3 * (x1 + 2)) / 4.0 + + # constraint functions + Cf = ((4.0 * (x2 / x3) - 1) / (4.0 * (x2 / x3) - 4)) + (0.615 * x3 / x2) + Fmax = 1000.0 + S = 189000.0 + G = 11.5 * 1e+6 + K = (G * x3 * x3 * x3 * x3) / (8 * x1 * x2 * x2 * x2) + lmax = 14.0 + lf = (Fmax / K) + 1.05 * (x1 + 2) * x3 + dmin = 0.2 + Dmax = 3 + Fp = 300.0 + sigmaP = Fp / K + sigmaPM = 6 + sigmaW = 1.25 + + g[:,0] = -((8 * Cf * Fmax * x2) / (np.pi * x3 * x3 * x3)) + S + g[:,1] = -lf + lmax + g[:,2] = -3 + (x2 / x3) + g[:,3] = -sigmaP + sigmaPM + g[:,4] = -sigmaP - ((Fmax - Fp) / K) - 1.05 * (x1 + 2) * x3 + lf + g[:,5] = sigmaW - ((Fmax - Fp) / K) + + g = np.where(g < 0, -g, 0) + f[:,1] = g[:,0] + g[:,1] + g[:,2] + g[:,3] + g[:,4] + g[:,5] + + f_norm = (f - self.ideal) / (self.nadir - self.ideal) + return f_norm + + def _evaluate_torch(self, x): + pass + + +class RE31(mop): + def __init__(self, n_obj=3, n_var=3): + self.problem_name = 'RE31' + self.n_obj = n_obj + self.n_var = n_var + self.n_cons = 0 + self.n_original_constraints = 3 + + self.ubound = np.zeros(self.n_var) + self.lbound = np.zeros(self.n_var) + self.lbound[0] = 0.00001 + self.lbound[1] = 0.00001 + self.lbound[2] = 1.0 + self.ubound[0] = 100.0 + self.ubound[1] = 100.0 + self.ubound[2] = 3.0 + + self.ideal = np.array([5.53731918799e-05, 0.333333333333, 0.0]) + self.nadir = np.array([500.002668442, 8246211.25124, 19359919.7502]) + + + def _evaluate_numpy(self, x): + n_sub = len(x) + f = np.zeros( (n_sub, self.n_obj) ) + g = np.zeros( (n_sub, self.n_original_constraints) ) + + x1 = x[:,0] + x2 = x[:,1] + x3 = x[:,2] + + # First original objective function + f[:,0] = x1 * np.sqrt(16.0 + (x3 * x3)) + x2 * np.sqrt(1.0 + x3 * x3) + # Second original objective function + f[:,1] = (20.0 * np.sqrt(16.0 + (x3 * x3))) / (x1 * x3) + + # Constraint functions + g[:,0] = 0.1 - f[:,0] + g[:,1] = 100000.0 - f[:,1] + g[:,2] = 100000 - ((80.0 * np.sqrt(1.0 + x3 * x3)) / (x3 * x2)) + g = np.where(g < 0, -g, 0) + f[:,2] = g[:,0] + g[:,1] + g[:,2] + + f_norm = (f - self.ideal) / (self.nadir - self.ideal) + return f_norm + + def _evaluate_torch(self, x): + pass + + +class RE37(mop): + def __init__(self, n_obj=3, n_var=4): + self.problem_name = 'RE37' + self.n_obj = n_obj + self.n_var = n_var + self.n_cons = 0 + self.n_original_constraints = 0 + + self.lbound = np.full(self.n_var, 0) + self.ubound = np.full(self.n_var, 1) + + self.ideal = np.array([0.00889341391106, 0.00488, -0.431499999825]) + self.nadir = np.array([0.98949120096, 0.956587924661, 0.987530948586]) + + + def _evaluate_numpy(self, x): + n_sub = len(x) + f = np.zeros( (n_sub, self.n_obj) ) + + xAlpha = x[:,0] + xHA = x[:,1] + xOA = x[:,2] + xOPTT = x[:,3] + + # f1 (TF_max) + f[:,0] = 0.692 + (0.477 * xAlpha) - (0.687 * xHA) - (0.080 * xOA) - (0.0650 * xOPTT) - ( + 0.167 * xAlpha * xAlpha) - (0.0129 * xHA * xAlpha) + (0.0796 * xHA * xHA) - ( + 0.0634 * xOA * xAlpha) - (0.0257 * xOA * xHA) + (0.0877 * xOA * xOA) - ( + 0.0521 * xOPTT * xAlpha) + (0.00156 * xOPTT * xHA) + (0.00198 * xOPTT * xOA) + ( + 0.0184 * xOPTT * xOPTT) + # f2 (X_cc) + f[:,1] = 0.153 - (0.322 * xAlpha) + (0.396 * xHA) + (0.424 * xOA) + (0.0226 * xOPTT) + ( + 0.175 * xAlpha * xAlpha) + (0.0185 * xHA * xAlpha) - (0.0701 * xHA * xHA) - ( + 0.251 * xOA * xAlpha) + (0.179 * xOA * xHA) + (0.0150 * xOA * xOA) + ( + 0.0134 * xOPTT * xAlpha) + (0.0296 * xOPTT * xHA) + (0.0752 * xOPTT * xOA) + ( + 0.0192 * xOPTT * xOPTT) + # f3 (TT_max) + f[:,2] = 0.370 - (0.205 * xAlpha) + (0.0307 * xHA) + (0.108 * xOA) + (1.019 * xOPTT) - ( + 0.135 * xAlpha * xAlpha) + (0.0141 * xHA * xAlpha) + (0.0998 * xHA * xHA) + ( + 0.208 * xOA * xAlpha) - (0.0301 * xOA * xHA) - (0.226 * xOA * xOA) + ( + 0.353 * xOPTT * xAlpha) - (0.0497 * xOPTT * xOA) - (0.423 * xOPTT * xOPTT) + ( + 0.202 * xHA * xAlpha * xAlpha) - (0.281 * xOA * xAlpha * xAlpha) - ( + 0.342 * xHA * xHA * xAlpha) - (0.245 * xHA * xHA * xOA) + (0.281 * xOA * xOA * xHA) - ( + 0.184 * xOPTT * xOPTT * xAlpha) - (0.281 * xHA * xAlpha * xOA) + + f_norm = (f - self.ideal) / (self.nadir - self.ideal) + return f_norm + + + +class RE41(mop): + def __init__(self, n_obj=4, n_var=7): + self.problem_name = 'RE41' + self.n_obj = n_obj + self.n_var = n_var + self.n_cons = 0 + self.n_original_constraints = 10 + + self.lbound = np.zeros(self.n_var) + self.ubound = np.zeros(self.n_var) + self.lbound[0] = 0.5 + self.lbound[1] = 0.45 + self.lbound[2] = 0.5 + self.lbound[3] = 0.5 + self.lbound[4] = 0.875 + self.lbound[5] = 0.4 + self.lbound[6] = 0.4 + self.ubound[0] = 1.5 + self.ubound[1] = 1.35 + self.ubound[2] = 1.5 + self.ubound[3] = 1.5 + self.ubound[4] = 2.625 + self.ubound[5] = 1.2 + self.ubound[6] = 1.2 + + self.ideal = np.array([15.576004, 3.58525, 10.61064375, 0.0]) + self.nadir = np.array([39.2905121788, 4.42725, 13.09138125, 9.49401929991]) + + + def _evaluate_numpy(self, x): + n_sub = len(x) + + f = np.zeros( (n_sub, self.n_obj) ) + g = np.zeros( (n_sub, self.n_original_constraints) ) + + x1 = x[:,0] + x2 = x[:,1] + x3 = x[:,2] + x4 = x[:,3] + x5 = x[:,4] + x6 = x[:,5] + x7 = x[:,6] + + # First original objective function + f[:,0] = 1.98 + 4.9 * x1 + 6.67 * x2 + 6.98 * x3 + 4.01 * x4 + 1.78 * x5 + 0.00001 * x6 + 2.73 * x7 + # Second original objective function + f[:,1] = 4.72 - 0.5 * x4 - 0.19 * x2 * x3 + # Third original objective function + Vmbp = 10.58 - 0.674 * x1 * x2 - 0.67275 * x2 + Vfd = 16.45 - 0.489 * x3 * x7 - 0.843 * x5 * x6 + f[:,2] = 0.5 * (Vmbp + Vfd) + + # Constraint functions + g[:,0] = 1 - (1.16 - 0.3717 * x2 * x4 - 0.0092928 * x3) + g[:,1] = 0.32 - (0.261 - 0.0159 * x1 * x2 - 0.06486 * x1 - 0.019 * x2 * x7 + 0.0144 * x3 * x5 + 0.0154464 * x6) + g[:,2] = 0.32 - ( + 0.214 + 0.00817 * x5 - 0.045195 * x1 - 0.0135168 * x1 + 0.03099 * x2 * x6 - 0.018 * x2 * x7 + 0.007176 * x3 + 0.023232 * x3 - 0.00364 * x5 * x6 - 0.018 * x2 * x2) + g[:,3] = 0.32 - (0.74 - 0.61 * x2 - 0.031296 * x3 - 0.031872 * x7 + 0.227 * x2 * x2) + g[:,4] = 32 - (28.98 + 3.818 * x3 - 4.2 * x1 * x2 + 1.27296 * x6 - 2.68065 * x7) + g[:,5] = 32 - (33.86 + 2.95 * x3 - 5.057 * x1 * x2 - 3.795 * x2 - 3.4431 * x7 + 1.45728) + g[:,6] = 32 - (46.36 - 9.9 * x2 - 4.4505 * x1) + g[:,7] = 4 - f[:,1] + g[:,8] = 9.9 - Vmbp + g[:,9] = 15.7 - Vfd + g = np.where(g < 0, -g, 0) + f[:,3] = g[:,0] + g[:,1] + g[:,2] + g[:,3] + g[:,4] + g[:,5] + g[:,6] + g[:,7] + g[:,8] + g[:,9] + f_norm = (f - self.ideal) / (self.nadir - self.ideal) + return f_norm + + def _evaluate_torch(self, x): + pass + + +class RE42(mop): + def __init__(self): + self.problem_name = 'RE42' + + self.n_obj = 4 + self.n_var = 6 + self.n_cons = 0 + self.n_original_constraints = 9 + + self.lbound = np.zeros(self.n_var ) + self.ubound = np.zeros(self.n_var ) + self.lbound[0] = 150.0 + self.lbound[1] = 20.0 + self.lbound[2] = 13.0 + self.lbound[3] = 10.0 + self.lbound[4] = 14.0 + self.lbound[5] = 0.63 + self.ubound[0] = 274.32 + self.ubound[1] = 32.31 + self.ubound[2] = 25.0 + self.ubound[3] = 11.71 + self.ubound[4] = 18.0 + self.ubound[5] = 0.75 + + self.ideal = np.array([-2756.2590400638524, 3962.557843228888, 1947.880856925791, 0.0]) + self.nadir = np.array([-1010.5229595219643, 13827.138456300128, 2611.9668107424536, 12.437669929732023 ]) + + + + def _evaluate_numpy(self, x): + n_sub = len(x) + + f = np.zeros( (n_sub, self.n_obj) ) + # NOT g + constraintFuncs = np.zeros( (n_sub, self.n_original_constraints) ) + + x_L = x[:,0] + x_B = x[:,1] + x_D = x[:,2] + x_T = x[:,3] + x_Vk = x[:,4] + x_CB = x[:,5] + + displacement = 1.025 * x_L * x_B * x_T * x_CB + V = 0.5144 * x_Vk + g = 9.8065 + Fn = V / np.power(g * x_L, 0.5) + a = (4977.06 * x_CB * x_CB) - (8105.61 * x_CB) + 4456.51 + b = (-10847.2 * x_CB * x_CB) + (12817.0 * x_CB) - 6960.32 + + power = (np.power(displacement, 2.0 / 3.0) * np.power(x_Vk, 3.0)) / (a + (b * Fn)) + outfit_weight = 1.0 * np.power(x_L, 0.8) * np.power(x_B, 0.6) * np.power(x_D, 0.3) * np.power(x_CB, 0.1) + steel_weight = 0.034 * np.power(x_L, 1.7) * np.power(x_B, 0.7) * np.power(x_D, 0.4) * np.power(x_CB, 0.5) + machinery_weight = 0.17 * np.power(power, 0.9) + light_ship_weight = steel_weight + outfit_weight + machinery_weight + + ship_cost = 1.3 * ((2000.0 * np.power(steel_weight, 0.85)) + (3500.0 * outfit_weight) + ( + 2400.0 * np.power(power, 0.8))) + capital_costs = 0.2 * ship_cost + + DWT = displacement - light_ship_weight + + running_costs = 40000.0 * np.power(DWT, 0.3) + + round_trip_miles = 5000.0 + sea_days = (round_trip_miles / 24.0) * x_Vk + handling_rate = 8000.0 + + daily_consumption = ((0.19 * power * 24.0) / 1000.0) + 0.2 + fuel_price = 100.0 + fuel_cost = 1.05 * daily_consumption * sea_days * fuel_price + port_cost = 6.3 * np.power(DWT, 0.8) + + fuel_carried = daily_consumption * (sea_days + 5.0) + miscellaneous_DWT = 2.0 * np.power(DWT, 0.5) + + cargo_DWT = DWT - fuel_carried - miscellaneous_DWT + port_days = 2.0 * ((cargo_DWT / handling_rate) + 0.5) + RTPA = 350.0 / (sea_days + port_days) + + voyage_costs = (fuel_cost + port_cost) * RTPA + annual_costs = capital_costs + running_costs + voyage_costs + annual_cargo = cargo_DWT * RTPA + + f[:,0] = annual_costs / annual_cargo + f[:,1] = light_ship_weight + # f_2 is dealt as a minimization problem + f[:,2] = -annual_cargo + + # Reformulated objective functions + constraintFuncs[:,0] = (x_L / x_B) - 6.0 + constraintFuncs[:,1] = -(x_L / x_D) + 15.0 + constraintFuncs[:,2] = -(x_L / x_T) + 19.0 + constraintFuncs[:,3] = 0.45 * np.power(DWT, 0.31) - x_T + constraintFuncs[:,4] = 0.7 * x_D + 0.7 - x_T + constraintFuncs[:,5] = 500000.0 - DWT + constraintFuncs[:,6] = DWT - 3000.0 + constraintFuncs[:,7] = 0.32 - Fn + + KB = 0.53 * x_T + BMT = ((0.085 * x_CB - 0.002) * x_B * x_B) / (x_T * x_CB) + KG = 1.0 + 0.52 * x_D + constraintFuncs[:,8] = (KB + BMT - KG) - (0.07 * x_B) + + constraintFuncs = np.where(constraintFuncs < 0, -constraintFuncs, 0) + f[:,3] = constraintFuncs[:,0] + constraintFuncs[:,1] + constraintFuncs[:,2] + constraintFuncs[:,3] + constraintFuncs[:,4] + \ + constraintFuncs[:,5] + constraintFuncs[:,6] + constraintFuncs[:,7] + constraintFuncs[:,8] + + f_norm = (f - self.ideal) / (self.nadir - self.ideal) + return f + + + + + diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/re_original.py b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/re_original.py new file mode 100644 index 0000000000000000000000000000000000000000..b1ce39c4c368606151e87e6201698c21d95ad05c --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/re_original.py @@ -0,0 +1,1335 @@ +#!/usr/bin/env python +""" + A real-world multimnist-objective problem suite (the RE benchmark set) + Reference: + Ryoji Tanabe, Hisao Ishibuchi, "An Easy-to-use Real-world Multi-objective Problem Suite" Applied Soft Computing. 89: 106078 (2020) + Copyright (c) 2020 Ryoji Tanabe + + I re-implemented the RE problem set by referring to its C source code (reproblem.c). While variables directly copied from the C source code are written in CamelCase, the other variables are written in snake_case. It is somewhat awkward. + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" +import numpy as np + +class RE21(): + def __init__(self, n_var=4, n_obj=2, lower_bound=np.zeros(30), + upper_bound=np.ones(30)): + self.problem_name = 'RE21' + self.n_constraints = 0 + self.n_original_constraints = 0 + + F = 10.0 + sigma = 10.0 + tmp_val = F / sigma + + self.ubound = np.full(self.n_variables, 3 * tmp_val) + self.lbound = np.zeros(self.n_variables) + self.lbound[0] = tmp_val + self.lbound[1] = np.sqrt(2.0) * tmp_val + self.lbound[2] = np.sqrt(2.0) * tmp_val + self.lbound[3] = tmp_val + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + x1 = x[0] + x2 = x[1] + x3 = x[2] + x4 = x[3] + + F = 10.0 + sigma = 10.0 + E = 2.0 * 1e5 + L = 200.0 + + f[0] = L * ((2 * x1) + np.sqrt(2.0) * x2 + np.sqrt(x3) + x4) + f[1] = ((F * L) / E) * ((2.0 / x1) + (2.0 * np.sqrt(2.0) / x2) - (2.0 * np.sqrt(2.0) / x3) + (2.0 / x4)) + + return f + + +class RE22(): + def __init__(self): + self.problem_name = 'RE22' + self.n_objectives = 2 + self.n_variables = 3 + + self.n_constraints = 0 + self.n_original_constraints = 2 + + self.ubound = np.zeros(self.n_variables) + self.lbound = np.zeros(self.n_variables) + self.lbound[0] = 0.2 + self.lbound[1] = 0.0 + self.lbound[2] = 0.0 + self.ubound[0] = 15 + self.ubound[1] = 20 + self.ubound[2] = 40 + + self.feasible_vals = np.array( + [0.20, 0.31, 0.40, 0.44, 0.60, 0.62, 0.79, 0.80, 0.88, 0.93, 1.0, 1.20, 1.24, 1.32, 1.40, 1.55, 1.58, 1.60, + 1.76, 1.80, 1.86, 2.0, 2.17, 2.20, 2.37, 2.40, 2.48, 2.60, 2.64, 2.79, 2.80, 3.0, 3.08, 3, 10, 3.16, 3.41, + 3.52, 3.60, 3.72, 3.95, 3.96, 4.0, 4.03, 4.20, 4.34, 4.40, 4.65, 4.74, 4.80, 4.84, 5.0, 5.28, 5.40, 5.53, + 5.72, 6.0, 6.16, 6.32, 6.60, 7.11, 7.20, 7.80, 7.90, 8.0, 8.40, 8.69, 9.0, 9.48, 10.27, 11.0, 11.06, 11.85, + 12.0, 13.0, 14.0, 15.0]) + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_original_constraints) + # Reference: getNearestValue_sample2.py (https://gist.github.com/icchi-h/1d0bb1c52ebfdd31f14b3e811328390a) + idx = np.abs(np.asarray(self.feasible_vals) - x[0]).argmin() + x1 = self.feasible_vals[idx] + x2 = x[1] + x3 = x[2] + + # First original objective function + f[0] = (29.4 * x1) + (0.6 * x2 * x3) + + # Original constraint functions + g[0] = (x1 * x3) - 7.735 * ((x1 * x1) / x2) - 180.0 + g[1] = 4.0 - (x3 / x2) + g = np.where(g < 0, -g, 0) + f[1] = g[0] + g[1] + + return f + + +class RE23(): + def __init__(self): + self.problem_name = 'RE23' + self.n_objectives = 2 + self.n_variables = 4 + self.n_constraints = 0 + self.n_original_constraints = 3 + + self.ubound = np.zeros(self.n_variables) + self.lbound = np.zeros(self.n_variables) + self.lbound[0] = 1 + self.lbound[1] = 1 + self.lbound[2] = 10 + self.lbound[3] = 10 + self.ubound[0] = 100 + self.ubound[1] = 100 + self.ubound[2] = 200 + self.ubound[3] = 240 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_original_constraints) + + x1 = 0.0625 * int(np.round(x[0])) + x2 = 0.0625 * int(np.round(x[1])) + x3 = x[2] + x4 = x[3] + + # First original objective function + f[0] = (0.6224 * x1 * x3 * x4) + (1.7781 * x2 * x3 * x3) + (3.1661 * x1 * x1 * x4) + (19.84 * x1 * x1 * x3) + + # Original constraint functions + g[0] = x1 - (0.0193 * x3) + g[1] = x2 - (0.00954 * x3) + g[2] = (np.pi * x3 * x3 * x4) + ((4.0 / 3.0) * (np.pi * x3 * x3 * x3)) - 1296000 + g = np.where(g < 0, -g, 0) + f[1] = g[0] + g[1] + g[2] + + return f + + +class RE24(): + def __init__(self): + self.problem_name = 'RE24' + self.n_objectives = 2 + self.n_variables = 2 + self.n_constraints = 0 + self.n_original_constraints = 4 + + self.ubound = np.zeros(self.n_variables) + self.lbound = np.zeros(self.n_variables) + self.lbound[0] = 0.5 + self.lbound[1] = 0.5 + self.ubound[0] = 4 + self.ubound[1] = 50 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_original_constraints) + + x1 = x[0] + x2 = x[1] + + # First original objective function + f[0] = x1 + (120 * x2) + + E = 700000 + sigma_b_max = 700 + tau_max = 450 + delta_max = 1.5 + sigma_k = (E * x1 * x1) / 100 + sigma_b = 4500 / (x1 * x2) + tau = 1800 / x2 + delta = (56.2 * 10000) / (E * x1 * x2 * x2) + + g[0] = 1 - (sigma_b / sigma_b_max) + g[1] = 1 - (tau / tau_max) + g[2] = 1 - (delta / delta_max) + g[3] = 1 - (sigma_b / sigma_k) + g = np.where(g < 0, -g, 0) + f[1] = g[0] + g[1] + g[2] + g[3] + + return f + + +class RE25(): + def __init__(self): + self.problem_name = 'RE25' + self.n_objectives = 2 + self.n_variables = 3 + self.n_constraints = 0 + self.n_original_constraints = 6 + + self.ubound = np.zeros(self.n_variables) + self.lbound = np.zeros(self.n_variables) + self.lbound[0] = 1 + self.lbound[1] = 0.6 + self.lbound[2] = 0.09 + self.ubound[0] = 70 + self.ubound[1] = 3 + self.ubound[2] = 0.5 + + self.feasible_vals = np.array( + [0.009, 0.0095, 0.0104, 0.0118, 0.0128, 0.0132, 0.014, 0.015, 0.0162, 0.0173, 0.018, 0.02, 0.023, 0.025, + 0.028, 0.032, 0.035, 0.041, 0.047, 0.054, 0.063, 0.072, 0.08, 0.092, 0.105, 0.12, 0.135, 0.148, 0.162, + 0.177, 0.192, 0.207, 0.225, 0.244, 0.263, 0.283, 0.307, 0.331, 0.362, 0.394, 0.4375, 0.5]) + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_original_constraints) + + x1 = np.round(x[0]) + x2 = x[1] + # Reference: getNearestValue_sample2.py (https://gist.github.com/icchi-h/1d0bb1c52ebfdd31f14b3e811328390a) + idx = np.abs(np.asarray(self.feasible_vals) - x[2]).argmin() + x3 = self.feasible_vals[idx] + + # first original objective function + f[0] = (np.pi * np.pi * x2 * x3 * x3 * (x1 + 2)) / 4.0 + + # constraint functions + Cf = ((4.0 * (x2 / x3) - 1) / (4.0 * (x2 / x3) - 4)) + (0.615 * x3 / x2) + Fmax = 1000.0 + S = 189000.0 + G = 11.5 * 1e+6 + K = (G * x3 * x3 * x3 * x3) / (8 * x1 * x2 * x2 * x2) + lmax = 14.0 + lf = (Fmax / K) + 1.05 * (x1 + 2) * x3 + dmin = 0.2 + Dmax = 3 + Fp = 300.0 + sigmaP = Fp / K + sigmaPM = 6 + sigmaW = 1.25 + + g[0] = -((8 * Cf * Fmax * x2) / (np.pi * x3 * x3 * x3)) + S + g[1] = -lf + lmax + g[2] = -3 + (x2 / x3) + g[3] = -sigmaP + sigmaPM + g[4] = -sigmaP - ((Fmax - Fp) / K) - 1.05 * (x1 + 2) * x3 + lf + g[5] = sigmaW - ((Fmax - Fp) / K) + + g = np.where(g < 0, -g, 0) + f[1] = g[0] + g[1] + g[2] + g[3] + g[4] + g[5] + + return f + + +class RE31(): + def __init__(self): + self.problem_name = 'RE31' + self.n_objectives = 3 + self.n_variables = 3 + self.n_constraints = 0 + self.n_original_constraints = 3 + + self.ubound = np.zeros(self.n_variables) + self.lbound = np.zeros(self.n_variables) + self.lbound[0] = 0.00001 + self.lbound[1] = 0.00001 + self.lbound[2] = 1.0 + self.ubound[0] = 100.0 + self.ubound[1] = 100.0 + self.ubound[2] = 3.0 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_original_constraints) + + x1 = x[0] + x2 = x[1] + x3 = x[2] + + # First original objective function + f[0] = x1 * np.sqrt(16.0 + (x3 * x3)) + x2 * np.sqrt(1.0 + x3 * x3) + # Second original objective function + f[1] = (20.0 * np.sqrt(16.0 + (x3 * x3))) / (x1 * x3) + + # Constraint functions + g[0] = 0.1 - f[0] + g[1] = 100000.0 - f[1] + g[2] = 100000 - ((80.0 * np.sqrt(1.0 + x3 * x3)) / (x3 * x2)) + g = np.where(g < 0, -g, 0) + f[2] = g[0] + g[1] + g[2] + + return f + + +class RE32(): + def __init__(self): + self.problem_name = 'RE32' + self.n_objectives = 3 + self.n_variables = 4 + self.n_constraints = 0 + self.n_original_constraints = 4 + + self.ubound = np.zeros(self.n_variables) + self.lbound = np.zeros(self.n_variables) + self.lbound[0] = 0.125 + self.lbound[1] = 0.1 + self.lbound[2] = 0.1 + self.lbound[3] = 0.125 + self.ubound[0] = 5.0 + self.ubound[1] = 10.0 + self.ubound[2] = 10.0 + self.ubound[3] = 5.0 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_original_constraints) + + x1 = x[0] + x2 = x[1] + x3 = x[2] + x4 = x[3] + + P = 6000 + L = 14 + E = 30 * 1e6 + + # // deltaMax = 0.25 + G = 12 * 1e6 + tauMax = 13600 + sigmaMax = 30000 + + # First original objective function + f[0] = (1.10471 * x1 * x1 * x2) + (0.04811 * x3 * x4) * (14.0 + x2) + # Second original objective function + f[1] = (4 * P * L * L * L) / (E * x4 * x3 * x3 * x3) + + # Constraint functions + M = P * (L + (x2 / 2)) + tmpVar = ((x2 * x2) / 4.0) + np.power((x1 + x3) / 2.0, 2) + R = np.sqrt(tmpVar) + tmpVar = ((x2 * x2) / 12.0) + np.power((x1 + x3) / 2.0, 2) + J = 2 * np.sqrt(2) * x1 * x2 * tmpVar + + tauDashDash = (M * R) / J + tauDash = P / (np.sqrt(2) * x1 * x2) + tmpVar = tauDash * tauDash + ((2 * tauDash * tauDashDash * x2) / (2 * R)) + (tauDashDash * tauDashDash) + tau = np.sqrt(tmpVar) + sigma = (6 * P * L) / (x4 * x3 * x3) + tmpVar = 4.013 * E * np.sqrt((x3 * x3 * x4 * x4 * x4 * x4 * x4 * x4) / 36.0) / (L * L) + tmpVar2 = (x3 / (2 * L)) * np.sqrt(E / (4 * G)) + PC = tmpVar * (1 - tmpVar2) + + g[0] = tauMax - tau + g[1] = sigmaMax - sigma + g[2] = x4 - x1 + g[3] = PC - P + g = np.where(g < 0, -g, 0) + f[2] = g[0] + g[1] + g[2] + g[3] + + return f + + +class RE33(): + def __init__(self): + self.problem_name = 'RE33' + self.n_objectives = 3 + self.n_variables = 4 + self.n_constraints = 0 + self.n_original_constraints = 4 + + self.ubound = np.zeros(self.n_variables) + self.lbound = np.zeros(self.n_variables) + self.lbound[0] = 55 + self.lbound[1] = 75 + self.lbound[2] = 1000 + self.lbound[3] = 11 + self.ubound[0] = 80 + self.ubound[1] = 110 + self.ubound[2] = 3000 + self.ubound[3] = 20 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_original_constraints) + + x1 = x[0] + x2 = x[1] + x3 = x[2] + x4 = x[3] + + # First original objective function + f[0] = 4.9 * 1e-5 * (x2 * x2 - x1 * x1) * (x4 - 1.0) + # Second original objective function + f[1] = ((9.82 * 1e6) * (x2 * x2 - x1 * x1)) / (x3 * x4 * (x2 * x2 * x2 - x1 * x1 * x1)) + + # Reformulated objective functions + g[0] = (x2 - x1) - 20.0 + g[1] = 0.4 - (x3 / (3.14 * (x2 * x2 - x1 * x1))) + g[2] = 1.0 - (2.22 * 1e-3 * x3 * (x2 * x2 * x2 - x1 * x1 * x1)) / np.power((x2 * x2 - x1 * x1), 2) + g[3] = (2.66 * 1e-2 * x3 * x4 * (x2 * x2 * x2 - x1 * x1 * x1)) / (x2 * x2 - x1 * x1) - 900.0 + g = np.where(g < 0, -g, 0) + f[2] = g[0] + g[1] + g[2] + g[3] + + return f + + +class RE34(): + def __init__(self): + self.problem_name = 'RE34' + self.n_objectives = 3 + self.n_variables = 5 + self.n_constraints = 0 + self.n_original_constraints = 0 + + self.lbound = np.full(self.n_variables, 1) + self.ubound = np.full(self.n_variables, 3) + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_original_constraints) + + x1 = x[0] + x2 = x[1] + x3 = x[2] + x4 = x[3] + x5 = x[4] + + f[0] = 1640.2823 + (2.3573285 * x1) + (2.3220035 * x2) + (4.5688768 * x3) + (7.7213633 * x4) + (4.4559504 * x5) + f[1] = 6.5856 + (1.15 * x1) - (1.0427 * x2) + (0.9738 * x3) + (0.8364 * x4) - (0.3695 * x1 * x4) + ( + 0.0861 * x1 * x5) + (0.3628 * x2 * x4) - (0.1106 * x1 * x1) - (0.3437 * x3 * x3) + ( + 0.1764 * x4 * x4) + f[2] = -0.0551 + (0.0181 * x1) + (0.1024 * x2) + (0.0421 * x3) - (0.0073 * x1 * x2) + (0.024 * x2 * x3) - ( + 0.0118 * x2 * x4) - (0.0204 * x3 * x4) - (0.008 * x3 * x5) - (0.0241 * x2 * x2) + (0.0109 * x4 * x4) + + return f + + +class RE35(): + def __init__(self): + self.problem_name = 'RE35' + self.n_objectives = 3 + self.n_variables = 7 + self.n_constraints = 0 + self.n_original_constraints = 11 + + self.lbound = np.zeros(self.n_variables) + self.ubound = np.zeros(self.n_variables) + self.lbound[0] = 2.6 + self.lbound[1] = 0.7 + self.lbound[2] = 17 + self.lbound[3] = 7.3 + self.lbound[4] = 7.3 + self.lbound[5] = 2.9 + self.lbound[6] = 5.0 + self.ubound[0] = 3.6 + self.ubound[1] = 0.8 + self.ubound[2] = 28 + self.ubound[3] = 8.3 + self.ubound[4] = 8.3 + self.ubound[5] = 3.9 + self.ubound[6] = 5.5 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_original_constraints) + + x1 = x[0] + x2 = x[1] + x3 = np.round(x[2]) + x4 = x[3] + x5 = x[4] + x6 = x[5] + x7 = x[6] + + # First original objective function (weight) + f[0] = 0.7854 * x1 * (x2 * x2) * (((10.0 * x3 * x3) / 3.0) + (14.933 * x3) - 43.0934) - 1.508 * x1 * ( + x6 * x6 + x7 * x7) + 7.477 * (x6 * x6 * x6 + x7 * x7 * x7) + 0.7854 * (x4 * x6 * x6 + x5 * x7 * x7) + + # Second original objective function (stress) + tmpVar = np.power((745.0 * x4) / (x2 * x3), 2.0) + 1.69 * 1e7 + f[1] = np.sqrt(tmpVar) / (0.1 * x6 * x6 * x6) + + # Constraint functions + g[0] = -(1.0 / (x1 * x2 * x2 * x3)) + 1.0 / 27.0 + g[1] = -(1.0 / (x1 * x2 * x2 * x3 * x3)) + 1.0 / 397.5 + g[2] = -(x4 * x4 * x4) / (x2 * x3 * x6 * x6 * x6 * x6) + 1.0 / 1.93 + g[3] = -(x5 * x5 * x5) / (x2 * x3 * x7 * x7 * x7 * x7) + 1.0 / 1.93 + g[4] = -(x2 * x3) + 40.0 + g[5] = -(x1 / x2) + 12.0 + g[6] = -5.0 + (x1 / x2) + g[7] = -1.9 + x4 - 1.5 * x6 + g[8] = -1.9 + x5 - 1.1 * x7 + g[9] = -f[1] + 1300.0 + tmpVar = np.power((745.0 * x5) / (x2 * x3), 2.0) + 1.575 * 1e8 + g[10] = -np.sqrt(tmpVar) / (0.1 * x7 * x7 * x7) + 1100.0 + g = np.where(g < 0, -g, 0) + f[2] = g[0] + g[1] + g[2] + g[3] + g[4] + g[5] + g[6] + g[7] + g[8] + g[9] + g[10] + + return f + + +class RE36(): + def __init__(self): + self.problem_name = 'RE36' + self.n_objectives = 3 + self.n_variables = 4 + self.n_constraints = 0 + self.n_original_constraints = 1 + + self.lbound = np.full(self.n_variables, 12) + self.ubound = np.full(self.n_variables, 60) + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_original_constraints) + + # all the four variables must be inverger values + x1 = np.round(x[0]) + x2 = np.round(x[1]) + x3 = np.round(x[2]) + x4 = np.round(x[3]) + + # First original objective function + f[0] = np.abs(6.931 - ((x3 / x1) * (x4 / x2))) + # Second original objective function (the maximum value among the four variables) + l = [x1, x2, x3, x4] + f[1] = max(l) + + g[0] = 0.5 - (f[0] / 6.931) + g = np.where(g < 0, -g, 0) + f[2] = g[0] + + return f + + +class RE37(): + def __init__(self): + self.problem_name = 'RE37' + self.n_objectives = 3 + self.n_variables = 4 + self.n_constraints = 0 + self.n_original_constraints = 0 + + self.lbound = np.full(self.n_variables, 0) + self.ubound = np.full(self.n_variables, 1) + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + + xAlpha = x[0] + xHA = x[1] + xOA = x[2] + xOPTT = x[3] + + # f1 (TF_max) + f[0] = 0.692 + (0.477 * xAlpha) - (0.687 * xHA) - (0.080 * xOA) - (0.0650 * xOPTT) - ( + 0.167 * xAlpha * xAlpha) - (0.0129 * xHA * xAlpha) + (0.0796 * xHA * xHA) - ( + 0.0634 * xOA * xAlpha) - (0.0257 * xOA * xHA) + (0.0877 * xOA * xOA) - ( + 0.0521 * xOPTT * xAlpha) + (0.00156 * xOPTT * xHA) + (0.00198 * xOPTT * xOA) + ( + 0.0184 * xOPTT * xOPTT) + # f2 (X_cc) + f[1] = 0.153 - (0.322 * xAlpha) + (0.396 * xHA) + (0.424 * xOA) + (0.0226 * xOPTT) + ( + 0.175 * xAlpha * xAlpha) + (0.0185 * xHA * xAlpha) - (0.0701 * xHA * xHA) - ( + 0.251 * xOA * xAlpha) + (0.179 * xOA * xHA) + (0.0150 * xOA * xOA) + ( + 0.0134 * xOPTT * xAlpha) + (0.0296 * xOPTT * xHA) + (0.0752 * xOPTT * xOA) + ( + 0.0192 * xOPTT * xOPTT) + # f3 (TT_max) + f[2] = 0.370 - (0.205 * xAlpha) + (0.0307 * xHA) + (0.108 * xOA) + (1.019 * xOPTT) - ( + 0.135 * xAlpha * xAlpha) + (0.0141 * xHA * xAlpha) + (0.0998 * xHA * xHA) + ( + 0.208 * xOA * xAlpha) - (0.0301 * xOA * xHA) - (0.226 * xOA * xOA) + ( + 0.353 * xOPTT * xAlpha) - (0.0497 * xOPTT * xOA) - (0.423 * xOPTT * xOPTT) + ( + 0.202 * xHA * xAlpha * xAlpha) - (0.281 * xOA * xAlpha * xAlpha) - ( + 0.342 * xHA * xHA * xAlpha) - (0.245 * xHA * xHA * xOA) + (0.281 * xOA * xOA * xHA) - ( + 0.184 * xOPTT * xOPTT * xAlpha) - (0.281 * xHA * xAlpha * xOA) + + return f + + +class RE41(): + def __init__(self): + self.problem_name = 'RE41' + self.n_objectives = 4 + self.n_variables = 7 + self.n_constraints = 0 + self.n_original_constraints = 10 + + self.lbound = np.zeros(self.n_variables) + self.ubound = np.zeros(self.n_variables) + self.lbound[0] = 0.5 + self.lbound[1] = 0.45 + self.lbound[2] = 0.5 + self.lbound[3] = 0.5 + self.lbound[4] = 0.875 + self.lbound[5] = 0.4 + self.lbound[6] = 0.4 + self.ubound[0] = 1.5 + self.ubound[1] = 1.35 + self.ubound[2] = 1.5 + self.ubound[3] = 1.5 + self.ubound[4] = 2.625 + self.ubound[5] = 1.2 + self.ubound[6] = 1.2 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_original_constraints) + + x1 = x[0] + x2 = x[1] + x3 = x[2] + x4 = x[3] + x5 = x[4] + x6 = x[5] + x7 = x[6] + + # First original objective function + f[0] = 1.98 + 4.9 * x1 + 6.67 * x2 + 6.98 * x3 + 4.01 * x4 + 1.78 * x5 + 0.00001 * x6 + 2.73 * x7 + # Second original objective function + f[1] = 4.72 - 0.5 * x4 - 0.19 * x2 * x3 + # Third original objective function + Vmbp = 10.58 - 0.674 * x1 * x2 - 0.67275 * x2 + Vfd = 16.45 - 0.489 * x3 * x7 - 0.843 * x5 * x6 + f[2] = 0.5 * (Vmbp + Vfd) + + # Constraint functions + g[0] = 1 - (1.16 - 0.3717 * x2 * x4 - 0.0092928 * x3) + g[1] = 0.32 - (0.261 - 0.0159 * x1 * x2 - 0.06486 * x1 - 0.019 * x2 * x7 + 0.0144 * x3 * x5 + 0.0154464 * x6) + g[2] = 0.32 - ( + 0.214 + 0.00817 * x5 - 0.045195 * x1 - 0.0135168 * x1 + 0.03099 * x2 * x6 - 0.018 * x2 * x7 + 0.007176 * x3 + 0.023232 * x3 - 0.00364 * x5 * x6 - 0.018 * x2 * x2) + g[3] = 0.32 - (0.74 - 0.61 * x2 - 0.031296 * x3 - 0.031872 * x7 + 0.227 * x2 * x2) + g[4] = 32 - (28.98 + 3.818 * x3 - 4.2 * x1 * x2 + 1.27296 * x6 - 2.68065 * x7) + g[5] = 32 - (33.86 + 2.95 * x3 - 5.057 * x1 * x2 - 3.795 * x2 - 3.4431 * x7 + 1.45728) + g[6] = 32 - (46.36 - 9.9 * x2 - 4.4505 * x1) + g[7] = 4 - f[1] + g[8] = 9.9 - Vmbp + g[9] = 15.7 - Vfd + + g = np.where(g < 0, -g, 0) + f[3] = g[0] + g[1] + g[2] + g[3] + g[4] + g[5] + g[6] + g[7] + g[8] + g[9] + + return f + + +class RE42(): + def __init__(self): + self.problem_name = 'RE42' + self.n_objectives = 4 + self.n_variables = 6 + self.n_constraints = 0 + self.n_original_constraints = 9 + + self.lbound = np.zeros(self.n_variables) + self.ubound = np.zeros(self.n_variables) + self.lbound[0] = 150.0 + self.lbound[1] = 20.0 + self.lbound[2] = 13.0 + self.lbound[3] = 10.0 + self.lbound[4] = 14.0 + self.lbound[5] = 0.63 + self.ubound[0] = 274.32 + self.ubound[1] = 32.31 + self.ubound[2] = 25.0 + self.ubound[3] = 11.71 + self.ubound[4] = 18.0 + self.ubound[5] = 0.75 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + # NOT g + constraintFuncs = np.zeros(self.n_original_constraints) + + x_L = x[0] + x_B = x[1] + x_D = x[2] + x_T = x[3] + x_Vk = x[4] + x_CB = x[5] + + displacement = 1.025 * x_L * x_B * x_T * x_CB + V = 0.5144 * x_Vk + g = 9.8065 + Fn = V / np.power(g * x_L, 0.5) + a = (4977.06 * x_CB * x_CB) - (8105.61 * x_CB) + 4456.51 + b = (-10847.2 * x_CB * x_CB) + (12817.0 * x_CB) - 6960.32 + + power = (np.power(displacement, 2.0 / 3.0) * np.power(x_Vk, 3.0)) / (a + (b * Fn)) + outfit_weight = 1.0 * np.power(x_L, 0.8) * np.power(x_B, 0.6) * np.power(x_D, 0.3) * np.power(x_CB, 0.1) + steel_weight = 0.034 * np.power(x_L, 1.7) * np.power(x_B, 0.7) * np.power(x_D, 0.4) * np.power(x_CB, 0.5) + machinery_weight = 0.17 * np.power(power, 0.9) + light_ship_weight = steel_weight + outfit_weight + machinery_weight + + ship_cost = 1.3 * ((2000.0 * np.power(steel_weight, 0.85)) + (3500.0 * outfit_weight) + ( + 2400.0 * np.power(power, 0.8))) + capital_costs = 0.2 * ship_cost + + DWT = displacement - light_ship_weight + + running_costs = 40000.0 * np.power(DWT, 0.3) + + round_trip_miles = 5000.0 + sea_days = (round_trip_miles / 24.0) * x_Vk + handling_rate = 8000.0 + + daily_consumption = ((0.19 * power * 24.0) / 1000.0) + 0.2 + fuel_price = 100.0 + fuel_cost = 1.05 * daily_consumption * sea_days * fuel_price + port_cost = 6.3 * np.power(DWT, 0.8) + + fuel_carried = daily_consumption * (sea_days + 5.0) + miscellaneous_DWT = 2.0 * np.power(DWT, 0.5) + + cargo_DWT = DWT - fuel_carried - miscellaneous_DWT + port_days = 2.0 * ((cargo_DWT / handling_rate) + 0.5) + RTPA = 350.0 / (sea_days + port_days) + + voyage_costs = (fuel_cost + port_cost) * RTPA + annual_costs = capital_costs + running_costs + voyage_costs + annual_cargo = cargo_DWT * RTPA + + f[0] = annual_costs / annual_cargo + f[1] = light_ship_weight + # f_2 is dealt as a minimization problem + f[2] = -annual_cargo + + # Reformulated objective functions + constraintFuncs[0] = (x_L / x_B) - 6.0 + constraintFuncs[1] = -(x_L / x_D) + 15.0 + constraintFuncs[2] = -(x_L / x_T) + 19.0 + constraintFuncs[3] = 0.45 * np.power(DWT, 0.31) - x_T + constraintFuncs[4] = 0.7 * x_D + 0.7 - x_T + constraintFuncs[5] = 500000.0 - DWT + constraintFuncs[6] = DWT - 3000.0 + constraintFuncs[7] = 0.32 - Fn + + KB = 0.53 * x_T + BMT = ((0.085 * x_CB - 0.002) * x_B * x_B) / (x_T * x_CB) + KG = 1.0 + 0.52 * x_D + constraintFuncs[8] = (KB + BMT - KG) - (0.07 * x_B) + + constraintFuncs = np.where(constraintFuncs < 0, -constraintFuncs, 0) + f[3] = constraintFuncs[0] + constraintFuncs[1] + constraintFuncs[2] + constraintFuncs[3] + constraintFuncs[4] + \ + constraintFuncs[5] + constraintFuncs[6] + constraintFuncs[7] + constraintFuncs[8] + + return f + + +class RE61(): + def __init__(self): + self.problem_name = 'RE61' + self.n_objectives = 6 + self.n_variables = 3 + self.n_constraints = 0 + self.n_original_constraints = 7 + + self.lbound = np.zeros(self.n_variables) + self.ubound = np.zeros(self.n_variables) + self.lbound[0] = 0.01 + self.lbound[1] = 0.01 + self.lbound[2] = 0.01 + self.ubound[0] = 0.45 + self.ubound[1] = 0.10 + self.ubound[2] = 0.10 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_original_constraints) + + # First original objective function + f[0] = 106780.37 * (x[1] + x[2]) + 61704.67 + # Second original objective function + f[1] = 3000 * x[0] + # Third original objective function + f[2] = 305700 * 2289 * x[1] / np.power(0.06 * 2289, 0.65) + # Fourth original objective function + f[3] = 250 * 2289 * np.exp(-39.75 * x[1] + 9.9 * x[2] + 2.74) + # Fifth original objective function + f[4] = 25 * (1.39 / (x[0] * x[1]) + 4940 * x[2] - 80) + + # Constraint functions + g[0] = 1 - (0.00139 / (x[0] * x[1]) + 4.94 * x[2] - 0.08) + g[1] = 1 - (0.000306 / (x[0] * x[1]) + 1.082 * x[2] - 0.0986) + g[2] = 50000 - (12.307 / (x[0] * x[1]) + 49408.24 * x[2] + 4051.02) + g[3] = 16000 - (2.098 / (x[0] * x[1]) + 8046.33 * x[2] - 696.71) + g[4] = 10000 - (2.138 / (x[0] * x[1]) + 7883.39 * x[2] - 705.04) + g[5] = 2000 - (0.417 * x[0] * x[1] + 1721.26 * x[2] - 136.54) + g[6] = 550 - (0.164 / (x[0] * x[1]) + 631.13 * x[2] - 54.48) + + g = np.where(g < 0, -g, 0) + f[5] = g[0] + g[1] + g[2] + g[3] + g[4] + g[5] + g[6] + + return f + + +class RE91(): + def __init__(self): + self.problem_name = 'RE91' + self.n_objectives = 9 + self.n_variables = 7 + self.n_constraints = 0 + self.n_original_constraints = 0 + + self.lbound = np.zeros(self.n_variables) + self.ubound = np.zeros(self.n_variables) + self.lbound[0] = 0.5 + self.lbound[1] = 0.45 + self.lbound[2] = 0.5 + self.lbound[3] = 0.5 + self.lbound[4] = 0.875 + self.lbound[5] = 0.4 + self.lbound[6] = 0.4 + self.ubound[0] = 1.5 + self.ubound[1] = 1.35 + self.ubound[2] = 1.5 + self.ubound[3] = 1.5 + self.ubound[4] = 2.625 + self.ubound[5] = 1.2 + self.ubound[6] = 1.2 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_original_constraints) + + x1 = x[0] + x2 = x[1] + x3 = x[2] + x4 = x[3] + x5 = x[4] + x6 = x[5] + x7 = x[6] + # stochastic variables + x8 = 0.006 * (np.random.normal(0, 1)) + 0.345 + x9 = 0.006 * (np.random.normal(0, 1)) + 0.192 + x10 = 10 * (np.random.normal(0, 1)) + 0.0 + x11 = 10 * (np.random.normal(0, 1)) + 0.0 + + # First function + f[0] = 1.98 + 4.9 * x1 + 6.67 * x2 + 6.98 * x3 + 4.01 * x4 + 1.75 * x5 + 0.00001 * x6 + 2.73 * x7 + # Second function + f[1] = max(0.0, (1.16 - 0.3717 * x2 * x4 - 0.00931 * x2 * x10 - 0.484 * x3 * x9 + 0.01343 * x6 * x10) / 1.0) + # Third function + f[2] = max(0.0, ( + 0.261 - 0.0159 * x1 * x2 - 0.188 * x1 * x8 - 0.019 * x2 * x7 + 0.0144 * x3 * x5 + 0.87570001 * x5 * x10 + 0.08045 * x6 * x9 + 0.00139 * x8 * x11 + 0.00001575 * x10 * x11) / 0.32) + # Fourth function + f[3] = max(0.0, ( + 0.214 + 0.00817 * x5 - 0.131 * x1 * x8 - 0.0704 * x1 * x9 + 0.03099 * x2 * x6 - 0.018 * x2 * x7 + 0.0208 * x3 * x8 + 0.121 * x3 * x9 - 0.00364 * x5 * x6 + 0.0007715 * x5 * x10 - 0.0005354 * x6 * x10 + 0.00121 * x8 * x11 + 0.00184 * x9 * x10 - 0.018 * x2 * x2) / 0.32) + # Fifth function + f[4] = max(0.0, ( + 0.74 - 0.61 * x2 - 0.163 * x3 * x8 + 0.001232 * x3 * x10 - 0.166 * x7 * x9 + 0.227 * x2 * x2) / 0.32) + # Sixth function + tmp = (( + 28.98 + 3.818 * x3 - 4.2 * x1 * x2 + 0.0207 * x5 * x10 + 6.63 * x6 * x9 - 7.77 * x7 * x8 + 0.32 * x9 * x10) + ( + 33.86 + 2.95 * x3 + 0.1792 * x10 - 5.057 * x1 * x2 - 11 * x2 * x8 - 0.0215 * x5 * x10 - 9.98 * x7 * x8 + 22 * x8 * x9) + ( + 46.36 - 9.9 * x2 - 12.9 * x1 * x8 + 0.1107 * x3 * x10)) / 3 + f[5] = max(0.0, tmp / 32) + # Seventh function + f[6] = max(0.0, ( + 4.72 - 0.5 * x4 - 0.19 * x2 * x3 - 0.0122 * x4 * x10 + 0.009325 * x6 * x10 + 0.000191 * x11 * x11) / 4.0) + # EighthEighth function + f[7] = max(0.0, ( + 10.58 - 0.674 * x1 * x2 - 1.95 * x2 * x8 + 0.02054 * x3 * x10 - 0.0198 * x4 * x10 + 0.028 * x6 * x10) / 9.9) + # Ninth function + f[8] = max(0.0, ( + 16.45 - 0.489 * x3 * x7 - 0.843 * x5 * x6 + 0.0432 * x9 * x10 - 0.0556 * x9 * x11 - 0.000786 * x11 * x11) / 15.7) + + return f + + +class CRE21(): + def __init__(self): + self.problem_name = 'CRE21' + self.n_objectives = 2 + self.n_variables = 3 + self.n_constraints = 3 + + self.ubound = np.zeros(self.n_variables) + self.lbound = np.zeros(self.n_variables) + self.lbound[0] = 0.00001 + self.lbound[1] = 0.00001 + self.lbound[2] = 1.0 + self.ubound[0] = 100.0 + self.ubound[1] = 100.0 + self.ubound[2] = 3.0 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_constraints) + + x1 = x[0] + x2 = x[1] + x3 = x[2] + + # First original objective function + f[0] = x1 * np.sqrt(16.0 + (x3 * x3)) + x2 * np.sqrt(1.0 + x3 * x3) + # Second original objective function + f[1] = (20.0 * np.sqrt(16.0 + (x3 * x3))) / (x1 * x3) + + # Constraint functions + g[0] = 0.1 - f[0] + g[1] = 100000.0 - f[1] + g[2] = 100000 - ((80.0 * np.sqrt(1.0 + x3 * x3)) / (x3 * x2)) + g = np.where(g < 0, -g, 0) + + return f, g + + +class CRE22(): + def __init__(self): + self.problem_name = 'CRE22' + self.n_objectives = 2 + self.n_variables = 4 + self.n_constraints = 4 + + self.ubound = np.zeros(self.n_variables) + self.lbound = np.zeros(self.n_variables) + self.lbound[0] = 0.125 + self.lbound[1] = 0.1 + self.lbound[2] = 0.1 + self.lbound[3] = 0.125 + self.ubound[0] = 5.0 + self.ubound[1] = 10.0 + self.ubound[2] = 10.0 + self.ubound[3] = 5.0 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_constraints) + + x1 = x[0] + x2 = x[1] + x3 = x[2] + x4 = x[3] + + P = 6000 + L = 14 + E = 30 * 1e6 + + # // deltaMax = 0.25 + G = 12 * 1e6 + tauMax = 13600 + sigmaMax = 30000 + + # First original objective function + f[0] = (1.10471 * x1 * x1 * x2) + (0.04811 * x3 * x4) * (14.0 + x2) + # Second original objective function + f[1] = (4 * P * L * L * L) / (E * x4 * x3 * x3 * x3) + + # Constraint functions + M = P * (L + (x2 / 2)) + tmpVar = ((x2 * x2) / 4.0) + np.power((x1 + x3) / 2.0, 2) + R = np.sqrt(tmpVar) + tmpVar = ((x2 * x2) / 12.0) + np.power((x1 + x3) / 2.0, 2) + J = 2 * np.sqrt(2) * x1 * x2 * tmpVar + + tauDashDash = (M * R) / J + tauDash = P / (np.sqrt(2) * x1 * x2) + tmpVar = tauDash * tauDash + ((2 * tauDash * tauDashDash * x2) / (2 * R)) + (tauDashDash * tauDashDash) + tau = np.sqrt(tmpVar) + sigma = (6 * P * L) / (x4 * x3 * x3) + tmpVar = 4.013 * E * np.sqrt((x3 * x3 * x4 * x4 * x4 * x4 * x4 * x4) / 36.0) / (L * L) + tmpVar2 = (x3 / (2 * L)) * np.sqrt(E / (4 * G)) + PC = tmpVar * (1 - tmpVar2) + + g[0] = tauMax - tau + g[1] = sigmaMax - sigma + g[2] = x4 - x1 + g[3] = PC - P + g = np.where(g < 0, -g, 0) + + return f, g + + +class CRE23(): + def __init__(self): + self.problem_name = 'CRE23' + self.n_objectives = 2 + self.n_variables = 4 + self.n_constraints = 4 + + self.ubound = np.zeros(self.n_variables) + self.lbound = np.zeros(self.n_variables) + self.lbound[0] = 55 + self.lbound[1] = 75 + self.lbound[2] = 1000 + self.lbound[3] = 11 + self.ubound[0] = 80 + self.ubound[1] = 110 + self.ubound[2] = 3000 + self.ubound[3] = 20 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_constraints) + + x1 = x[0] + x2 = x[1] + x3 = x[2] + x4 = x[3] + + # First original objective function + f[0] = 4.9 * 1e-5 * (x2 * x2 - x1 * x1) * (x4 - 1.0) + # Second original objective function + f[1] = ((9.82 * 1e6) * (x2 * x2 - x1 * x1)) / (x3 * x4 * (x2 * x2 * x2 - x1 * x1 * x1)) + + # Reformulated objective functions + g[0] = (x2 - x1) - 20.0 + g[1] = 0.4 - (x3 / (3.14 * (x2 * x2 - x1 * x1))) + g[2] = 1.0 - (2.22 * 1e-3 * x3 * (x2 * x2 * x2 - x1 * x1 * x1)) / np.power((x2 * x2 - x1 * x1), 2) + g[3] = (2.66 * 1e-2 * x3 * x4 * (x2 * x2 * x2 - x1 * x1 * x1)) / (x2 * x2 - x1 * x1) - 900.0 + g = np.where(g < 0, -g, 0) + + return f, g + + +class CRE24(): + def __init__(self): + self.problem_name = 'CRE24' + self.n_objectives = 2 + self.n_variables = 7 + self.n_constraints = 11 + + self.lbound = np.zeros(self.n_variables) + self.ubound = np.zeros(self.n_variables) + + self.lbound[0] = 2.6 + self.lbound[1] = 0.7 + self.lbound[2] = 17 + self.lbound[3] = 7.3 + self.lbound[4] = 7.3 + self.lbound[5] = 2.9 + self.lbound[6] = 5.0 + self.ubound[0] = 3.6 + self.ubound[1] = 0.8 + self.ubound[2] = 28 + self.ubound[3] = 8.3 + self.ubound[4] = 8.3 + self.ubound[5] = 3.9 + self.ubound[6] = 5.5 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_constraints) + + x1 = x[0] + x2 = x[1] + x3 = np.round(x[2]) + x4 = x[3] + x5 = x[4] + x6 = x[5] + x7 = x[6] + + # First original objective function (weight) + f[0] = 0.7854 * x1 * (x2 * x2) * (((10.0 * x3 * x3) / 3.0) + (14.933 * x3) - 43.0934) - 1.508 * x1 * ( + x6 * x6 + x7 * x7) + 7.477 * (x6 * x6 * x6 + x7 * x7 * x7) + 0.7854 * (x4 * x6 * x6 + x5 * x7 * x7) + + # Second original objective function (stress) + tmpVar = np.power((745.0 * x4) / (x2 * x3), 2.0) + 1.69 * 1e7 + f[1] = np.sqrt(tmpVar) / (0.1 * x6 * x6 * x6) + + # Constraint functions + g[0] = -(1.0 / (x1 * x2 * x2 * x3)) + 1.0 / 27.0 + g[1] = -(1.0 / (x1 * x2 * x2 * x3 * x3)) + 1.0 / 397.5 + g[2] = -(x4 * x4 * x4) / (x2 * x3 * x6 * x6 * x6 * x6) + 1.0 / 1.93 + g[3] = -(x5 * x5 * x5) / (x2 * x3 * x7 * x7 * x7 * x7) + 1.0 / 1.93 + g[4] = -(x2 * x3) + 40.0 + g[5] = -(x1 / x2) + 12.0 + g[6] = -5.0 + (x1 / x2) + g[7] = -1.9 + x4 - 1.5 * x6 + g[8] = -1.9 + x5 - 1.1 * x7 + g[9] = -f[1] + 1300.0 + tmpVar = np.power((745.0 * x5) / (x2 * x3), 2.0) + 1.575 * 1e8 + g[10] = -np.sqrt(tmpVar) / (0.1 * x7 * x7 * x7) + 1100.0 + g = np.where(g < 0, -g, 0) + + return f, g + + +class CRE25(): + def __init__(self): + self.problem_name = 'CRE25' + self.n_objectives = 2 + self.n_variables = 4 + self.n_constraints = 1 + + self.lbound = np.full(self.n_variables, 12) + self.ubound = np.full(self.n_variables, 60) + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_constraints) + + # all the four variables must be inverger values + x1 = np.round(x[0]) + x2 = np.round(x[1]) + x3 = np.round(x[2]) + x4 = np.round(x[3]) + + # First original objective function + f[0] = np.abs(6.931 - ((x3 / x1) * (x4 / x2))) + # Second original objective function (the maximum value among the four variables) + l = [x1, x2, x3, x4] + f[1] = max(l) + + g[0] = 0.5 - (f[0] / 6.931) + g = np.where(g < 0, -g, 0) + + return f, g + + +class CRE31(): + def __init__(self): + self.problem_name = 'CRE31' + self.n_objectives = 3 + self.n_variables = 7 + self.n_constraints = 10 + + self.lbound = np.zeros(self.n_variables) + self.ubound = np.zeros(self.n_variables) + self.lbound[0] = 0.5 + self.lbound[1] = 0.45 + self.lbound[2] = 0.5 + self.lbound[3] = 0.5 + self.lbound[4] = 0.875 + self.lbound[5] = 0.4 + self.lbound[6] = 0.4 + self.ubound[0] = 1.5 + self.ubound[1] = 1.35 + self.ubound[2] = 1.5 + self.ubound[3] = 1.5 + self.ubound[4] = 2.625 + self.ubound[5] = 1.2 + self.ubound[6] = 1.2 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_constraints) + + x1 = x[0] + x2 = x[1] + x3 = x[2] + x4 = x[3] + x5 = x[4] + x6 = x[5] + x7 = x[6] + + # First original objective function + f[0] = 1.98 + 4.9 * x1 + 6.67 * x2 + 6.98 * x3 + 4.01 * x4 + 1.78 * x5 + 0.00001 * x6 + 2.73 * x7 + # Second original objective function + f[1] = 4.72 - 0.5 * x4 - 0.19 * x2 * x3 + # Third original objective function + Vmbp = 10.58 - 0.674 * x1 * x2 - 0.67275 * x2 + Vfd = 16.45 - 0.489 * x3 * x7 - 0.843 * x5 * x6 + f[2] = 0.5 * (Vmbp + Vfd) + + # Constraint functions + g[0] = 1 - (1.16 - 0.3717 * x2 * x4 - 0.0092928 * x3) + g[1] = 0.32 - (0.261 - 0.0159 * x1 * x2 - 0.06486 * x1 - 0.019 * x2 * x7 + 0.0144 * x3 * x5 + 0.0154464 * x6) + g[2] = 0.32 - ( + 0.214 + 0.00817 * x5 - 0.045195 * x1 - 0.0135168 * x1 + 0.03099 * x2 * x6 - 0.018 * x2 * x7 + 0.007176 * x3 + 0.023232 * x3 - 0.00364 * x5 * x6 - 0.018 * x2 * x2) + g[3] = 0.32 - (0.74 - 0.61 * x2 - 0.031296 * x3 - 0.031872 * x7 + 0.227 * x2 * x2) + g[4] = 32 - (28.98 + 3.818 * x3 - 4.2 * x1 * x2 + 1.27296 * x6 - 2.68065 * x7) + g[5] = 32 - (33.86 + 2.95 * x3 - 5.057 * x1 * x2 - 3.795 * x2 - 3.4431 * x7 + 1.45728) + g[6] = 32 - (46.36 - 9.9 * x2 - 4.4505 * x1) + g[7] = 4 - f[1] + g[8] = 9.9 - Vmbp + g[9] = 15.7 - Vfd + g = np.where(g < 0, -g, 0) + + return f, g + + +class CRE32(): + def __init__(self): + self.problem_name = 'CRE32' + self.n_objectives = 3 + self.n_variables = 6 + self.n_constraints = 9 + + self.lbound = np.zeros(self.n_variables) + self.ubound = np.zeros(self.n_variables) + self.lbound[0] = 150.0 + self.lbound[1] = 20.0 + self.lbound[2] = 13.0 + self.lbound[3] = 10.0 + self.lbound[4] = 14.0 + self.lbound[5] = 0.63 + self.ubound[0] = 274.32 + self.ubound[1] = 32.31 + self.ubound[2] = 25.0 + self.ubound[3] = 11.71 + self.ubound[4] = 18.0 + self.ubound[5] = 0.75 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + # NOT g + constraintFuncs = np.zeros(self.n_constraints) + + x_L = x[0] + x_B = x[1] + x_D = x[2] + x_T = x[3] + x_Vk = x[4] + x_CB = x[5] + + displacement = 1.025 * x_L * x_B * x_T * x_CB + V = 0.5144 * x_Vk + g = 9.8065 + Fn = V / np.power(g * x_L, 0.5) + a = (4977.06 * x_CB * x_CB) - (8105.61 * x_CB) + 4456.51 + b = (-10847.2 * x_CB * x_CB) + (12817.0 * x_CB) - 6960.32 + + power = (np.power(displacement, 2.0 / 3.0) * np.power(x_Vk, 3.0)) / (a + (b * Fn)) + outfit_weight = 1.0 * np.power(x_L, 0.8) * np.power(x_B, 0.6) * np.power(x_D, 0.3) * np.power(x_CB, 0.1) + steel_weight = 0.034 * np.power(x_L, 1.7) * np.power(x_B, 0.7) * np.power(x_D, 0.4) * np.power(x_CB, 0.5) + machinery_weight = 0.17 * np.power(power, 0.9) + light_ship_weight = steel_weight + outfit_weight + machinery_weight + + ship_cost = 1.3 * ((2000.0 * np.power(steel_weight, 0.85)) + (3500.0 * outfit_weight) + ( + 2400.0 * np.power(power, 0.8))) + capital_costs = 0.2 * ship_cost + + DWT = displacement - light_ship_weight + + running_costs = 40000.0 * np.power(DWT, 0.3) + + round_trip_miles = 5000.0 + sea_days = (round_trip_miles / 24.0) * x_Vk + handling_rate = 8000.0 + + daily_consumption = ((0.19 * power * 24.0) / 1000.0) + 0.2 + fuel_price = 100.0 + fuel_cost = 1.05 * daily_consumption * sea_days * fuel_price + port_cost = 6.3 * np.power(DWT, 0.8) + + fuel_carried = daily_consumption * (sea_days + 5.0) + miscellaneous_DWT = 2.0 * np.power(DWT, 0.5) + + cargo_DWT = DWT - fuel_carried - miscellaneous_DWT + port_days = 2.0 * ((cargo_DWT / handling_rate) + 0.5) + RTPA = 350.0 / (sea_days + port_days) + + voyage_costs = (fuel_cost + port_cost) * RTPA + annual_costs = capital_costs + running_costs + voyage_costs + annual_cargo = cargo_DWT * RTPA + + f[0] = annual_costs / annual_cargo + f[1] = light_ship_weight + # f_2 is dealt as a minimization problem + f[2] = -annual_cargo + + # Reformulated objective functions + constraintFuncs[0] = (x_L / x_B) - 6.0 + constraintFuncs[1] = -(x_L / x_D) + 15.0 + constraintFuncs[2] = -(x_L / x_T) + 19.0 + constraintFuncs[3] = 0.45 * np.power(DWT, 0.31) - x_T + constraintFuncs[4] = 0.7 * x_D + 0.7 - x_T + constraintFuncs[5] = 500000.0 - DWT + constraintFuncs[6] = DWT - 3000.0 + constraintFuncs[7] = 0.32 - Fn + + KB = 0.53 * x_T + BMT = ((0.085 * x_CB - 0.002) * x_B * x_B) / (x_T * x_CB) + KG = 1.0 + 0.52 * x_D + constraintFuncs[8] = (KB + BMT - KG) - (0.07 * x_B) + constraintFuncs = np.where(constraintFuncs < 0, -constraintFuncs, 0) + + return f, constraintFuncs + + +class CRE51(): + def __init__(self): + self.problem_name = 'CRE51' + self.n_objectives = 5 + self.n_variables = 3 + self.n_constraints = 7 + + self.lbound = np.zeros(self.n_variables) + self.ubound = np.zeros(self.n_variables) + self.lbound[0] = 0.01 + self.lbound[1] = 0.01 + self.lbound[2] = 0.01 + self.ubound[0] = 0.45 + self.ubound[1] = 0.10 + self.ubound[2] = 0.10 + + def evaluate(self, x): + f = np.zeros(self.n_objectives) + g = np.zeros(self.n_constraints) + + # First original objective function + f[0] = 106780.37 * (x[1] + x[2]) + 61704.67 + # Second original objective function + f[1] = 3000 * x[0] + # Third original objective function + f[2] = 305700 * 2289 * x[1] / np.power(0.06 * 2289, 0.65) + # Fourth original objective function + f[3] = 250 * 2289 * np.exp(-39.75 * x[1] + 9.9 * x[2] + 2.74) + # Fifth original objective function + f[4] = 25 * (1.39 / (x[0] * x[1]) + 4940 * x[2] - 80) + + # Constraint functions + g[0] = 1 - (0.00139 / (x[0] * x[1]) + 4.94 * x[2] - 0.08) + g[1] = 1 - (0.000306 / (x[0] * x[1]) + 1.082 * x[2] - 0.0986) + g[2] = 50000 - (12.307 / (x[0] * x[1]) + 49408.24 * x[2] + 4051.02) + g[3] = 16000 - (2.098 / (x[0] * x[1]) + 8046.33 * x[2] - 696.71) + g[4] = 10000 - (2.138 / (x[0] * x[1]) + 7883.39 * x[2] - 705.04) + g[5] = 2000 - (0.417 * x[0] * x[1] + 1721.26 * x[2] - 136.54) + g[6] = 550 - (0.164 / (x[0] * x[1]) + 631.13 * x[2] - 54.48) + g = np.where(g < 0, -g, 0) + + return f, g + + + + +if __name__ == '__main__': + np.random.seed(seed=1) + fun = RE21() + + x = fun.lbound + (fun.ubound - fun.lbound) * np.random.rand(fun.n_variables) + print("Problem = {}".format(fun.problem_name)) + print("Number of objectives = {}".format(fun.n_objectives)) + print("Number of variables = {}".format(fun.n_variables)) + print("Number of constraints = {}".format(fun.n_constraints)) + print("Lower bounds = ", fun.lbound) + print("Upper bounds = ", fun.ubound) + print("x = ", x) + + if 'CRE' in fun.problem_name: + f, g = fun.evaluate(x) + print("f(x) = {}".format(f)) + print("g(x) = {}".format(g)) + else: + f = fun.evaluate(x) + print("f(x) = {}".format(f)) \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/vlmop.py b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/vlmop.py new file mode 100644 index 0000000000000000000000000000000000000000..cb18014bc180be4a293946800bda2d924bdab774 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/vlmop.py @@ -0,0 +1,67 @@ +import matplotlib.pyplot as plt +import torch +import numpy as np + +from ..mop import mop + + + +class VLMOP1(mop): + def __init__( self, n_var=10, n_obj=2, lbound=-np.ones(10), ubound=np.ones(10) ): + super().__init__(n_var=n_var, + n_obj=n_obj, + lbound=lbound, + ubound=ubound, ) + self.problem_name = 'VLMOP1' + + def _evaluate_torch(self, x): + f1 = 1 - torch.exp(-1 * torch.sum((x - 1 / np.sqrt(self.n_var))**2, dim=1)) + f2 = 1 - torch.exp(-1 * torch.sum((x + 1 / np.sqrt(self.n_var))**2, dim=1)) + return torch.stack((f1, f2), dim=1) + + def _evaluate_numpy(self, x): + f1 = 1 - np.exp(-1 * np.sum((x - 1 / np.sqrt(self.n_var)) ** 2, axis=1 ) ) + f2 = 1 - np.exp(-1 * np.sum((x + 1 / np.sqrt(self.n_var)) ** 2, axis=1 ) ) + return np.stack((f1, f2), axis=1) + + + def get_pf(self): + x = torch.linspace(-1 / np.sqrt(self.n_var), 1 / np.sqrt(self.n_var), 100) + x = torch.tile(x.unsqueeze(1), (1, self.n_var)) + with torch.no_grad(): + return self._evaluate_torch(x).numpy() + + + + +class VLMOP2(mop): + def __init__(self, n_var=10, n_obj=2, lbound=-np.ones(10), ubound=np.ones(10)): + super().__init__(n_var=n_var, + n_obj=n_obj, + lbound=lbound, + ubound=ubound, ) + self.problem_name = 'VLMOP2' + + def _evaluate_torch(self, x): + f1 = torch.norm(x - 1 / np.sqrt(self.n_var), dim=1)**2 / 4 + f2 = torch.norm(x + 1 / np.sqrt(self.n_var), dim=1)**2 / 4 + return torch.stack((f1, f2), dim=1) + + def _evaluate_numpy(self, x): + f1 = np.linalg.norm(x - 1 / np.sqrt(self.n_var), axis=1)**2 / 4 + f2 = np.linalg.norm(x + 1 / np.sqrt(self.n_var), axis=1)**2 / 4 + return np.stack((f1, f2), axis=1) + + def get_pf(self): + x = torch.linspace(-1 / np.sqrt(self.n_var), 1 / np.sqrt(self.n_var), 100) + x = torch.tile(x.unsqueeze(1), (1, self.n_var)) + with torch.no_grad(): + return self._evaluate_torch(x).numpy() + + +if __name__ == '__main__': + problem = VLMOP2() + pf = problem.get_pf() + plt.plot(pf[:, 0], pf[:, 1], '-') + plt.show() + print() \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/wfg.py b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/wfg.py new file mode 100644 index 0000000000000000000000000000000000000000..b28b04f643122b019e912540f228c8ed20be9eeb --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/wfg.py @@ -0,0 +1,3 @@ + + + diff --git a/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/zdt.py b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/zdt.py new file mode 100644 index 0000000000000000000000000000000000000000..fcbbf25e3e7119ddc06aaf23c793f97fede08fa2 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/problem/synthetic/zdt.py @@ -0,0 +1,175 @@ +""" +ZDT test suite for multi-objective problem + +Reference +---------- + Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective + evolutionary algorithms: Empirical results. Evolutionary computation, + 8(2), 173-195. DOI: 10.1162/106365600568202 +""" +import numpy as np +import torch +from matplotlib import pyplot as plt +from ..mop import mop + +class ZDT1(mop): + + def __init__(self, n_var=30, n_obj=2, lbound=np.zeros(30), + ubound=np.ones(30)): + super().__init__(n_var=n_var, + n_obj=n_obj, + lbound=lbound, + ubound=ubound, ) + self.problem_name = 'ZDT1' + + + def _evaluate_torch(self, x: torch.Tensor): + f1 = x[:, 0] + n = len(x) + g = 1 + 9/(n-1) * torch.sum(x[:, 1:], dim=1) + h = 1 - torch.sqrt(f1 / g) + f2 = g * h + return torch.stack((f1, f2), dim=1) + + def _evaluate_numpy(self, x: np.ndarray): + n = len(x) + f1 = x[:, 0] + g = 1 + 9 / (n-1) * np.sum(x[:, 1:], axis=1) + f2 = 1 - np.sqrt(f1 / g) + return np.stack((f1, f2), axis=1) + + def _get_pf(self, n_points: int = 100): + f1 = np.linspace(0, 1, n_points) + f2 = 1 - np.sqrt(f1) + return np.stack((f1, f2), axis=1) + + +class ZDT2(mop): + + def __init__(self, n_var=30, n_obj=2, lbound=np.zeros(30), ubound=np.ones(30)): + super().__init__(n_var=n_var, + n_obj=n_obj, + lbound=lbound, + ubound=ubound) + self.problem_name = 'ZDT2' + + def _evaluate_torch(self, x: torch.Tensor): + f1 = x[:, 0] + g = 1 + 9 * torch.mean(x[:, 1:], dim=1) + f2 = g * (1 - (f1 / g) ** 2) + return torch.stack((f1, f2), dim=1) + + def _evaluate_numpy(self, x: np.ndarray): + f1 = x[:, 0] + g = 1 + 9 * np.mean(x[:, 1:], axis=1) + f2 = g * (1 - (f1 / g) ** 2) + return np.stack((f1, f2), axis=1) + + def _get_pf(self, n_points: int = 100): + f1 = np.linspace(0, 1, n_points) + f2 = 1 - f1 ** 2 + return np.stack((f1, f2), axis=1) + + +class ZDT3(mop): + + def __init__(self, n_var=30, n_obj=2, lbound=np.zeros(30), ubound=np.ones(30)): + super().__init__(n_var=n_var, + n_obj=n_obj, + lbound=lbound, + ubound=ubound, ) + self.problem_name = 'ZDT3' + + + def _evaluate_torch(self, x: torch.Tensor): + f1 = x[:, 0] + g = 1 + 9 * torch.mean(x[:, 1:], dim=1) + f2 = g * (1 - torch.sqrt(f1 / g) - f1 / g * torch.sin(10 * np.pi * f1)) + return torch.stack((f1, f2), dim=1) + + def _evaluate_numpy(self, x: np.ndarray): + f1 = x[:, 0] + g = 1 + 9 * np.mean(x[:, 1:], axis=1) + f2 = g * (1 - np.sqrt(f1 / g) - f1 / g * np.sin(10 * np.pi * f1)) + return np.stack((f1, f2), axis=1) + + def _get_pf(self, n_points: int = 100): + f1 = np.hstack([np.linspace(0, 0.0830, int(n_points / 5)), + np.linspace(0.1822, 0.2578, int(n_points / 5)), + np.linspace(0.4093, 0.4539, int(n_points / 5)), + np.linspace(0.6183, 0.6525, int(n_points / 5)), + np.linspace(0.8233, 0.8518, n_points - 4 * int(n_points / 5))]) + f2 = 1 - np.sqrt(f1) - f1 * np.sin(10 * np.pi * f1) + return np.stack((f1, f2), axis=1) + + +class ZDT4(mop): + + def __init__(self, n_var=10, n_obj=2, lbound=-5*np.ones(10), ubound=5*np.ones(10)): + lbound[0] = 0 + ubound[0] = 1 + + super().__init__(n_var=n_var, + n_obj=n_obj, + lbound=lbound, + ubound=ubound, ) + self.problem_name = 'ZDT4' + + + def _evaluate_torch(self, x: torch.Tensor): + f1 = x[:, 0] + g = 1 + 10 * (self.n_var - 1) + torch.sum(x[:, 1:] ** 2 - 10 * torch.cos(4 * np.pi * x[:, 1:]), dim=1) + f2 = g * (1 - torch.sqrt(f1 / g)) + return torch.stack((f1, f2), dim=1) + + def _evaluate_numpy(self, x: np.ndarray): + f1 = x[:, 0] + g = 1 + 10 * (self.n_var - 1) + np.sum(x[:, 1:] ** 2 - 10 * np.cos(4 * np.pi * x[:, 1:]), axis=1) + f2 = g * (1 - np.sqrt(f1 / g)) + return np.stack((f1, f2), axis=1) + + def _get_pf(self, n_points: int = 100): + f1 = np.linspace(0, 1, n_points) + f2 = 1 - np.sqrt(f1) + return np.stack((f1, f2), axis=1) + + +class ZDT6(mop): + + def __init__(self, n_var=30, n_obj=2, lbound=np.zeros(30), ubound=np.ones(30) ) -> None: + super().__init__(n_var=n_var, + n_obj=n_obj, + lbound=lbound, + ubound=ubound, ) + self.problem_name = 'ZDT6' + + + def _evaluate_torch(self, x: torch.Tensor): + f1 = 1 - torch.exp(-4 * x[:, 0]) * (torch.sin(6 * np.pi * x[:, 0])) ** 6 + g = 1 + 9 * (torch.sum(x[:, 1:], dim=1) / (self.n_var - 1)) ** 0.25 + f2 = g * (1 - (f1 / g) ** 2) + return torch.stack((f1, f2), dim=1) + + def _evaluate_numpy(self, x: np.ndarray): + f1 = 1 - np.exp(-4 * x[:, 0]) * (np.sin(6 * np.pi * x[:, 0])) ** 6 + g = 1 + 9 * (np.sum(x[:, 1:], axis=1) / (self.n_var - 1)) ** 0.25 + f2 = g * (1 - (f1 / g) ** 2) + return np.stack((f1, f2), axis=1) + + def _get_pf(self, n_points: int = 100): + f1 = np.linspace(0, 1, n_points) + f2 = 1 - f1 ** 2 + return np.stack((f1, f2), axis=1) + + + +if __name__ == '__main__': + problem = ZDT3() + + res = problem.evaluate(torch.rand(10, problem.get_number_variable)) + pf = problem.get_pf() + x = np.random.rand(10, problem.get_number_variable) + y = problem.evaluate(x) + + plt.scatter(pf[:, 0], pf[:, 1], c='none', edgecolors='r') + plt.show() diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/__init__.py b/bike_bench_internal/benchmark_models/libmoon/solver/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a3a1f67dcc0108cf7dca5fe8f6c1ba137c4afd3f --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/__init__.py @@ -0,0 +1 @@ +# from gradient.epo_solver import EPOSolver diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/__init__.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4a619c4d9cdb71d8da0a844d5accb9f9dfd2484a --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/__init__.py @@ -0,0 +1,24 @@ +from .base_solver import GradAggSolver +from .mgda_solver import MGDASolver +from .epo_solver import EPOSolver +from .moosvgd import MOOSVGDSolver +from .gradhv import GradHVSolver +from .pmtl import PMTLSolver + + + +from .core_solver import CoreAgg, CoreMGDA, CoreEPO, CoreMOOSVGD, CoreHVGrad + +def get_core_solver(args, pref=None): + if args.mtd == 'agg': + return CoreAgg(pref=pref, agg_mtd=args.agg_mtd) + elif args.mtd == 'mgda': + return CoreMGDA() + elif args.mtd == 'epo': + return CoreEPO(pref=pref) + elif args.mtd == 'moosvgd': + return CoreMOOSVGD(args=args) + elif args.mtd == 'hvgrad': + return CoreHVGrad(args=args) + else: + assert False, 'not implemented' \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/base_solver.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/base_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..6478b7e15e1a046b59f1ffd2abbceec472349010 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/base_solver.py @@ -0,0 +1,82 @@ +from numpy import array +from torch.autograd import Variable +from torch.optim import SGD +from torch import Tensor +from ...util_global.constant import scalar_dict, solution_eps, get_hv_ref_dict +import torch +from tqdm import tqdm +from pymoo.indicators.hv import HV + + +class GradBaseSolver: + def __init__(self, step_size, max_iter, tol): + self.step_size = step_size + self.max_iter = max_iter + self.tol = tol + + def solve(self, problem, x, prefs, args): + ''' + :param problem: + :param x: + :param agg: + :return: + is a dict with keys: x, y + ''' + + # The abstract class cannot be implemented directly. + raise NotImplementedError + + + +class GradAggSolver(GradBaseSolver): + def __init__(self, step_size, max_iter, tol, device='cpu'): + self.device = device + super().__init__(step_size, max_iter, tol) + + def solve(self, problem, x, prefs, args, ref_point): + x = torch.tensor(x, dtype=torch.float32, requires_grad=True, device=self.device) + + # ref_point = array([2.0, 2.0]) + # ind = HV(ref_point = get_hv_ref_dict(args.problem_name)) + # ind = HV(ref_point = array([1.0, 1.0])) + + # ind = HV(ref_point = ref_point) + + + + + # hv_arr = [] + y_arr = [] + + if not isinstance(prefs, torch.Tensor): + prefs = torch.tensor(prefs, dtype=torch.float32, device=self.device) + else: + prefs = prefs.to(dtype=torch.float32, device=self.device) + + # prefs = Tensor(prefs) + optimizer = SGD([x], lr=self.step_size) + agg_func = scalar_dict[args.agg] + res = {} + for i in tqdm(range(self.max_iter)): + y = problem.evaluate(x) + + # hv_arr.append(ind.do(y.detach().cpu().numpy())) + + agg_val = agg_func(y, prefs) + optimizer.zero_grad() + torch.sum(agg_val).backward() + optimizer.step() + + y_arr.append(y.detach().cpu().numpy()) + + if 'lbound' in dir(problem): + x.data = torch.clamp(x.data, + torch.tensor(problem.lbound, device=x.device, dtype=torch.float32) + solution_eps, + torch.tensor(problem.ubound, device=x.device, dtype=torch.float32) - solution_eps) + + + res['x'] = x.detach().cpu().numpy() + res['y'] = y.detach().cpu().numpy() + # res['hv_arr'] = hv_arr + res['y_arr'] = y_arr + return res \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/core_solver.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/core_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..71ced71da32430387cc28f044a9b75966c3610d0 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/core_solver.py @@ -0,0 +1,82 @@ +import numpy as np +from .mgda_core import solve_mgda +from .epo_solver import EPO_LP +import torch +from .gradhv import HvMaximization +from ...util_global.constant import get_hv_ref_dict + + + +class CoreHVGrad: + def __init__(self, args): + self.args = args + self.hv_solver = HvMaximization(args.n_prob, args.n_obj, get_hv_ref_dict(args.problem)) + + def get_alpha(self, losses): + alpha = self.hv_solver.compute_weights(losses).T + return alpha + + + +class CoreMOOSVGD: + def __init__(self): + pass + + def get_alpha(self): + return 0 + + +class CoreMGDA: + def __init__(self): + pass + + def get_alpha(self, G, losses=None, pref=None): + _, alpha = solve_mgda(G, return_coeff=True) + return alpha + + +class CoreGrad: + def __init__(self): + pass + + + +class CoreEPO(CoreGrad): + def __init__(self, pref): + self.pref = pref + self.epo_lp = EPO_LP(m=len(pref), n=1, r=1/np.array(pref)) + + + def get_alpha(self, G, losses): + if type(G) == torch.Tensor: + G = G.detach().cpu().numpy().copy() + GG = G @ G.T + + alpha = self.epo_lp.get_alpha(losses, G=GG, C=True) + return alpha + + + + +class CoreAgg(CoreGrad): + def __init__(self, pref, agg_mtd='ls'): + self.agg_mtd = agg_mtd + self.pref = pref + + def get_alpha(self, G, losses): + if self.agg_mtd == 'ls': + alpha = self.pref + elif self.agg_mtd == 'mtche': + idx = np.argmax(losses) + alpha = np.zeros_like(self.pref ) + alpha[idx] = 1.0 + else: + assert False + return alpha + + + +if __name__ == '__main__': + agg = CoreAgg( pref=np.array([1, 0]) ) + + diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/epo_solver.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/epo_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..1ee8bb5d86007660884f96539a9711166a52d42c --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/epo_solver.py @@ -0,0 +1,198 @@ +import numpy as np +import cvxpy as cp +import cvxopt + +from .base_solver import GradBaseSolver +from torch.autograd import Variable +from tqdm import tqdm +import torch +from torch.optim import SGD +from numpy import array +from pymoo.indicators.hv import HV +import warnings +warnings.filterwarnings("ignore") + +from ...util_global.constant import solution_eps + + + +class EPO_LP(object): + def __init__(self, m, n, r, eps=1e-4): + cvxopt.glpk.options["msg_lev"] = "GLP_MSG_OFF" + self.m = m + self.n = n + self.r = r + self.eps = eps + self.last_move = None + self.a = cp.Parameter(m) # Adjustments + self.C = cp.Parameter((m, m)) # C: Gradient inner products, G^T G + self.Ca = cp.Parameter(m) # d_bal^TG + self.rhs = cp.Parameter(m) # RHS of constraints for balancing + + self.alpha = cp.Variable(m) # Variable to optimize + + obj_bal = cp.Maximize(self.alpha @ self.Ca) # objective for balance + constraints_bal = [self.alpha >= 0, cp.sum(self.alpha) == 1, # Simplex + self.C @ self.alpha >= self.rhs] + self.prob_bal = cp.Problem(obj_bal, constraints_bal) # LP balance + + obj_dom = cp.Maximize(cp.sum(self.alpha @ self.C)) # obj for descent + constraints_res = [self.alpha >= 0, cp.sum(self.alpha) == 1, # Restrict + self.alpha @ self.Ca >= -cp.neg(cp.max(self.Ca)), + self.C @ self.alpha >= 0] + constraints_rel = [self.alpha >= 0, cp.sum(self.alpha) == 1, # Relaxed + self.C @ self.alpha >= 0] + self.prob_dom = cp.Problem(obj_dom, constraints_res) # LP dominance + self.prob_rel = cp.Problem(obj_dom, constraints_rel) # LP dominance + + self.gamma = 0 # Stores the latest Optimum value of the LP problem + self.mu_rl = 0 # Stores the latest non-uniformity + + + def get_alpha(self, l, G, r=None, C=False, relax=False): + + r = self.r if r is None else r + assert len(l) == len(G) == len(r) == self.m, "length != m" + + rl, self.mu_rl, self.a.value = adjustments(l, r) + + self.C.value = G if C else G @ G.T + self.Ca.value = self.C.value @ self.a.value + + + if self.mu_rl > self.eps: + J = self.Ca.value > 0 + if len(np.where(J)[0]) > 0: + J_star_idx = np.where(rl == np.max(rl))[0] + self.rhs.value = self.Ca.value.copy() + self.rhs.value[J] = -np.inf # Not efficient; but works. + self.rhs.value[J_star_idx] = 0 + else: + self.rhs.value = np.zeros_like(self.Ca.value) + self.gamma = self.prob_bal.solve(solver=cp.GLPK, verbose=False) + # self.gamma = self.prob_bal.solve(verbose=False) + self.last_move = "bal" + else: + if relax: + self.gamma = self.prob_rel.solve(solver=cp.GLPK, verbose=False) + else: + self.gamma = self.prob_dom.solve(solver=cp.GLPK, verbose=False) + # self.gamma = self.prob_dom.solve(verbose=False) + self.last_move = "dom" + + return self.alpha.value + + +def mu(rl, normed=False): + if len(np.where(rl < 0)[0]): + raise ValueError(f"rl<0 \n rl={rl}") + return None + m = len(rl) + l_hat = rl if normed else rl / rl.sum() + eps = np.finfo(rl.dtype).eps + l_hat = l_hat[l_hat > eps] + return np.sum(l_hat * np.log(l_hat * m)) + + +def adjustments(l, r=1): + m = len(l) + rl = r * l + l_hat = rl / rl.sum() + mu_rl = mu(l_hat, normed=True) + + eps = 1e-3 # clipping by eps is to avoid log(0), zxy Dec. 5. + a = r * ( np.log( np.clip(l_hat * m, eps, np.inf) ) - mu_rl) + return rl, mu_rl, a + + + +def solve_epo(grad_arr, losses, pref, epo_lp): + + ''' + input: grad_arr: (m,n). + losses : (m,). + pref: (m,) inv. + + return : gw: (n,). + ''' + if type(pref) == torch.Tensor: + pref = pref.numpy() + + pref = np.array(pref) + G = grad_arr.detach().clone().numpy() + + if type(losses) == torch.Tensor: + losses_np = losses.detach().clone().numpy().squeeze() + else: + losses_np = losses + + m = G.shape[0] + n = G.shape[1] + GG = G @ G.T + + # epo_lp = EPO_LP(m=m, n=n, r=np.array(pref)) + + alpha = epo_lp.get_alpha(losses_np, G=GG, C=True) + if alpha is None: # A patch for the issue in cvxpy + alpha = pref / pref.sum() + gw = alpha @ G + + # return torch.Tensor(gw).unsqueeze(0) + return torch.Tensor(gw), alpha + + + + + + + +class EPOSolver(GradBaseSolver): + def __init__(self, step_size, max_iter, tol): + super().__init__(step_size, max_iter, tol) + + + def solve(self, problem, x, prefs, args, ref_point): + x = Variable(x, requires_grad=True) + + epo_arr = [ EPO_LP(m=args.n_obj, n=args.n_var, r=np.array( 1/pref )) for pref in prefs ] + optimizer = SGD([x], lr=self.step_size) + + ind = HV(ref_point=ref_point) + hv_arr = [] + y_arr = [] + + + for i in tqdm( range(self.max_iter) ): + + # optimizer.zero_grad() + y = problem.evaluate(x) + y_arr.append(y.detach().numpy() ) + + alpha_arr = [0] * args.n_prob + for prob_idx in range( args.n_prob ): + grad_arr = [0] * args.n_obj + for obj_idx in range(args.n_obj): + y[prob_idx][obj_idx].backward(retain_graph=True) + grad_arr[obj_idx] = x.grad[prob_idx].clone() + x.grad.zero_() + + grad_arr = torch.stack(grad_arr) + _, alpha = solve_epo(grad_arr, losses=y[prob_idx], pref=prefs[prob_idx], epo_lp=epo_arr[prob_idx]) + alpha_arr[prob_idx] = alpha + + optimizer.zero_grad() + alpha_arr = torch.Tensor( np.array(alpha_arr) ) + torch.sum(alpha_arr * y).backward() + optimizer.step() + + if 'lbound' in dir(problem): + x.data = torch.clamp(x.data, torch.Tensor(problem.lbound) + solution_eps, torch.Tensor(problem.ubound)-solution_eps ) + + + res = {} + res['x'] = x.detach().numpy() + res['y'] = y.detach().numpy() + res['hv_arr'] = [0] + res['y_arr'] = y_arr + + return res \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/functions_evaluation.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/functions_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..1f5609a2779d498ab945cbcd8198b263d9179e49 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/functions_evaluation.py @@ -0,0 +1,94 @@ +""" + The function fastNonDominatedSort is based on the sorting algorithm described by + Deb, Kalyanmoy, et al. + "A fast and elitist multiobjective genetic algorithm: NSGA-II." + IEEE transactions on evolutionary computation 6.2 (2002): 182-197. +""" + + +import numpy as np +import pdb +from .functions_hv_python3 import HyperVolume + + +def determine_non_dom_mo_sol(mo_obj_val): + # get set of non-dominated solutions, returns indices of non-dominated and booleans of dominated mo_sol + n_mo_sol = mo_obj_val.shape[1] + domination_rank = fastNonDominatedSort(mo_obj_val) + non_dom_indices = np.where(domination_rank == 0) + non_dom_indices = non_dom_indices[0] # np.where returns a tuple, so we need to get the array inside the tuple + # non_dom_mo_sol = mo_sol[:,non_dom_indices] + # non_dom_mo_obj_val = mo_obj_val[:,non_dom_indices] + mo_sol_is_non_dominated = np.zeros(n_mo_sol,dtype = bool) + mo_sol_is_non_dominated[non_dom_indices] = True + mo_sol_is_dominated = np.bitwise_not(mo_sol_is_non_dominated) + return(non_dom_indices,mo_sol_is_dominated) + +def fastNonDominatedSort(objVal): + # Based on Deb et al. (2002) NSGA-II + N_OBJECTIVES = objVal.shape[0] + N_SOLUTIONS = objVal.shape[1] + + rankIndArray = - 999 * np.ones(N_SOLUTIONS, dtype = int) # -999 indicates unassigned rank + solIndices = np.arange(0,N_SOLUTIONS) # array of 0 1 2 ... N_SOLUTIONS + ## compute the entire domination matrix + # dominationMatrix: (i,j) is True if solution i dominates solution j + dominationMatrix = np.zeros((N_SOLUTIONS,N_SOLUTIONS), dtype = bool) + for p in solIndices: + objValA = objVal[:,p][:,None] # add [:,None] to preserve dimensions + # objValArray = np.delete(objVal, obj = p axis = 1) # dont delete solution p because it messes up indices + dominates = checkDomination(objValA,objVal) + dominationMatrix[p,:] = dominates + + # count the number of times a solution is dominated + dominationCounter = np.sum(dominationMatrix, axis = 0) + + ## find rank 0 solutions to initialize loop + isRankZero = (dominationCounter == 0) # column and row binary indices of solutions that are rank 0 + + rankZeroRowInd = solIndices[isRankZero] + # mark rank 0's solutions by -99 so that they are not considered as members of next rank + dominationCounter[rankZeroRowInd] = -99 + # initialize rank counter at 0 + rankCounter = 0 + # assign solutions in rank 0 rankIndArray = 0 + rankIndArray[isRankZero] = rankCounter + + isInCurRank = isRankZero + # while the current rank is not empty + while not (np.sum(isInCurRank) == 0): + curRankRowInd = solIndices[isInCurRank] # column and row numbers of solutions that are in current rank + # for each solution in current rank + for p in curRankRowInd: + # decrease domination counter of each solution dominated by solution p which is in the current rank + dominationCounter[dominationMatrix[p,:]] -= 1 #dominationMatrix[p,:] contains indices of the solutions dominated by p + # all solutions that now have dominationCounter == 0, are in the next rank + isInNextRank = (dominationCounter == 0) + rankIndArray[isInNextRank] = rankCounter + 1 + # mark next rank's solutions by -99 so that they are not considered as members of future ranks + dominationCounter[isInNextRank] = -99 + # increase front counter + rankCounter += 1 + # check which solutions are in current rank (next rank became current rank) + isInCurRank = (rankIndArray == rankCounter) + if not np.all(isInNextRank == isInCurRank): # DEBUGGING, if it works fine, replace above assignment + pdb.set_trace() + return(rankIndArray) + +def checkDomination(objValA,objValArray): + dominates = ( np.any(objValA < objValArray, axis = 0) & np.all(objValA <= objValArray , axis = 0) ) + return(dominates) + +def compute_hv_in_higher_dimensions(mo_obj_val,ref_point): + n_mo_obj = mo_obj_val.shape[0] + n_mo_sol = mo_obj_val.shape[1] + assert len(ref_point) == n_mo_obj + # initialize hv computation instance + hv_computation_instance = HyperVolume(tuple(ref_point)) + # turn numpy array to list of tuples + list_of_mo_obj_val = list() + for i_mo_sol in range(n_mo_sol): + list_of_mo_obj_val.append(tuple(mo_obj_val[:,i_mo_sol])) + + hv = float(hv_computation_instance.compute(list_of_mo_obj_val)) + return(hv) \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/functions_hv_grad_3d.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/functions_hv_grad_3d.py new file mode 100644 index 0000000000000000000000000000000000000000..a6b19289393c5390023e6332c5a473a1f14b6633 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/functions_hv_grad_3d.py @@ -0,0 +1,215 @@ +''' +The function grad_multi_sweep and its subfunctions in this file are based on the algorithm described in: +Emmerich, Michael, and André Deutz. +"Time complexity and zeros of the hypervolume indicator gradient field." +EVOLVE-a bridge between probability, set oriented numerics, +and evolutionary computation III. Springer, Heidelberg, 2014. 169-193. +''' +import numpy as np +import copy +from .functions_hv_python3 import HyperVolume +from .functions_evaluation import determine_non_dom_mo_sol + +def determine_mo_sol_in_exterior(mo_obj_val,ref_point): + # select only mo-solutions that are in the exterior + ref_point_temp = ref_point[:,None] # add axis so that comparison works + exterior_booleans = np.any(mo_obj_val > ref_point_temp, axis = 0) + exterior_indices = np.where(exterior_booleans == True) + return(exterior_indices,exterior_booleans) + +def determine_mo_sol_in_interior(mo_obj_val,ref_point): + # select only mo-solutions that are in the interior + ref_point_temp = ref_point[:,None] # add axis so that comparison works + interior_booleans = np.all(mo_obj_val < ref_point_temp, axis = 0) + interior_indices = np.where(interior_booleans == True) + return(interior_indices,interior_booleans) + +def determine_mo_sol_on_ref_boundary(mo_obj_val,ref_point): + # select only mo-solutions that are on the reference boundary + ref_point_temp = ref_point[:,None] # add axis so that comparison works + boundary_booleans = np.logical_and( np.all(mo_obj_val <= ref_point_temp, axis = 0) , np.any(mo_obj_val == ref_point_temp, axis = 0) ) + boundary_indices = np.where(boundary_booleans == True) + return(boundary_indices,boundary_booleans) + +def compute_domination_properties(mo_obj_val): + ''' + compute properties needed for HV gradient computation + ''' + n_mo_sol = mo_obj_val.shape[1] + # mo_sol i stricly dominates j (all entries in i < j ) if strong_domination_matrix[i,j] = True + strong_domination_matrix = np.zeros((n_mo_sol,n_mo_sol), dtype = np.bool_) + for i in range(0,n_mo_sol): + cur_col = mo_obj_val[:,i][:,None] + strong_domination_matrix[i,:] = np.all(cur_col < mo_obj_val,axis = 0) + # mo_sol i weakly dominates j (all entries in i <= j and at least one entry i < j ) if weak_domination_matrix[i,j] = True + weak_domination_matrix = np.zeros((n_mo_sol,n_mo_sol), dtype = np.bool_) + for i in range(0,n_mo_sol): + cur_col = mo_obj_val[:,i][:,None] + weak_domination_matrix[i,:] = np.logical_and( np.all(cur_col <= mo_obj_val,axis = 0) , np.any(cur_col < mo_obj_val,axis = 0) ) + # a mo_sol i is strongly dominated if any other solutions j strongly dominates it (any True in the column strong_domination_matrix[:,i] ) + is_strongly_dominated = np.any(strong_domination_matrix, axis = 0) + # a mo_sol i is weakly dominated if any other solutions j weakly dominates it (any True in the column weak_domination_matrix[:,i] ) + is_weakly_dominated = np.any(weak_domination_matrix, axis = 0) + # weakly but not strongly dominated + is_weakly_but_not_strongly_dominated = np.logical_and( np.logical_not(is_strongly_dominated) , np.any(weak_domination_matrix, axis = 0) ) + + # no other solution weakly dominates it + is_not_weakly_dominated = np.logical_not(is_weakly_dominated) + + # mo_sol i shares coordinate with j if at least 1 entry i = j + coordinate_sharing_matrix = np.zeros((n_mo_sol,n_mo_sol), dtype = np.bool_) + for i in range(0,n_mo_sol): + cur_col = mo_obj_val[:,i][:,None] + coordinate_sharing_matrix[i,:] = np.any(cur_col == mo_obj_val,axis = 0) + + ## is not weakly dominated but share some coordinate with another not weakly dominated mo_sol + # set diagonal entries of coordinate_sharing_matrix to zero to make check work (we do not care of shared coordinates with itself) + subset_of_coordinate_sharing_matrix_with_diag_zero = copy.copy(coordinate_sharing_matrix) + np.fill_diagonal(subset_of_coordinate_sharing_matrix_with_diag_zero,False) # inplace operation + # select subset of columns so that the comparison is for all mo-solutions but only with respect to all non-weakly dominated solutions + subset_of_coordinate_sharing_matrix_with_diag_zero = subset_of_coordinate_sharing_matrix_with_diag_zero[:,is_not_weakly_dominated] + # check per row if any coordinate is shared with a non-weakly dominated solution + has_shared_coordinates = np.any(subset_of_coordinate_sharing_matrix_with_diag_zero,axis = 1) + is_not_weakly_dominated_and_shares_coordinates_with_other_not_weakly_dominated = np.logical_and( is_not_weakly_dominated , has_shared_coordinates ) + + ## is not weakly dominated and share no coordinate with another not weakly dominated mo_sol + is_not_weakly_dominated_and_shares_no_coordinates_with_other_not_weakly_dominated = np.logical_and( is_not_weakly_dominated , np.logical_not(has_shared_coordinates) ) + + + return(is_strongly_dominated,is_weakly_dominated,is_weakly_but_not_strongly_dominated,is_not_weakly_dominated,is_not_weakly_dominated_and_shares_coordinates_with_other_not_weakly_dominated,is_not_weakly_dominated_and_shares_no_coordinates_with_other_not_weakly_dominated) + + +def compute_subsets(mo_obj_val,ref_point): + is_strongly_dominated,is_weakly_dominated,is_weakly_but_not_strongly_dominated, \ + is_not_weakly_dominated,is_not_weakly_dominated_and_shares_coordinates_with_other_not_weakly_dominated, \ + is_not_weakly_dominated_and_shares_no_coordinates_with_other_not_weakly_dominated = compute_domination_properties(mo_obj_val) + ## Z: indices of mo-solutions for which all partial derivatives are zero + # E: in exterior of reference space + E,_ = determine_mo_sol_in_exterior(mo_obj_val,ref_point) + # S: that are strictly dominated + S = np.where(is_strongly_dominated == True) + # Z = E cup S + Z = np.union1d(E,S) + + ## U: indices of mo-solutions for which some partial derivatives are undefined + # D: that are non-dominated but have duplicate coordinates + D = np.where(is_not_weakly_dominated_and_shares_coordinates_with_other_not_weakly_dominated == True) + # W: that are weakly dominated, i.e. not strictly dominated but there is a weakly better (Pareto dominating) solution + W = np.where(is_weakly_but_not_strongly_dominated == True) + # B: that are on the boundary of the reference space + B,_ = determine_mo_sol_on_ref_boundary(mo_obj_val,ref_point) + # # U = D cup (W \ E) cup (B \ S) + # U = np.union1d( np.union1d(D, np.setdiff1d(W,E)) , np.setdiff1d(B,S) ) + #DEVIATION FROM EMMERICH & DEUTZ PAPER: + # U = (D \ E) cup (W \ E) cup (B \ S) + U = np.union1d( np.union1d( np.setdiff1d(D,E) , np.setdiff1d(W,E) ) , np.setdiff1d(B,S) ) + + ## P: indices of mo-solutions for which all partial derivatives are positive (negative??? which one makes sense in our case) + # N: that are not Pareto dominated AND have no duplicate coordinate with any other non-dominated solution + N = np.where(is_not_weakly_dominated_and_shares_no_coordinates_with_other_not_weakly_dominated == True) + # I: that are in the interior of the reference space + I,_ = determine_mo_sol_in_interior(mo_obj_val,ref_point) + # P = N intersect I + P = np.intersect1d(N,I) + + return(P,U,Z) + +def grad_multi_sweep_with_duplicate_handling(mo_obj_val,ref_point): + # find unique mo_obj_val (it also sorts columns which is unnecessary but gets fixed in when using mapping_indices) + unique_mo_obj_val, mapping_indices = np.unique(mo_obj_val, axis = 1, return_inverse = True) + # compute hv_grad for unique mo-solutions + unique_hv_grad = grad_multi_sweep(unique_mo_obj_val,ref_point) + # assign the same gradients to duplicate mo_obj_val (and undo the unnecessary sorting) + hv_grad = unique_hv_grad[:,mapping_indices] + return(hv_grad) + +def grad_multi_sweep(mo_obj_val,ref_point): + ''' + Based on: + Emmerich, Michael, and André Deutz. + "Time complexity and zeros of the hypervolume indicator gradient field." + EVOLVE-a bridge between probability, set oriented numerics, + and evolutionary computation III. Springer, Heidelberg, 2014. 169-193. + ''' + n_obj = mo_obj_val.shape[0] + n_mo_sol = mo_obj_val.shape[1] + assert n_obj == len(ref_point) + hv_grad = np.zeros_like(mo_obj_val) + + + + P, U, Z = compute_subsets(mo_obj_val,ref_point) + ##### + if not (len(U) == 0): + # raise ValueError("Partial derivatives might be only one-sided in indice" + str(U)) + print("Partial derivatives might be only one-sided in indices" + str(U)) + print(mo_obj_val[:,U]) + hv_grad[:,U] = 0 + + for k in range(0,n_obj): + temp_ref_point = copy.copy(ref_point) + temp_ref_point = np.delete(temp_ref_point,k,axis = 0) + temp_ref_point = tuple(temp_ref_point) + hv_instance = HyperVolume(temp_ref_point) + sorted_P = copy.copy(mo_obj_val[:,P]) + # descending order sorting + sort_order= np.argsort(-sorted_P[k,:]) + sorted_P = sorted_P[:,sort_order] + # remove k-th row + sorted_P = np.delete(sorted_P,k,0) + # initialize queue by turning array of columns into list of columns + Q = sorted_P.T.tolist() # it should be possible to delete this row, Q is overwritten in the next line + Q = list() + for i in range(sorted_P.shape[1]): + Q.append(tuple(sorted_P[:,i])) + queue_index = len(P) # this initialization is actually index of last queue entry +1. The +1 is convenient because the while loop will always update the index by -1, so it all matches up in the end + T = list() + while (len(Q) > 0): + # take last element in list + q = Q.pop() + # compute hypervolume contribution of q when added to T + + if len(T) == 0: + T_with_q = list() + T_with_q.append(q) + hv_contribution = hv_instance.compute(T_with_q) + else: + T_with_q = copy.copy(T) + T_with_q.append(q) + hv_contribution = hv_instance.compute(T_with_q) - hv_instance.compute(T) + # queue_index is the index of q + queue_index = queue_index - 1 # -1 because the counter is always lagging 1 and it was initialized with +1 + # mo_sol_index is the index of q in mo_obj_val + mo_sol_index = P[sort_order[queue_index]] + hv_grad[k,mo_sol_index] = hv_contribution + + ## add q to T and remove points dominated by q + # initialize T by q in first iteration + if len(T) == 0: + T.append(q) + # T = q + else: + ## remove columns in T that are dominated by q + # loop through T + i = 0 + while (i < len(T)): + # remove entry if dominated by q + if check_weak_domination_in_tuple(q,T[i]): + del T[i] + else: + # if entry not deleted, move to next entry + i = i + 1 + + # add q to T + T.append(q) + return(hv_grad) + +def check_weak_domination_in_tuple(tuple_A,tuple_B): + assert len(tuple_A) == len(tuple_B) + # initialize as True + A_weakly_dominates_B = True + for i in range(len(tuple_B)): + if tuple_A[i] > tuple_B[i]: + A_weakly_dominates_B = False + break + return(A_weakly_dominates_B) \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/functions_hv_python3.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/functions_hv_python3.py new file mode 100644 index 0000000000000000000000000000000000000000..d37aca8fc7176d9c282ba09ffec95f1dc8d73e1b --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/functions_hv_python3.py @@ -0,0 +1,274 @@ +""" +Copyright (C) 2010 Simon Wessing +TU Dortmund University + +This program is free software: you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation, either version 3 of the License, or +(at your option) any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License +along with this program. If not, see . + +__author__ = "Simon Wessing" +""" + + +class HyperVolume: + """ + Hypervolume computation based on variant 3 of the algorithm in the paper: + C. M. Fonseca, L. Paquete, and M. Lopez-Ibanez. An improved dimension-sweep + algorithm for the hypervolume indicator. In IEEE Congress on Evolutionary + Computation, pages 1157-1163, Vancouver, Canada, July 2006. + Minimization is implicitly assumed here! + """ + + def __init__(self, referencePoint): + """Constructor.""" + self.referencePoint = referencePoint + self.list = [] + + def compute(self, front): + """Returns the hypervolume that is dominated by a non-dominated front. + Before the HV computation, front and reference point are translated, so + that the reference point is [0, ..., 0]. + """ + + def weaklyDominates(point, other): + for i in range(len(point)): + if point[i] > other[i]: + return False + return True + + relevantPoints = [] + referencePoint = self.referencePoint + dimensions = len(referencePoint) + for point in front: + # only consider points that dominate the reference point + if weaklyDominates(point, referencePoint): + relevantPoints.append(point) + if any(referencePoint): + # shift points so that referencePoint == [0, ..., 0] + # this way the reference point doesn't have to be explicitly used + # in the HV computation + for j in range(len(relevantPoints)): + relevantPoints[j] = [relevantPoints[j][i] - referencePoint[i] for i in range(dimensions)] + self.preProcess(relevantPoints) + bounds = [-1.0e308] * dimensions + hyperVolume = self.hvRecursive(dimensions - 1, len(relevantPoints), bounds) + return hyperVolume + + + def hvRecursive(self, dimIndex, length, bounds): + """Recursive call to hypervolume calculation. + In contrast to the paper, the code assumes that the reference point + is [0, ..., 0]. This allows the avoidance of a few operations. + """ + hvol = 0.0 + sentinel = self.list.sentinel + if length == 0: + return hvol + elif dimIndex == 0: + # special case: only one dimension + # why using hypervolume at all? + return -sentinel.next[0].cargo[0] + elif dimIndex == 1: + # special case: two dimensions, end recursion + q = sentinel.next[1] + h = q.cargo[0] + p = q.next[1] + while p is not sentinel: + pCargo = p.cargo + hvol += h * (q.cargo[1] - pCargo[1]) + if pCargo[0] < h: + h = pCargo[0] + q = p + p = q.next[1] + hvol += h * q.cargo[1] + return hvol + else: + remove = self.list.remove + reinsert = self.list.reinsert + hvRecursive = self.hvRecursive + p = sentinel + q = p.prev[dimIndex] + while q.cargo != None: + if q.ignore < dimIndex: + q.ignore = 0 + q = q.prev[dimIndex] + q = p.prev[dimIndex] + while length > 1 and (q.cargo[dimIndex] > bounds[dimIndex] or q.prev[dimIndex].cargo[dimIndex] >= bounds[dimIndex]): + p = q + remove(p, dimIndex, bounds) + q = p.prev[dimIndex] + length -= 1 + qArea = q.area + qCargo = q.cargo + qPrevDimIndex = q.prev[dimIndex] + if length > 1: + hvol = qPrevDimIndex.volume[dimIndex] + qPrevDimIndex.area[dimIndex] * (qCargo[dimIndex] - qPrevDimIndex.cargo[dimIndex]) + else: + qArea[0] = 1 + qArea[1:dimIndex+1] = [qArea[i] * -qCargo[i] for i in range(dimIndex)] + q.volume[dimIndex] = hvol + if q.ignore >= dimIndex: + qArea[dimIndex] = qPrevDimIndex.area[dimIndex] + else: + qArea[dimIndex] = hvRecursive(dimIndex - 1, length, bounds) + if qArea[dimIndex] <= qPrevDimIndex.area[dimIndex]: + q.ignore = dimIndex + while p is not sentinel: + pCargoDimIndex = p.cargo[dimIndex] + hvol += q.area[dimIndex] * (pCargoDimIndex - q.cargo[dimIndex]) + bounds[dimIndex] = pCargoDimIndex + reinsert(p, dimIndex, bounds) + length += 1 + q = p + p = p.next[dimIndex] + q.volume[dimIndex] = hvol + if q.ignore >= dimIndex: + q.area[dimIndex] = q.prev[dimIndex].area[dimIndex] + else: + q.area[dimIndex] = hvRecursive(dimIndex - 1, length, bounds) + if q.area[dimIndex] <= q.prev[dimIndex].area[dimIndex]: + q.ignore = dimIndex + hvol -= q.area[dimIndex] * q.cargo[dimIndex] + return hvol + + + def preProcess(self, front): + """Sets up the list data structure needed for calculation.""" + dimensions = len(self.referencePoint) + nodeList = MultiList(dimensions) + nodes = [MultiList.Node(dimensions, point) for point in front] + for i in range(dimensions): + # sort by dimension + nodes = sorted(nodes, key=lambda node: node.cargo[i]) + nodeList.extend(nodes, i) + self.list = nodeList + + + +class MultiList: + """A special data structure needed by FonsecaHyperVolume. + + It consists of several doubly linked lists that share common nodes. So, + every node has multiple predecessors and successors, one in every list. + """ + + class Node: + + def __init__(self, numberLists, cargo=None): + self.cargo = cargo + self.next = [None] * numberLists + self.prev = [None] * numberLists + self.ignore = 0 + self.area = [0.0] * numberLists + self.volume = [0.0] * numberLists + + def __str__(self): + return str(self.cargo) + + + def __init__(self, numberLists): + """Constructor. + + Builds 'numberLists' doubly linked lists. + """ + self.numberLists = numberLists + self.sentinel = MultiList.Node(numberLists) + self.sentinel.next = [self.sentinel] * numberLists + self.sentinel.prev = [self.sentinel] * numberLists + + + def __str__(self): + strings = [] + for i in range(self.numberLists): + currentList = [] + node = self.sentinel.next[i] + while node != self.sentinel: + currentList.append(str(node)) + node = node.next[i] + strings.append(str(currentList)) + stringRepr = "" + for string in strings: + stringRepr += string + "\n" + return stringRepr + + + def __len__(self): + """Returns the number of lists that are included in this MultiList.""" + return self.numberLists + + + def getLength(self, i): + """Returns the length of the i-th list.""" + length = 0 + sentinel = self.sentinel + node = sentinel.next[i] + while node != sentinel: + length += 1 + node = node.next[i] + return length + + + def append(self, node, index): + """Appends a node to the end of the list at the given index.""" + lastButOne = self.sentinel.prev[index] + node.next[index] = self.sentinel + node.prev[index] = lastButOne + # set the last element as the new one + self.sentinel.prev[index] = node + lastButOne.next[index] = node + + + def extend(self, nodes, index): + """Extends the list at the given index with the nodes.""" + sentinel = self.sentinel + for node in nodes: + lastButOne = sentinel.prev[index] + node.next[index] = sentinel + node.prev[index] = lastButOne + # set the last element as the new one + sentinel.prev[index] = node + lastButOne.next[index] = node + + + def remove(self, node, index, bounds): + """Removes and returns 'node' from all lists in [0, 'index'[.""" + for i in range(index): + predecessor = node.prev[i] + successor = node.next[i] + predecessor.next[i] = successor + successor.prev[i] = predecessor + if bounds[i] > node.cargo[i]: + bounds[i] = node.cargo[i] + return node + + + def reinsert(self, node, index, bounds): + """ + Inserts 'node' at the position it had in all lists in [0, 'index'[ + before it was removed. This method assumes that the next and previous + nodes of the node that is reinserted are in the list. + """ + for i in range(index): + node.prev[i].next[i] = node + node.next[i].prev[i] = node + if bounds[i] > node.cargo[i]: + bounds[i] = node.cargo[i] + + + +if __name__ == "__main__": + + # Example: + referencePoint = [2, 2, 2] + hv = HyperVolume(referencePoint) + front = [[1,0,1], [0,1,0]] + volume = hv.compute(front) \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/gradhv.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/gradhv.py new file mode 100644 index 0000000000000000000000000000000000000000..5b30fa793c526056ead529680d18d2beea9d31c9 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/gradhv.py @@ -0,0 +1,122 @@ +""" +The class HvMaximization is based on the algorithm described by +Wang, Hao, et al. +"Hypervolume indicator gradient ascent multimnist-objective optimization." +International conference on evolutionary multimnist-criterion optimization. Springer, Cham, 2017. +""" + +import numpy as np +import torch + +from .functions_evaluation import fastNonDominatedSort +from .functions_hv_grad_3d import grad_multi_sweep_with_duplicate_handling +from .base_solver import GradBaseSolver +from torch.autograd import Variable + + +from tqdm import tqdm +from pymoo.indicators.hv import HV +from ...util_global.constant import solution_eps, get_hv_ref_dict + +""" +The class HvMaximization is based on the algorithm described by +Wang, Hao, et al. +"Hypervolume indicator gradient ascent multi-objective optimization." +International conference on evolutionary multi-criterion optimization. Springer, Cham, 2017. +""" + +import numpy as np +import torch + +from .functions_evaluation import fastNonDominatedSort +from .functions_hv_grad_3d import grad_multi_sweep_with_duplicate_handling + + +class HvMaximization(object): + """ + Mo optimizer for calculating dynamic weights using higamo style hv maximization + based on Hao Wang et al.'s HIGA-MO + uses non-dominated sorting to create multiple fronts, and maximize hypervolume of each + """ + + def __init__(self, n_mo_sol, n_mo_obj, ref_point, obj_space_normalize=True): + super(HvMaximization, self).__init__() + self.name = 'hv_maximization' + self.ref_point = np.array(ref_point) + self.n_mo_sol = n_mo_sol + self.n_mo_obj = n_mo_obj + self.obj_space_normalize = obj_space_normalize + + def compute_weights(self, mo_obj_val): + n_mo_obj = self.n_mo_obj + n_mo_sol = self.n_mo_sol + + # non-dom sorting to create multiple fronts + hv_subfront_indices = fastNonDominatedSort(mo_obj_val) + dyn_ref_point = 1.1 * np.max(mo_obj_val, axis=1) + for i_obj in range(0, n_mo_obj): + dyn_ref_point[i_obj] = np.maximum(self.ref_point[i_obj], dyn_ref_point[i_obj]) + number_of_fronts = np.max(hv_subfront_indices) + 1 # +1 because of 0 indexing + + obj_space_multifront_hv_gradient = np.zeros((n_mo_obj, n_mo_sol)) + for i_fronts in range(0, number_of_fronts): + # compute HV gradients for current front + temp_grad_array = grad_multi_sweep_with_duplicate_handling(mo_obj_val[:, (hv_subfront_indices == i_fronts)], + dyn_ref_point) + obj_space_multifront_hv_gradient[:, (hv_subfront_indices == i_fronts)] = temp_grad_array + + # normalize the hv_gradient in obj space (||dHV/dY|| == 1) + normalized_obj_space_multifront_hv_gradient = np.zeros((n_mo_obj, n_mo_sol)) + for i_mo_sol in range(0, n_mo_sol): + w = np.sqrt(np.sum(obj_space_multifront_hv_gradient[:, i_mo_sol] ** 2.0)) + if np.isclose(w, 0): + w = 1 + if self.obj_space_normalize: + normalized_obj_space_multifront_hv_gradient[:, i_mo_sol] = obj_space_multifront_hv_gradient[:, + i_mo_sol] / w + else: + normalized_obj_space_multifront_hv_gradient[:, i_mo_sol] = obj_space_multifront_hv_gradient[:, i_mo_sol] + + dynamic_weights = torch.tensor(normalized_obj_space_multifront_hv_gradient, dtype=torch.float) + return (dynamic_weights) + + + +class GradHVSolver(GradBaseSolver): + def __init__(self, step_size, max_iter, tol): + + super().__init__(step_size, max_iter, tol) + + def solve(self, problem, x, prefs, args, ref_point): + if args.n_obj != 2: + assert False, 'hvgrad only supports 2 obj problem' + + hv_maximizer = HvMaximization(args.n_prob, args.n_obj, ref_point) + + + x = Variable(x, requires_grad=True) + optimizer = torch.optim.SGD([x,], lr=self.step_size) + hv_ind = HV(ref_point= ref_point) + hv_arr = [0] * self.max_iter + y_arr=[] + for iter_idx in tqdm(range(self.max_iter)): + y = problem.evaluate(x) + y_np = y.detach().numpy() + y_arr.append(y_np) + hv_arr[iter_idx] = hv_ind.do(y_np) + weight = hv_maximizer.compute_weights(y_np.T) + weight = torch.tensor(weight.T, dtype=torch.float) + optimizer.zero_grad() + torch.sum(weight*y).backward() + optimizer.step() + if 'lbound' in dir(problem): + x.data = torch.clamp(x.data, torch.Tensor(problem.lbound) + solution_eps, torch.Tensor(problem.ubound)-solution_eps ) + + + res = {} + res['x'] = x.detach().numpy() + res['y'] = y.detach().numpy() + res['hv_arr'] = hv_arr + res['y_arr'] = y_arr + + return res \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/mgda_core.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/mgda_core.py new file mode 100644 index 0000000000000000000000000000000000000000..e484b8ddaf8fda43aa445d068924bcc0314e0edd --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/mgda_core.py @@ -0,0 +1,72 @@ +import torch +from numpy import array +import numpy as np +from cvxopt import matrix, solvers +solvers.options['show_progress'] = False + +def solve_mgda_analy(grad_1, grad_2, return_coeff = False): + ''' + Noted that, solve_mgda_analy only support 2-objective case. + grad_i.shape: (n,). + This function support grad_i as both Tensor and numpy. + ''' + + v1v1 = grad_1 @ grad_1 + v2v2 = grad_2 @ grad_2 + v1v2 = grad_1 @ grad_2 + + if v1v2 >= v1v1: + gamma = 0.999 + elif v1v2 >= v2v2: + gamma = 0.001 + else: + gamma = -1.0 * ((v1v2 - v2v2) / (v1v1 + v2v2 - 2 * v1v2)) + + coeff = array([gamma, 1-gamma]) + gw = coeff[0] * grad_1 + coeff[1] * grad_2 + if return_coeff: + return gw, coeff + else: + return gw + + +def solve_mgda(G, return_coeff=False): + ''' + input G: (m,n). + output gw (n,). + comments: This function is used to solve the dual MGDA problem. It can handle m>2. + ''' + if type(G) == torch.Tensor: + G = G.detach().cpu().numpy().copy() + + + m = G.shape[0] + if m == 2: + return solve_mgda_analy(G[0], G[1], return_coeff=return_coeff) + else: + Q = G @ G.T + Q = matrix(np.float64(Q)) + p = np.zeros(m) + A = np.ones(m) + + A = matrix(A, (1, m)) + b = matrix(1.0) + + G_cvx = -np.eye(m) + h = [0.0] * m + h = matrix(h) + + G_cvx = matrix(G_cvx) + p = matrix(p) + sol = solvers.qp(Q, p, G_cvx, h, A, b) + + res = np.array(sol['x']).squeeze() + res = res / sum(res) # important + gw = torch.Tensor( res @ G ) + + if return_coeff: + return gw, res + else: + return gw + + diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/mgda_solver.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/mgda_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..41beea161a2d79e244800252839fb01d3718a847 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/mgda_solver.py @@ -0,0 +1,71 @@ +import torch.autograd + +from .mgda_core import solve_mgda + +from .base_solver import GradBaseSolver +from torch.autograd import Variable +from torch.optim import SGD +from tqdm import tqdm +from torch import Tensor +import numpy as np +from ...util_global.constant import solution_eps, get_hv_ref_dict +from pymoo.indicators.hv import HV + + +''' + MGDA solver, published in: + 1. Multiple-gradient descent algorithm (MGDA) for multiobjective optimizationAlgorithme de descente à gradients multiples pour lʼoptimisation multiobjectif + 2. Sener, Ozan, and Vladlen Koltun. "Multi-task learning as multimnist-objective optimization." Advances in neural information processing systems 31 (2018). +''' + + +class MGDASolver(GradBaseSolver): + def __init__(self, step_size, max_iter, tol): + super().__init__(step_size, max_iter, tol) + + + def solve(self, problem, x, prefs, args, ref_point): + x = Variable(x, requires_grad=True) + optimizer = SGD([x], lr=self.step_size) + + ind = HV(ref_point=ref_point) + hv_arr = [] + y_arr = [] + + for i in tqdm(range(self.max_iter)): + grad_arr = [0] * args.n_prob + y = problem.evaluate(x) + y_np = y.detach().numpy() + y_arr.append(y_np) + hv_arr.append(ind.do(y_np)) + + for prob_idx in range( args.n_prob ): + grad_arr[prob_idx] = [0] * args.n_obj + for obj_idx in range(args.n_obj): + y[prob_idx][obj_idx].backward(retain_graph=True) + grad_arr[prob_idx][obj_idx] = x.grad[prob_idx].clone() + x.grad.zero_() + grad_arr[prob_idx] = torch.stack(grad_arr[prob_idx]) + + grad_arr = torch.stack(grad_arr) + gw_arr = [solve_mgda(G, return_coeff=True) for G in grad_arr] + optimizer.zero_grad() + weights = Tensor( np.array([gw[1] for gw in gw_arr]) ) + # weights = Tensor( np.array([1.0, 0.0]) ) + torch.sum(weights * y).backward() + optimizer.step() + + if 'lbound' in dir(problem): + x.data = torch.clamp(x.data, torch.Tensor(problem.lbound) + solution_eps, torch.Tensor(problem.ubound) - solution_eps ) + + res = {} + res['x'] = x.detach().numpy() + res['y'] = y.detach().numpy() + res['hv_arr'] = hv_arr + res['y_arr'] = y_arr + + return res + + +if __name__ == '__main__': + print() \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/min_norm_solvers_numpy.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/min_norm_solvers_numpy.py new file mode 100644 index 0000000000000000000000000000000000000000..9f187916888d9053ed65a0a50df11d976ebd97e5 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/min_norm_solvers_numpy.py @@ -0,0 +1,386 @@ +# This code is from +# Multi-Task Learning as Multi-Objective Optimization +# Ozan Sener, Vladlen Koltun +# Neural Information Processing Systems (NeurIPS) 2018 +# https://github.com/intel-isl/MultiObjectiveOptimization + +import numpy as np +import torch + +class MinNormSolver: + MAX_ITER = 250 + STOP_CRIT = 1e-5 + + def _min_norm_element_from2(v1v1, v1v2, v2v2): + """ + Analytical solution for min_{c} |cx_1 + (1-c)x_2|_2^2 + d is the distance (objective) optimzed + v1v1 = + v1v2 = + v2v2 = + """ + if v1v2 >= v1v1: + # Case: Fig 1, third column + gamma = 0.999 + cost = v1v1 + return gamma, cost + if v1v2 >= v2v2: + # Case: Fig 1, first column + gamma = 0.001 + cost = v2v2 + return gamma, cost + # Case: Fig 1, second column + gamma = -1.0 * ((v1v2 - v2v2) / (v1v1 + v2v2 - 2 * v1v2)) + cost = v2v2 + gamma * (v1v2 - v2v2) + return gamma, cost + + def _min_norm_2d(vecs, dps): + """ + Find the minimum norm solution as combination of two points + This is correct only in 2D + ie. min_c |\sum c_i x_i|_2^2 st. \sum c_i = 1 , 1 >= c_1 >= 0 for all i, c_i + c_j = 1.0 for some i, j + """ + dmin = 1e8 + for i in range(len(vecs)): + for j in range(i + 1, len(vecs)): + if (i, j) not in dps: + dps[(i, j)] = 0.0 + for k in range(len(vecs[i])): + dps[(i, j)] += torch.mul(vecs[i][k], vecs[j][k]).sum().data.cpu() + dps[(j, i)] = dps[(i, j)] + if (i, i) not in dps: + dps[(i, i)] = 0.0 + for k in range(len(vecs[i])): + dps[(i, i)] += torch.mul(vecs[i][k], vecs[i][k]).sum().data.cpu() + if (j, j) not in dps: + dps[(j, j)] = 0.0 + for k in range(len(vecs[i])): + dps[(j, j)] += torch.mul(vecs[j][k], vecs[j][k]).sum().data.cpu() + c, d = MinNormSolver._min_norm_element_from2(dps[(i, i)], dps[(i, j)], dps[(j, j)]) + if d < dmin: + dmin = d + sol = [(i, j), c, d] + return sol, dps + + def _projection2simplex(y): + """ + Given y, it solves argmin_z |y-z|_2 st \sum z = 1 , 1 >= z_i >= 0 for all i + """ + m = len(y) + sorted_y = np.flip(np.sort(y), axis=0) + tmpsum = 0.0 + tmax_f = (np.sum(y) - 1.0) / m + for i in range(m - 1): + tmpsum += sorted_y[i] + tmax = (tmpsum - 1) / (i + 1.0) + if tmax > sorted_y[i + 1]: + tmax_f = tmax + break + return np.maximum(y - tmax_f, np.zeros(y.shape)) + + def _next_point(cur_val, grad, n): + proj_grad = grad - (np.sum(grad) / n) + tm1 = -1.0 * cur_val[proj_grad < 0] / proj_grad[proj_grad < 0] + tm2 = (1.0 - cur_val[proj_grad > 0]) / (proj_grad[proj_grad > 0]) + + # tm1 = np.array(tm1) + # tm2 = np.array(tm2) + + # skippers = np.sum(tm1 < 1e-7) + np.sum(tm2 < 1e-7) + t = 1 + if len(tm1[tm1 > 1e-7]) > 0: + t = np.min(tm1[tm1 > 1e-7]) + if len(tm2[tm2 > 1e-7]) > 0: + t = min(t, np.min(tm2[tm2 > 1e-7])) + + next_point = proj_grad * t + cur_val + next_point = MinNormSolver._projection2simplex(next_point) + return next_point + + def find_min_norm_element(vecs): + """ + Given a list of vectors (vecs), this method finds the minimum norm element in the convex hull. + as min |u|_2 st. u = \sum c_i vecs[i] and \sum c_i = 1. + It is quite geometric, and the main idea is the fact that if d_{ij} = min |u|_2 st u = c x_i + (1-c) x_j; the solution lies in (0, d_{i,j}). + Hence, we find the best 2-task solution, and then run the projected gradient descent until convergence. + """ + # Solution lying at the combination of two points + dps = {} # What does dps mean? + + init_sol, dps = MinNormSolver._min_norm_2d(vecs, dps) + + n = len(vecs) + sol_vec = np.zeros(n) + sol_vec[init_sol[0][0]] = init_sol[1] + sol_vec[init_sol[0][1]] = 1 - init_sol[1] + + if n < 3: + # This is optimal for n=2, so return the solution + return sol_vec, init_sol[2] + + iter_count = 0 + + grad_mat = np.zeros((n, n)) + for i in range(n): + for j in range(n): + grad_mat[i, j] = dps[(i, j)] + + while iter_count < MinNormSolver.MAX_ITER: + grad_dir = -1.0 * np.dot(grad_mat, sol_vec) + new_point = MinNormSolver._next_point(sol_vec, grad_dir, n) + # Re-compute the inner products for line search + v1v1 = 0.0 + v1v2 = 0.0 + v2v2 = 0.0 + for i in range(n): + for j in range(n): + v1v1 += sol_vec[i] * sol_vec[j] * dps[(i, j)] + v1v2 += sol_vec[i] * new_point[j] * dps[(i, j)] + v2v2 += new_point[i] * new_point[j] * dps[(i, j)] + nc, nd = MinNormSolver._min_norm_element_from2(v1v1, v1v2, v2v2) + new_sol_vec = nc * sol_vec + (1 - nc) * new_point + change = new_sol_vec - sol_vec + change = np.array(change) + + if np.sum( np.abs(change) ) < MinNormSolver.STOP_CRIT: + return sol_vec, nd + + sol_vec = new_sol_vec + + def find_min_norm_element_FW(vecs): + """ + Given a list of vectors (vecs), this method finds the minimum norm element in the convex hull + as min |u|_2 st. u = \sum c_i vecs[i] and \sum c_i = 1. + It is quite geometric, and the main idea is the fact that if d_{ij} = min |u|_2 st u = c x_i + (1-c) x_j; the solution lies in (0, d_{i,j}) + Hence, we find the best 2-task solution, and then run the Frank Wolfe until convergence + """ + # Solution lying at the combination of two points + dps = {} + init_sol, dps = MinNormSolver._min_norm_2d(vecs, dps) + + n = len(vecs) + sol_vec = np.zeros(n) + sol_vec[init_sol[0][0]] = init_sol[1] + sol_vec[init_sol[0][1]] = 1 - init_sol[1] + + if n < 3: + # This is optimal for n=2, so return the solution + return sol_vec, init_sol[2] + + iter_count = 0 + + grad_mat = np.zeros((n, n)) + for i in range(n): + for j in range(n): + grad_mat[i, j] = dps[(i, j)] + + while iter_count < MinNormSolver.MAX_ITER: + t_iter = np.argmin(np.dot(grad_mat, sol_vec)) + + v1v1 = np.dot(sol_vec, np.dot(grad_mat, sol_vec)) + v1v2 = np.dot(sol_vec, grad_mat[:, t_iter]) + v2v2 = grad_mat[t_iter, t_iter] + + nc, nd = MinNormSolver._min_norm_element_from2(v1v1, v1v2, v2v2) + new_sol_vec = nc * sol_vec + new_sol_vec[t_iter] += 1 - nc + + change = new_sol_vec - sol_vec + if np.sum(np.abs(change)) < MinNormSolver.STOP_CRIT: + return sol_vec, nd + sol_vec = new_sol_vec + + +def gradient_normalizers(grads, losses, normalization_type): + gn = {} + if normalization_type == 'l2': + for t in grads: + gn[t] = np.sqrt(np.sum([gr.pow(2).sum().data.cpu() for gr in grads[t]])) + elif normalization_type == 'loss': + for t in grads: + gn[t] = losses[t] + elif normalization_type == 'loss+': + for t in grads: + gn[t] = losses[t] * np.sqrt(np.sum([gr.pow(2).sum().data.cpu() for gr in grads[t]])) + elif normalization_type == 'none': + for t in grads: + gn[t] = 1.0 + else: + print('ERROR: Invalid Normalization Type') + return gn + + +class MinNormSolverNumpy: + MAX_ITER = 250 + STOP_CRIT = 1e-6 + + def _min_norm_element_from2(v1v1, v1v2, v2v2): + """ + Analytical solution for min_{c} |cx_1 + (1-c)x_2|_2^2 + d is the distance (objective) optimzed + v1v1 = + v1v2 = + v2v2 = + """ + if v1v2 >= v1v1: + # Case: Fig 1, third column + gamma = 0.999 + cost = v1v1 + return gamma, cost + if v1v2 >= v2v2: + # Case: Fig 1, first column + gamma = 0.001 + cost = v2v2 + return gamma, cost + # Case: Fig 1, second column + gamma = -1.0 * ((v1v2 - v2v2) / (v1v1 + v2v2 - 2 * v1v2)) + cost = v2v2 + gamma * (v1v2 - v2v2) + return gamma, cost + + def _min_norm_2d(vecs, dps): + """ + Find the minimum norm solution as combination of two points + This solution is correct if vectors(gradients) lie in 2D + ie. min_c |\sum c_i x_i|_2^2 st. \sum c_i = 1 , 1 >= c_1 >= 0 for all i, c_i + c_j = 1.0 for some i, j + """ + dmin = 1e8 + for i in range(len(vecs)): + for j in range(i + 1, len(vecs)): + if (i, j) not in dps: + dps[(i, j)] = 0.0 + dps[(i, j)] = np.dot(vecs[i], vecs[j]) + dps[(j, i)] = dps[(i, j)] + if (i, i) not in dps: + dps[(i, i)] = 0.0 + dps[(i, i)] = np.dot(vecs[i], vecs[i]) + if (j, j) not in dps: + dps[(j, j)] = 0.0 + dps[(j, j)] = np.dot(vecs[j], vecs[j]) + c, d = MinNormSolver._min_norm_element_from2(dps[(i, i)], dps[(i, j)], dps[(j, j)]) + if d < dmin: + dmin = d + sol = [(i, j), c, d] + return sol, dps + + def _projection2simplex(y): + """ + Given y, it solves argmin_z |y-z|_2 st \sum z = 1 , 1 >= z_i >= 0 for all i + """ + m = len(y) + sorted_y = np.flip(np.sort(y), axis=0) + tmpsum = 0.0 + tmax_f = (np.sum(y) - 1.0) / m + for i in range(m - 1): + tmpsum += sorted_y[i] + tmax = (tmpsum - 1) / (i + 1.0) + if tmax > sorted_y[i + 1]: + tmax_f = tmax + break + return np.maximum(y - tmax_f, np.zeros(y.shape)) + + def _next_point(cur_val, grad, n): + proj_grad = grad - (np.sum(grad) / n) + tm1 = -1.0 * cur_val[proj_grad < 0] / proj_grad[proj_grad < 0] + tm2 = (1.0 - cur_val[proj_grad > 0]) / (proj_grad[proj_grad > 0]) + + skippers = np.sum(tm1 < 1e-7) + np.sum(tm2 < 1e-7) + t = 1 + if len(tm1[tm1 > 1e-7]) > 0: + t = np.min(tm1[tm1 > 1e-7]) + if len(tm2[tm2 > 1e-7]) > 0: + t = min(t, np.min(tm2[tm2 > 1e-7])) + + next_point = proj_grad * t + cur_val + next_point = MinNormSolver._projection2simplex(next_point) + return next_point + + def find_min_norm_element(vecs): + """ + Given a list of vectors (vecs), this method finds the minimum norm element in the convex hull + as min |u|_2 st. u = \sum c_i vecs[i] and \sum c_i = 1. + It is quite geometric, and the main idea is the fact that if d_{ij} = min |u|_2 st u = c x_i + (1-c) x_j; the solution lies in (0, d_{i,j}) + Hence, we find the best 2-task solution, and then run the projected gradient descent until convergence + """ + # Solution lying at the combination of two points + dps = {} + init_sol, dps = MinNormSolver._min_norm_2d(vecs, dps) + + n = len(vecs) + sol_vec = np.zeros(n) + sol_vec[init_sol[0][0]] = init_sol[1] + sol_vec[init_sol[0][1]] = 1 - init_sol[1] + + if n < 3: + # This is optimal for n=2, so return the solution + return sol_vec, init_sol[2] + + iter_count = 0 + + grad_mat = np.zeros((n, n)) + for i in range(n): + for j in range(n): + grad_mat[i, j] = dps[(i, j)] + + while iter_count < MinNormSolver.MAX_ITER: + grad_dir = -1.0 * np.dot(grad_mat, sol_vec) + new_point = MinNormSolver._next_point(sol_vec, grad_dir, n) + # Re-compute the inner products for line search + v1v1 = 0.0 + v1v2 = 0.0 + v2v2 = 0.0 + for i in range(n): + for j in range(n): + v1v1 += sol_vec[i] * sol_vec[j] * dps[(i, j)] + v1v2 += sol_vec[i] * new_point[j] * dps[(i, j)] + v2v2 += new_point[i] * new_point[j] * dps[(i, j)] + nc, nd = MinNormSolver._min_norm_element_from2(v1v1, v1v2, v2v2) + new_sol_vec = nc * sol_vec + (1 - nc) * new_point + change = new_sol_vec - sol_vec + if np.sum(np.abs(change)) < MinNormSolver.STOP_CRIT: + return sol_vec, nd + sol_vec = new_sol_vec + return sol_vec, nd + + def find_min_norm_element_FW(vecs): + """ + Given a list of vectors (vecs), this method finds the minimum norm element in the convex hull + as min |u|_2 st. u = \sum c_i vecs[i] and \sum c_i = 1. + It is quite geometric, and the main idea is the fact that if d_{ij} = min |u|_2 st u = c x_i + (1-c) x_j; the solution lies in (0, d_{i,j}) + Hence, we find the best 2-task solution, and then run the Frank Wolfe until convergence + """ + # Solution lying at the combination of two points + dps = {} + init_sol, dps = MinNormSolver._min_norm_2d(vecs, dps) + + n = len(vecs) + sol_vec = np.zeros(n) + sol_vec[init_sol[0][0]] = init_sol[1] + sol_vec[init_sol[0][1]] = 1 - init_sol[1] + + if n < 3: + # This is optimal for n=2, so return the solution + return sol_vec, init_sol[2] + + iter_count = 0 + + grad_mat = np.zeros((n, n)) + for i in range(n): + for j in range(n): + grad_mat[i, j] = dps[(i, j)] + + while iter_count < MinNormSolver.MAX_ITER: + t_iter = np.argmin(np.dot(grad_mat, sol_vec)) + + v1v1 = np.dot(sol_vec, np.dot(grad_mat, sol_vec)) + v1v2 = np.dot(sol_vec, grad_mat[:, t_iter]) + v2v2 = grad_mat[t_iter, t_iter] + + nc, nd = MinNormSolver._min_norm_element_from2(v1v1, v1v2, v2v2) + new_sol_vec = nc * sol_vec + new_sol_vec[t_iter] += 1 - nc + + change = new_sol_vec - sol_vec + if np.sum(np.abs(change)) < MinNormSolver.STOP_CRIT: + return sol_vec, nd + sol_vec = new_sol_vec + return sol_vec, nd \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/moosvgd.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/moosvgd.py new file mode 100644 index 0000000000000000000000000000000000000000..3c8e1d23e237c9e053e9bc38bf547465d9e19f2f --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/moosvgd.py @@ -0,0 +1,101 @@ +from .mgda_core import solve_mgda +from .base_solver import GradBaseSolver + +import torch +import math +from torch.autograd import Variable +from torch.optim import SGD +import sys +from ...util_global.constant import solution_eps +from tqdm import tqdm + +def kernel_functional_rbf(losses): + ''' + input losses: (n_prob, n_obj) + output kernel_matrix: (n_prob, n_prob) + comments: This function is used to compute the kernel matrix for SVGD. + ''' + n = losses.shape[0] + # losses shape : (10,) * (3,) + pairwise_distance = torch.norm(losses[:, None] - losses, dim=2).pow(2) + h = median(pairwise_distance) / math.log(n) + A = 5e-6 # Noted, this bandwith parameter is important. + kernel_matrix = torch.exp(-pairwise_distance / A*h) # 5e-6 for zdt1,2,3, zxy, Dec 5, 2023 + return kernel_matrix + +def median(tensor): + """ + torch.median() acts differently from np.median(). We want to simulate numpy implementation. + """ + tensor = tensor.detach().flatten() + tensor_max = tensor.max()[None] + return (torch.cat((tensor, tensor_max)).median() + tensor.median()) / 2. + +def get_svgd_gradient(G, inputs, losses): + ''' + :param G.shape: (n_prob, n_obj, n_var) + :param inputs.shape: (n_prob, n_var) + :param losses.shape: (n_prob, n_obj) + :return: + ''' + n_prob = inputs.size(0) + # G shape (n_prob, n_obj, n_var) + g_w = [0] * n_prob + + for idx in range(n_prob): + g_w[idx] = torch.Tensor( solve_mgda(G[idx], return_coeff=False) ) + + g_w = torch.stack(g_w) # (n_prob, n_var) + # See https://github.com/activatedgeek/svgd/issues/1#issuecomment-649235844 for why there is a factor -0.5 + + kernel = kernel_functional_rbf(losses) + kernel_grad = -0.5 * torch.autograd.grad(kernel.sum(), inputs, allow_unused=True)[0] # (n_prob, n_var) + gradient = (kernel.mm(g_w) - kernel_grad) / n_prob + + return gradient + + + +class MOOSVGDSolver(GradBaseSolver): + def __init__(self, step_size, max_iter, tol): + super().__init__(step_size, max_iter, tol) + + def solve(self, problem, x, prefs, args, ref_point): + x = Variable(x, requires_grad=True) + optimizer = SGD([x], lr=self.step_size) + for i in tqdm(range(self.max_iter)): + y = problem.evaluate(x) + grad_arr = [0] * args.n_prob + for prob_idx in range(args.n_prob): + grad_arr[prob_idx] = [0] * args.n_obj + for obj_idx in range(args.n_obj): + y[prob_idx][obj_idx].backward(retain_graph=True) + grad_arr[prob_idx][obj_idx] = x.grad[prob_idx].clone() + x.grad.zero_() + grad_arr[prob_idx] = torch.stack(grad_arr[prob_idx]) + + grad_arr = torch.stack(grad_arr).detach() + gw = get_svgd_gradient(grad_arr, x, y) + optimizer.zero_grad() + x.grad = gw + optimizer.step() + + if 'lbound' in dir(problem): + x.data = torch.clamp(x.data, torch.Tensor(problem.lbound) + solution_eps, torch.Tensor(problem.ubound) - solution_eps) + + res={} + res['x'] = x.detach().numpy() + res['y'] = y.detach().numpy() + res['hv_arr'] = [0] + return res + + + +if __name__ == '__main__': + losses = torch.rand(10, 3) + kernel = kernel_functional_rbf(losses) + + + + + diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/pmgda.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/pmgda.py new file mode 100644 index 0000000000000000000000000000000000000000..394e01e4934c88d4fb40162967efa484eea8c5c6 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/pmgda.py @@ -0,0 +1,14 @@ +from .base_solver import GradBaseSolver + + + +class PMGDASolver(GradBaseSolver): + def __init__(self, step_size, max_iter, tol): + print('pmgda solver') + print() + + super().__init__(step_size, max_iter, tol) + + def solve(self, problem, x, prefs, args): + print() + diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/pmtl.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/pmtl.py new file mode 100644 index 0000000000000000000000000000000000000000..a7222170abea274fe99cbda8a4a6325c7fc4bf43 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/pmtl.py @@ -0,0 +1,147 @@ +import torch + +from .base_solver import GradBaseSolver +from matplotlib import pyplot as plt +from .min_norm_solvers_numpy import MinNormSolver +import numpy as np + + + +from torch.autograd import Variable +from tqdm import tqdm +from ...util_global.constant import solution_eps + +from .mgda_core import solve_mgda + + + + +def get_d_moomtl(grads): + """ + calculate the gradient direction for MOO-MTL + """ + nobj, dim = grads.shape + sol, nd = MinNormSolver.find_min_norm_element(grads) + return sol + + +def get_d_paretomtl(grads, value, weights, i): + # calculate the gradient direction for Pareto MTL + nobj, dim = grads.shape + + # check active constraints + normalized_current_weight = weights[i] / np.linalg.norm(weights[i]) + normalized_rest_weights = np.delete(weights, (i), axis=0) / np.linalg.norm(np.delete(weights, (i), axis=0), axis=1, + keepdims=True) + # shape: (9, 2) + w = normalized_rest_weights - normalized_current_weight + # solve QP + gx = np.dot(w, value / np.linalg.norm(value)) + idx = gx > 0 + vec = np.concatenate((grads, np.dot(w[idx], grads)), axis=0) + # use MinNormSolver to solve QP + # vec.shape: + + # sol, nd = MinNormSolver.find_min_norm_element( vec ) + _, sol = solve_mgda( torch.Tensor(vec), return_coeff=True) + + # reformulate ParetoMTL as linear scalarization method, return the weights + weight0 = sol[0] + np.sum(np.array([sol[j] * w[idx][j - 2, 0] for j in np.arange(2, 2 + np.sum(idx))])) + weight1 = sol[1] + np.sum(np.array([sol[j] * w[idx][j - 2, 1] for j in np.arange(2, 2 + np.sum(idx))])) + weight = np.stack([weight0, weight1]) + + return weight + + + + +def get_d_paretomtl_init(grads, value, weights, i): + # calculate the gradient direction for Pareto MTL initialization + nobj, dim = grads.shape + + # check active constraints + normalized_current_weight = weights[i] / np.linalg.norm(weights[i]) + normalized_rest_weights = np.delete(weights, (i), axis=0) / np.linalg.norm(np.delete(weights, (i), axis=0), axis=1, + keepdims=True) + w = normalized_rest_weights - normalized_current_weight + gx = np.dot(w, value / np.linalg.norm(value)) + idx = gx > 0 + + + if np.sum(idx) <= 0: + return np.zeros(nobj) + if np.sum(idx) == 1: + sol = np.ones(1) + else: + vecs = np.dot(w[idx], grads) + _, sol = solve_mgda( torch.Tensor(vecs), return_coeff=True) + # print() + + # calculate the weights + weight0 = np.sum(np.array([sol[j] * w[idx][j, 0] for j in np.arange(0, np.sum(idx))])) + weight1 = np.sum(np.array([sol[j] * w[idx][j, 1] for j in np.arange(0, np.sum(idx))])) + weight = np.stack([weight0, weight1]) + + return weight + + +def circle_points(r, n): + # generate evenly distributed preference vector + circles = [] + for r, n in zip(r, n): + t = np.linspace(0, 0.5 * np.pi, n) + x = r * np.cos(t) + y = r * np.sin(t) + circles.append(np.c_[x, y]) + return circles + + + + +class PMTLSolver(GradBaseSolver): + def __init__(self, step_size, max_iter, tol): + super().__init__(step_size, max_iter, tol) + + def solve(self, problem, x, prefs, args, ref_point): + if args.n_obj != 2: + assert False, 'hvgrad only supports 2 obj problem' + + x = Variable(x, requires_grad=True) + warmup_iter = self.max_iter // 5 + optimizer = torch.optim.SGD([x], lr=self.step_size) + + y_arr = [] + for iter_idx in tqdm( range(self.max_iter) ): + y = problem.evaluate(x) + y_np = y.detach().numpy() + y_arr.append(y_np) + + grad_arr = [0] * args.n_prob + for prob_idx in range(args.n_prob): + grad_arr[prob_idx] = [0] * args.n_obj + for obj_idx in range(args.n_obj): + y[prob_idx][obj_idx].backward(retain_graph=True) + grad_arr[prob_idx][obj_idx] = x.grad[prob_idx].clone() + x.grad.zero_() + grad_arr[prob_idx] = torch.stack(grad_arr[prob_idx]) + + grad_arr = torch.stack(grad_arr) + grad_arr_np = grad_arr.detach().numpy() + if iter_idx < warmup_iter: + weights = [ get_d_paretomtl_init(grad_arr_np[i], y_np[i], prefs, i) for i in range(args.n_prob) ] + else: + weights = [ get_d_paretomtl(grad_arr_np[i], y_np[i], prefs, i) for i in range(args.n_prob) ] + + optimizer.zero_grad() + torch.sum(torch.tensor(weights) * y).backward() + optimizer.step() + + if 'lbound' in dir(problem): + x.data = torch.clamp(x.data, torch.Tensor(problem.lbound) + solution_eps, torch.Tensor(problem.ubound) - solution_eps) + + res={} + res['x'] = x.detach().numpy() + res['y'] = y.detach().numpy() + res['hv_arr'] = [0] + res['y_arr'] = y_arr + return res \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/run/__init__.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/run/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/run/run_grad.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/run/run_grad.py new file mode 100644 index 0000000000000000000000000000000000000000..6947fbb333eece98a60438414e88ce3754bc7dde --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/run/run_grad.py @@ -0,0 +1,99 @@ +import numpy as np +import os +import sys +print(f"vscode current run path is {os.getcwd()}") +os.chdir(sys.path[0]) +print(f"set py path as current path ") +print(f"vscode current run path is {os.getcwd()}") + +from libmoon.solver.gradient import MGDASolver, GradAggSolver, EPOSolver, MOOSVGDSolver, GradHVSolver, PMTLSolver +from libmoon.util_global.weight_factor.funs import uniform_pref +from libmoon.util_global.constant import problem_dict +from libmoon.visulization.view_res import vis_res, vedio_res +import argparse +import torch +from matplotlib import pyplot as plt +import pickle +import time + + + +if __name__ == '__main__': + parser = argparse.ArgumentParser( description= 'example script' ) + parser.add_argument( '--n-partition', type=int, default=10 ) + parser.add_argument( '--agg', type=str, default='tche') # If solve is agg, then choose a specific agg method. + parser.add_argument('--solver', type=str, default='hvgrad') + # ['agg', 'epo', 'moosvgd', 'hvgrad', 'pmtl', 'mgda'] + parser.add_argument( '--problem-name', type=str, default='VLMOP2') + parser.add_argument('--iter', type=int, default=2000) + parser.add_argument('--step-size', type=float, default=0.1) + parser.add_argument('--tol', type=float, default=1e-6) + parser.add_argument('--plt-pref-flag', type=str, default='N') + + args = parser.parse_args() + problem = problem_dict[args.problem_name] + args.n_obj, args.n_var = problem.n_obj, problem.n_var + root_name = os.path.dirname(os.path.dirname(__file__)) + + + if args.solver == 'mgda': + solver = MGDASolver(args.step_size, args.iter, args.tol) + elif args.solver == 'agg': + solver = GradAggSolver(args.step_size, args.iter, args.tol) + elif args.solver == 'epo': + solver = EPOSolver(args.step_size, args.iter, args.tol) + elif args.solver == 'moosvgd': + solver = MOOSVGDSolver(args.step_size, args.iter, args.tol) + elif args.solver == 'hvgrad': + solver = GradHVSolver(args.step_size, args.iter, args.tol) + elif args.solver == 'pmtl': + solver = PMTLSolver(args.step_size, args.iter, args.tol) + elif args.solver=='pmgda': + assert False, 'will be implemented soon' + else: + raise Exception('solver not supported') + + if args.solver == 'agg': + args.folder_name = os.path.join(root_name, 'output', args.problem_name, '{}_{}'.format(args.solver, args.agg)) + else: + args.folder_name = os.path.join(root_name, 'output', args.problem_name, args.solver) + + os.makedirs(args.folder_name, exist_ok=True) + prefs = uniform_pref( args.n_partition, problem.n_obj, clip_eps=1e-2) + + args.n_prob = len(prefs) + if 'lbound' in dir(problem): + if args.problem_name == 'VLMOP1': + x0 = torch.rand(args.n_prob, problem.n_var) * 2 / np.sqrt(problem.n_var) - 1 / np.sqrt(problem.n_var) + else: + x0 = torch.rand(args.n_prob, problem.n_var) + else: + x0 = torch.rand( args.n_prob, problem.n_var ) * 20 - 10 + + + ts = time.time() + res = solver.solve( problem, x=x0, prefs=prefs, args=args) + + elapsed = time.time() - ts + res['elapsed'] = elapsed + + + use_fig=False + if use_fig: + vis_res(res, problem, prefs, args) + fig_name = os.path.join(args.folder_name, 'res.svg') + plt.savefig(fig_name) + print('Save fig to %s' % fig_name) + plt.show() + + + use_vedio=True + if use_vedio: + vedio_res(res, problem, prefs, args) + + + pickle_name = os.path.join(args.folder_name, 'res.pkl') + with open(pickle_name, 'wb') as f: + pickle.dump(res, f) + + print('Save pickle to %s' % pickle_name) diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/utils/__init__.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/gradient/utils/util.py b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/utils/util.py new file mode 100644 index 0000000000000000000000000000000000000000..52d76fc68f195272a442095f630d0ecb0af4f036 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/gradient/utils/util.py @@ -0,0 +1,26 @@ +''' + This file contains the utility functions for the gradient descent solver. +''' +import torch + +def get_grads_from_model(loss, model): + G = [0,] * len(loss) + for idx, l in enumerate(loss): + model.zero_grad() + l.backward(retain_graph=True) + G[idx] = get_flatten_grad(model) + return torch.stack(G) + +def get_flatten_grad(model): + grad = [] + for param in model.parameters(): + if param.grad is not None: + grad.append(param.grad.view(-1)) + else: + grad.append(torch.zeros_like(param.view(-1))) + grad = torch.cat(grad) + return grad + + +def numel_params(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/mobo/__init__.py b/bike_bench_internal/benchmark_models/libmoon/solver/mobo/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/mobo/dirhvego.py b/bike_bench_internal/benchmark_models/libmoon/solver/mobo/dirhvego.py new file mode 100644 index 0000000000000000000000000000000000000000..247f4f30b9fee6f1d6850028f7a43094a4724c30 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/mobo/dirhvego.py @@ -0,0 +1,135 @@ +# -*- coding: utf-8 -*- +""" +------------------------------- Reference -------------------------------- + L. Zhao and Q. Zhang, Hypervolume-Guided Decomposition for Parallel + Expensive Multiobjective Optimization. IEEE Transactions on Evolutionary + Computation, 2023. +""" +import numpy as np +from scipy import stats +from sklearn.preprocessing import MinMaxScaler +import matplotlib.pyplot as plt +from solver.mobo.mobod import MOBOD + + +class DirHVEGO(MOBOD): + def __init__(self, + mop: any = None, + ref_vecs: np.ndarray = None, + max_iter: int = None, + batch_size: int = 5 + ) -> None: + super().__init__(mop, ref_vecs, max_iter, batch_size) + + def evaluator_acquisition(self, u, sigma, ref_vec, pref_inc): + ''' + Parameters: + ---------- + u : + sigma : + ref_vec: direction vector + pref_inc : preference-conditional incumbent + + Returns + ------- + EI_D : preference-conditional EI: DirHV-EI(X|pref_vec) + + ''' + xi_minus_u = pref_inc - u # N*M + tau = xi_minus_u / sigma # N*M + temp = xi_minus_u * stats.norm(0, 1).cdf(tau) + sigma * stats.norm(0, 1).pdf(tau) # N*M + EI_D = np.prod(temp, axis=1) + return EI_D + + def step(self): + # normalization + X_norm = self.X.copy() + Y_scaler = MinMaxScaler(feature_range=(0, 1)) + Y_norm = Y_scaler.fit_transform(self.Y) + + # GP modeling + self.construct_model(X_norm, Y_norm) + + # Utopian point + Z = -0.01 * np.ones(shape=[1, self.n_obj]) + # Calculate the Intersection points and Direction vectors + Xi, Lambda = self.get_Xi(Y_norm[self.pf_idx[0]], self.ref_vecs, Z) + # Use MOEA/D to maximize DirHV-EI + Pop_Dec, Pop_u, Pop_s = self.MOEAD_GR_(Lambda, Xi) + # Discard duplicate candidates + PopDec, ia = np.unique(Pop_Dec, axis=0, return_index=True) + Pop_u = Pop_u[ia, :] + Pop_s = Pop_s[ia, :] + N = self.ref_vecs.shape[0] # pop size + + # Compute EI_D for all the points in Q + L = PopDec.shape[0] + EIDs = np.zeros((L, N)) + for j in range(L): + EIDs[j, :] = self.evaluator_acquisition(np.tile(Pop_u[j], (N, 1)), np.tile(Pop_s[j], (N, 1)), Lambda, Xi) + + # Find q solutions with the greedy algorithm + # Batch_size = np.min(Problem.maxFE - Problem.FE, q) # the total budget is Problem.maxFE + Qb = self.batchSelection(EIDs, self.batch_size) + + return PopDec[Qb, :] + + def batchSelection(self, EIDs, q): + # Algorithm 3: Submodularity-based Batch Selection + L, N = EIDs.shape + Qb = [] + temp = EIDs.copy() + beta = np.zeros(N) + for i in range(q): + index = np.argmax(np.sum(temp, axis=1)) + Qb.append(index) + beta = beta + temp[index, :] + # temp: [EI_D(x|\lambda) - beta]_+ + temp = EIDs - np.repeat(beta[None, :], L, axis=0) + temp[temp < 0] = 0 + return Qb + + def get_Xi(self, A, W, Z): + N = self.ref_vecs.shape[0] + W_ = 1.1 * self.ref_vecs - Z + Lambda = W_ / np.linalg.norm(W_, axis=1, keepdims=True) + # Eq. 11, compute the intersection points + Lambda_ = 1.0 / Lambda + A = A - Z # L*M + G = np.outer(Lambda_[:, 0], A[:, 0]) # N*L, f1 + for j in range(1, self.n_obj): + G = np.maximum(G, np.outer(Lambda_[:, j], A[:, j])) # N*L, max(fi,fj) + + # minimum of mTch for each direction vector + Lmin = np.min(G, axis=1, keepdims=True) # N*1 one for each direction vector + + # N*M Intersection points + Xi = Z + np.multiply(Lmin, Lambda) + + return Xi, Lambda + + + + + +if __name__ == '__main__': + from libmoon.problem.synthetic.zdt import ZDT1 + from pyDOE3 import lhs + + prob = ZDT1(n_var=8, + n_obj=2, + lower_bound=np.array([.0] * 8), + upper_bound=np.array([1.] * 8)) + alg = DirHVEGO() + alg.setup(prob, max_iter=10, batch_size=5) + + xdoe = (prob.ub - prob.lb) * lhs(prob.n_var, samples=11 * prob.n_var - 1, + criterion='maximin', iterations=10) + prob.lb + + + ydoe = prob.evaluate(xdoe) + + alg.solve(xdoe, ydoe) + + plt.scatter(alg.Y[alg.pf_idx[0], 0], alg.Y[alg.pf_idx[0], 1], c='none', edgecolors='r') + plt.show() \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/mobo/mobod.py b/bike_bench_internal/benchmark_models/libmoon/solver/mobo/mobod.py new file mode 100644 index 0000000000000000000000000000000000000000..3cdb555ea678d0e6184deb879cea3437b3391619 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/mobo/mobod.py @@ -0,0 +1,202 @@ +import numpy as np +from smt.surrogate_models import KRG + +from scipy.spatial.distance import cdist + +from pymoo.indicators.hv import HV +from pymoo.util.nds.non_dominated_sorting import NonDominatedSorting +from pymoo.util.ref_dirs import get_reference_directions +from pyDOE3 import lhs + +from solver.mobo.utils.termination import termination + + +''' + Main algorithm framework for Decomposition-based Multi-objective Bayesian Optimization. +''' + + + +class MOBOD(object): + def __init__(self, + mop: any = None, + ref_vecs: np.ndarray = None, + max_iter: int = None, + batch_size: int = 5, + ): + self.mop = mop + self.ref_vecs = ref_vecs + self.max_iter = max_iter + self.batch_size = batch_size + self.termina = termination() + + def setup(self, + mop: any = None, + ref_vec: np.ndarray = None, + max_iter: int = None, + batch_size: int = 5, + ) -> None: + if mop is not None: + self.mop = mop + self.n_var, self.n_obj = mop.n_var, mop.n_obj + assert not mop.has_constraint + self.termina.setup(max_gen=max_iter) + + if self.ref_vecs is None: + self.ref_vecs = get_reference_directions("uniform", mop.get_number_objective, n_partitions=199) + self.pop_size = len(self.ref_vecs) + # to keep track of data + self.X = None + self.Y = None + self.sample_num = 0 + self.i_iter = 0 + self.pf_idx = None # idx of nondominated solutions + self.ref_point = None + self.hv_all_value = np.zeros([max_iter + 1, 1]) + + def construct_model(self, X_obs, Y_obs): + # build surrogate models + theta = [self.sample_num ** (-1. / self.sample_num)] * self.n_var + self.surrogate_model = [KRG(theta0=theta) for i in range(self.n_obj)] + for i in range(self.n_obj): + self.surrogate_model[i].options.__setitem__('print_global', False) + self.surrogate_model[i].set_training_values(X_obs, Y_obs[:, i]) + self.surrogate_model[i].train() + + def predict_model(self, X): + N = X.shape[0] + u = np.zeros(shape=(N, self.n_obj), dtype=float) + MSE = np.zeros(shape=(N, self.n_obj), dtype=float) + + for j in range(self.n_obj): + u[:, j] = self.surrogate_model[j].predict_values(X)[:, 0] + # MSE[:,j] = np.sqrt(model[j].predict_variances(PopDec)) + MSE[:, j] = self.surrogate_model[j].predict_variances(X)[:, 0] + + MSE[MSE < 0] = 0 + s = np.sqrt(MSE) + return u, s + + def evaluator_acquisition(self, u, sigma, ref_vec, pref_inc): + pass + + def aggregate_data(self, X_new, Y_new): + if self.sample_num == 0: + self.X = X_new.copy() + self.Y = Y_new.copy() + else: + self.X = np.vstack([self.X, X_new]) + self.Y = np.vstack([self.Y, Y_new]) + self.sample_num += len(X_new) + + # nondominated X, Y + nds = NonDominatedSorting() + self.pf_idx = nds.do(self.Y) + # X_nds = self.X[self.pf_idx[0]] + Y_nds = self.Y[self.pf_idx[0]] + + hv = HV(ref_point=self.ref_point) + hv_value = hv(Y_nds) + self.hv_all_value[self.i_iter, 0] = hv_value + + def step(self): + pass + + def solve(self, X_init, Y_init): + if self.ref_point is None: + self.ref_point = np.max(Y_init, axis=0) + self.aggregate_data(X_init, Y_init) + + s = str('n_iter').center(12) + " | " + str('n_eval').center(12) + " | " + str('HV').center(12) + print("=" * len(s)) + print(s) + print("=" * len(s)) + print(str(self.i_iter).center(12), "|", str(self.sample_num).center(12), "| ", + f"%.{10}f" % self.hv_all_value[self.i_iter, 0]) + + while self.termina.has_next: + self.i_iter += 1 + # generate new samples + X_next = self.step() # check + Y_next = self.mop.evaluate(X_next) + self.termina(nfe=self.batch_size, gen=1) + self.aggregate_data(X_next, Y_next) + print(str(self.i_iter).center(12), "|", str(self.sample_num).center(12), "| ", + f"%.{10}f" % self.hv_all_value[self.i_iter, 0]) + + print("=" * len(s)) + + def MOEAD_GR_(self, ref_vecs, pref_incs): + # using MOEA/D-GR to solve subproblems + maxIter = 50 + N = self.pop_size + T = int(np.ceil(N / 10)) # size of neighbourhood: 0.1*N + B = np.argsort(cdist(ref_vecs, ref_vecs), axis=1, kind='quicksort')[:, :T] + + # the initial population for MOEA/D + + Pop_Dec = (self.mop.get_upper_bound - self.mop.get_lower_bound) * lhs(self.n_var, + samples=N) + self.mop.get_lower_bound + Pop_u, Pop_sigma = self.predict_model(Pop_Dec) + Pop_EID = self.evaluator_acquisition(Pop_u, Pop_sigma, ref_vecs, pref_incs) + + # optimization + for gen in range(maxIter - 1): + for i in range(N): + if np.random.random() < 0.8: # delta + P = B[i, np.random.permutation(B.shape[1])] + else: + P = np.random.permutation(N) + # generate an offspring 1*d + Off_Dec = self._OperatorDE(Pop_Dec[i, :][None, :], Pop_Dec[P[0], :][None, :], Pop_Dec[P[1], :][None, :]) + Off_u, Off_sigma = self.predict_model(Off_Dec) + # Global Replacement MOEA/D-GR + # Find the most approprite subproblem and its neighbourhood + EID_all = self.evaluator_acquisition(np.repeat(Off_u, N, axis=0), np.repeat(Off_sigma, N, axis=0), + ref_vecs, pref_incs) + best_index = np.argmax(EID_all) + P = B[best_index, :] # replacement neighborhood + + offindex = P[Pop_EID[P] < EID_all[P]] + if len(offindex) > 0: + Pop_Dec[offindex, :] = np.repeat(Off_Dec, len(offindex), axis=0) + Pop_u[offindex, :] = np.repeat(Off_u, len(offindex), axis=0) + Pop_sigma[offindex, :] = np.repeat(Off_sigma, len(offindex), axis=0) + Pop_EID[offindex] = EID_all[offindex] + + return Pop_Dec, Pop_u, Pop_sigma + + def _OperatorDE(self, Parent1, Parent2, Parent3): + ''' + generate one offspring by P1 + 0.5*(P2-P3) and polynomial mutation. + ''' + # Parameter + CR = 1 + F = 0.5 + proM = 1 + disM = 20 + # + N, D = Parent1.shape + # Differental evolution + Site = np.random.rand(N, D) < CR + Offspring = Parent1.copy() + Offspring[Site] = Offspring[Site] + F * (Parent2[Site] - Parent3[Site]) + # Polynomial mutation + Lower = np.atleast_2d(self.mop.get_lower_bound) # numpy Upper=np.array(Upper)[None,:] + Upper = np.atleast_2d(self.mop.get_upper_bound) # Lower = np.atleast_2d(Lower) + U_L = Upper - Lower + Site = np.random.rand(N, D) < proM / D + mu = np.random.rand(N, D) + temp = np.logical_and(Site, mu <= 0.5) + Offspring = np.minimum(np.maximum(Offspring, Lower), Upper) + delta1 = (Offspring - Lower) / U_L + delta2 = (Upper - Offspring) / U_L + # mu <= 0.5 + val = 2. * mu + (1 - 2. * mu) * (np.power(1. - delta1, disM + 1)) + Offspring[temp] = Offspring[temp] + (np.power(val[temp], 1.0 / (disM + 1)) - 1.) * U_L[temp] + # mu > 0.5 + temp = np.logical_and(Site, mu > 0.5) + val = 2. * (1.0 - mu) + 2. * (mu - 0.5) * (np.power(1. - delta2, disM + 1)) + Offspring[temp] = Offspring[temp] + (1.0 - np.power(val[temp], 1.0 / (disM + 1))) * U_L[temp] + + return Offspring \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/mobo/utils/__init__.py b/bike_bench_internal/benchmark_models/libmoon/solver/mobo/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/mobo/utils/termination.py b/bike_bench_internal/benchmark_models/libmoon/solver/mobo/utils/termination.py new file mode 100644 index 0000000000000000000000000000000000000000..0164e1822ee1c67b4fdc6115f474f9aae7b140e0 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/mobo/utils/termination.py @@ -0,0 +1,30 @@ +from typing import Any +import numpy as np + + +class termination: + + def __init__(self) -> None: + pass + + def __call__(self, + nfe: int = 0, + gen: int = 0, + ) -> bool: + self.nfe += nfe + self.gen += gen + + def setup(self, + nfe: int = 0, + gen: int = 0, + max_nfe: int = np.inf, + max_gen: int = np.inf, + ) -> None: + self.nfe = nfe + self.gen = gen + self.max_nfe = max_nfe + self.max_gen = max_gen + + @property + def has_next(self) -> bool: + return (self.nfe < self.max_nfe) and (self.gen < self.max_gen) diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/moea/__init__.py b/bike_bench_internal/benchmark_models/libmoon/solver/moea/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/moea/moead.py b/bike_bench_internal/benchmark_models/libmoon/solver/moea/moead.py new file mode 100644 index 0000000000000000000000000000000000000000..81e2097cdeeaf4a2d77563d4279caf59e46f38fb --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/moea/moead.py @@ -0,0 +1,233 @@ +import matplotlib.pyplot as plt +import numpy as np +from scipy.spatial.distance import cdist + +import copy + +from pymoo.util.ref_dirs import get_reference_directions +from solver.moea.utils import get_decomposition +from solver.moea.utils.population import population, external_population +from libmoon.solver.moea.utils.termination import termination +from solver.moea.utils.genetic_operator import cross_sbx, mut_pm +from solver.moea.utils.utils_ea import population_initialization, neighborhood_selection, OperatorDE +from libmoon.util_global.constant import FONT_SIZE, problem_dict, root_name + +from libmoon.visulization.util import plot_simplex, plot_unit_sphere +import argparse +import time +from pymoo.indicators.hv import HV +from indicator.indicator import compute_indicators + + +import pickle +import os + + +class MOEAD(): + def __init__(self, + mop: any = None, + ref_vec: np.ndarray = None, + n_neighbors: int = 10, + ): + + self.name = 'MOEAD' + self.mop = copy.deepcopy(mop) + self.ref_vec = ref_vec + self.n_neighbors = n_neighbors + self.pop = population() + self.ep = external_population() + self.termina = termination() + + @property + def solution_ep(self): + return self.ep.non_dominate_sol + + @property + def solution_pop(self): + return self.pop.X, self.pop.F + + def setup(self, + mop: any = None, + args: argparse.Namespace = None, + max_gen: int = 100, + pop_size: int = 100, + ) -> None: + + + self.args = args + if mop is not None: + self.mop = mop + assert not mop.has_constraint + self.termina.setup(max_gen=max_gen) + + if self.ref_vec is None: + self.ref_vec = get_reference_directions("uniform", mop.get_number_objective, n_partitions=pop_size-1) + self.ref_vec = np.clip(self.ref_vec, 1e-4, 1-1e-4) + + + self.n_pop = len(self.ref_vec) + pop = population_initialization(self.n_pop, self.mop) + f = self.mop(pop) + self.z_star = np.min(f, axis=0) + + self.pop(pop, f) + self.termina(nfe=self.n_pop) + self.neighbors = np.argsort(cdist(self.ref_vec, self.ref_vec), axis=1)[:, :self.n_neighbors] + + self.decomposition = get_decomposition('tch') + self.gen = 0 + + + def solve(self): + while self.termina.has_next: + self.step() + if self.gen % 500 == 0: + print('gen: {}'.format(self.gen)) + + + def reset(self, + problem: any): + """ + Initialization. + """ + + def step(self): + self.gen += 1 + for k in np.random.permutation(self.n_pop): + if self.args.crossover == 'sbx': + P = neighborhood_selection(self.n_pop, self.neighbors[k]) + X = self.pop.parent_select(P) + off_cross = cross_sbx(X, self.mop.get_lower_bound, self.mop.get_upper_bound) + else: + P = neighborhood_selection(self.n_pop, self.neighbors[k], n_selects=3) + X = self.pop.parent_select(P) + off_cross = OperatorDE(np.atleast_2d(X[0]), np.atleast_2d(X[1]), np.atleast_2d(X[2]), self.mop) + + off = mut_pm(off_cross, self.mop.get_lower_bound, self.mop.get_upper_bound) + off_f = self.mop(off).squeeze() + self.z_star = np.minimum(self.z_star, off_f) + self._update_neighbor(off, off_f, self.neighbors[k]) + self.ep(off, off_f) + self.termina(nfe=self.n_pop, gen=1) + + def _update_neighbor(self, off, off_f, neighbors): + for i in neighbors: + if self.decomposition(off_f, self.ref_vec[i], self.z_star) <= self.decomposition(self.pop.F[i], + self.ref_vec[i], + + self.z_star): self.pop(off, F=off_f, ind=i) +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--n-gen', type=int, default=2000 ) + parser.add_argument('--crossover', type=str, default='sbx') # crossover operator ['de', 'sbx'] + parser.add_argument('--problem-name', type=str, default='ZDT4') # should be in lowwer case + args = parser.parse_args() + + problem = problem_dict[args.problem_name] + ref_point = np.array( 1.2 * np.ones(problem.n_obj) ) + ind = HV(ref_point=ref_point) + + alg = MOEAD() + print('{} on {}'.format(alg.name, problem.problem_name)) + alg.setup(problem, args, max_gen=args.n_gen, pop_size=10) + + ref_vec = alg.ref_vec + ts = time.time() + alg.solve() + print( 'solving over {:.2f}m'.format( (time.time() - ts )/60)) + indicator_res = compute_indicators(alg.pop.F) + + if problem.n_obj == 2: + plt.scatter(alg.pop.F[:, 0], alg.pop.F[:, 1], c='none', edgecolors='r', label='solution') + plt.plot(alg.pop.F[:, 0], alg.pop.F[:, 1], c='r') + + if not problem.problem_name.startswith('RE'): + pf = problem.get_pf() + plt.plot(pf[:, 0], pf[:, 1], c='b', label='Pareto front') + + plt.xlabel('$f_1$', fontsize=FONT_SIZE ) + plt.ylabel('$f_2$', fontsize=FONT_SIZE ) + + ref_vec_norm = ref_vec / np.linalg.norm(ref_vec, axis=1, keepdims=True) + for ref in ref_vec_norm: + plt.plot([0, ref[0]], [0, ref[1]], c='k') + + else: + if problem.n_obj==3: + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.scatter(alg.pop.F[:, 0], alg.pop.F[:, 1], alg.pop.F[:, 2], c='none', edgecolors='r') + ax.set_xlabel('$f_1$', fontsize=FONT_SIZE) + ax.set_ylabel('$f_2$', fontsize=FONT_SIZE) + ax.set_zlabel('$f_3$', fontsize=FONT_SIZE) + + ref_norm = np.copy(alg.ref_vec) + ref_norm = ref_norm / np.linalg.norm(ref_norm, axis=1, keepdims=True) + + for ref in ref_norm: + if problem.problem_name == 'DTLZ1': + ax.plot([0, 0.5*ref[0]], [0, 0.5*ref[1]], [0, 0.5*ref[2]], c='k') + else: + ax.plot([0, ref[0]], [0, ref[1]], [0, ref[2]], c='k') + + elif problem.n_obj==4: + # plt.subplot(4, 1, 1) + fig = plt.figure(figsize=(4, 10)) + ax = fig.add_subplot(4, 1, 1, projection='3d') + ax.scatter(alg.pop.F[:, 0], alg.pop.F[:, 1], alg.pop.F[:, 2], c='none', edgecolors='r') + ax.set_xlabel('$f_1$', fontsize=FONT_SIZE) + ax.set_ylabel('$f_2$', fontsize=FONT_SIZE) + ax.set_zlabel('$f_3$', fontsize=FONT_SIZE) + ax.set_title('$f_1, f_2, f_3$') + + ax = fig.add_subplot(4, 1, 2, projection='3d') + ax.scatter(alg.pop.F[:, 0], alg.pop.F[:, 1], alg.pop.F[:, 3], c='none', edgecolors='r') + ax.set_xlabel('$f_1$', fontsize=FONT_SIZE) + ax.set_ylabel('$f_2$', fontsize=FONT_SIZE) + ax.set_zlabel('$f_4$', fontsize=FONT_SIZE) + ax.set_title('$f_1, f_2, f_4$') + + ax = fig.add_subplot(4, 1, 3, projection='3d') + ax.scatter(alg.pop.F[:, 0], alg.pop.F[:, 2], alg.pop.F[:, 3], c='none', edgecolors='r') + ax.set_xlabel('$f_1$', fontsize=FONT_SIZE) + ax.set_ylabel('$f_3$', fontsize=FONT_SIZE) + ax.set_zlabel('$f_4$', fontsize=FONT_SIZE) + ax.set_title('$f_1, f_3, f_4$') + + ax = fig.add_subplot(4, 1, 4, projection='3d') + ax.scatter(alg.pop.F[:, 1], alg.pop.F[:, 2], alg.pop.F[:, 3], c='none', edgecolors='r') + ax.set_xlabel('$f_2$', fontsize=FONT_SIZE) + ax.set_ylabel('$f_3$', fontsize=FONT_SIZE) + ax.set_zlabel('$f_4$', fontsize=FONT_SIZE) + ax.set_title('$f_2, f_3, f_4$') + + + if problem.problem_name == 'DTLZ1': + p1 = np.array([0, 0, 0.5]) + p2 = np.array([0, 0.5, 0.0]) + p3 = np.array([0.5, 0, 0.0]) + plot_simplex(ax, p1, p2, p3) + elif problem.problem_name.startswith('DTLZ'): + plot_unit_sphere(ax) + + plt.legend() + + + + pickle_folder = os.path.join(root_name, 'output', problem.problem_name, alg.name) + os.makedirs(pickle_folder, exist_ok=True) + pickle_name = os.path.join(pickle_folder, 'res.pkl') + with open(pickle_name, 'wb') as f: + pickle.dump(alg.pop.F, f) + pickle.dump(indicator_res, f) + + + txt_name = os.path.join(pickle_folder, 'res.txt') + with open(txt_name, 'w') as f: + for k, v in indicator_res.items(): + f.write('{}: {}\n'.format(k, v)) + + plt.title('MOEA/D({}) on {} with {}'.format(args.crossover, problem.problem_name, args.n_gen ) ) + plt.show() + + plt.figure() diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/moea/moead_pfl.py b/bike_bench_internal/benchmark_models/libmoon/solver/moea/moead_pfl.py new file mode 100644 index 0000000000000000000000000000000000000000..54156ba29c7d5894271161d4c1db3b0d6d39b608 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/moea/moead_pfl.py @@ -0,0 +1,221 @@ +import os.path + +from solver.moea.moead import MOEAD +import numpy as np +import argparse +import time +from pymoo.indicators.hv import HV +from libmoon.util_global.constant import FONT_SIZE, problem_dict, root_name + +from solver.moea.utils.genetic_operator import cross_sbx, mut_pm +from solver.moea.utils.utils_ea import neighborhood_selection, OperatorDE + +from matplotlib import pyplot as plt +from solver.pfl.model.simple import PFLModel +import torch +from torch import Tensor +from torch.optim import Adam + +from torch.autograd import Variable +from indicator.indicator import get_MMS, pref2angle, angle2pref + +import pickle + + + +class MOEAD_PFL(MOEAD): + def __init__(self, + mop: any = None, + ref_vec: np.ndarray = None, + n_neighbors: int = 10, + ): + super().__init__(mop, ref_vec, n_neighbors) + # self.pfl_update_num = 100 + self.name = 'MOEAD_PFL' + # self.pfl_model = PFLModel(self.mop.n_obj) + + def setup(self, + mop: any = None, + args: argparse.Namespace = None, + max_gen: int = 100, + pop_size: int = 100, + ) -> None: + + super().setup(mop, args, max_gen, pop_size) + self.pfl_model = PFLModel(self.mop.n_obj) + + self.optimizer = Adam(self.pfl_model.parameters(), lr=0.001) + self.pfl_update_num = args.n_pfl_update + + + def pfl_update(self): + loss_arr = [] + + angle_ts = pref2angle(self.ref_vec, self.mop.n_obj) + angle_ts = torch.Tensor( angle_ts ) + + for idx in range(500): + pred_y = self.pfl_model.forward( angle_ts ) + loss = torch.sum( torch.pow(pred_y - Tensor(self.pop.F), 2) ) + # print() + self.optimizer.zero_grad() + loss.backward() + loss_arr.append(loss.item()) + self.optimizer.step() + + + use_plot = False + if use_plot: + plt.plot(loss_arr) + plt.xlabel('iteration') + plt.ylabel('loss') + plt.show() + assert False + + + def pref_adjust(self): + pref = self.ref_vec + pref_old = np.copy(pref) + angle = pref2angle(pref, self.mop.n_obj) + angle_ts = Variable( Tensor(angle), requires_grad=True ) + angle_optimizer = Adam([angle_ts], lr=0.01) + + loss_arr = [] + for idx in range(1000): + pred_y = self.pfl_model.forward(angle_ts) + mms = get_MMS( pred_y ) + angle_optimizer.zero_grad() + mms.backward() + loss_arr.append(mms.item()) + + angle_optimizer.step() + angle_ts.data = torch.clip(angle_ts.data, 1e-3, np.pi/2-1e-3) + + pref_ts = angle2pref(angle_ts, self.mop.n_obj) + self.ref_vec = pref_ts.detach().numpy() + + + use_plot = False + if use_plot: + plt.plot(loss_arr) + plt.xlabel('iteration') + plt.ylabel('mms') + plt.show() + assert False + + return pref_old + + + + + def step(self): + # print('using PFL updation') + self.gen += 1 + + if self.gen % self.pfl_update_num == 0: + + # for idx, ref in enumerate(self.ref_vec): + # if idx == 0: + # plt.scatter(ref[0], ref[1], label='old', color='r') + # else: + # plt.scatter(ref[0], ref[1], color='r') + + self.pfl_update() + pref_old = self.pref_adjust() + print('adjust at {}'.format(self.gen)) + + use_plt=False + if use_plt: + plt.scatter(pref_old[:,0], pref_old[:,1], label='old', color='r') + plt.scatter(self.ref_vec[:,0], self.ref_vec[:,1], label='new', color='b') + plt.legend() + plt.show() + + for k in np.random.permutation(self.n_pop): + if self.args.crossover == 'sbx': + P = neighborhood_selection(self.n_pop, self.neighbors[k]) + X = self.pop.parent_select(P) + off_cross = cross_sbx(X, self.mop.get_lower_bound, self.mop.get_upper_bound) + else: + P = neighborhood_selection(self.n_pop, self.neighbors[k], n_selects=3) + X = self.pop.parent_select(P) + off_cross = OperatorDE(np.atleast_2d(X[0]), np.atleast_2d(X[1]), np.atleast_2d(X[2]), self.mop) + off = mut_pm(off_cross, self.mop.get_lower_bound, self.mop.get_upper_bound) + off_f = self.mop(off).squeeze() + self.z_star = np.minimum(self.z_star, off_f) + self._update_neighbor(off, off_f, self.neighbors[k]) + self.ep(off, off_f) + self.termina(nfe=self.n_pop, gen=1) + + + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--n-gen', type=int, default=2000) + parser.add_argument('--n-pfl-update', type=int, default=500) + + parser.add_argument('--crossover', type=str, default='sbx') # crossover operator ['de', 'sbx'] + parser.add_argument('--problem-name', type=str, default='RE24') # should be in lowwer case + + args = parser.parse_args() + problem = problem_dict[args.problem_name] + ref_point = np.array(1.2 * np.ones(problem.n_obj)) + ind = HV(ref_point=ref_point) + + alg = MOEAD_PFL() + print('{} on {}'.format(alg.name, problem.problem_name)) + alg.setup(problem, args, max_gen=args.n_gen, pop_size=10) + + ref_vec = alg.ref_vec + ts = time.time() + alg.solve() + print('solving over {:.2f}m'.format((time.time() - ts) / 60)) + + # statistics and save. + hv_val = ind.do(alg.pop.F) + mms = get_MMS(alg.pop.F) + + print('hv: {:.4f}'.format(hv_val)) + print('mms: {:.4f}'.format(mms)) + + + pickle_folder = os.path.join(root_name, 'output', problem.problem_name, alg.name) + os.makedirs(pickle_folder, exist_ok=True) + pickle_name = os.path.join(pickle_folder, 'res.pkl') + with open(pickle_name, 'wb') as f: + pickle.dump(alg.pop.F, f) + + txt_name = os.path.join(pickle_folder, 'res.txt') + with open(txt_name, 'w') as f: + f.write('hv: {:.4f}\n'.format(hv_val)) + f.write('mms: {:.4f}\n'.format(mms)) + + + if problem.n_obj == 2: + plt.scatter(alg.pop.F[:, 0], alg.pop.F[:, 1], c='none', edgecolors='r', label='solution') + plt.plot(alg.pop.F[:, 0], alg.pop.F[:, 1], c='r') + + if not problem.problem_name.startswith('RE'): + pf = problem.get_pf() + plt.plot(pf[:, 0], pf[:, 1], c='b') + + plt.legend() + plt.xlabel('$f_1$', fontsize=FONT_SIZE ) + plt.ylabel('$f_2$', fontsize=FONT_SIZE ) + + ref_vec_norm = ref_vec / np.linalg.norm(ref_vec, axis=1, keepdims=True) + for ref in ref_vec_norm: + plt.plot([0, ref[0]], [0, ref[1]], c='k') + elif problem.n_obj == 3: + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.scatter(alg.pop.F[:, 0], alg.pop.F[:, 1], alg.pop.F[:, 2], c='none', edgecolors='r') + ax.set_xlabel('$f_1$', fontsize=FONT_SIZE) + ax.set_ylabel('$f_2$', fontsize=FONT_SIZE) + ax.set_zlabel('$f_3$', fontsize=FONT_SIZE) + else: + assert False, 'n_obj should be 2 or 3' + + plt.show() + diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/__init__.py b/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..535b0c42fde54ef2f67a115db699655cf916c318 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/__init__.py @@ -0,0 +1,19 @@ +from solver.moea.utils.decomposition import weighted_sum +from solver.moea.utils.decomposition import Tchebycheff, modified_Tchebycheff + +from solver.moea.utils.decomposition import penalty_boundary_intersection +from solver.moea.utils.utils_ea import dominance_min + +def get_decomposition(name): + + decom_methods = { + "ws": weighted_sum, + "tch": Tchebycheff, + "mtch": modified_Tchebycheff, + "pbi": penalty_boundary_intersection, + } + + if name not in decom_methods: + raise Exception("Decomposition method not found.") + + return decom_methods[name] \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/decomposition.py b/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/decomposition.py new file mode 100644 index 0000000000000000000000000000000000000000..d638bdc77a688af24e268dd9fa615463f98af26a --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/decomposition.py @@ -0,0 +1,38 @@ +import numpy as np +from numpy import linalg as LA + + +def weighted_sum(f: np.ndarray, + lamda: np.ndarray + ) -> np.ndarray: + + assert f.ndim * lamda.ndim == 1 + return np.sum(f * lamda) + +def Tchebycheff(f: np.ndarray, + lamda: np.ndarray, + ref_point: np.ndarray, + ) -> np.ndarray: + + assert f.ndim * lamda.ndim * ref_point.ndim == 1 + return np.max(lamda * (f-ref_point)) + +def modified_Tchebycheff(f: np.ndarray, + lamda: np.ndarray, + ref_point: np.ndarray, + ) -> np.ndarray: + + return Tchebycheff(f, 1/lamda, ref_point) + + + +def penalty_boundary_intersection(f: np.ndarray, + lamda: np.ndarray, + ref_point: np.ndarray, + theta: float, + ) -> np.ndarray: + + assert f.ndim * lamda.ndim * ref_point.ndim == 1 + d1 = np.dot(f-ref_point, lamda) / LA.norm(lamda) + d2 = LA.norm(f - (ref_point+d1*lamda)) + return d1 + theta * d2 \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/genetic_operator.py b/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/genetic_operator.py new file mode 100644 index 0000000000000000000000000000000000000000..a5b39971dac5c859784844321dbd9d561e8eb234 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/genetic_operator.py @@ -0,0 +1,155 @@ +import numpy as np + +from solver.moea.utils.utils_ea import repair_clamp + + + + + +def cross_sbx(X, xl, xu, eta=15, prob_var=0.5, prob_bin=0.5, eps=1.0e-14, n_offsprings=1): + n_parents, n_var = X.shape + + # the probability of a crossover for each of the variables + cross = np.random.random((n_var)) < prob_var + + # when solutions are too close -> do not apply sbx crossover + too_close = np.abs(X[0] - X[1]) <= eps + + # disable if two individuals are already too close + cross[too_close] = False + # disable crossover when lower and upper bound are identical + cross[xl == xu] = False + + # assign y1 the smaller and y2 the larger value + y1 = np.min(X, axis=0)[cross] + y2 = np.max(X, axis=0)[cross] + + # mask all the values that should be crossovered + _xl = xl[cross] + _xu = xu[cross] + eta = np.full((n_var, ), eta)[cross] + prob_bin = np.full((n_var, ), prob_bin)[cross] + + + # random values for each individual + rand = np.random.random(len(eta)) + def calc_betaq(beta): + alpha = 2.0 - np.power(beta, -(eta + 1.0)) + + mask, mask_not = (rand <= (1.0 / alpha)), (rand > (1.0 / alpha)) + + betaq = np.zeros(mask.shape) + betaq[mask] = np.power((rand * alpha), (1.0 / (eta + 1.0)))[mask] + betaq[mask_not] = np.power((1.0 / (2.0 - rand * alpha)), (1.0 / (eta + 1.0)))[mask_not] + + return betaq + + # difference between all variables + delta = (y2 - y1) + + beta = 1.0 + (2.0 * (y1 - _xl) / delta) + betaq = calc_betaq(beta) + c1 = 0.5 * ((y1 + y2) - betaq * delta) + + beta = 1.0 + (2.0 * (_xu - y2) / delta) + betaq = calc_betaq(beta) + c2 = 0.5 * ((y1 + y2) + betaq * delta) + + # with the given probability either assign the value from the first or second parent + b = np.random.random(len(prob_bin)) < prob_bin + tmp = np.copy(c1[b]) + c1[b] = c2[b] + c2[b] = tmp + + # first copy the unmodified parents + Q = np.copy(X) + + # copy the positions where the crossover was done + Q[0, cross] = c1 + Q[1, cross] = c2 + + Q[0] = repair_clamp(Q[0], xl, xu) + Q[1] = repair_clamp(Q[1], xl, xu) + + if n_offsprings == 1: + rand = np.random.random() < 0.5 + Q[0, rand] = Q[1, rand] + Q = Q[[0]] + + return Q + + +def mut_pm(X, xl, xu, eta=15, prob=None, at_least_once=False): + n, n_var = X.shape + if prob is None: prob = min(0.5, 1 / n_var) + + eta = np.full((n,), eta) + prob = np.full((n,), prob) + + Xp = np.full(X.shape, np.inf) + + mut = mut_binomial(n, n_var, prob, at_least_once=at_least_once) + mut[:, xl == xu] = False + + Xp[:, :] = X + + _xl = np.repeat(xl[None, :], X.shape[0], axis=0)[mut] + _xu = np.repeat(xu[None, :], X.shape[0], axis=0)[mut] + + X = X[mut] + eta = np.tile(eta[:, None], (1, n_var))[mut] + + delta1 = (X - _xl) / (_xu - _xl) + delta2 = (_xu - X) / (_xu - _xl) + + mut_pow = 1.0 / (eta + 1.0) + + rand = np.random.random(X.shape) + mask = rand <= 0.5 + mask_not = np.logical_not(mask) + + deltaq = np.zeros(X.shape) + + xy = 1.0 - delta1 + val = 2.0 * rand + (1.0 - 2.0 * rand) * (np.power(xy, (eta + 1.0))) + d = np.power(val, mut_pow) - 1.0 + deltaq[mask] = d[mask] + + xy = 1.0 - delta2 + val = 2.0 * (1.0 - rand) + 2.0 * (rand - 0.5) * (np.power(xy, (eta + 1.0))) + d = 1.0 - (np.power(val, mut_pow)) + deltaq[mask_not] = d[mask_not] + + # mutated values + _Y = X + deltaq * (_xu - _xl) + + # back in bounds if necessary (floating point issues) + _Y[_Y < _xl] = _xl[_Y < _xl] + _Y[_Y > _xu] = _xu[_Y > _xu] + + # set the values for output + Xp[mut] = _Y + + return Xp + + +def row_at_least_once_true(M): + _, d = M.shape + for k in np.where(~np.any(M, axis=1))[0]: + M[k, np.random.randint(d)] = True + return M + + +def mut_binomial(n, m, prob, at_least_once=True): + prob = np.ones(n) * prob + M = np.random.random((n, m)) < prob[:, None] + + if at_least_once: + M = row_at_least_once_true(M) + + return M + + + + + diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/population.py b/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/population.py new file mode 100644 index 0000000000000000000000000000000000000000..e6506054cf11c0d9d7158dba94ea37c555f286a2 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/population.py @@ -0,0 +1,93 @@ +from typing import Any +import numpy as np + +from solver.moea.utils.utils_ea import dominance_min + +class population: + + def __init__(self): + pass + + def __call__(self, pop, F=None, G=None, ind=None) -> Any: + if ind is None: + self.pop = pop + self.n_pop, self.n_var = self.pop.shape + self.F = F + self.G = G + else: + self.pop[ind] = pop + if F is not None: self.F[ind] = F + if G is not None: self.G[ind] = G + + def __getitem__(self, index): + return self.pop[index] + + @property + def X(self) -> np.ndarray: + return self.pop + + def evaluate(self, problem): + self.F = problem(self.pop) + + def update(self, pop): + self.pop = pop + + def parent_select(self, P): + return np.atleast_2d(self.pop[P]) + +class external_population: + + def __init__(self): + self.pop = None + self.F = None + + def __call__(self, pop, pop_f): + pop = np.atleast_2d(pop) + pop_f = np.atleast_2d(pop_f) + + if self.pop is None: + self.pop = pop + self.F = pop_f + else: + for i in range(pop.shape[0]): self._update_par(pop[i], pop_f[i]) + + self.n_pop = self.pop.shape[0] + + @property + def non_dominate_sol(self): + return self.pop, self.F + + def _update_par(self, pop, pop_f): + add_ind = np.full((self.n_pop, ), False) + for i in range(self.n_pop): + if dominance_min(self.F[i], pop_f): + add_ind[i] = True + break + + if not add_ind.any(): + domi_ind = np.full((self.n_pop, ), False) + for i in range(self.n_pop): + if dominance_min(pop_f, self.F[i]): domi_ind[i] = True + + self.pop = np.delete(self.pop, domi_ind, axis=0) + self.F = np.delete(self.F, domi_ind, axis=0) + + self.pop = np.vstack([self.pop, pop]) + self.F = np.vstack([self.F, pop_f]) + + + +if __name__ == '__main__': + + from libmoon.problem.synthetic.zdt import ZDT1 + from utils_ea import population_initialization + + problem = ZDT1() + pop = population(pop=population_initialization(10, problem)) + + pop.evaluate(problem) + + print(pop.F) + print() + + print() \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/termination.py b/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/termination.py new file mode 100644 index 0000000000000000000000000000000000000000..41852e569d963637114bc055fefc3b624bfc8db8 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/termination.py @@ -0,0 +1,32 @@ +from typing import Any +import numpy as np + + + +class termination: + + def __init__(self) -> None: + pass + + def __call__(self, + nfe: int=0, + gen: int=0, + ) -> bool: + self.nfe += nfe + self.gen += gen + + def setup(self, + nfe: int=0, + gen: int=0, + max_nfe: int=np.inf, + max_gen: int=np.inf, + ) -> None: + self.nfe = nfe + self.gen = gen + self.max_nfe = max_nfe + self.max_gen = max_gen + + @property + def has_next(self) -> bool: + return (self.nfe < self.max_nfe) and (self.gen < self.max_gen) + diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/utils_ea.py b/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/utils_ea.py new file mode 100644 index 0000000000000000000000000000000000000000..d2fb7ea2cc79a04ffcb9bb9a4f70ea51fae7b45c --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/utils_ea.py @@ -0,0 +1,109 @@ +import numpy as np + + + + +def population_initialization(n_pop, + problem, + method: str='random'): + + lb = problem.get_lower_bound + ub = problem.get_upper_bound + dim = problem.get_number_variable + if method == 'random': + return lb + (ub - lb) * np.random.rand(n_pop, dim) + + +def repair_clamp(Xp, xl, xu): + + I = np.where(Xp < xl) + Xp[I] = xl[I] + + I = np.where(Xp > xu) + Xp[I] = xu[I] + + return Xp + + +def dominance_min(u, v): + assert u.ndim * v.ndim == 1 + assert len(u) == len(v) + for i in range(len(u)): + if v[i] < u[i]: return False + if (u==v).all(): return False + return True + + + +def neighborhood_selection(n_pop, neighbors, n_selects=2, prob=.9): + + assert neighbors.ndim == 1 + + if np.random.random() < prob: + P = np.random.choice(neighbors, n_selects, replace=False) + else: + P = np.random.permutation(n_pop)[:n_selects] + + return P + + + +def OperatorDE(Parent1, Parent2, Parent3, mop): + ''' + generate one offspring by P1 + 0.5*(P2-P3) and polynomial mutation. + ''' + # Parameter + CR = 1 + F = 0.5 + proM = 1 + disM = 20 + # + N, D = Parent1.shape + # Differental evolution + Site = np.random.rand(N, D) < CR + Offspring = Parent1.copy() + Offspring[Site] = Offspring[Site] + F * (Parent2[Site] - Parent3[Site]) + # Polynomial mutation + Lower = np.atleast_2d( mop.get_lower_bound ) # numpy Upper=np.array(Upper)[None,:] + Upper = np.atleast_2d( mop.get_upper_bound) # Lower = np.atleast_2d(Lower) + U_L = Upper - Lower + Site = np.random.rand(N, D) < proM / D + mu = np.random.rand(N, D) + temp = np.logical_and(Site, mu <= 0.5) + Offspring = np.minimum(np.maximum(Offspring, Lower), Upper) + delta1 = (Offspring - Lower) / U_L + delta2 = (Upper - Offspring) / U_L + # mu <= 0.5 + val = 2. * mu + (1 - 2. * mu) * (np.power(1. - delta1, disM + 1)) + Offspring[temp] = Offspring[temp] + (np.power(val[temp], 1.0 / (disM + 1)) - 1.) * U_L[temp] + # mu > 0.5 + temp = np.logical_and(Site, mu > 0.5) + val = 2. * (1.0 - mu) + 2. * (mu - 0.5) * (np.power(1. - delta2, disM + 1)) + Offspring[temp] = Offspring[temp] + (1.0 - np.power(val[temp], 1.0 / (disM + 1))) * U_L[temp] + + return Offspring + + + + + + +if __name__ == '__main__': + from libmoon.problem.synthetic.zdt import ZDT1 + + problem = ZDT1() + + # p1 = np.array( [[1,2,3],] ) + # p2 = np.array( [[2,3,4],] ) + + p1 = np.random.random( (1, 30) ) + p2 = np.random.random( (1, 30) ) + p3 = np.random.random( (1, 30) ) + + + print(p1) + print(p2) + + res = _OperatorDE(p1, p2, p1, problem) + print( res ) + diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/weight_vector.py b/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/weight_vector.py new file mode 100644 index 0000000000000000000000000000000000000000..8c0867feb16acdf7004eb436eba3f95659f86391 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/moea/utils/weight_vector.py @@ -0,0 +1,2 @@ +import numpy as np + diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/pfl/__init__.py b/bike_bench_internal/benchmark_models/libmoon/solver/pfl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/pfl/model/__init__.py b/bike_bench_internal/benchmark_models/libmoon/solver/pfl/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/pfl/model/simple.py b/bike_bench_internal/benchmark_models/libmoon/solver/pfl/model/simple.py new file mode 100644 index 0000000000000000000000000000000000000000..8b3b6616c552a32ae06be6b123f3071a3b518563 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/pfl/model/simple.py @@ -0,0 +1,33 @@ + +import torch +from torch import nn + + +class PFLModel(nn.Module): + def __init__(self, n_obj=2): + super().__init__() + # self.args = args + hidden_size = 128 + self.model = nn.Sequential( + nn.Linear(n_obj-1, hidden_size), + nn.ReLU(), + nn.Linear(hidden_size, hidden_size), + nn.ReLU(), + nn.Linear(hidden_size, hidden_size), + nn.ReLU(), + nn.Linear(hidden_size, n_obj) + ) + + def forward(self, x): + ''' + :param x: input + :return: the predicted Pareto front + ''' + # raise NotImplementedError + return self.model(x) + + def get_pf(self): + ''' + :return: the true Pareto front + ''' + raise NotImplementedError diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/pfl/run.py b/bike_bench_internal/benchmark_models/libmoon/solver/pfl/run.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/psl/__init__.py b/bike_bench_internal/benchmark_models/libmoon/solver/psl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/psl/model/__init__.py b/bike_bench_internal/benchmark_models/libmoon/solver/psl/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8734b91e9b2a00c1eca066f29e347e688a6e8f1c --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/psl/model/__init__.py @@ -0,0 +1,2 @@ +from .simple import SimplePSLModel +from .mtl import HyperNet, LeNetTarget \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/psl/model/mtl.py b/bike_bench_internal/benchmark_models/libmoon/solver/psl/model/mtl.py new file mode 100644 index 0000000000000000000000000000000000000000..bbba986090656c43636c471d91fdfcd292ba0a70 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/psl/model/mtl.py @@ -0,0 +1,182 @@ +import torch +from torch import nn +from libmoon.util_global.constant import get_param_num + +import torch.nn.functional as F + + +class HyperNet(nn.Module): + def __init__(self, + kernel_size, + ray_hidden_dim=100, + out_dim=10, + target_hidden_dim=50, + n_kernels=10, + n_conv_layers=2, + n_hidden=1, + n_tasks=2): + + super().__init__() + self.n_conv_layers = n_conv_layers + self.n_hidden = n_hidden + self.n_tasks = n_tasks + + assert len(kernel_size) == n_conv_layers, ( + 'kernel size should be the same as the number of conv layers' + 'conv layers holding kernel size for earch conv layer' + ) + + self.ray_mlp = nn.Sequential( + nn.Linear(2, ray_hidden_dim), + nn.ReLU(inplace=True), + nn.Linear(ray_hidden_dim, ray_hidden_dim), + nn.ReLU(inplace=True), + nn.Linear(ray_hidden_dim, ray_hidden_dim) + ) + + self.conv_0_weights = nn.Linear( + ray_hidden_dim, n_kernels * kernel_size[0] * kernel_size[0] # n_kernel : 10 + ) + # output_size: 10*9*9 + + self.conv_0_bias = nn.Linear(ray_hidden_dim, n_kernels) + for i in range(1, n_conv_layers): + # previous number of kernels. + p = 2**(i-1) * n_kernels + # current number of kernels + c = 2**i*n_kernels + setattr( + self, f"conv_{i}_weights", nn.Linear(ray_hidden_dim, c*p*kernel_size[i]*kernel_size[i]), + ) + setattr(self, f"conv_{i}_bias", nn.Linear(ray_hidden_dim, c)) + + latent = 25 + self.hidden_0_weights = nn.Linear( + ray_hidden_dim, target_hidden_dim * 2 ** i * n_kernels * latent # self.hidden_0_weights: 100 -> 25000 + ) + + self.hidden_0_bias = nn.Linear(ray_hidden_dim, target_hidden_dim) + for j in range(n_tasks): + setattr( + self, + f"task_{j}_weights", + nn.Linear(ray_hidden_dim, target_hidden_dim * out_dim) + ) + setattr(self, f"task_{j}_bias", nn.Linear(ray_hidden_dim, out_dim)) + + def shared_parameters(self): + return list([p for n,p in self.named_parameters() if 'task' not in n]) + + def forward(self, ray): + features = self.ray_mlp(ray) + # features.shape: (batch_size, ray_hidden_dim) + out_dict={} # task 1, task 2. Task specfic parameters. + # features.shape : [128, 100] + + layer_types = ['conv', 'hidden', 'task'] + for i in layer_types: + # Sequential layers. Two convs, two hiddens, two tasks. + if i == 'conv': + n_layers = self.n_conv_layers + elif i == 'hidden': + n_layers = self.n_hidden + elif i == 'task': + n_layers = self.n_tasks + + for j in range( n_layers ): + out_dict[f"{i}{j}.weights"] = getattr(self, f"{i}_{j}_weights")( + features + ) + + out_dict[f"{i}{j}.bias"] = getattr(self, f"{i}_{j}_bias")( + features + ).flatten() + + return out_dict + + + +class LeNetTarget(nn.Module): + ''' + LeNet target network + ''' + def __init__(self, + kernel_size, + n_kernels=10, + out_dim=10, + target_hidden_dim=50, + n_conv_layers=2, + n_tasks=2 + ): + + super().__init__() + assert len(kernel_size) == n_conv_layers, ( + 'kernel size should be the same as the number of conv layers' + 'conv layers holding kernel size for earch conv layer' + ) + self.n_kernels = n_kernels + self.kernel_size= kernel_size + self.out_dim = out_dim + self.n_conv_layers = n_conv_layers + self.n_tasks = n_tasks + self.target_hidden_dim= target_hidden_dim + + def forward(self, x, weights=None): + # weights['conv0.weights'].shape : (bs, 810) + x = F.conv2d( + x, + weight = weights['conv0.weights'].reshape( + self.n_kernels, 1, self.kernel_size[0], self.kernel_size[0] + ), + bias=weights['conv0.bias'], + stride=1, + ) + + x = F.relu(x) + x = F.max_pool2d(x, 2) + + for i in range(1, self.n_conv_layers): + x = F.conv2d( + x, + weight = weights[f"conv{i}.weights"].reshape( + int(2**i * self.n_kernels), int(2**(i-1) * self.n_kernels), self.kernel_size[i], self.kernel_size[i] + ), + bias=weights[f"conv{i}.bias"], + stride=1, + ) + x = F.relu(x) + x = F.max_pool2d(x, 2) + + x = torch.flatten(x, 1) + + x = F.linear( + x, + weight = weights['hidden0.weights'].reshape( + self.target_hidden_dim, x.shape[-1] + ), + bias=weights['hidden0.bias'], + ) + + logits = [] + for j in range(self.n_tasks): + logits.append( + F.linear( + x, + weight = weights[f"task{j}.weights"].reshape( + self.out_dim, self.target_hidden_dim + ), + bias=weights[f"task{j}.bias"], + ) + ) + + return logits + + + +if __name__ == '__main__': + prefs = torch.rand(10, 2) + hyper_model = HyperNet(kernel_size=[5,5]) + model_num = get_param_num(hyper_model) #model num: 3,186,850. It is too large, unacceptable. + print('model_num', model_num) + out_dict = hyper_model(prefs) + # print() \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/psl/model/simple.py b/bike_bench_internal/benchmark_models/libmoon/solver/psl/model/simple.py new file mode 100644 index 0000000000000000000000000000000000000000..b909711257e61358b0f088cbead008c46ce994c9 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/psl/model/simple.py @@ -0,0 +1,41 @@ +import torch +from torch import nn + + +class SimplePSLModel(nn.Module): + + def __init__(self, problem, args): + + ''' + :param dim: a 3d-array. [chanel, height, width] + :param + ''' + super().__init__() + self.problem = problem + self.args = args + self.hidden_size = 128 + + if 'lb' in dir(problem): + self.psl_model = nn.Sequential( + nn.Linear(self.problem.n_obj, self.hidden_size), + nn.ReLU(), + nn.Linear(self.hidden_size, self.hidden_size), + nn.ReLU(), + nn.Linear(self.hidden_size, self.args.n_var), + nn.Sigmoid() + ) + else: + self.psl_model = nn.Sequential( + nn.Linear(self.problem.n_obj, self.hidden_size), + nn.ReLU(), + nn.Linear(self.hidden_size, self.hidden_size), + nn.ReLU(), + nn.Linear(self.hidden_size, self.args.n_var), + ) + + def forward(self, pref): + mid = self.psl_model(pref) + if 'lb' in dir(self.problem): + return mid * (self.problem.ub - self.problem.lb) + self.problem.lb + else: + return mid diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/psl/run_mtl_condition.py b/bike_bench_internal/benchmark_models/libmoon/solver/psl/run_mtl_condition.py new file mode 100644 index 0000000000000000000000000000000000000000..f4a4eb658ee66116a38961a9fb6da470da303cd4 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/psl/run_mtl_condition.py @@ -0,0 +1,80 @@ +import torch +import argparse +import numpy as np +# from util_global.constant import get_device +from libmoon.problem.mtl.objectives import from_name +from libmoon.util_global.constant import get_device + +loss_func_arr = from_name( names=['CrossEntropyLoss', 'CrossEntropyLoss'], task_names=['l', 'r'] ) +from model.mtl import HyperNet, LeNetTarget +from libmoon.problem.mtl.loaders import MultiMNISTData +from tqdm import tqdm + +from matplotlib import pyplot as plt + + + +if __name__ == '__main__': + parse = argparse.ArgumentParser() + parse.add_argument('--n-epoch', type=int, default=5) + parse.add_argument('--batch-size', type=int, default=128) + parse.add_argument('--lr', type=float, default=1e-3) + parse.add_argument('--n-obj', type=int, default=2) + parse.add_argument('--model', type=str, default='lenet') + parse.add_argument('--ray-hidden', type=int, default=100) + + dataset = MultiMNISTData('mnist', 'train') + args = parse.parse_args() + loader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True, + num_workers=0) + dataset_test = MultiMNISTData('mnist', 'test') + loader_test = torch.utils.data.DataLoader(dataset_test, batch_size=args.batch_size, shuffle=True, + num_workers=0) + + args.device = get_device() + if args.model == 'lenet': + hnet = HyperNet( [9,5] ) + net = LeNetTarget( [9,5] ) + else: + assert False, 'model not supported' + + hnet.to(args.device) + optimizer = torch.optim.Adam(hnet.parameters(), lr=args.lr) + + + + loss_history = [] + + for idx in tqdm(range( args.n_epoch)): + for i, batch in enumerate(loader): + + ray = torch.from_numpy( + np.random.dirichlet((1, 1), 1).astype(np.float32).flatten() + ).to(args.device) # ray.shape (1,2) + + batch_ = {} + for k, v in batch.items(): + batch_[k] = v.to(args.device) + + hnet.train() + weights = hnet( ray ) + logits_l, logits_r = net(batch_['data'], weights) + + batch_['logits_l'] = logits_l + batch_['logits_r'] = logits_r + + loss_arr = torch.stack([loss(**batch_) for loss in loss_func_arr]) + # print() + optimizer.zero_grad() + loss = ray@loss_arr + (loss).backward() + loss_item = loss.cpu().detach().numpy() + loss_history.append(loss_item) + + optimizer.step() + + + plt.plot( np.log( np.array(loss_history) ) ) + plt.show() + + diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/psl/run_mtl_psl.py b/bike_bench_internal/benchmark_models/libmoon/solver/psl/run_mtl_psl.py new file mode 100644 index 0000000000000000000000000000000000000000..f4a4eb658ee66116a38961a9fb6da470da303cd4 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/psl/run_mtl_psl.py @@ -0,0 +1,80 @@ +import torch +import argparse +import numpy as np +# from util_global.constant import get_device +from libmoon.problem.mtl.objectives import from_name +from libmoon.util_global.constant import get_device + +loss_func_arr = from_name( names=['CrossEntropyLoss', 'CrossEntropyLoss'], task_names=['l', 'r'] ) +from model.mtl import HyperNet, LeNetTarget +from libmoon.problem.mtl.loaders import MultiMNISTData +from tqdm import tqdm + +from matplotlib import pyplot as plt + + + +if __name__ == '__main__': + parse = argparse.ArgumentParser() + parse.add_argument('--n-epoch', type=int, default=5) + parse.add_argument('--batch-size', type=int, default=128) + parse.add_argument('--lr', type=float, default=1e-3) + parse.add_argument('--n-obj', type=int, default=2) + parse.add_argument('--model', type=str, default='lenet') + parse.add_argument('--ray-hidden', type=int, default=100) + + dataset = MultiMNISTData('mnist', 'train') + args = parse.parse_args() + loader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True, + num_workers=0) + dataset_test = MultiMNISTData('mnist', 'test') + loader_test = torch.utils.data.DataLoader(dataset_test, batch_size=args.batch_size, shuffle=True, + num_workers=0) + + args.device = get_device() + if args.model == 'lenet': + hnet = HyperNet( [9,5] ) + net = LeNetTarget( [9,5] ) + else: + assert False, 'model not supported' + + hnet.to(args.device) + optimizer = torch.optim.Adam(hnet.parameters(), lr=args.lr) + + + + loss_history = [] + + for idx in tqdm(range( args.n_epoch)): + for i, batch in enumerate(loader): + + ray = torch.from_numpy( + np.random.dirichlet((1, 1), 1).astype(np.float32).flatten() + ).to(args.device) # ray.shape (1,2) + + batch_ = {} + for k, v in batch.items(): + batch_[k] = v.to(args.device) + + hnet.train() + weights = hnet( ray ) + logits_l, logits_r = net(batch_['data'], weights) + + batch_['logits_l'] = logits_l + batch_['logits_r'] = logits_r + + loss_arr = torch.stack([loss(**batch_) for loss in loss_func_arr]) + # print() + optimizer.zero_grad() + loss = ray@loss_arr + (loss).backward() + loss_item = loss.cpu().detach().numpy() + loss_history.append(loss_item) + + optimizer.step() + + + plt.plot( np.log( np.array(loss_history) ) ) + plt.show() + + diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/psl/run_simple_psl.py b/bike_bench_internal/benchmark_models/libmoon/solver/psl/run_simple_psl.py new file mode 100644 index 0000000000000000000000000000000000000000..a7d0f033f874d8b04a377090d9e3e120de8b1766 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/psl/run_simple_psl.py @@ -0,0 +1,65 @@ +# from problem.sy +from solver.psl.model import SimplePSLModel +from libmoon.util_global.constant import problem_dict, FONT_SIZE +import argparse +from tqdm import tqdm +import numpy as np +import torch +from libmoon.util_global.scalarization import tche +from libmoon.util_global.weight_factor.funs import uniform_pref + + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--problem', default='zdt1', type=str) + parser.add_argument('--n_var', default=30, type=int) + parser.add_argument('--dist', default='dirichlet', type=str ) + parser.add_argument('--epoch', default=100, type=int ) + parser.add_argument('--batch-size', default=128, type=int ) + parser.add_argument('--lr', default=1e-3, type=float ) + + + args = parser.parse_args() + if torch.cuda.is_available(): + device = torch.device("cuda") + print('cuda is available') + else: + device = torch.device("cpu") + print('cuda is not available') + + args.device = device + problem = problem_dict[args.problem] + model = SimplePSLModel(problem, args).to(args.device) + optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) + + + loss_history = [] + for _ in tqdm(range(args.epoch)): + prefs = torch.Tensor( np.random.dirichlet(np.ones(problem.n_obj), args.batch_size) ).to(args.device) + + xs = model(prefs) + fs = problem.evaluate(xs) + g = tche(fs, prefs) + loss = torch.mean(g) + loss_history.append(loss.cpu().detach().numpy()) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + + from matplotlib import pyplot as plt + + test_pref = torch.Tensor( uniform_pref(20, problem.n_obj) ).to(args.device) + predict_x = model(test_pref) + predict_y = problem.evaluate(predict_x) + predict_y_np = predict_y.cpu().detach().numpy() + + plt.scatter(predict_y_np[:, 0], predict_y_np[:, 1], marker='o', color='r', facecolors='none', label='predict') + + plt.xlabel('$L_1$', fontsize=FONT_SIZE) + plt.ylabel('$L_2$', fontsize=FONT_SIZE) + plt.legend(fontsize=FONT_SIZE) + + + plt.show() \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/solver/psl/util.py b/bike_bench_internal/benchmark_models/libmoon/solver/psl/util.py new file mode 100644 index 0000000000000000000000000000000000000000..a29442e1c4bd8a5cce35c00d26ef3d6caa1d1e6c --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/solver/psl/util.py @@ -0,0 +1,12 @@ + + +import numpy as np + +def get_random_prefs(batch_size, n_obj): + return np.random.dirichlet(np.ones(n_obj), batch_size) + + + +# if __name__ == '__main__': +# prefs = get_random_prefs(10, 2) + # print() diff --git a/bike_bench_internal/benchmark_models/libmoon/util_global/__init__.py b/bike_bench_internal/benchmark_models/libmoon/util_global/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/util_global/constant.py b/bike_bench_internal/benchmark_models/libmoon/util_global/constant.py new file mode 100644 index 0000000000000000000000000000000000000000..74e54400a6f890aee3e5cda3a0d03796ee42d1d2 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/util_global/constant.py @@ -0,0 +1,82 @@ +from .scalarization import ls, mtche, tche, pbi, cosmos +from ..problem.synthetic import VLMOP1, VLMOP2, ZDT1, ZDT2, ZDT3, ZDT4, ZDT6 +from ..problem.synthetic import MAF1 +from ..problem.synthetic.dtlz import DTLZ1, DTLZ2, DTLZ3, DTLZ4 +from ..problem.synthetic.re import RE21, RE22, RE23, RE24, RE25, RE31, RE37, RE41, RE42 + + +import os +from numpy import array +import torch + + + +FONT_SIZE = 20 +solution_eps = 1e-5 +scalar_dict = { + 'ls' : ls, + 'mtche' : mtche, + 'tche' : tche, + 'pbi' : pbi, + 'cosmos' : cosmos +} + +problem_dict = { + 'ZDT1': ZDT1(), + 'ZDT2': ZDT2(), + 'ZDT3': ZDT3(), + 'ZDT4': ZDT4(), + 'ZDT6': ZDT6(), + 'DTLZ1': DTLZ1(), + 'DTLZ2': DTLZ2(), + 'DTLZ3': DTLZ3(), + 'DTLZ4': DTLZ4(), + 'VLMOP1': VLMOP1(), + 'VLMOP2': VLMOP2(), + 'MAF1': MAF1(), + 'RE21' : RE21(), + 'RE22': RE22(), + 'RE23': RE23(), + 'RE24' : RE24(), + 'RE25' : RE25(), + 'RE31' : RE31(), + 'RE37' : RE37(), + 'RE41': RE41(), + 'RE42': RE42(), + +} + + +hv_ref_dict = { + 'VLMOP1': array([1.0, 1.0]), + 'VLMOP2': array([4.0, 4.0]), + 'MAF1': array([2.0, 2.0, 2.0]), + 'MNIST': array([3.0, 3.0]) +} + +def get_hv_ref_dict(problem_name): + if problem_name.startswith('ZDT'): + ref = array([1.0, 1.0]) + else: + ref = hv_ref_dict[problem_name] + return ref + 0.5 + +root_name = os.path.dirname(os.path.dirname(__file__)) +def is_pref_based(mtd): + if mtd in ['epo', 'mgda', 'agg', 'pmgda']: + return True + else: + return False + + +def get_device(): + if torch.cuda.is_available(): + device = torch.device("cuda") + print('cuda is available') + else: + device = torch.device("cpu") + print('cuda is not available') + return device + +def get_param_num(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/util_global/scalarization.py b/bike_bench_internal/benchmark_models/libmoon/util_global/scalarization.py new file mode 100644 index 0000000000000000000000000000000000000000..6610410903bbd452500f582e8cc82d7db7138f61 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/util_global/scalarization.py @@ -0,0 +1,74 @@ +import numpy as np +import torch +from torch import Tensor + +''' + numpy evaluation may have some problems, please check. + Here, an interesting thing is that, PBI and COSMOS are not MOO agg functions. + I.e., the optimas may not be Pareto optimal. +''' + +def tche(f_arr, w, z=0): + if type(f_arr) == Tensor: + idx = torch.argmax(w * (f_arr - z), axis=1) + return f_arr[torch.arange(f_arr.shape[0]), idx] + # return [0] + + elif type(f_arr) == np.ndarray: + idx = np.argmax(w * (f_arr - z), axis=1) + return f_arr[np.arange(f_arr.shape[0]), idx] + # return np.max(w * (f_arr - z), axis=1) + else: + raise Exception('type not supported') + + +def mtche(f_arr, w, z=0): + return tche(f_arr, 1/w, z) + + +def ls(f_arr, w, z=0): + if type(f_arr) == Tensor: + return torch.sum(w * (f_arr - z), axis=1) + elif type(f_arr) == np.ndarray: + return np.sum(w * (f_arr - z), axis=1) + else: + raise Exception('type not supported') + + +def pbi(f_arr, w, coeff=5, z=0): + + if type(f_arr) == Tensor: + w0 = w / torch.norm(w, dim=1).unsqueeze(1) + d1 = torch.sum(f_arr * w0, axis=1) + d2 = torch.norm(f_arr - d1.unsqueeze(1) * w0, dim=1) + return d1 + coeff * d2 + else: + w0 = w / np.linalg.norm(w) + d1 = np.sum(f_arr * w0, axis=1) + d2 = np.linalg.norm(f_arr - np.outer(d1, w0), axis=1) + return d1 + coeff * d2 + + + +def cosmos(f_arr, w, coeff=10, z=0): + if type(f_arr) == Tensor: + w0 = w / torch.norm(w, dim=1).unsqueeze(1) + + d1 = torch.sum(f_arr * w0, axis=1) + d2 = d1 / torch.norm(f_arr, dim=1) + return d1 - coeff * d2 + else: + w0 = w / np.linalg.norm(w) + d1 = f_arr @ w0 + d2 = f_arr @ w0 / np.linalg.norm(f_arr, axis=1) + return d1 - coeff * d2 + + + +if __name__ == '__main__': + f_arr = torch.rand(100, 2) + w = torch.Tensor([1, 1]) + z = torch.rand(100, 2) + + # print(Tche(f_arr, w, z)) + print(pbi(f_arr, w)) \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/util_global/weight_factor/__init__.py b/bike_bench_internal/benchmark_models/libmoon/util_global/weight_factor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/util_global/weight_factor/das_dennis.py b/bike_bench_internal/benchmark_models/libmoon/util_global/weight_factor/das_dennis.py new file mode 100644 index 0000000000000000000000000000000000000000..e16d6f2f38acad1276558355a099863a1e3d9a74 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/util_global/weight_factor/das_dennis.py @@ -0,0 +1,23 @@ + +import numpy as np + +def das_dennis(n_partitions, n_dim): + if n_partitions == 0: + return np.full((1, n_dim), 1 / n_dim) + else: + ref_dirs = [] + ref_dir = np.full(n_dim, np.nan) + das_dennis_recursion(ref_dirs, ref_dir, n_partitions, n_partitions, 0) + return np.concatenate(ref_dirs, axis=0) + + +def das_dennis_recursion(ref_dirs, ref_dir, n_partitions, beta, depth): + if depth == len(ref_dir) - 1: + ref_dir[depth] = beta / (1.0 * n_partitions) + ref_dirs.append(ref_dir[None, :]) + else: + for i in range(beta + 1): + ref_dir[depth] = 1.0 * i / (1.0 * n_partitions) + das_dennis_recursion(ref_dirs, np.copy(ref_dir), n_partitions, beta - i, depth + 1) + + diff --git a/bike_bench_internal/benchmark_models/libmoon/util_global/weight_factor/funs.py b/bike_bench_internal/benchmark_models/libmoon/util_global/weight_factor/funs.py new file mode 100644 index 0000000000000000000000000000000000000000..ecb4a1462f7704f444457c872f2e1421f28f4c02 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/util_global/weight_factor/funs.py @@ -0,0 +1,17 @@ +import numpy as np +from .das_dennis import das_dennis + + +def uniform_pref(n_partition, n_obj, clip_eps=0, mtd='uniform'): + + if n_obj == 2: + pref_1 = np.linspace(clip_eps, 1-clip_eps, n_partition) + pref_2 = 1 - pref_1 + prefs = np.stack((pref_1, pref_2), axis=1) + else: + prefs = das_dennis(n_partition, n_obj) + + prefs = np.clip(prefs, clip_eps, 1-clip_eps) + prefs = prefs / prefs.sum(axis=1, keepdims=True) + + return prefs \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/visulization/__init__.py b/bike_bench_internal/benchmark_models/libmoon/visulization/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/benchmark_models/libmoon/visulization/util.py b/bike_bench_internal/benchmark_models/libmoon/visulization/util.py new file mode 100644 index 0000000000000000000000000000000000000000..c02a92d80dbe8106aac70601a6df3457f437ea31 --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/visulization/util.py @@ -0,0 +1,24 @@ +import numpy as np +from matplotlib import pyplot as plt +from mpl_toolkits.mplot3d.art3d import Poly3DCollection + + +def plot_simplex(ax, p1, p2, p3, color='k'): + # x1 = np.array([0, 0, 2]) + # y1 = np.array([0, 2, 0]) + # z1 = np.array([2, 0, 0]) # z1 should have 3 coordinates, right? + # ax.scatter(x1, y1, z1) + # 1. create vertices from points + verts = [list(zip(p1, p2, p3))] + # 2. create 3d polygons and specify parameters + srf = Poly3DCollection(verts, alpha=.25, facecolor='#800000') + # 3. add polygon to the figure (current axes) + plt.gca().add_collection3d(srf) + + +def plot_unit_sphere(ax): + u, v = np.mgrid[0:np.pi / 2:30j, 0:np.pi / 2:20j] + x = np.cos(u) * np.sin(v) + y = np.sin(u) * np.sin(v) + z = np.cos(v) + ax.plot_surface(x, y, z, color='b', alpha=0.2) \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/libmoon/visulization/view_res.py b/bike_bench_internal/benchmark_models/libmoon/visulization/view_res.py new file mode 100644 index 0000000000000000000000000000000000000000..e01ef871be73f51eef59056a010fd60e4665734a --- /dev/null +++ b/bike_bench_internal/benchmark_models/libmoon/visulization/view_res.py @@ -0,0 +1,142 @@ +import numpy as np +from matplotlib import pyplot as plt +from libmoon.util_global.constant import FONT_SIZE +from mpl_toolkits.mplot3d.art3d import Poly3DCollection + + + +def vis_res(res, problem, prefs, args): + if args.n_obj == 2: + fig = plt.figure(figsize=(8, 20)) + plt.subplot(3,1,1) + plt.title(args.solver, fontsize=FONT_SIZE) + plt.scatter(res['y'][:, 0], res['y'][:, 1], label='solution') + pf = problem.get_pf() + plt.plot(pf[:, 0], pf[:, 1], 'r--', label='PF') + plt.xlabel('$f_1$', fontsize=FONT_SIZE) + plt.ylabel('$f_2$', fontsize=FONT_SIZE) + pf_norm = np.max( np.linalg.norm(pf, axis=1) ) + prefs_norm = prefs / np.linalg.norm(prefs, axis=1)[:, np.newaxis] * pf_norm + if args.plt_pref_flag == 'Y': + for idx, pref in enumerate(prefs_norm) : + if idx == 0: + plt.plot([0, pref[0]], [0, pref[1]], 'k--', linewidth=1, label='Prefs') + else: + plt.plot([0, pref[0]], [0, pref[1]], 'k--', linewidth=1) + plt.legend(fontsize=FONT_SIZE) + + plt.subplot(3,1,2) + plt.plot(res['hv_arr']) + plt.xlabel('iteration', fontsize=FONT_SIZE) + plt.ylabel('hypervolume', fontsize=FONT_SIZE) + + plt.subplot(3,1,3) + for xx in res['x']: + plt.plot(xx, 'o-', color='k') + plt.xlabel('variable index', fontsize=FONT_SIZE) + plt.ylabel('variable value', fontsize=FONT_SIZE) + elif args.n_obj==3: + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + + ax.scatter(res['y'][:, 0], res['y'][:, 1], res['y'][:, 2], label='solution',) + if args.problem_name == 'maf1': + # ax draw rectangle. + x1 = np.array([0, 0, 2]) + y1 = np.array([0, 2, 0]) + z1 = np.array([2, 0, 0]) # z1 should have 3 coordinates, right? + ax.scatter(x1, y1, z1) + # 1. create vertices from points + verts = [list(zip(x1, y1, z1))] + # 2. create 3d polygons and specify parameters + srf = Poly3DCollection(verts, alpha=.25, facecolor='#800000') + # 3. add polygon to the figure (current axes) + plt.gca().add_collection3d(srf) + + ax.set_xlabel('$f_1$', fontsize=FONT_SIZE) + ax.set_ylabel('$f_2$', fontsize=FONT_SIZE) + ax.set_zlabel('$f_3$', fontsize=FONT_SIZE) + ax.view_init(elev=30, azim=125) + + # ax2 = fig.add_subplot(312) + # ax2.plot(res['hv_arr']) + else: + raise Exception('n_obj not supported') + + +import matplotlib.cm as cm +# import matplotlib.animation as animation + +from matplotlib.animation import FuncAnimation +import matplotlib.animation as manimation +from matplotlib.animation import PillowWriter + +from libmoon.util_global.constant import FONT_SIZE + +def vedio_res(res, problem, prefs, args): + print('vedio making') + # print() + # FFMpegWriter = manimation.writers['ffmpeg'] + metadata = dict(title='Movie Test', artist='Matplotlib', + comment='a red circle following a blue sine wave') + # writer = PillowWriter(fps=15, metadata=metadata) + fps=15 + from matplotlib.animation import FuncAnimation + from matplotlib.animation import FFMpegWriter + + # Create some data + # x = np.linspace(0, 2 * np.pi, 100) + # y = np.sin(x) + subsample = 20 + y_arr = res['y_arr'][::subsample] + n_frame = len( y_arr ) + + # Create a figure and axis + fig, ax = plt.subplots() + ax.set_xlim(0, 1.2) + ax.set_ylim(0, 1.2) + + ax.set_xlabel('$f_1$', fontsize=FONT_SIZE) + ax.set_ylabel('$f_2$', fontsize=FONT_SIZE) + + + # Create a function to update the plot for each frame of the animation + if args.solver == 'agg': + file_name = '{}_{}_{}'.format(args.problem_name, args.solver, args.agg) + else: + file_name = '{}_{}'.format(args.problem_name, args.solver) + def update(frame): + ax.clear() + ax.scatter(y_arr[frame][:,0], y_arr[frame][:,1]) + ax.set_xlim(0, 1.2) + ax.set_ylim(0, 1.2) + + ax.set_xlabel('$f_1$', fontsize=FONT_SIZE) + ax.set_ylabel('$f_2$', fontsize=FONT_SIZE) + + ax.set_title(file_name, fontsize=FONT_SIZE) + + + # Create the animation + ani = FuncAnimation(fig, update, frames=n_frame, interval=100) + + # Define the writer for the animation using FFMpegWriter + writer = FFMpegWriter(fps=15, metadata=dict(artist='Me'), bitrate=1800) + + + + + mp4_file_name = file_name + '.mp4' + + # Save the animation as a video file + ani.save(mp4_file_name, writer=writer) + print('Vedio saved: {}'.format(mp4_file_name)) + + plt.show() + + + + + + + diff --git a/bike_bench_internal/benchmark_models/openai_files/condition_dataset.txt b/bike_bench_internal/benchmark_models/openai_files/condition_dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..6bcff417d493c820a802ac7c0fedd72c35068fbc --- /dev/null +++ b/bike_bench_internal/benchmark_models/openai_files/condition_dataset.txt @@ -0,0 +1,10 @@ +Rider Body Dimensions: Upper leg length - 0.3815820515155792, Lower leg length - 0.49641406536102295, Arm length - 0.678210973739624, Torso length - 0.5025676488876343, Neck and head length - 0.3184974789619446, Torso width - 0.34167566895484924. Use Case: Road Biking. Marketing Description: Experience excellence with the cloudy-blue Echo Infinity, crafted with a resilient steel frame, a sleek precision-engineered disc rear wheel, a cargo rack for extra storage, dual bottle mounts on the seat tube and down tube for quick and easy hydration on the go, and adventure-ready mountain bars. +Rider Body Dimensions: Upper leg length - 0.35833755135536194, Lower leg length - 0.5139421820640564, Arm length - 0.6388106942176819, Torso length - 0.5596845149993896, Neck and head length - 0.3166768550872803, Torso width - 0.3037489056587219. Use Case: Mountain Biking. Marketing Description: The azure Adventurer Force offers an aerodynamic carbon frame, a cargo rack for extra storage, outstanding composite wheels, a set of aerodynamic bars, a seat tube-mounted bottle holder for quick and easy hydration on the go, and road-ready race bars. +Rider Body Dimensions: Upper leg length - 0.38213708996772766, Lower leg length - 0.5036822557449341, Arm length - 0.6267454028129578, Torso length - 0.5271315574645996, Neck and head length - 0.3037262558937073, Torso width - 0.3358073830604553. Use Case: Mountain Biking. Marketing Description: Conquer any road with the sap-green Peak Drive 630, featuring a seat tube-mounted bottle holder for quick and easy access to water, a cargo rack for extra storage, a rear fender for trail comfort, rugged trail bars, and an aerodynamic composite front wheel for exceptional performance. +Rider Body Dimensions: Upper leg length - 0.3527318239212036, Lower leg length - 0.5175962448120117, Arm length - 0.603912889957428, Torso length - 0.5023996829986572, Neck and head length - 0.29468992352485657, Torso width - 0.3591054379940033. Use Case: Mountain Biking. Marketing Description: The greeny-blue Thunder Wave offers aerobars for triathlon dominance, a modern carbon frame, ergonomic track bullhorns, fenders for added protection, a versatile rack for added utility, and a lightweight tri-spoked composite rear wheel, ready to make every ride memorable. +Rider Body Dimensions: Upper leg length - 0.35784658789634705, Lower leg length - 0.4858334958553314, Arm length - 0.609955370426178, Torso length - 0.5037460923194885, Neck and head length - 0.2751818895339966, Torso width - 0.3398345410823822. Use Case: Mountain Biking. Marketing Description: Conquer any road with the dark-sky-blue Titan Impact-X, with a competition-grade carbon frame, road-ready road handlebars, outstanding composite wheels, an integrated cargo rack for commuting, and fenders for added protection for exceptional performance. +Rider Body Dimensions: Upper leg length - 0.36563923954963684, Lower leg length - 0.49396321177482605, Arm length - 0.6390037536621094, Torso length - 0.5377609729766846, Neck and head length - 0.3034992814064026, Torso width - 0.2764577269554138. Use Case: Commuting. Marketing Description: Experience the thrill of the ride with the light-royal-blue Blitz Sprint, designed with a lightweight disc front wheel and composite rear wheel, a low-maintenance belt drive, a corrosion-resistant aluminium frame, and urban track bullhorns. +Rider Body Dimensions: Upper leg length - 0.42793118953704834, Lower leg length - 0.4857039451599121, Arm length - 0.6159527897834778, Torso length - 0.5410552024841309, Neck and head length - 0.32506775856018066, Torso width - 0.38092488050460815. Use Case: Commuting. Marketing Description: The Lightning Pulse 710 includes an ultra-lightweight carbon frame, a sturdy rear rack for carrying gear, a seat tube-mounted bottle holder for your convenience, a sleek race-optimized tri-spoked composite rear wheel, and urban track bullhorns and is the solution to your cycling needs. +Rider Body Dimensions: Upper leg length - 0.38297030329704285, Lower leg length - 0.49398431181907654, Arm length - 0.648978054523468, Torso length - 0.4963388442993164, Neck and head length - 0.3469637334346771, Torso width - 0.3404614329338074. Use Case: Commuting. Marketing Description: Feel the power of the road with the Canyon Advance, boasting a durable steel frame, a seat tube-mounted bottle holder for your convenience, off-road mountain bike bars, a lightweight ultra-efficient composite front wheel and disc rear wheel, and racing aerobars for streamlined control. +Rider Body Dimensions: Upper leg length - 0.3871501386165619, Lower leg length - 0.5298420786857605, Arm length - 0.6361967325210571, Torso length - 0.5753913521766663, Neck and head length - 0.33915889263153076, Torso width - 0.3392464518547058. Use Case: Mountain Biking. Marketing Description: The rose Blitz Velocity includes a stiff carbon frame, a superior ultra-efficient composite front wheel, versatile drop handlebars, and an integrated cargo rack for commuting. +Rider Body Dimensions: Upper leg length - 0.42270979285240173, Lower leg length - 0.4352869391441345, Arm length - 0.6196873784065247, Torso length - 0.5234108567237854, Neck and head length - 0.3142751157283783, Torso width - 0.360833078622818. Use Case: Road Biking. Marketing Description: The warm-grey Hunter Elite 480 features a down tube-mounted bottle holder so you never miss a sip, aerodynamic bars for speed, a sturdy rear rack for carrying gear, ergonomic urban bullhorns, a front fender for splatter resistance, a sleek disc rear wheel, and a reliable aluminium frame and is the solution to your cycling needs. diff --git a/bike_bench_internal/benchmark_models/openai_files/criterion_descriptions.txt b/bike_bench_internal/benchmark_models/openai_files/criterion_descriptions.txt new file mode 100644 index 0000000000000000000000000000000000000000..ac843b53776618c252846396cf1bf4c82dab47eb --- /dev/null +++ b/bike_bench_internal/benchmark_models/openai_files/criterion_descriptions.txt @@ -0,0 +1,33 @@ +Descriptions of all objectives and constraints in the standard bike--bench multi-obejctive engineering design benchmark. +Some evaluation criteria are contingent on both the conditional information, while others are contingent on only the bike design. By convention, objectives are minimized at 0, with lower values being better. +Constraints are also minimized, with 0 being the critical value. Larger positive values being more constraint violating and larger magnitude negative values are more more constraint-satisfying. +In general, once a constraint is satisfied, we no longer care about further minimizing the value. However, achieving constraints is critical because designs that violate constraints are invalid. + +General format is: +'evaluation criterion name': [criterion type] Description. + +Usability Score: [Objective] The predicted 'usability,' as rated by a human, with 0 being the most usable and 1 being the least usable. Predicted by a regression model trained on human-collected ratings. +Drag Force: [Objective] The predicted drag force in N incurred by the cyclist in a 10 m/s headwind, as predicted by a regression model trained on computational fluid dynamics simulation data. +Knee Angle Error: [Objective] The difference between the minimum knee angle of the cyclist and the optimal reference range. May include a penalty term if the rider's geometry is completely incompatible with the bike. +Hip Angle Error: [Objective] The difference between the torso-to-upper-leg angle of the cyclist and the optimal reference range. May include a penalty term if the rider's geometry is completely incompatible with the bike. +Arm Angle Error: [Objective] The difference between the torso-to-arm angle of the cyclist and the optimal reference range. May include a penalty term if the rider's geometry is completely incompatible with the bike. +Cosine Distance to Embedding: [Objective] The cosine distance in the CLIP embedding space between the rendered bike image and the target text or image embedding. +Mass: [Objective] The mass in kg of the bike frame, as predicted by a regression model trained on finite element analysis data. +Planar Compliance: [Objective] A composite planar compliance score for the bike frame, as predicted by a regression model trained on finite element analysis data. +Transverse Compliance: [Objective] A transverse compliance score for the bike frame, as predicted by a regression model trained on finite element analysis data. +Eccentric Compliance: [Objective] A composite eccentric compliance score for the bike frame, as predicted by a regression model trained on finite element analysis data. +Planar Safety Factor: [Constraint] Constraint quantified as 1.5 minus the safety factor under planar loading, as predicted by a regression model trained on finite element analysis data. +Eccentric Safety Factor: [Constraint] Constraint quantified as 1.5 minus the safety factor under eccentric loading, as predicted by a regression model trained on finite element analysis data. +Saddle height too small: [Constraint] Constraint indicating that the saddle height collides with the top of the seat tube. +Seat post too short: [Constraint] Constraint indicating that the seat post doesn't reach the seat tube given the prescribed saddle height. +Head tube lower extension too great: [Constraint] Constraint indicating that the down tube doesn't properly intersect with the head tube. +Head tube length too great: [Constraint] Constraint indicating that the head tube is so short that the top tube and down tube intersect. +Certain parameters must be positive: [Constraint] Constraint indicating that at least one parameter that should be strictly positive is negative. +Chain stay should be greater than wheel radius: [Constraint] Constraint indicating that the chain stay length is smaller than the wheel radius, creating a collision. +Chain stay should be greater than BB: [Constraint] Constraint indicating that the vertical drop from the rear axle to bottom bracket is greater than the chain stay length, creating an impossibility. +Seat stay should be greater than wheel radius: [Constraint] Constraint indicating that the seat stay length is smaller than the wheel radius, creating a collision. +Down tube must reach head tube: [Constraint] Constraint indicating that the down tube is too short to reach the head tube. +The pedal shouldn't intersect the front wheel: [Constraint] Constraint indicating that the front wheel would intersect the pedal in its forward position, causing a collision when turning. +The crank shouldn't hit the ground when it is in its lower position: [Constraint] Constraint indicating that the crank hits the ground during its rotation. +RGB value should be less than 255: [Constraint] Constraint indicating that frame RGB values were set at higher than 255. +Predicted Frame Validity: [Constraint] Constraint indicating some abstract issue with the frame, as predicted by a classification model trained to identify CAD models that failed to regenerate. \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/openai_files/design_dataset.csv b/bike_bench_internal/benchmark_models/openai_files/design_dataset.csv new file mode 100644 index 0000000000000000000000000000000000000000..448bf0db58476104d85fe2daa37052e24b40c1c1 --- /dev/null +++ b/bike_bench_internal/benchmark_models/openai_files/design_dataset.csv @@ -0,0 +1,11 @@ +,CS textfield,BB textfield,Stack,Head angle,Head tube length textfield,Seat stay junction0,Seat tube length,Seat angle,DT Length,FORK0R,BB diameter,ttd,dtd,csd,ssd,Chain stay position on BB,SSTopZOFFSET,MATERIAL,Head tube upper extension2,Seat tube extension2,Head tube lower extension2,SEATSTAYbrdgshift,CHAINSTAYbrdgshift,SEATSTAYbrdgdia1,CHAINSTAYbrdgdia1,SEATSTAYbrdgCheck,CHAINSTAYbrdgCheck,Dropout spacing,Wall thickness Bottom Bracket,Wall thickness Top tube,Wall thickness Head tube,Wall thickness Down tube,Wall thickness Chain stay,Wall thickness Seat stay,Wall thickness Seat tube,Wheel diameter front,RDBSD,Wheel diameter rear,FDBSD,Fork type,Stem kind,Display AEROBARS,Handlebar style,Head tube type,BB length,Head tube diameter,Wheel cut,Seat tube diameter,bottle SEATTUBE0 show,bottle DOWNTUBE0 show,Front Fender include,Rear Fender include,BELTorCHAIN,Number of cogs,Number of chainrings,Display RACK,FIRST color R_RGB,FIRST color G_RGB,FIRST color B_RGB,RIM_STYLE front,RIM_STYLE rear,SPOKES composite front,SPOKES composite rear,SBLADEW front,SBLADEW rear,Saddle length,Saddle height,Down tube diameter,Seatpost LENGTH,Seat tube type +0,425.0,66.0,575.2029642,73.0,105.2,45.0,555.0,74.0,644.8496592,47.0,40.0,25.4,36.5,23.35,13.0,15.0,9.0,STEEL,63.1,48.0,40.1,330.0,350.0,16.0,18.0,False,False,130.0,2.984925050987317,1.7715343952277929,2.4985541836279714,1.89419119522216,1.509739706979634,1.4104879858706545,1.3872913729986849,674.0,52.0,674.0,52.0,0,0,False,1,1,68.0,31.8,692.0,26.8,False,False,True,True,True,8,1,False,0,0,255,SPOKED,SPOKED,1,1,80.0,80.0,278.0,718.0,26.8,250,1 +1,405.0,70.0,591.9108569,73.0,145.0,45.0,515.2,74.0,653.4145038,45.0,40.0,28.6,36.5,23.35,13.0,15.0,9.0,STEEL,28.9,49.2,49.2,330.0,350.0,16.0,18.0,True,True,130.0,0.2046520282233931,0.305932490991244,2.319972198156796,0.8702181810647862,1.5425441917667475,0.806345347435681,0.92790071049802,674.0,52.0,674.0,52.0,0,0,False,1,1,68.0,45.0,692.0,31.8,False,False,False,False,True,6,1,True,255,0,0,SPOKED,SPOKED,1,1,80.0,80.0,298.0,718.0,34.9,330,1 +2,450.0,35.0,512.9319064,64.0,133.8,45.0,380.0,80.0,757.6759997,45.0,40.0,28.6,36.5,27.35,21.5,15.0,9.0,CARBON,25.0,128.4,30.0,330.0,350.0,16.0,18.0,True,True,157.0,8.484778180322339,0.8488915365612826,3.1148345945692566,4.191635779849775,1.7240195862586758,3.896331957214665,0.6390156039354706,744.0,122.0,744.0,122.0,1,0,False,1,2,68.0,53.0,692.0,35.0,False,True,False,False,True,11,0,False,255,255,255,SPOKED,SPOKED,1,1,80.0,80.0,250.0,715.0,43.0,480,2 +3,455.0,70.0,721.2682239,71.0,180.4,35.0,546.2,73.5,748.4959838,45.0,39.0,27.0,36.5,21.25,14.5,15.0,10.1,STEEL,36.3,46.2,56.2,330.0,350.0,16.0,18.0,False,False,142.0,5.466905446143302,8.521785908456947,4.204791146184713,0.9166758036231312,4.12050892151602,2.697186294023933,4.6994853143713895,736.0,114.0,736.0,114.0,0,0,False,0,1,68.0,46.3,692.0,28.6,False,False,True,True,True,9,1,False,0,255,0,SPOKED,SPOKED,1,1,80.0,80.0,265.0,830.0,34.9,300,2 +4,415.0,70.0,637.9091156,73.0,191.4,45.0,630.0,73.5,656.6201875,45.0,40.0,33.6,36.5,23.35,13.0,15.0,9.0,CARBON,27.8,32.1,43.5,330.0,350.0,16.0,18.0,False,False,130.0,3.6756400104414495,2.004391254209075,2.5044697624511856,1.340251983765522,0.6731900960204301,5.772791309765022,0.8908183145343465,674.0,52.0,674.0,52.0,0,0,False,0,1,68.0,45.0,692.0,31.8,False,False,False,False,True,9,1,False,255,51,51,SPOKED,SPOKED,1,1,80.0,80.0,275.0,865.0,34.9,300,2 +5,420.0,70.0,641.0649213,73.0,151.0,100.0,540.0,72.5,668.1348776,45.0,40.0,39.75,50.0,28.5,13.0,15.0,9.0,CARBON,30.6,40.0,55.8,330.0,350.0,16.0,18.0,False,False,130.0,5.214529705739492,1.9237993824502804,1.4867755647923897,0.8627442835832468,5.247645572356713,2.4721990321348235,0.6039291976593105,676.0,54.0,676.0,54.0,0,0,False,0,3,68.0,55.0,692.0,50.0,False,True,False,False,True,10,1,False,255,255,255,SPOKED,SPOKED,1,1,80.0,80.0,210.0,709.0,34.9,310,2 +6,550.0,29.5,496.9336227,67.0,100.0,45.0,533.0,65.0,627.6098218,45.0,40.0,28.6,36.5,23.35,13.0,15.0,9.0,BAMBOO,46.9,44.9,50.1,330.0,350.0,16.0,18.0,False,False,135.0,10.246325085448763,1.0571538465985395,3.320203681882855,1.821909962603824,1.3504986571871185,1.5312390491638666,2.50149356595328,628.0,69.0,628.0,69.0,1,0,False,1,1,68.0,45.0,692.0,31.8,False,False,True,True,True,8,2,True,204,204,255,SPOKED,SPOKED,1,1,80.0,80.0,278.0,700.0,34.9,300,2 +7,405.0,70.0,593.536575,73.0,145.0,45.0,515.0,74.0,648.3152394,45.0,40.0,28.6,36.5,23.35,13.0,15.0,9.0,BAMBOO,34.0,49.0,49.9,330.0,350.0,16.0,18.0,False,False,130.0,1.1647590566025448,0.7426686627208833,0.3143101269390225,0.2668311202837158,0.2891763139624213,0.2958998478109877,0.2262816326792515,674.0,52.0,674.0,52.0,0,0,False,0,1,68.0,45.0,692.0,31.8,False,True,False,False,True,8,1,False,204,204,255,SPOKED,SPOKED,1,1,80.0,80.0,278.0,718.0,34.9,300,2 +8,380.0,47.01358832,561.1033732,74.00206468,168.8,45.0,552.4,76.00206468,623.9028894,45.0,40.0,28.6,36.5,23.35,13.0,15.0,9.0,STEEL,33.0,32.4,46.4,330.0,350.0,16.0,18.0,True,True,130.0,20.48369695835277,6.034812926706038,1.5929308922857068,4.880546522303409,9.48464280499604,15.08677458400779,1.7309129777278605,674.0,52.0,674.0,52.0,0,0,False,0,1,68.0,45.0,692.0,31.8,False,False,False,False,True,0,0,False,255,0,255,SPOKED,SPOKED,1,1,80.0,80.0,298.0,700.0,34.9,300,1 +9,400.0,7.0,511.5560042,68.0,123.0,45.0,331.0,75.0,640.6227443,45.0,40.0,42.75,49.0,35.0,21.5,15.0,9.0,ALUMINIUM,31.0,60.0,42.0,330.0,300.0,16.0,18.0,False,False,130.0,7.737970318079169,12.947741764920302,3.2944381729785044,6.863696733810227,4.51413833563366,2.7508253329283927,5.072657023145851,674.0,52.0,674.0,52.0,1,0,False,1,2,73.0,57.0,692.0,31.8,False,False,True,True,True,6,0,False,0,0,0,SPOKED,SPOKED,1,1,80.0,80.0,230.0,400.0,34.9,200,2 diff --git a/bike_bench_internal/benchmark_models/openai_files/parameter_descriptions.txt b/bike_bench_internal/benchmark_models/openai_files/parameter_descriptions.txt new file mode 100644 index 0000000000000000000000000000000000000000..672dd7f6e0f5d7dfa7b14c55327668cd8a5785ae --- /dev/null +++ b/bike_bench_internal/benchmark_models/openai_files/parameter_descriptions.txt @@ -0,0 +1,76 @@ +Descriptions of all parameters in the standard bike--bench design representation scheme. +General notes: All lengths are measured in mm. All angles are measured in degrees. General format is: +'parameter name': [Datatype] Description. + +The 70 variables are described as follows: + +'CS textfield': [Continuous] The length of the chain stay tubes. +'BB textfield': [Continuous] Bottom bracket drop, measured as the vertical drop from the rear axle to the center of the bottom bracket. By convention, positive values imply the bottom bracket lies below the axle. +'Stack': [Continuous] The vertical distance from the top of the head tube relative to the bottom bracket. +'Head angle': [Continuous] The angle of the head tube with respect to horizontal, in degrees. +'Head tube length textfield' [Continuous] The length of the head tube. +'Seat stay junction0': [Continuous] The length along the seat tube from the top of the seat tube to the junction with the seat stays. By convention, this is measured to the center of the seat stays. +'Seat tube length': [Continuous] The length of the seat stay tubes. +'Seat angle': [Continuous] The angle of the seat tube with respect to horizontal. +'DT Length': [Continuous] The length of the down tube. +'FORK0R': [Continuous] Fork offset, measured as the perpendicular distance from the front axle to the head tube axis. +'BB diameter': [Continuous] The diameter of the bottom bracket +'ttd': [Continuous] Top tube outer diameter. +'csd': [Continuous] Chain stay outer diameter. +'ssd': [Continuous] Seat stay outer diameter. +'dtd': [Continuous] Down tube outer diameter. +'Chain stay position on BB': [Continuous] The distance along the length of the bottom bracket from its edge to the center of the chain stay tubes. +'SSTopZOFFSET': [Continuous] The offset from center plane of the bike of the joints connecting the seat stays to the the seat tube. +'Head tube upper extension2': [Continuous] The length from the top of the head tube to the junction with the top tube. By convention, this is measured to the center of the top tube. +'Seat tube extension2': [Continuous] The length from the top of the seat tube to the junction with the top tube. By convention, this is measured to the center of the top tube. +'Head tube lower extension2': [Continuous] The length from the bottom of the head tube to the junction with the down tube. By convention, this is measured to the center of the down tube. +'SEATSTAYbrdgshift': [Continuous] The distance along the center plane of the bike from the seat stay and seat tube junction to the seat stay bridge, if present on the bike. +'CHAINSTAYbrdgshift': [Continuous] The distance along the center plane of the bike from the outer rim of the bottom bracket to the chain stay bridge, if present on the bike. +'SEATSTAYbrdgdia1': [Continuous] The diameter of the seat stay bridge, if present on the bike. +'CHAINSTAYbrdgdia1': [Continuous] The diameter of the chain stay bridge, if present on the bike. +'SEATSTAYbrdgCheck': [Boolean] A boolean value indicating whether the seat stay bridge is present on the bike. +'CHAINSTAYbrdgCheck': [Boolean] A boolean value indicating whether the chain stay bridge is present on the bike. +'Dropout spacing': [Continuous] The distance between the rear dropouts. +'Wall thickness Bottom Bracket': [Continuous] The tube wall thickness of the bottom bracket. +'Wall thickness Top tube': [Continuous] The tube wall thickness of the top tube. +'Wall thickness Head tube': [Continuous] The tube wall thickness of the head tube. +'Wall thickness Down tube': [Continuous] The tube wall thickness of the down tube. +'Wall thickness Chain stay': [Continuous] The tube wall thickness of the chain stay. +'Wall thickness Seat stay': [Continuous] The tube wall thickness of the seat stay. +'Wall thickness Seat tube': [Continuous] The tube wall thickness of the seat tube. +'Wheel diameter front': [Continuous] The outer diameter of the front wheel. +'RDBSD': [Continuous] The difference between rear wheel outer diameter and bead seat diamater, roughly approximating the tire thickness. +'Wheel diameter rear': [Continuous] The outer diameter of the rear wheel. +'FDBSD': [Continuous] The difference between front wheel outer diameter and bead seat diamater, roughly approximating the tire thickness. +'Display AEROBARS': [Boolean] A boolean value indicating whether the bike has aerobars. +'BB length': [Continuous] The length of the bottom bracket. +'Head tube diameter': [Continuous] Head tube outer diameter. +'Wheel cut': [Continuous] The diameter of the cutout of seat tube for the rear wheel, if using an aerodynamic tube type. +'Seat tube diameter': [Continuous] Seat tube outer diameter. +'bottle SEATTUBE0 show': [Boolean] A boolean value indicating whether the bike has a bottle holder on the seat tube. +'bottle DOWNTUBE0 show': [Boolean] A boolean value indicating whether the bike has a bottle holder on the down tube. +'Front Fender include': [Boolean] A boolean value indicating whether the bike has a front fender. +'Rear Fender include': [Boolean] A boolean value indicating whether the bike has a rear fender. +'BELTorCHAIN': [Boolean] A boolean value indicating whether the bike has a chain (True) as opposed to a belt. +'Number of cogs' [Integer] The number of cogs on the rear wheel. +'Number of chainrings' [Integer] The number of chainrings attached to the crank. +'Display RACK': [Boolean] A boolean value indicating whether the bike has a rack. +'FIRST color R_RGB': [Continuous] The red component of the primary paint color of the bike. +'FIRST color G_RGB': [Continuous] The green component of the primary paint color of the bike. +'FIRST color B_RGB': [Continuous] The blue component of the primary paint color of the bike. +'SPOKES composite front': [Integer] If applicable, the number of composite spokes in the front wheel minus two (a value of 1 is a trispoke wheel). +'SPOKES composite rear': [Integer] If applicable, the number of composite spokes in the rear wheel minus two (a value of 1 is a trispoke wheel). +'SBLADEW front': [Continuous] If applicable, the width of the front wheel composite spokes. +'SBLADEW rear': [Continuous] If applicable, the width of the rear wheel composite spokes. +'Saddle length': [Continuous] The length of the saddle. +'Saddle height': [Continuous] The vertical distance from the saddle to the bottom bracket. +'Down tube diameter': [Continuous] The diameter of the down tube. +'Seatpost LENGTH': [Continuous] The length of the seat post. +'MATERIAL': [Categorical] The material of the bike frame. Possible values are: 'ALUMINIUM', 'CARBON', 'STEEL', 'TITANIUM', 'BAMBOO', 'OTHER'. +'Head tube type': [Categorical] The style of head tube. Possible values are: '0', '1', '2', '3'. 0 is aerodynamic, while 1 and 2 are standard round tubes with no distinction in this representation scheme. 3 is a tapered head tube. +'RIM_STYLE front': [Categorical] The style of the front rim. Possible values are: 'DISC', 'SPOKED', 'TRISPOKE'. Despite the name, trispoke class implies composite spokes but does not necessarily imply three composite spokes. +'RIM_STYLE rear': [Categorical] The style of the rear rim. Possible values are: 'DISC', 'SPOKED', 'TRISPOKE'. Despite the name, trispoke class implies composite spokes but does not necessarily imply three composite spokes. +'Handlebar style': [Categorical] The style of the handlebars. Possible values are: '0', '1', '2'. 0 is a drop bar, 1 is a mountain bike bar, 2 is a bullhorn bar. +'Stem kind': [Categorical] The style of stem. Possible values are: '0', '1', '2'. 0 is a stem that features a sharp and immediate angle away from the head tube. 1 is a stem that features a sharp angle some distance away from the head tube. 2 is a stem that features a gradual angle away from the head tube after intially extending in line with the head tube. +'Fork type': [Categorical] The style of fork. Possible values are: '0', '1', '2'. 0 is a standard fork, 1 is a fork with mountain bike shocks, 2 is a time trial bike fork. +'Seat tube type': [Categorical] The style of seat tube. Possible values are: '0', '1', '2'. 0 is aerodynamic, while 1 and 2 are standard round tubes with no distinction in this representation scheme. diff --git a/bike_bench_internal/benchmark_models/openai_files/prompt1.txt b/bike_bench_internal/benchmark_models/openai_files/prompt1.txt new file mode 100644 index 0000000000000000000000000000000000000000..99d68b722cc52c179bba59b15f57daa30b2a9e53 --- /dev/null +++ b/bike_bench_internal/benchmark_models/openai_files/prompt1.txt @@ -0,0 +1,5 @@ +I will ask you to create some bicycle designs. The bicycle designs are subject to a set of conditions: a text prompt, some rider dimensions, and a use case. +Each design is defined by 70 variables, which I will describe. Some of these are categorical variables, and I will provide you with the possible values for these variables. Others are continuous. +I will describe the design variables shortly. +Designs are evaluated according to a set of 25 criteria. The first 10 are objectives, while the last 15 are constraints. +Here are the design variable descriptions and the evaluation criteria: diff --git a/bike_bench_internal/benchmark_models/openai_files/prompt2.txt b/bike_bench_internal/benchmark_models/openai_files/prompt2.txt new file mode 100644 index 0000000000000000000000000000000000000000..de172d9101ff8deb40c6f6e0ad54004974fd213a --- /dev/null +++ b/bike_bench_internal/benchmark_models/openai_files/prompt2.txt @@ -0,0 +1,5 @@ +I will also provide a dataset of existing bicycle designs. These are useful as a reference point, because it may be difficult to satisfy constraints and objectives if you deviate too far from the space of existing designs. +I will also provide a set of objective scores for these bicycle designs. Since these criteria are dependent on the conditional information, each bike design is paired with a randomized condition set for the purpose of evaluation. +Thus, I will provide: 1) a text file with 10 rows of conditions, 2) a csv file with 10 rows of bikes and 70 columns of parameters, and 3) a csv file of 10 sets (rows) of 25 scores (columns). + +Here are these files. Please have a look to try to gain an understanding of the design space and the evaluation criteria. diff --git a/bike_bench_internal/benchmark_models/openai_files/prompt3.txt b/bike_bench_internal/benchmark_models/openai_files/prompt3.txt new file mode 100644 index 0000000000000000000000000000000000000000..1874b114501d9ccecdfae9c3239d6ce572749f5b --- /dev/null +++ b/bike_bench_internal/benchmark_models/openai_files/prompt3.txt @@ -0,0 +1,5 @@ +Having examined the existing designs and the evaluation of these designs alongside their associated conditions, I hope you have gained an understanding of the design space and design objectives. +I will now ask you to create bicycle designs. Please deliberate on a strategy for creating high-performing designs that satisfy the constraints and objectives. +I will provide you with 10 new conditions. Please create 10 unique bicycle design that satisfies the constraints and objectives. Important: You are not allowed to generate the same bike 10 times. +Each design must be unique! Please provide the designs in a 10x70 csv file. No index or headers. Only the 10x70 values. +Here are the 10 conditions. Remember! Every design must be unique. \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/openai_files/prompt3_uncond.txt b/bike_bench_internal/benchmark_models/openai_files/prompt3_uncond.txt new file mode 100644 index 0000000000000000000000000000000000000000..b47d9720f637058b460269a8344de70ed9b12905 --- /dev/null +++ b/bike_bench_internal/benchmark_models/openai_files/prompt3_uncond.txt @@ -0,0 +1,5 @@ +Having examined the existing designs and the evaluation of these designs alongside their associated conditions, I hope you have gained an understanding of the design space and design objectives. +I will now ask you to create bicycle designs. Please deliberate on a strategy for creating high-performing designs that satisfy the constraints and objectives. +I will provide you with a new condition. Please create 10 unique bicycle design that satisfies the constraints and objectives. Important: You are not allowed to generate the same bike 10 times. +Each design must be unique! Please provide the designs in a 10x70 csv file. No index or headers. Only the 10x70 values. +Here is the condition. Remember! Every design must be unique and no output except the csv itself. \ No newline at end of file diff --git a/bike_bench_internal/benchmark_models/openai_files/score_dataset.csv b/bike_bench_internal/benchmark_models/openai_files/score_dataset.csv new file mode 100644 index 0000000000000000000000000000000000000000..bacc869de1fc5e322d0df00841000f8167b360f5 --- /dev/null +++ b/bike_bench_internal/benchmark_models/openai_files/score_dataset.csv @@ -0,0 +1,11 @@ +,Usability Score - 0 to 1,Drag Force,Knee Angle Error,Hip Angle Error,Arm Angle Error,Cosine Distance to Text,Mass,Planar Compliance,Transverse Compliance,Eccentric Compliance,Planar Safety Factor,Eccentric Safety Factor,Saddle height too small,Seat post too short,Head tube lower extension too great,Head tube length too great,Certain parameters must be positive,Chain stay should be greater than wheel radius,Chain stay should be greater than BB,Seat stay should be greater than wheel radius,Down tube must reach head tube,The pedal shouldn't intersect the front wheel,The crank shouldn't hit the ground when it is in its lower position,RGB value should be less than 255,Predicted Frame Validity +0,0.6381937,24.956564,23.760986,55.881233,88.08154,0.40155026,3.6412504,1.5981448,1.0493705,1.0579363,1.1308582,0.6499475,-123.0,-117.0,-65.1,-2.0,0.0,-88.0,-359.0,-170.91113,-131.90216,-30.847107,-98.5,-510.0,-0.5 +1,0.38727024,23.659071,19.567719,56.624653,85.85516,0.39944986,2.7167168,3.0971503,1.4603455,2.1541839,1.2934663,1.111268,-162.79999,-157.20001,-95.8,-66.9,0.0,-68.0,-335.0,-130.3706,-153.11768,-53.489685,-94.5,-510.0,-0.5 +2,0.6102355,21.929268,29.035683,70.10344,95.04921,0.40687305,4.997476,0.29123527,0.920715,1.1357151,0.4111116,0.67816997,-295.0,-175.0,-103.8,-78.8,0.0,-78.0,-415.0,-117.32089,-338.03897,-284.72675,-164.5,0.0,-0.5 +3,0.57361186,23.047443,0.0,75.530624,96.39838,0.40678328,6.3496933,1.7697401,1.1470501,1.3980099,1.1257372,0.671633,-243.79999,-46.200012,-124.2,-87.899994,0.0,-87.0,-385.0,-150.82654,-144.66113,-96.83441,-125.5,-510.0,-0.5 +4,0.40103874,21.800613,0.0,86.510056,98.75029,0.40379068,2.6870232,2.3896227,2.437431,1.9756782,1.0132189,0.42796385,-195.0,-95.0,-147.9,-120.09999,0.0,-78.0,-345.0,-210.71802,-160.14853,-61.691162,-94.5,-408.0,-0.4948822 +5,0.6271787,21.670937,13.546101,69.403175,94.605484,0.40416533,3.6477923,1.3099455,0.8439656,0.9789104,1.0435773,0.5509871,-129.0,-171.0,-95.2,-64.6,0.0,-82.0,-350.0,-111.07092,-118.11017,-26.884644,-95.5,0.0,-0.4998589 +6,0.38559347,23.070572,28.915787,72.55081,101.05913,0.39921436,2.514029,0.96191776,2.1896625,1.7227731,0.7302422,0.62384546,-127.0,-163.0,-49.9,-3.0,0.0,-236.0,-520.5,-222.66992,-176.60938,-138.41193,-112.0,-102.0,-0.5 +7,0.38364825,23.285517,31.28086,77.8617,98.741104,0.4007411,1.0120034,6.3366594,4.8389273,4.0411663,1.2812575,1.087194,-163.0,-127.0,-95.1,-61.1,0.0,-68.0,-335.0,-130.24524,-145.72327,-43.487793,-94.5,-102.0,-0.49999914 +8,0.45155436,22.62312,43.77021,72.679504,89.14049,0.39918146,9.456675,0.0064820647,0.28811574,0.45228663,0.0044898987,-0.101011395,-107.599976,-182.40002,-122.4,-89.4,0.0,-43.0,-332.98642,-175.83661,-180.45917,-51.689453,-117.48642,-255.0,-0.5 +9,0.28031018,14.451009,94.02806,26.597836,85.255356,0.39893124,4.8296537,0.064380705,0.5205397,0.5060564,-0.180902,-0.5630548,-29.0,-161.0,-81.0,-50.0,0.0,-63.0,-393.0,-85.75256,-204.16864,-141.52081,-157.5,-765.0,-0.5 diff --git a/bike_bench_internal/benchmark_models/results/designs/CTGAN.pt b/bike_bench_internal/benchmark_models/results/designs/CTGAN.pt new file mode 100644 index 0000000000000000000000000000000000000000..74fda960ee420a3186c4a6a4cdc4e95e1c15b919 --- /dev/null +++ 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b/bike_bench_internal/benchmark_models/results/models/TVAE.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9bdf3070e85d642be9f23f2a562215169821535f033cf72b94e9e4b2c5d188f4 +size 2339380 diff --git a/bike_bench_internal/benchmark_models/scoring.ipynb b/bike_bench_internal/benchmark_models/scoring.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1ca5088e3abb0c57d0a15ec5442da765cb59f4d5 --- /dev/null +++ b/bike_bench_internal/benchmark_models/scoring.ipynb @@ -0,0 +1,1567 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "8d323700", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\Lyler\\mambaforge\\envs\\bike-bench-cuda\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "from bikebench.design_evaluation.design_evaluation import *\n", + "from bikebench.conditioning import conditioning\n", + "from bikebench.resource_utils import datasets_path\n", + "from bikebench.benchmark_models import benchmarking_utils\n", + "from bikebench.benchmark_models.generative_modeling_utils import sample_continuous\n", + "from bikebench.design_evaluation.score_report import ScoreReportDashboard" + ] + }, + { + "cell_type": "markdown", + "id": "ebea9c08", + "metadata": {}, + "source": [ + "Model scores are calcualted after model training, but in case the evaluation functions change, the following funcitons can be used to rescore all models in the result folders" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0c5303ad", + "metadata": {}, + "outputs": [], + "source": [ + "# data = data_loading.load_bike_bench_train()\n", + "# benchmarking_utils.rescore_unconditional(data.columns, device=\"cuda\")\n", + "# benchmarking_utils.rescore_conditional(data.columns, device=\"cuda\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "5992c008", + "metadata": {}, + "source": [ + "## Main Scores" + ] + }, + { + "cell_type": "markdown", + "id": "7180a8f3", + "metadata": {}, + "source": [ + "Score report for coniditonal generation:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ac0198c0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Constraint Satisfaction RateHypervolumeMaximum Mean Discrepancy
CTGAN0.37%0.20620.0376
DDPM_conditional38.76%0.26430.0295
DDPM_conditional-CI17.44%0.25060.0316
DDPM_guided28.70%0.26290.0346
DDPM_guided-CI14.03%0.23720.0359
Dataset1.18%0.28340.0007
GAN27.32%0.21330.0941
GAN-CI16.90%0.21900.1474
O4-mini0.54%0.20090.3140
TVAE0.00%0.00000.0358
VAE34.08%0.22870.0444
VAE-CI25.40%0.21820.0430
\n", + "
" + ], + "text/plain": [ + " Constraint Satisfaction Rate Hypervolume \\\n", + "CTGAN 0.37% 0.2062 \n", + "DDPM_conditional 38.76% 0.2643 \n", + "DDPM_conditional-CI 17.44% 0.2506 \n", + "DDPM_guided 28.70% 0.2629 \n", + "DDPM_guided-CI 14.03% 0.2372 \n", + "Dataset 1.18% 0.2834 \n", + "GAN 27.32% 0.2133 \n", + "GAN-CI 16.90% 0.2190 \n", + "O4-mini 0.54% 0.2009 \n", + "TVAE 0.00% 0.0000 \n", + "VAE 34.08% 0.2287 \n", + "VAE-CI 25.40% 0.2182 \n", + "\n", + " Maximum Mean Discrepancy \n", + "CTGAN 0.0376 \n", + "DDPM_conditional 0.0295 \n", + "DDPM_conditional-CI 0.0316 \n", + "DDPM_guided 0.0346 \n", + "DDPM_guided-CI 0.0359 \n", + "Dataset 0.0007 \n", + "GAN 0.0941 \n", + "GAN-CI 0.1474 \n", + "O4-mini 0.3140 \n", + "TVAE 0.0358 \n", + "VAE 0.0444 \n", + "VAE-CI 0.0430 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import importlib\n", + "importlib.reload(benchmarking_utils)\n", + "conditional_scores = benchmarking_utils.create_score_report_conditional()\n", + "#turn constraint satisfaction rate into a percent with two decimal places\n", + "conditional_scores['Constraint Satisfaction Rate'] = conditional_scores['Constraint Satisfaction Rate'] * 100\n", + "conditional_scores['Constraint Satisfaction Rate'] = conditional_scores['Constraint Satisfaction Rate'].apply(lambda x: \"{:.2f}%\".format(x))\n", + "\n", + "#four decimals for Hypervolume and Maximum Mean Discrepancy\n", + "conditional_scores['Hypervolume'] = conditional_scores['Hypervolume'].apply(lambda x: \"{:.4f}\".format(x))\n", + "conditional_scores['Maximum Mean Discrepancy'] = conditional_scores['Maximum Mean Discrepancy'].apply(lambda x: \"{:.4f}\".format(x))\n", + "\n", + "\n", + "savedir = os.path.join(\"results\", \"cond_overall_scores.csv\")\n", + "conditional_scores.to_csv(savedir, index=False)\n", + "display(conditional_scores)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9222b4dc", + "metadata": {}, + "source": [ + "Score report for unconditional generation:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b61de423", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Constraint Satisfaction RateHypervolumeMaximum Mean Discrepancy
Agg-LS93.82%0.28170.5668
Agg-LS-CI6.00%0.07590.5462
CTGAN0.21%0.06300.0283
DDPM_conditional33.10%0.16720.0403
DDPM_conditional-CI14.84%0.16040.0417
DDPM_guided30.46%0.16970.0353
DDPM_guided-CI16.22%0.16560.0344
Dataset1.00%0.15600.0007
GAN33.28%0.13240.1741
GAN-CI15.68%0.10660.3056
NSGA2100.00%0.26240.4813
NSGA2-CI17.20%0.13410.4571
O4-mini0.20%0.00630.4525
TVAE0.04%0.02570.0397
VAE52.19%0.13350.0580
VAE-CI31.51%0.13800.0490
\n", + "
" + ], + "text/plain": [ + " Constraint Satisfaction Rate Hypervolume \\\n", + "Agg-LS 93.82% 0.2817 \n", + "Agg-LS-CI 6.00% 0.0759 \n", + "CTGAN 0.21% 0.0630 \n", + "DDPM_conditional 33.10% 0.1672 \n", + "DDPM_conditional-CI 14.84% 0.1604 \n", + "DDPM_guided 30.46% 0.1697 \n", + "DDPM_guided-CI 16.22% 0.1656 \n", + "Dataset 1.00% 0.1560 \n", + "GAN 33.28% 0.1324 \n", + "GAN-CI 15.68% 0.1066 \n", + "NSGA2 100.00% 0.2624 \n", + "NSGA2-CI 17.20% 0.1341 \n", + "O4-mini 0.20% 0.0063 \n", + "TVAE 0.04% 0.0257 \n", + "VAE 52.19% 0.1335 \n", + "VAE-CI 31.51% 0.1380 \n", + "\n", + " Maximum Mean Discrepancy \n", + "Agg-LS 0.5668 \n", + "Agg-LS-CI 0.5462 \n", + "CTGAN 0.0283 \n", + "DDPM_conditional 0.0403 \n", + "DDPM_conditional-CI 0.0417 \n", + "DDPM_guided 0.0353 \n", + "DDPM_guided-CI 0.0344 \n", + "Dataset 0.0007 \n", + "GAN 0.1741 \n", + "GAN-CI 0.3056 \n", + "NSGA2 0.4813 \n", + "NSGA2-CI 0.4571 \n", + "O4-mini 0.4525 \n", + "TVAE 0.0397 \n", + "VAE 0.0580 \n", + "VAE-CI 0.0490 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.set_option('display.max_rows', 100)\n", + "unconditional_scores = benchmarking_utils.create_score_report_unconditional()\n", + "#turn constraint satisfaction rate into a percent with two decimal places\n", + "unconditional_scores['Constraint Satisfaction Rate'] = unconditional_scores['Constraint Satisfaction Rate'] * 100\n", + "unconditional_scores['Constraint Satisfaction Rate'] = unconditional_scores['Constraint Satisfaction Rate'].apply(lambda x: \"{:.2f}%\".format(x))\n", + "\n", + "#four decimals for Hypervolume and Maximum Mean Discrepancy\n", + "unconditional_scores['Hypervolume'] = unconditional_scores['Hypervolume'].apply(lambda x: \"{:.4f}\".format(x))\n", + "unconditional_scores['Maximum Mean Discrepancy'] = unconditional_scores['Maximum Mean Discrepancy'].apply(lambda x: \"{:.4f}\".format(x))\n", + "\n", + "savedir = os.path.join(\"results\", \"uncond_overall_scores.csv\")\n", + "unconditional_scores.to_csv(savedir, index=False)\n", + "display(unconditional_scores)\n" + ] + }, + { + "cell_type": "markdown", + "id": "afa97eb0", + "metadata": {}, + "source": [ + "## Score Scatterplots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f64d1b03", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from matplotlib.patches import Rectangle\n", + "from matplotlib.lines import Line2D\n", + "\n", + "def plot_scores(\n", + " scores_subset,\n", + " zoom_hv=(0.13, 0.175),\n", + " zoom_mmd=(0.033, 0.063),\n", + " cmap_name='cividis_r',\n", + " marker_map=None,\n", + " show_ci=\"Both\",\n", + " text_offset=8,\n", + " axis_fontsize=14,\n", + " legend_fontsize=13,\n", + " cbar_fontsize=14,\n", + " legend_loc = 'center right'\n", + "):\n", + " \"\"\"\n", + " Scatter + zoom plot of model scores with optional CI models and custom annotation locations.\n", + "\n", + " Parameters\n", + " ----------\n", + " scores_subset : pd.DataFrame\n", + " Indexed by model name, with columns:\n", + " - 'Constraint Satisfaction Rate' (e.g. \"93.82%\")\n", + " - 'Hypervolume' (float)\n", + " - 'Maximum Mean Discrepancy' (float)\n", + " - 'class' (str)\n", + " - 'annotation_location' (str): one of 'top', 'left'\n", + " zoom_hv : tuple(float, float)\n", + " zoom_mmd : tuple(float, float)\n", + " cmap_name : str\n", + " marker_map : dict or None\n", + " show_ci : bool\n", + " text_offset : float\n", + " axis_fontsize, legend_fontsize, cbar_fontsize : int\n", + " \"\"\"\n", + " # 1) Prepare DataFrame\n", + " df = scores_subset.copy()\n", + " df['Hypervolume'] = df['Hypervolume'].astype(float)\n", + " df['Maximum Mean Discrepancy'] = df['Maximum Mean Discrepancy'].astype(float)\n", + " df['CSR'] = df['Constraint Satisfaction Rate'].str.rstrip('%').astype(float)\n", + "\n", + " # 2) Optionally drop CI rows\n", + " if show_ci == \"CI\":\n", + " # Find base models that have corresponding '-CI' versions\n", + " ci_suffix = '-CI'\n", + " base_models_with_ci = {\n", + " nm[:-len(ci_suffix)]\n", + " for nm in df.index\n", + " if nm.endswith(ci_suffix) and (nm[:-len(ci_suffix)] in df.index)\n", + " }\n", + "\n", + " # Drop those base models (but keep CI versions)\n", + " df = df[~df.index.isin(base_models_with_ci)]\n", + " \n", + " elif show_ci == \"Base\":\n", + " df = df[~df.index.str.endswith('-CI')]\n", + "\n", + " # 3) Marker mapping\n", + " default_map = {'Opt':'o','Data':'s','LLM':'D','TGM':'^','OAGM':'v'}\n", + " marker_map = marker_map or default_map\n", + " df['marker'] = df['class'].map(marker_map)\n", + "\n", + "\n", + " # 4) Identify CI connectors\n", + " ci_pairs = []\n", + " if show_ci == \"Both\":\n", + " ci_pairs = [\n", + " (nm[:-3], nm)\n", + " for nm in df.index\n", + " if nm.endswith('-CI') and (nm[:-3] in df.index)\n", + " ]\n", + "\n", + " # 5) Plot settings\n", + " ms = 100\n", + " cmap = plt.get_cmap(cmap_name)\n", + " inside = (\n", + " (df['Hypervolume'] >= zoom_hv[0]) & (df['Hypervolume'] <= zoom_hv[1]) &\n", + " (df['Maximum Mean Discrepancy'] >= zoom_mmd[0]) & (df['Maximum Mean Discrepancy'] <= zoom_mmd[1])\n", + " )\n", + "\n", + " # Helper to compute annotation parameters\n", + " def get_annot_params(loc):\n", + " if loc == 'left':\n", + " return {'xytext':(-text_offset, 0), 'ha':'right', 'va':'center'}\n", + " elif loc == 'right':\n", + " return {'xytext':(text_offset, 0), 'ha':'left', 'va':'center'}\n", + " # default to top\n", + " return {'xytext':(0, text_offset), 'ha':'center', 'va':'bottom'}\n", + "\n", + " # 6) Create figure\n", + " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6), dpi=300)\n", + "\n", + " # Main scatter + connectors\n", + " for cls, grp in df.groupby('class'):\n", + " ax1.scatter(\n", + " grp['Hypervolume'], grp['Maximum Mean Discrepancy'],\n", + " c=grp['CSR'], cmap=cmap,\n", + " vmin=df['CSR'].min(), vmax=df['CSR'].max(),\n", + " marker=marker_map.get(cls, 'o'), s=ms, alpha=0.8,\n", + " edgecolor='k', linewidth=0.5\n", + " )\n", + " for base, ci in ci_pairs:\n", + " ax1.plot(\n", + " [df.at[base, 'Hypervolume'], df.at[ci, 'Hypervolume']],\n", + " [df.at[base, 'Maximum Mean Discrepancy'], df.at[ci, 'Maximum Mean Discrepancy']],\n", + " linestyle=':', color='gray'\n", + " )\n", + "\n", + " # Highlight rectangle + label\n", + " rect = Rectangle(\n", + " (zoom_hv[0], zoom_mmd[0]),\n", + " zoom_hv[1] - zoom_hv[0], zoom_mmd[1] - zoom_mmd[0],\n", + " edgecolor='gray', facecolor='none', linestyle='--', linewidth=1.2\n", + " )\n", + " ax1.add_patch(rect)\n", + " x_center = (zoom_hv[0] + zoom_hv[1]) / 2\n", + " ax1.text(\n", + " x_center, zoom_mmd[1] + 0.005, \"Highlight:\",\n", + " ha='center', va='bottom', fontsize=axis_fontsize, fontweight='bold'\n", + " )\n", + "\n", + " # Annotate outside-zoom points with custom locations\n", + " for nm, row in df[~inside].iterrows():\n", + " params = get_annot_params(row.get('annotation_location', 'top'))\n", + " ax1.annotate(\n", + " f\"{nm}\\n({row['CSR']:.1f}%)\",\n", + " (row['Hypervolume'], row['Maximum Mean Discrepancy']),\n", + " textcoords='offset points',\n", + " **params,\n", + " fontsize=axis_fontsize - 1\n", + " )\n", + "\n", + " ax1.set_xlabel(r'Optimality (Hypervolume) $\\uparrow$', fontsize=axis_fontsize)\n", + " ax1.set_ylabel(r'Distributional Similarity (MMD) $\\downarrow$', fontsize=axis_fontsize)\n", + " ax1.tick_params(axis='both', labelsize=axis_fontsize - 1)\n", + " for sp in ['top','right']:\n", + " ax1.spines[sp].set_visible(False)\n", + "\n", + " # Zoom subplot\n", + " for cls, grp in df.groupby('class'):\n", + " ax2.scatter(\n", + " grp['Hypervolume'], grp['Maximum Mean Discrepancy'],\n", + " c=grp['CSR'], cmap=cmap,\n", + " vmin=df['CSR'].min(), vmax=df['CSR'].max(),\n", + " marker=marker_map.get(cls, 'o'), s=ms, alpha=0.8,\n", + " edgecolor='k', linewidth=0.5\n", + " )\n", + " for base, ci in ci_pairs:\n", + " ax2.plot(\n", + " [df.at[base, 'Hypervolume'], df.at[ci, 'Hypervolume']],\n", + " [df.at[base, 'Maximum Mean Discrepancy'], df.at[ci, 'Maximum Mean Discrepancy']],\n", + " linestyle=':', color='gray'\n", + " )\n", + " for nm, row in df[inside].iterrows():\n", + " params = get_annot_params(row.get('annotation_location', 'top'))\n", + " ax2.annotate(\n", + " f\"{nm}\\n({row['CSR']:.1f}%)\",\n", + " (row['Hypervolume'], row['Maximum Mean Discrepancy']),\n", + " textcoords='offset points',\n", + " **params,\n", + " fontsize=axis_fontsize - 1\n", + " )\n", + "\n", + " ax2.set_xlim(*zoom_hv)\n", + " ax2.set_ylim(*zoom_mmd)\n", + " ax2.set_xlabel(r'Optimality (Hypervolume) $\\uparrow$', fontsize=axis_fontsize)\n", + " ax2.set_ylabel(r'Distributional Similarity (MMD) $\\downarrow$', fontsize=axis_fontsize)\n", + " ax2.tick_params(axis='both', labelsize=axis_fontsize - 1)\n", + " for sp in ['top','right']:\n", + " ax2.spines[sp].set_visible(False)\n", + "\n", + " # Colorbar\n", + " sm = plt.cm.ScalarMappable(\n", + " cmap=cmap, norm=plt.Normalize(vmin=df['CSR'].min(), vmax=df['CSR'].max())\n", + " )\n", + " sm.set_array([])\n", + " cbar = fig.colorbar(sm, ax=ax2, pad=0.02)\n", + " cbar.set_label(r'Constraint Satisfaction Rate (%) $\\uparrow$', fontsize=cbar_fontsize)\n", + " cbar.ax.tick_params(labelsize=cbar_fontsize - 1)\n", + "\n", + "\n", + " classes = df['class'].unique()\n", + " if len(classes)==5:\n", + " classes = classes[[0,1,4,2,3]]\n", + " else:\n", + " classes = classes[[0,3,1,2]]\n", + " legend_elements = [\n", + " Line2D([0], [0],\n", + " marker=marker_map.get(cls, 'o'),\n", + " color='black', linestyle='None',\n", + " markersize=8, label=cls)\n", + " for cls in classes\n", + " ]\n", + " ax1.legend(\n", + " handles=legend_elements,\n", + " title='Algorithm:',\n", + " loc=legend_loc,\n", + " fontsize=legend_fontsize,\n", + " title_fontsize=legend_fontsize,\n", + " frameon=False\n", + " )\n", + "\n", + " # ax1.set_xlim(0, ax1.get_xlim()[1] * 1.05)\n", + " # ax1.plot(\n", + " # 0.98, 0.02, # 95% across, 5% up in axes coords\n", + " # marker='*',\n", + " # color='black',\n", + " # markersize=10,\n", + " # transform=ax1.transAxes,\n", + " # zorder=5 # on top\n", + " # )\n", + " plt.tight_layout()\n", + "\n", + " \n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77395010", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "rename_dict = {\n", + "\"DDPM_conditional\": \"C-DDPM\",\n", + "\"DDPM_guided\": \"G-DDPM\",\n", + "\"DDPM_conditional-CI\": \"C-DDPM-CI\",\n", + "\"DDPM_guided-CI\": \"G-DDPM-CI\",\n", + "\"Agg-LS\": \"GD-AGG\",\n", + "\"Agg-LS-CI\": \"GD-AGG-CI\",\n", + "\"EPO\": \"GD-EPO\",\n", + "\"EPO-CI\": \"GD-EPO-CI\",\n", + "}\n", + "\n", + "#rename indices of classes\n", + "scores = conditional_scores.rename(index=rename_dict)\n", + "\n", + "classes = [\"TGM\", \"OAGM\", \"OAGM\", \"OAGM\", \"OAGM\", \"Data\", \"OAGM\", \"OAGM\", \"LLM\", \"TGM\", \"OAGM\", \"OAGM\"]\n", + "\n", + "\n", + "classes = [namedict.get(cls, cls) for cls in classes]\n", + "\n", + "\n", + "\n", + "scores[\"class\"] = classes\n", + "\n", + "\n", + "\n", + "scores[\"annotation_location\"] = \"top\"\n", + "scores.loc[scores.index == \"GD-EPO-CI\", \"annotation_location\"] = \"left\"\n", + "scores.loc[scores.index == \"G-DDPM-CI\", \"annotation_location\"] = \"left\"\n", + "scores.loc[scores.index == \"Dataset\", \"annotation_location\"] = \"left\"\n", + "\n", + "\n", + "plot_scores(scores, show_ci=\"CI\", zoom_hv=(0.2, 0.28), zoom_mmd=(0.028, 0.05), legend_loc = None) # to drop CI models\n", + "plot_scores(scores, show_ci=\"Base\", zoom_hv=(0.2, 0.28), zoom_mmd=(0.028, 0.05), legend_loc = None) # to include them with dotted lines\n", + "plot_scores(scores, show_ci=\"Both\", zoom_hv=(0.2, 0.28), zoom_mmd=(0.028, 0.05), legend_loc = None) # to include them with dotted lines" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "4b4a978f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Constraint Satisfaction RateHypervolumeMaximum Mean Discrepancyclassannotation_location
GD-AGG93.82%0.28170.5668Optimizationtop
GD-AGG-CI6.00%0.07590.5462Optimizationtop
CTGAN0.21%0.06300.0283GenAItop
C-DDPM33.10%0.16720.0403GenAI+Opt.top
C-DDPM-CI14.84%0.16040.0417GenAI+Opt.top
G-DDPM30.46%0.16970.0353GenAI+Opt.top
G-DDPM-CI16.22%0.16560.0344GenAI+Opt.left
GAN33.28%0.13240.1741GenAI+Opt.top
GAN-CI15.68%0.10660.3056GenAI+Opt.top
NSGA2100.00%0.26240.4813Optimizationtop
NSGA2-CI17.20%0.13410.4571Optimizationtop
O4-mini0.20%0.00630.4525LLMright
TVAE0.04%0.02570.0397GenAItop
VAE52.19%0.13350.0580GenAI+Opt.top
VAE-CI31.51%0.13800.0490GenAI+Opt.top
\n", + "
" + ], + "text/plain": [ + " Constraint Satisfaction Rate Hypervolume Maximum Mean Discrepancy \\\n", + "GD-AGG 93.82% 0.2817 0.5668 \n", + "GD-AGG-CI 6.00% 0.0759 0.5462 \n", + "CTGAN 0.21% 0.0630 0.0283 \n", + "C-DDPM 33.10% 0.1672 0.0403 \n", + "C-DDPM-CI 14.84% 0.1604 0.0417 \n", + "G-DDPM 30.46% 0.1697 0.0353 \n", + "G-DDPM-CI 16.22% 0.1656 0.0344 \n", + "GAN 33.28% 0.1324 0.1741 \n", + "GAN-CI 15.68% 0.1066 0.3056 \n", + "NSGA2 100.00% 0.2624 0.4813 \n", + "NSGA2-CI 17.20% 0.1341 0.4571 \n", + "O4-mini 0.20% 0.0063 0.4525 \n", + "TVAE 0.04% 0.0257 0.0397 \n", + "VAE 52.19% 0.1335 0.0580 \n", + "VAE-CI 31.51% 0.1380 0.0490 \n", + "\n", + " class annotation_location \n", + "GD-AGG Optimization top \n", + "GD-AGG-CI Optimization top \n", + "CTGAN GenAI top \n", + "C-DDPM GenAI+Opt. top \n", + "C-DDPM-CI GenAI+Opt. top \n", + "G-DDPM GenAI+Opt. top \n", + "G-DDPM-CI GenAI+Opt. left \n", + "GAN GenAI+Opt. top \n", + "GAN-CI GenAI+Opt. top \n", + "NSGA2 Optimization top \n", + "NSGA2-CI Optimization top \n", + "O4-mini LLM right \n", + "TVAE GenAI top \n", + "VAE GenAI+Opt. top \n", + "VAE-CI GenAI+Opt. top " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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9VlJvBFqemgCK5tZbb02LnXDCCdG/f/+c1rnkkkvSYnPmzIkpU6bknVsuVq9eHXfddVda/Pzzz4927dplvU67du3ivPPOS4vfddddsXr16qzWuP3222PTpk0NYj169IgxY8ZknUdExJgxY6JHjx4NYps2bYrbb789p3WgkuhJDRWjJwH5qZZ+VE7skSB/ehJQTqqlJ7Vq1aoo63z5y1/OGH/kkUeyXsM+CfKnJzVUjJ4EADS/atnTVKOjjjoqamrSP6Y0f/78rI6vtHuGarF8FVqLlUg9Nr/nnnsufvzjHzeI/fznP49OnToV9TyV1hsj1GMpNFc9Vhq1WL30xobUI7lQi03j4osvjiRJGsS+8pWvxMEHH9xsOeiNDanH0tZjpVGL5eedd96JP/7xj2nx0aNHx3bbbZf1OnpjQ+oxP8WqR4ByZxABFMmSJUviscceS4ufcsopOa+13377xeDBg9Pid999d1655erBBx+MlStXNoi1bt06Ro8enfNaX/rSl6J169YNYitXrowHH3wwq+PvueeetNgXv/jFtDW3pU2bNhnzb67XFJqbnpRZoT0JyF019aNyYo8E+dGTgHKiJ6Xbddddo0+fPmnxhQsXZr2GfRLkR09KV4yeBAA0L3ua8ta2bdvo1q1bWnz58uVZHV9J9wzVYnkrtBYrjXpsfhs2bIgxY8Y0GJZ53HHHxWc/+9min6uSemOEeiyF5qzHSqIWq5veqB7Jj1psGi+++GI8/PDDDWKtW7eOyy+/vFnz0BvVY0T51GMlUYvl6X//939jzZo1afEzzjgjp3X0RvVYDMWqR4ByZxABFMnf//73qK+vbxBr27ZtHHHEEXmtd8wxx6TFJk6cmNdauZowYUJa7JBDDokuXbrkvFaXLl3i4x//eFo8m2tZuHBhTJs2LS1+7LHH5pxHRObXdNq0aT44SlXSkzIrpCcB+ammflQu7JEgf3oSUE70pMx22mmntNiSJUuyOtY+CfKnJ2VWSE8CAJqfPU35W7duXVos29/GXEn3DNVi+SukFiuNemx+3//+9+Pll1/e8uftt98+brjhhiY5VyX1xgj1WArNWY+VRC1WN71RPZIftdg0fvvb36bFjjvuuOjevXuz5qE3qseI8qnHSqIWy9Mtt9ySFhswYEAceOCBOa2jN6rHYihWPQKUO4MIoEimTJmSFvvYxz4Wbdu2zWu94cOHp8VeeeWVZvmQY6ZrGTZsWN7rZbqWyZMn55VHmzZtMm7Qs3HwwQdHmzZtsjoPVDo9qXH59iQgP9XUj8qFPRLkT08CyomelNnatWvTYttvv31Wx9onQf70pMwK6UkAQPOzpylvCxcujBUrVqTF999//6yOr6R7hmqxvBVai5VGPTavGTNmxNVXX90g9r3vfS923XXXJjlfJfXGCPXY3Jq7HiuJWqxueuPwtJh6JBtqsWncc889abH/+q//avY89MbhaTH1+L5S1GMlUYvlZ+rUqfHCCy+kxfP57fN64/C0mHrMTTHrEaDcGUQARTJ16tS02AEHHJD3eo0d+/zzz+e9ZjZWrVoVs2fPzjqfbGQ6dvbs2bFq1aqtHpfpNd13330zfgA8G3V1ddG/f/+0eFO/plAKelLj8u1JQH6qpR+VE3skyJ+eBJQTPSndxo0b45VXXkmL9+jRI6vj7ZMgf3pSukJ7EgDQ/Oxpytsdd9yRFttpp51i6NCh2zy20u4ZqsXyVkgtViL12Hzq6+vj9NNPj/Xr12+JHXTQQXHWWWc1yfkqrTdGqMfm1Nz1WGnUYvXSG9Uj+VOLxTdz5sxYsGBBg1gqlYpPfOITzZqH3qgeI8qnHiuNWiw/v/71r9Ni7dq1iy996Us5raM3qsdiKFY9AlQCgwigSGbOnJkW++hHP5r3eh07doydd945LT5jxoy818zG7Nmzo76+Pi1eyLX069cvLVZfX59x4/5BxX5NG8ulqV9TKAU9qXH59qStufnmm+O0006LAQMGRI8ePaJNmzbRqVOn2H333WPIkCExduzYuO222+K1117L+xxQqaqlH5UTeyTIn57UvOyRYOv0pHSPPPJIrF69Oi2e7Y1T+yTIn56UrtCetDX2SQDQNOxpytf06dPjqquuSouff/750bp1620eXyn3DDdTi+Wr0Frc1tqXXXZZfOpTn4o99tgjOnbsGG3bto0ePXpE//7946ijjoorr7wyHnnkkVi3bl1B58qFemw+1113XTz99NNb/ty6deu4+eabo6amaT6iWWm9MUI9NqfmrsetKcf+qBabV3O+F6Y3qsdtKZf3ZvXGliHTb8Hu169fdO7cecufN2zYEH/4wx/iv/7rv6J///7RsWPHaNOmTey0006x7777xsiRI+P666+Pf/3rX3nnoTeqx4jyqcet0RvZltWrV8fvfve7tPjnPve56NSpU05r6Y3qsVDFrMetKcfeCLRMtaVOAKrBW2+9Fe+9915avE+fPgWtu/vuu8fixYsbxObOnVvQmtvy6quvpsVSqVT07t077zX79OkTqVQqkiRpEJ87d24MHjw4p1yK8Zp+WFO/ptDc9KSty7cnbc2ZZ56ZFluxYkWsWLEi5s6dG88991zccsstkUql4uijj47zzz8/RowYkde5oJJUUz8qJ/ZIkB89qfnZI0Hj9KTMrr/++rRY165d49BDD83qePskyI+elFmhPWlr7JMAoPjsacrXH//4x/jKV74SK1eubBA/5JBD4rzzzstqjUq5ZxihFstZMWpxa/7whz9kjC9evDgWL14cM2bMiAkTJkRExE477RRf+9rX4qtf/Wp07dq14HM3Rj02n1dffTUuvfTSBrELLrgg9t133yY954eVa2+MUI/NqRT1uDXl1h/VYvNrzvfC9Mb3qcfGlct7s3pjy/DPf/4zLfbBYce//e1v4+KLL077LfUREUuWLIklS5bE9OnT47777ouIiOOPPz6++93vxn777ZdTHnrj+9RjedTj1uiNbMs999wTK1asSIuPHTs257X0xvepx/wVsx63ptx6I9ByNf94U6hCmf7DFREZp0PlokePHlmfq1gyrd+lS5eCJr+3adMmunTpktW5tvV8Jb6m0Nz0pK3LtycVQ5Ik8de//jUOP/zw+OxnPxvLly9v8nNCKVVTPyon9kiQHz2pfNkj0RLpSekef/zxeOCBB9LiX/jCF6JVq1ZZrWGfBPnRk9IVoycVg30SAGTPnqb5rVixIt56660Gj4ULF8asWbPir3/9a1x55ZWx3377xec+97l46623Ghx76KGHxl/+8peC/r9XrvcM1WLza85aLJY333wzLr300thrr71i/PjxTXYe9dg8kiSJsWPHxurVq7fE9tprr7jkkkua9LyV1Bu3toZ6LK5S1WOxNEd/VIvlqxjvhemN71OPhSun92b1xso1a9astNiuu+4aa9asic9+9rNx6qmn5vR63HfffTFw4MD4wQ9+kFMeeuP71GN51GOx6I0t069//eu02F577RWf/OQnc15Lb3yfesxfMeuxWJrrPUegZTKIAIpg2bJlGeOFThDKtAlt7FzFkmn9YkxCyvVaNm3alHE6VFO8pu+8805s2rSpoHWhnOhJ21aKa/mwP/3pTzFw4MCMU0ahWlRTPyoX9kiQPz2pMtgj0VLoSQ2tWrUqxowZkxZv165dXHjhhVmtYZ8E+dOTGipGT2oK9kkAsHX2NM3v8MMPjx133LHBY5dddom99947/uM//iPGjRsX06ZNa3BMp06d4nvf+15MmjQpdthhh6zPVUn3DNVi82vOWiy2t99+O0444YT45je/2STrq8fmcfPNN8ekSZMaxG688cZo27Ztk563knrj1tZQj8VVqnostqbsj2qxMuT7Xpje+D71WFzl8t6s3lh5Mv0g54477hjHHHNM/OlPf8przfr6+rjoootizJgxab9xuzF64/vUY3nUY7HpjS3H7NmzY8qUKWnxfH/7vN74PvWYn2LXY7E19XuOQMtUW+oEoBq88847GeMdO3YsaN3tt98+63MVS6b1C72OiNyvZcWKFRn/Q9oUr2mSJPHuu+9G586dC1obyoWetG3Fupa99947PvWpT8X+++8fe++9d3Tr1i06duwYq1evjrfffjumTZsWU6ZMiT/+8Y8Z13/jjTfi6KOPjieffDJ22223PK4Eyls19aNyYY8E+dOTmo89EmybntTQl7/85ZgzZ05a/P/9v/8XPXv2zGoN+yTIn57UUDF6UmPskwCg6djTlLe99torvv3tb8cXvvCFjK/ptpT7PcNsjleL5aHQWsykQ4cOMWLEiDjooINi3333jd69e0fHjh2jTZs28fbbb8eiRYviySefjIceeij+8Y9/ZFzjuuuui+233z6uvPLKouS0mXpsegsWLIgLLrigQWzMmDExfPjwJj93JfXGra2hHounlPWYSbn2R7XYfErxXpje+D71mK5c3pvVG1tWLb755ptpsV/84hfx6quvNoj17NkzTjnllPjUpz4Vu+yyS9TW1saiRYviscceizvuuCNtsFpExK233hr9+vVL+7c/E73xfeqxPOoxE72xZdVivjL99vnWrVvHKaecktd6euP71GN+il2PmZRrbwRaLoMIoAjWrVuXMd6mTZuC1q2rq8v6XMWSaf1CryMi92tpztd0W7lApdGTtq2Qa+nZs2ecdtppceqpp0avXr0a/brdd989DjzwwDj11FPjuuuuixtuuCHGjRuXdp4FCxbEMcccE1OnTo3aWlszqks19aNyYY8E+dOTmpY9EuRGT/o/P/rRj+J///d/0+IHHnhgXHTRRVmvY58E+dOT/k+xetIH2ScBQPOwpylvr7zySlxxxRXxr3/9K84555zYddddczq+HO8ZNkYtlrdCa3Gz2traOOaYY+LLX/5yHHnkkY1+f3v06BH9+/ePI444Ii699NJ49tln4+tf/3o89dRTaV/73e9+N/bdd98YNWpUXjlloh6b3le/+tVYsWLFlj/vtNNO8cMf/rBZzl1JvXFra6jH4illPW5WCf1RLTatUr8Xpje+Tz2+r9T1uJnemN25qtHq1avTYh/+oe9vfvObcfXVV0e7du0axHv27Bkf+9jH4vzzz4+f/vSn8e1vfzvq6+sbfM3FF18cw4YNi4997GNbzUNvfJ96LI963ExvzO5cvG/Dhg1x++23p8WPO+646N69e15r6o3vU4+5a4p63KwSeiPQctWUOgGoBhs2bMgYL/RDgK1bt876XMWSaf1ifJgx12tpztd0W7lApdGTtq2Qa3n88cdj3LhxW7058WEdOnSICy+8MJ5++unYeeed055/+eWXM07Gg0pXTf2oXNgjQf70pKZljwS50ZPed++992b8rQidOnWKu+66K6fXwz4J8qcnva+YPemD7JMAoHnY05S/+fPnx7XXXht77rln/OhHP0r7wPrWlOM9w8aoxfJXSC1udtJJJ8UDDzwQxxxzTE4f+B4yZEhMnjw5zjnnnIzPX3DBBUX9sLd6bFp33nlnPPDAAw1iP/3pT6Nz587Ncv5K6o1bW0M9Fkep63GzSuiParFplfq9ML3xferxfaWux830xuzOVY229f0ZN25c/PjHP077oe8PatWqVZx33nlx2223pT23cePGuOyyy7aZh974PvVYHvW4md6Y3bl433333RdLlixJi48dOzbvNfXG96nH3DVFPW5WCb0RaLkMIoAiqKnJ/Fep0A3Y+vXrsz5XsWRavxgbyVyvpTlf023lApVGT9q2UlxLRMSAAQPiL3/5S2y33XZpz11xxRX+g0fVqaZ+VC7skSB/elL5skeiJdKTIh577LH44he/mPZh/9ra2vjd734Xu+++e07r2SdB/vSk4vekYrFPAoDs2dM0v+eeey6SJGnweO+992L+/Pnx8MMPx/e///3Yb7/90o5bu3ZtfOtb34rTTjst6x8Ar6R7hmqx+TVnLRZDbW1t/PSnP40vfvGLac+9/vrr8atf/apo51KPTWfp0qVx7rnnNoh95jOfiS984QvNlkMl9catraEeC1cO9VgMzdUf1WL5KsZ7YXrj+9Rj4crlvVm9sbJt7VoPPfTQnH5o++STT45TTjklLT5x4sR4/vnnc85Db9z6uapRudRjMeiNLU+mIUA9e/aMI488Mu819cb3qcfcNUU9FkNzvucItEz+dYAiaGzS0Nq1awtaN9PxuUw1ykem9Qu9jsbW2Nq1NOdruq1coNLoSdtWimvZbPDgwfHtb387Lb548eJ47LHHmiUHaC7V1I/KhT0S5E9PKm/2SLQ0Lb0nPfvss3Hsscem5VtTUxO33357HHXUUTmvaZ8E+dOTit+Tisk+CQCy09L3NOWiQ4cOscsuu8SIESPioosuipdffjkefPDB6N27d9rX3n777XHxxRdntW4l3TNUi+WhqWqxWFKpVNxwww3RpUuXtOfuuuuuop1HPTadr3/96/HWW29t+XOHDh3il7/8ZbPmUEm9cWtrqMfClUM9Fktz9Ee1WN4KfS9Mb2z8ePWYu3J5b1ZvrFxbu9bvfe97Of+g51VXXRWtWrVKi999990556E3bv1c1ahc6rFY9MaW44033oi//e1vafHTTz+9oB+Y1xsbP149Nq6p6rFYmus9R6BlKn2XgyqQaeJlRMSaNWsKWjfT8e3bty9ozW3JdC2FXkdja2ztWprzNd1WLlBp9KRtK8W1fNC3vvWtaNeuXVr8wQcfbLYcoDlUUz8qF/ZIkD89qfzZI9GStOSe9PLLL8dRRx0V7733XoN4KpWKm266KUaPHp3XuvZJkD89qfg9qdjskwBg21rynqbcHXXUUfHcc8/F/vvvn/bctddeG1OnTt3mGpV0z1Atlq9i1GIxderUKb72ta+lxZ9++ul4++23i3IO9dg0Hnjggfj973/fIHbVVVdFz549mzWPSuqNEeqxqZRLPRZTU/dHtVj+CnkvTG9s/Hj1mJ9yeW9Wb6xMjb2ue+21V3ziE5/Ieb1dd901jj766LT4448/nnMeeuP71GPz12Mx6Y0tw6233hr19fUNYjU1NXH66acXtK7e2Pjx6rFxTVWPxdQc7zkCLZNBBFAEmaYFRUSsWrWqoHUzHd+1a9eC1tyWTNdS6HU0tsbWrmW77baLurq6oueS6fi6urpGN+ZQifSkbSvFtXzQdtttF4cddlha/Kmnnmq2HKA5VFM/Khf2SJA/Pan82SPRkrTUnjRz5sw44ogjMt7Y+vnPfx5jxozJe237JMifnlT8nlRs9kkAsG0tdU9TKbp27Rr3339/2odY6+vr4wc/+ME2j6+ke4ZqsbwVWovFdswxx6TF6uvr49lnny3K+uqx+FasWBFf/epXG8SGDBkS55xzTrPnUkm9MUI9NoVyqsdia8r+qBbLXyHvhemNjR+vHvNTTu/N6o2Vp7FrHTZsWN5rZjr2ueeei/Xr1zd6jN7Y+PHqsfnrsdj0xupWX18fv/nNb9Lin/70p2O33XYraG29sfHj1WNmTVmPxdbU7zkCLZNBBFAE3bt3zxhfsGBBQetmOr6xcxVLpvUXL16cNrUpF/X19bF48eKszvVBO+64Y1qsEl9TaG560tbl25OK7cADD0yLvfnmm82aAzS1aupH5cQeCfKjJ1UGeyRaipbYk1555ZU4/PDDY+nSpWnP/ehHP4qzzz674HPYJ0F+9KSGitWTis0+CQC2riXuaSpNr1694itf+Upa/IEHHoh169Zt9dhKumeoFstfIbVYbIMHD45UKpUWL9ZeXz0W37e+9a0G119bWxs333xz1NQ0/8cwK6k3bm0N9Zi/cqrHYmvK/qgWK0O+74XpjY0frx7zVy7vzeqNlWennXbKGB84cGDea2Y6dsOGDbFs2bJGj9EbGz9ePTZ/PRab3ljdJk6cGG+88UZa/Iwzzih4bb2x8ePVY2ZNWY/F1tTvOQItU+W/4whloGfPnhnfwM+0ychFpuN79+5d0Jrbkmn9DRs2xKJFi/Jec+HChbFx48aszrWt5yvxNYXmpidtXb49qdgy/YDMkiVLmjUHaGrV1I/KiT0S5EdPqgz2SLQULa0n/fvf/44RI0Zk/L/c97///TjvvPOKch77JMiPnvR/itmTis0+CQC2rqXtaSrVf/7nf6bF1q5dGy+++OJWj6uke4ZqsTLkW4vF1qpVq4y/0a5Ye331WFzvvfde/PrXv24QGzt2bOyyyy7x1ltvZf1YsWJFxvWXL1+e9rXvvfdeo/lUUm+MUI/FVm71WGxN2R/VYmXI970wvbHx49Vj/srlvVm9sfL06dMnY7yQ3zTdrVu3jPGt/eC33tj48eqx+eux2PTG6vbh//NEvD9U49hjjy14bb2x8ePVY2ZNWY/F1tTvOQItk0EEUAStW7eOnj17psUL2dQlSRLz589Pi++xxx55r5mNvn37ZowXci2NHbuta8mUS1NslJv6NYXmpidtXb49qdgyvRmQJEmz5gBNrZr6UTmxR4L86EmVwR6JlqIl9aS5c+fGYYcdlnGK+pVXXhkXXXRR0c5lnwT50ZPeV+yeVGz2SQCwdS1pT1PJ9tprr4zxhQsXbvW4SrpnqBYrQ7612BSacq+vHotr06ZNabFf/epXseOOO+b0GDlyZMb1Bw8enPa1J598cqP5VFJvjFCPxVZu9dgUmqo/qsXKkO/3X29Uj02hnN6b1Rsry5577pkx3qFDh7zXbOzYd999t9Fj9Eb1GFE+9dgU9MbqtHTp0rj//vvT4qecckrU1tYWvL7eqB5z0dT12BTKaQ8LVAeDCKBIBgwYkBZ7/vnn815v+vTpsW7durT4wIED814zG7169YpOnTqlxQu5lkzHdu7cOXr16rXV4zK9pi+88ELeeURExun1Tf2aQinoSY3LtycVW6aJct27d2/WHKA5VEs/Kif2SJA/Pan82SPRkrSEnvT666/HYYcdFvPmzUt77tJLL41LL720qOezT4L86UnF70nFZp8EANvWEvY0lW6HHXbIGM/0On9Qpd0zVIvlL99aLLb6+vqMvymymHt99Vi9Kq03RqhHstfU/VEtlr983wvTG9VjUyiX92b1xsozaNCgjPH33nsv7zUbO7ax/+NE6I0R6jGifOqx2PTG6vXb3/42NmzYkBYfO3ZsUdbXG9VjLpq6HoutOd5zBFoegwigSIYOHZoWe+KJJ/JeL9Ox7du3j/79++e9ZraGDBmSVT7ZynTsgQceuM3jMr2mS5cujVdeeSWvPGbPnh1Lly5Ni2e6Xqh0elLj8u1JxZbph1n8545qVE39qFzYI0H+9KTyZ49ES1LtPWn+/PkxYsSIeP3119Oe+853vhNXXnll0c9pnwT505OK35OKzT4JALat2vc01SDTD/BEROy0007bPLaS7hmqxfJXSC0W08svvxz19fVp8WLu9dVjdauk3hihHsleU/dHtVj+CnkvTG9Uj8VWLu/N6o2VZ+jQoZFKpdLimX4wMFtvvfVWxni3bt22epzeqB7LqR6LSW+sXrfccktabNiwYbHnnnsW7Rx6o3rMVnPUYzE1x3uOQMtjEAEUyeGHH54WW7RoUcyZMyev9R5//PG02LBhw6K2tjav9XKR6VomT54cSZLkvFaSJDF58uS0+BFHHLHNY4cOHRrbb799WjzTa5ONTMd17Ngx44YcKp2elFkhPamY1q5dGw8//HBafPDgwc2aBzSHaupH5cIeCfKnJ5U3eyRammruSQsXLozDDjssXn311bTnvv3tb8f3vve9JjmvfRLkT08qb/ZJAJCdat7TVIuXX345Y7xHjx7bPLaS7hmqxfJXSC0W01/+8pe0WCqVavQ3VeZDPVa3SuqNEeqR7DV1f1SL5a3Q98L0RvVYTOX03qzeWHm6deuW8fvzwgsv5L1mpmPbtm0bXbt23epxeqN6LKd6LCa9sTpNmTIlZs2alRY/44wzinoevVE9ZqO56rGYmuM9R6DlMYgAimTIkCEZJ6PffvvtOa/17rvvxn333ZcWP+aYY/LKLVfHHntsWmz+/PkxadKknNd65JFHYsGCBWnxbK6ltrY2Pv3pT6fF83lNGzvu05/+tI0yVUlPyqyQnlRMP//5z2PlypVp8c985jPNmgc0h2rqR+XCHgnypyeVN3skWppq7UmLFy+OESNGZLzR+c1vfjOuvfbaJju3fRLkT08qb/ZJAJCdat3TVJO77rorLdatW7esfnNUJd0zVIvlr5BaLJaVK1fGz3/+87T4oEGDYueddy7aedRj8XTq1CmSJCn40Vjfmjt3btrXjh8/fqs5VVJvjFCPxVSO9VgszdEf1WJ5K/S9ML1RPRZTubw3qzdWruOPPz4t9thjj+W93qOPPpoWO/jgg7d5/1JvVI8R5VOPxaI3Vq9f//rXabHOnTvHZz/72aKeR29Uj9lornosluZ6zxFoeQwigCKpqamJ0aNHp8VvvfXW2LRpU05r3XnnnbFq1aoGsdatW8eoUaMKyjFb++yzTwwcODAtftNNN+W81s0335wWGzRoUOyzzz5ZHX/SSSelxSZPnhwzZ87MKY+ZM2fGlClTslofqoGelFmhPakYZs+eHVdffXVavGPHjjFixIhmywOaSzX1o3JijwT50ZPKlz0SLVE19qSlS5fGiBEjYvbs2WnPff3rX48f//jHTZ6DfRLkR08qX/ZJAJC9atzTVJPnnnsu/ud//ictPnLkyGjVqtU2j6+ke4ZqsbwVWovFcsEFF8Sbb76ZFj/hhBOKeh71WN0qqTdGqEey0xz9US2Wr2K8F6Y3qsdiKaf3ZvXGyvVf//VfkUqlGsTmzJmT1w9/z5s3LyZOnJgWz6Ye9Ub1GFE+9VgsemN1evfdd+Oee+5Ji5900knRtm3bop5Lb1SP29Kc9VgszfWeI9ACJUDRvPLKK0lNTU0SEQ0e1157bdZrLF++POnRo0faGieffHLWawwbNizt+F69euV0Lb/+9a/T1qipqUmefPLJrNf4xz/+kfH1uOWWW7JeY+PGjUmfPn3S1vjMZz6T0/UcffTRaWv06dMn2bhxY07rQCXRkxoqtCc9+uijycKFC3PK+8P+/e9/Z+xpEZFcc801Ba0N5aya+lEh5xo3blzR1rdHgvzpScXtSfZIUJhq6klvvfVWsv/++2f8u3z22WfntFYh7JMgf3pScdknAUBpVNOeppBzFeu9nwkTJiRvvvlmwevMnDkz2WWXXdLyTKVSydNPP531OuVwzzBbarE6a3H16tXJ+PHjk/r6+rxzqK+vT8aNG5dxn9+9e/dkxYoVea/dGPVY/Ht1hZg0aVLG7//cuXPzWq+SemOSqMdqrcdK7I9qsbi1WG7vhemN6rEc6lFvVIubjRw5Mu0cBx98cLJp06ac1jnppJPS1qmtrc363229UT0mSenrUW9Ui9vyy1/+MuP39qWXXmqS8+mN6nFrmqseK7E3Ai2PQQRQZF/84hfT/tFu27Zt8vLLL2/z2Pr6+ozH19bWJjNmzMg6h2Js6tatW5f07t07bZ299947qw3IO++8k3z0ox9NO753797JunXrcsrlpptuyrgZuvnmm7M6/le/+lXG42+66aac8oBKpCe9rxg96dxzz03q6uqSM888M3nyySdz+o/ehg0bkl//+tdJx44dM/ajPn36JGvWrMl6PahE1dKPCjlXsd/4skeC/OlJxetJ9khQuGroScuXL08GDRqU8e/yV7/61azXKRb7JMifnlQ89kkAUDrVsKfJRVO+93PKKack7du3T77+9a8nzzzzTM7Hr1u3LvnJT36S7LDDDhn3NV/5yldyXq/U9wxzoRarrxaXL1+eRETSv3//5Oabb07eeuutnPL417/+lXH44ebHjTfemPO1ZUs9Vt8Pfm9Wab0xSdRjNdZjpfZHtVi99wz1RvVYDvWoN6rFzV544YWMPzR6ySWXZL3Gb37zm4x1cNppp2W9ht6oHpOk9PWoN6rFbTnggAPSzj106NAmO5/eqB63prnqsVJ7I9CyGEQARTZv3rykQ4cOaf9wd+7cOXn88ccbPW7t2rXJ6NGjM/6j/81vfjOnHIq1qRs/fnzGfAYMGJDMnz+/0ePmzZuX7LfffhmPHT9+fM55bNq0KeMGrqamJvnpT3+61WN/8pOfZPzP8gEHHJDz5D6oRHpS8XrSueee2+DYXXbZJTnjjDOSG264IXn88ceT1157LXn77beTDRs2JO+++27y2muvJX/+85+TCy+8MNl1110b/Y9dly5dkunTp+f8ekClqaZ+tGrVqmTp0qVbfRx88MFp5/r2t7+9zePWr1+fdR72SJA/Pal4PckeCQpX6T3p3XffTYYOHZoxj89//vPJkiVLttlvtvV4++23c7oe+yTIn55UvJ5knwQApVPpe5oPKvV7P6ecckqDdXfbbbdk7NixyfXXX588/vjjydy5c7fsaVavXp28+eabyfPPP5/cdtttyZgxY5LOnTs3uq8ZPHhw8s477+T8mpT6nmEu1GL11eLmDwVvftTW1iaHH354ctlllyV/+MMfkunTpyeLFi1KVq1alaxfvz558803kxdeeCH5xS9+kRx77LEZ33PY/Mh1MEeu1GPx79Xlq9iDCJKksnrj5vOqx+qqx0rtj2qxuu8Z6o3qsdT1qDeqxQ/6+te/nvG1+cY3vpGsXr260eM2btyY/Pd//3fGeujUqVPO/2brjeoxSUpbj3qjWtyaF198MeNr1dS/NEJvVI+ZNGc9VmpvBFoWgwigCfzP//xPxn/AU6lUctxxxyX33ntvMm3atOT1119PHn/88eTqq69OevTokfGY/ffff6v/ocukmNOlxo4dmzGv9u3bJ2effXYyceLEZM6cOcm//vWvZMKECclXv/rVpF27dhmPOeOMM/LKIUmSZNasWRk3yxGRDBo0KLnllluSZ599Npk3b17yzDPPJDfffHMycODAjF/foUOHZNasWXnnApVGTypOT/rwDYpiPLp27Zo89dRTeb0WUImqpR+NGzeu6P1g82PSpEk55WKPBPnTk4rTk+yRoDgquSc19kHVYj7y6Y/2SZA/Pak4Pck+CQBKq5L3NB9U6vd+PvzD38V6fOxjH0uWL1+e8+uxWbl8jiEbarG6avHDHwou1uPkk09ulgGI6rE49ViophhEkCSV1RuTRD1WWz1Wcn9Ui8WpxXJ9L0xvVI+lrEe9US1+0Jo1a5JBgwZlXGu33XZLLrvssmTy5MnJq6++mrzxxhvJ008/nVx77bVJ//79G/1ePPDAAzm/JkmiN6rH0taj3qgWt+bss89OW7NDhw7Ju+++W9C62dAb1eOHNWc9VnJvBFoOgwigiVx++eUF/6Pfq1ev5PXXX8/53MX8od+1a9cmn/70pwu+lqOOOipZt25dXjlsNmHChKSurq6gPOrq6pIJEyYUlAdUIj2p8J5U7BsU//Ef/5EsWrQor9cBKlk19KNye+PLHgnypycV3pPskaB4KrUnldMP/X6YfRLkT08qvCfZJwFA6VXqnuaDSv3eT7F/+Luuri4ZN25csnbt2pxfiw8qp88xZEMtVk8tFvtDwZ06dUpuv/32nL8fhVCPhddjoZpqEEGl9cYkUY/VVI+V3h/VYuG1WK7vhemNuVGPxa1HvVEtftiCBQuSvfbaq+Bzt2rVKvn5z3+e8/k30xtzox6LW496o1pszJo1a5JOnTqlrTlmzJi818yF3pgb9Vhcld4bgZahJoAmMW7cuPjZz34WtbW1eR0/ZMiQmDJlSvTs2bPImeWmrq4u7rvvvjjllFPyXuPUU0+N8ePHR5s2bQrK5cgjj4yJEydG9+7d8zq+e/fuMXHixDjyyCMLygMqkZ70f/LtSQcddFAMGDAgamry3z7V1NTE0UcfHX/+85/jz3/+c+y88855rwWVqlr6UTmxR4L86UmFs0eC4tGTis8+CfKnJxXOPgkASs+epnBjxoyJ0047LT7ykY8UtM6OO+4Y55xzTkyfPj0uv/zyqKurK2i9cvocQzbUYuHKpRbbtm0bxx9/fHTp0qWgPD7ykY/EZZddFjNnzoyTTz65oLVypR6rV6X1xgj1WE0qvT+qxcKV63themPLVC71qDeqxQ/7yEc+ElOmTInDDz887zV22GGH+POf/xxf+9rX8l5DbySidPWoN6rFxtxzzz3xzjvvpMXHjh3bLOfXG/mg5q7HSu+NQMuQ3782QFbOOeecGDZsWJx//vnx97//PatjunbtGhdeeGF84xvfiNatWzdxhtmpq6uL2267LUaOHBkXXXRRzJ49O6vj+vXrF9dcc02MHDmyaLl88pOfjOnTp8fFF18ct912W6xfv36bx7Rp0yZOPfXUuPrqq6Nbt25FywUqjZ5UWE868cQT48QTT4zly5fH008/HS+++GK89NJL8eqrr8b8+fPjzTffjE2bNm35+jZt2sQOO+wQu+yySwwdOjQ+9rGPxeGHHx69evXK6/xQTaqlH5UTeyTIn55UGHskKC49qfjskyB/elJh7JMAoDzY0xTmE5/4RHziE5+IiIjZs2fH008/Hc8++2zMmjUrXnvttZg/f36sXbt2y9fX1NTE9ttvH926dYv99tsvBg0atGVfk+8HYhtTTp9jyIZaLEy51GLbtm1j/PjxkSRJ/POf/4ypU6fGSy+9FDNmzIh58+bFggUL4r333tvy9alUKjp06BBdunSJ/v37x8c+9rH4+Mc/HocddljR/07kQj1Wr0rrjRHqsVpUQ39Ui4Up5/fC9MaWp1zqUW9Ui5nsuOOOMXHixLjtttviyiuvjNdffz2r49q1axdf/epX46KLLoodd9yx4Dz0RiJKU496o1pszPr16+Pss89uEOvYsWMcdNBBzZaD3shmzV2P1dAbgeqXSpIkKXUS0BJMmzYt/vjHP8bkyZNj1qxZsWzZstiwYUN06NAhevXqFQMHDoyjjjoqRo4cGe3atSt1uo1KkiQefvjheOCBB+KZZ56JOXPmxIoVKyLi/al2ffv2jaFDh8Zxxx0XI0aMiFQq1WS5LF68OO6555545JFHYtq0abFo0aJYs2ZNtGvXLnr06BH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ysiperVo1l8atXr26JCQk2MTi4+NdGjM/x48fV8X8/PykatWqusesVq2a+Pn5iaIoNvH4+Hhp0qSJU3Mx4jW9m7tfU8DTqEl501uT8jJy5EhVLCkpSZKSkiQ+Pl527Nghs2fPFj8/P+nSpYu88MILEh0dretcgJX4Uj0yE9ZIgD7UJM9jjQTYR03S9tlnn6liYWFh0rp1a4eOZ50E6ENN0uZqTcoL6yQAAIzHmsa8Fi5cKKNGjZIbN27YxFu1aiXPP/+8Q2NYpWcoQi6amRG5mJeffvpJM56QkCAJCQkSFxcnq1atEhGR8PBw+de//iWjR4+WsLAwl89tD/noOcePH5cJEybYxF566SWpX7++W895N7PWRhHy0ZO8kY95MVt9JBc9z5OfhVEbbyMf7TPLZ7PUxoLhzz//VMXu3Ox43rx58uqrr6q+pV5E5OLFi3Lx4kU5cOCA/PzzzyIi0qNHD3njjTckMjLSqXlQG28jH82Rj3mhNiI/CxYskKSkJFV8+PDhTo9FbbyNfNTPyHzMi9lqI4CCy/PbmwI+SOt/uEREc3coZ0RERDh8LqNojV+6dGmXdn4PCgqS0qVLO3Su/B634msKeBo1KW96a5IRFEWR//3vf9KhQwd57LHH5OrVq24/J+BNvlSPzIQ1EqAPNcm8WCOhIKImqW3cuFGWLVumij/++ONSqFAhh8ZgnQToQ01SM6ImGYF1EgAAjmNN43lJSUly6dIlmz/nzp2TQ4cOyf/+9z+ZPHmyREZGSu/eveXSpUs2x7Zu3VqWL1/u0v/vmbVnSC56nidz0SgXLlyQCRMmSK1atWTJkiVuOw/56BmKosjw4cMlJSUlN1arVi0ZP368W89rpdqY1xjko7G8lY9G8UR9JBfNy4jPwqiNt5GPrjPTZ7PURus6dOiQKnbPPfdIamqqPPbYYzJkyBCnXo+ff/5ZGjVqJO+9955T86A23kY+miMfjUJtLJhmzZqlitWqVUvatm3r9FjUxtvIR/2MzEejeOozRwAFExsRAAa4fPmyZtzVHYS0FqH2zmUUrfGN2AnJ2eeSlZWluTuUO17Ta9euSVZWlkvjAmZCTcqfN57L3RYtWiSNGjXS3GUU8BW+VI/MgjUSoB81yRpYI6GgoCbZunnzpgwbNkwVDwkJkZdfftmhMVgnAfpRk2wZUZPcgXUSAAB5Y03jeR06dJCyZcva/KlYsaLUqVNH/vGPf8jEiRNl//79NseULFlS3n77bYmNjZUSJUo4fC4r9QzJRc/zZC4a7cqVK9KrVy957rnn3DI++egZM2fOlNjYWJvY9OnTpXDhwm49r5VqY15jkI/G8lY+Gs2d9ZFctAa9n4VRG28jH41lls9mqY3Wo/UPOcuWLSvdunWTRYsW6RozOztbXnnlFRk2bJjqG7ftoTbeRj6aIx+NRm0sOA4fPiybN29WxfV++zy18TbyUR+j89Fo7v7MEUDBFODtCQC+4Nq1a5rx4sWLuzRusWLFHD6XUbTGd/V5iDj/XJKSkjT/h9Qdr6miKHL9+nUpVaqUS2MDZkFNyp9Rz6VOnTry0EMPSYMGDaROnTpSpkwZKV68uKSkpMiVK1dk//79snnzZlm4cKHm+KdOnZIuXbrI77//LpUqVdLxTABz86V6ZBaskQD9qEmewxoJyB81ydZTTz0lx44dU8X//e9/S+XKlR0ag3USoB81yZYRNcke1kkAALgPaxpzq1WrlowbN04ef/xxzdc0P2bvGTpyPLloDq7mopbQ0FCJjo6WBx54QOrXry9Vq1aV4sWLS1BQkFy5ckXOnz8vv//+u6xcuVJ+++03zTE++ugjKVasmEyePNmQOeUgH93v7Nmz8tJLL9nEhg0bJu3atXP7ua1UG/Mag3w0jjfzUYtZ6yO56Dne+CyM2ngb+ahmls9mqY0FKxcvXLigin3xxRdy/Phxm1jlypUlJiZGHnroIalYsaIEBATI+fPnZcOGDTJ//nzVxmoiInPmzJHatWurfvdroTbeRj6aIx+1UBsLVi7qpfXt84GBgRITE6NrPGrjbeSjPkbnoxaz1kYABRcbEQAGuHXrlmY8KCjIpXGDg4MdPpdRtMZ39XmIOP9cPPma5jcXwGqoSflz5blUrlxZhg4dKkOGDJEqVarY/bnq1atLs2bNZMiQIfLRRx/J559/LhMnTlSd5+zZs9KtWzfZuXOnBASwNINv8aV6ZBaskQD9qEnuxRoJcA416W8ffPCBfPPNN6p4s2bN5JVXXnF4HNZJgH7UpL8ZVZPuxDoJAADPYE1jbkeOHJHXX39djh49KmPHjpV77rnHqePN2DO0h1w0N1dzMUdAQIB069ZNnnrqKXn44YftXt+IiAipV6+edOzYUSZMmCDbt2+Xp59+Wv744w/Vz77xxhtSv3596du3r645aSEf3W/06NGSlJSU+/fw8HB5//33PXJuK9XGvMYgH43jzXzMYYX6SC66l7c/C6M23kY+3ubtfMxBbXTsXL4oJSVFFbv7H30/99xz8tZbb0lISIhNvHLlynL//ffLCy+8IB9//LGMGzdOsrOzbX7m1VdflaioKLn//vvznAe18Tby0Rz5mIPa6Ni5cFtGRoZ89dVXqnj37t2lXLlyusakNt5GPjrPHfmYwwq1EUDB5e/tCQC+ICMjQzPu6k2AgYGBDp/LKFrjG3Ezo7PPxZOvaX5zAayGmpQ/V57Lxo0bZeLEiXk2J+4WGhoqL7/8smzdulXKly+venzfvn2aO+MBVudL9cgsWCMB+lGT3Is1EuAcatJtixcv1vxWhJIlS8oPP/zg1OvBOgnQj5p0m5E16U6skwAA8AzWNOZ35swZmTJlitSsWVM++OAD1Q3reTFjz9AectH8XMnFHAMHDpRly5ZJt27dnLrhu3nz5rJp0yYZO3as5uMvvfSSoTd7k4/u9e2338qyZctsYh9//LGUKlXKI+e3Um3Mawzy0RjezsccVqiP5KJ7efuzMGrjbeTjbd7OxxzURsfO5Yvyuz4TJ06U//73v6p/9H2nQoUKyfPPPy9z585VPZaZmSmvvfZavvOgNt5GPpojH3NQGx07F277+eef5eLFi6r48OHDdY9JbbyNfHSeO/IxhxVqI4CCi40IAAP4+2u/lVxdgKWnpzt8LqNojW/EQtLZ5+LJ1zS/uQBWQ03Knzeei4hIw4YNZfny5VK0aFHVY6+//jr/gwef40v1yCxYIwH6UZPMizUSCiJqksiGDRtkwIABqpv9AwIC5LvvvpPq1as7NR7rJEA/apLxNckorJMAAHAcaxrP27FjhyiKYvMnOTlZzpw5I2vXrpV33nlHIiMjVcelpaXJiy++KEOHDnX4H4BbqWdILnqeJ3PRCAEBAfLxxx/LgAEDVI+dPHlSpk2bZti5yEf3SUxMlGeeecYm1rVrV3n88cc9Ngcr1ca8xiAfXWeGfDSCp+ojuWheRnwWRm28jXx0nVk+m6U2Wltez7V169ZO/aPtQYMGSUxMjCq+evVq2bVrl9PzoDbmfS5fZJZ8NAK1seDR2gSocuXK8vDDD+sek9p4G/noPHfkoxE8+ZkjgIKJ3w6AAeztNJSWlubSuFrHO7OrkR5a47v6POyNkddz8eRrmt9cAKuhJuXPG88lR5MmTWTcuHGqeEJCgmzYsMEjcwA8xZfqkVmwRgL0oyaZG2skFDQFvSZt375dHnnkEdV8/f395auvvpLOnTs7PSbrJEA/apLxNclIrJMAAHBMQV/TmEVoaKhUrFhRoqOj5ZVXXpF9+/bJihUrpGrVqqqf/eqrr+TVV191aFwr9QzJRXNwVy4axc/PTz7//HMpXbq06rEffvjBsPOQj+7z9NNPy6VLl3L/HhoaKlOnTvXoHKxUG/Mag3x0nRny0SieqI/korm5+lkYtdH+8eSj88zy2Sy10bryeq5vv/220//Q880335RChQqp4j/++KPT86A25n0uX2SWfDQKtbHgOHXqlPz666+q+JNPPunSP5inNto/nny0z135aBRPfeYIoGDyfpUDfIDWjpciIqmpqS6Nq3V8kSJFXBozP1rPxdXnYW+MvJ6LJ1/T/OYCWA01KX/eeC53evHFFyUkJEQVX7FihcfmAHiCL9Ujs2CNBOhHTTI/1kgoSApyTdq3b5907txZkpOTbeJ+fn4yY8YM6d+/v65xWScB+lGTjK9JRmOdBABA/grymsbsOnfuLDt27JAGDRqoHpsyZYrs3Lkz3zGs1DMkF83LiFw0UsmSJeVf//qXKr5161a5cuWKIecgH91j2bJl8v3339vE3nzzTalcubJH52Gl2ihCPrqLWfLRSO6uj+Si+bnyWRi10f7x5KM+ZvlsltpoTfZe11q1akmbNm2cHu+ee+6RLl26qOIbN250eh7UxtvIR8/no5GojQXDnDlzJDs72ybm7+8vTz75pEvjUhvtH08+2ueufDSSJz5zBFAwsREBYACt3YJERG7evOnSuFrHh4WFuTRmfrSei6vPw94YeT2XokWLSnBwsOFz0To+ODjY7sIcsCJqUv688VzuVLRoUWnfvr0q/scff3hsDoAn+FI9MgvWSIB+1CTzY42EgqSg1qSDBw9Kx44dNRtbn376qQwbNkz32KyTAP2oScbXJKOxTgIAIH8FdU1jFWFhYbJ06VLVTazZ2dny3nvv5Xu8lXqG5KK5uZqLRuvWrZsqlp2dLdu3bzdkfPLReElJSTJ69GibWPPmzWXs2LEen4uVaqMI+egOZspHo7mzPpKL5ufKZ2HURvvHk4/6mOmzWWqj9dh7rlFRUbrH1Dp2x44dkp6ebvcYaqP948lHz+ej0aiNvi07O1u+/PJLVbxTp05SqVIll8amNto/nnzU5s58NJq7P3MEUDCxEQFggHLlymnGz54969K4WsfbO5dRtMZPSEhQ7drkjOzsbElISHDoXHcqW7asKmbF1xTwNGpS3vTWJKM1a9ZMFbtw4YJH5wC4my/VIzNhjQToQ02yBtZIKCgKYk06cuSIdOjQQRITE1WPffDBBzJmzBiXz8E6CdCHmmTLqJpkNNZJAADkrSCuaaymSpUqMmrUKFV82bJlcuvWrTyPtVLPkFw0P1dy0WhNmjQRPz8/VdyotT75aLwXX3zR5vkHBATIzJkzxd/f87dhWqk25jUG+aifmfLRaO6sj+SiNej9LIzaaP948lE/s3w2S220nvDwcM14o0aNdI+pdWxGRoZcvnzZ7jHURvvHk4+ez0ejURt92+rVq+XUqVOq+IgRI1wem9po/3jyUZs789Fo7v7MEUDBZP1PHAETqFy5suYH+FqLDGdoHV+1alWXxsyP1vgZGRly/vx53WOeO3dOMjMzHTpXfo9b8TUFPI2alDe9NcloWv9A5uLFix6dA+BuvlSPzIQ1EqAPNckaWCOhoChoNemvv/6S6Ohozf+Xe+edd+T555835DyskwB9qEl/M7ImGY11EgAAeStoaxqrevTRR1WxtLQ02bNnT57HWalnSC5ag95cNFqhQoU0v9HOqLU++Wis5ORkmTVrlk1s+PDhUrFiRbl06ZLDf5KSkjTHv3r1qupnk5OT7c7HSrVRhHw0mtny0WjurI/kojXo/SyM2mj/ePJRP7N8NktttJ5q1appxl35pukyZcpoxvP6h9/URvvHk4+ez0ejURt9293/zyNye1ONRx55xOWxqY32jycftbkzH43m7s8cARRMbEQAGCAwMFAqV66siruyqFMURc6cOaOK33vvvbrHdESNGjU04648F3vH5vdctObijoWyu19TwNOoSXnTW5OMpvVhgKIoHp0D4G6+VI/MhDUSoA81yRpYI6GgKEg1KT4+Xtq3b6+5i/rkyZPllVdeMexcrJMAfahJtxldk4zGOgkAgLwVpDWNldWqVUszfu7cuTyPs1LPkFy0Br256A7uXOuTj8bKyspSxaZNmyZly5Z16k/Pnj01x2/SpInqZwcNGmR3PlaqjSLko9HMlo/u4K76SC5ag97rT20kH93BTJ/NUhutpWbNmprx0NBQ3WPaO/b69et2j6E2ko8i5slHd6A2+qbExERZunSpKh4TEyMBAQEuj09tJB+d4e58dAczrWEB+AY2IgAM0rBhQ1Vs165dusc7cOCA3Lp1SxVv1KiR7jEdUaVKFSlZsqQq7spz0Tq2VKlSUqVKlTyP03pNd+/erXseIqK5e727X1PAG6hJ9umtSUbT2lGuXLlyHp0D4Am+Uo/MhDUSoB81yfxYI6EgKQg16eTJk9K+fXs5ffq06rEJEybIhAkTDD0f6yRAP2qS8TXJaKyTAADIX0FY01hdiRIlNONar/OdrNYzJBfNT28uGi07O1vzmyKNXOuTj77LarVRhHyE49xdH8lF89P7WRi1kXx0B7N8NktttJ7GjRtrxpOTk3WPae9Ye/+PI0JtFCEfRcyTj0ajNvquefPmSUZGhio+fPhwQ8anNpKPznB3PhrNE585Aih42IgAMEiLFi1UsS1btugeT+vYIkWKSL169XSP6ajmzZs7NB9HaR3brFmzfI/Tek0TExPlyJEjuuZx+PBhSUxMVMW1ni9gddQk+/TWJKNp/WMW/ucOvsiX6pFZsEYC9KMmmR9rJBQkvl6Tzpw5I9HR0XLy5EnVY//3f/8nkydPNvycrJMA/ahJxtcko7FOAgAgf76+pvEFWv+AR0QkPDw832Ot1DMkF83PlVw00r59+yQ7O1sVN3KtTz76NivVRhHyEY5zd30kF83Plc/CqI3ko9HM8tkstdF6WrRoIX5+fqq41j8MdNSlS5c042XKlMnzOGoj+WimfDQStdF3zZ49WxWLioqSmjVrGnYOaiP56ChP5KORPPGZI4CCh40IAIN06NBBFTt//rwcO3ZM13gbN25UxaKioiQgIEDXeM7Qei6bNm0SRVGcHktRFNm0aZMq3rFjx3yPbdGihRQrVkwV13ptHKF1XPHixTUX5IDVUZO0uVKTjJSWliZr165VxZs0aeLReQCe4Ev1yCxYIwH6UZPMjTUSChpfrknnzp2T9u3by/Hjx1WPjRs3Tt5++223nJd1EqAfNcncWCcBAOAYX17T+Ip9+/ZpxiMiIvI91ko9Q3LR/FzJRSMtX75cFfPz87P7TZV6kI++zUq1UYR8hOPcXR/JRXNz9bMwaiP5aCQzfTZLbbSeMmXKaF6f3bt36x5T69jChQtLWFhYnsdRG8lHM+WjkaiNvmnz5s1y6NAhVXzEiBGGnofaSD46wlP5aCRPfOYIoOBhIwLAIM2bN9fcGf2rr75yeqzr16/Lzz//rIp369ZN19yc9cgjj6hiZ86ckdjYWKfHWrdunZw9e1YVd+S5BAQESKdOnVRxPa+pveM6derEQhk+iZqkzZWaZKRPP/1Ubty4oYp37drVo/MAPMGX6pFZsEYC9KMmmRtrJBQ0vlqTEhISJDo6WrPR+dxzz8mUKVPcdm7WSYB+1CRzY50EAIBjfHVN40t++OEHVaxMmTIOfXOUlXqG5KL5uZKLRrlx44Z8+umnqnjjxo2lfPnyhp2HfDROyZIlRVEUl//Yq1vx8fGqn12yZEmec7JSbRQhH41kxnw0iifqI7lobq5+FkZtJB+NZJbPZqmN1tWjRw9VbMOGDbrHW79+vSrWsmXLfPuX1EbyUcQ8+WgUaqPvmjVrlipWqlQpeeyxxww9D7WRfHSEp/LRKJ76zBFAwcNGBIBB/P39pX///qr4nDlzJCsry6mxvv32W7l586ZNLDAwUPr27evSHB1Vt25dadSokSo+Y8YMp8eaOXOmKta4cWOpW7euQ8cPHDhQFdu0aZMcPHjQqXkcPHhQNm/e7ND4gC+gJmlztSYZ4fDhw/LWW2+p4sWLF5fo6GiPzQPwFF+qR2bCGgnQh5pkXqyRUBD5Yk1KTEyU6OhoOXz4sOqxp59+Wv773/+6fQ6skwB9qEnmxToJAADH+eKaxpfs2LFDvv76a1W8Z8+eUqhQoXyPt1LPkFw0N1dz0SgvvfSSXLhwQRXv1auXoechH32blWqjCPkIx3iiPpKL5mXEZ2HURvLRKGb6bJbaaF2DBw8WPz8/m9ixY8d0/ePv06dPy+rVq1VxR/KR2kg+ipgnH41CbfRN169flwULFqjiAwcOlMKFCxt6Lmoj+ZgfT+ajUTz1mSOAAkgBYJgjR44o/v7+iojY/JkyZYrDY1y9elWJiIhQjTFo0CCHx4iKilIdX6VKFaeey6xZs1Rj+Pv7K7///rvDY/z222+ar8fs2bMdHiMzM1OpVq2aaoyuXbs69Xy6dOmiGqNatWpKZmamU+MAVkJNsuVqTVq/fr1y7tw5p+Z9t7/++kuzpomI8u6777o0NmBmvlSPXDnXxIkTDRufNRKgHzXJ2JrEGglwjS/VpEuXLikNGjTQfC+PGTPGqbFcwToJ0I+aZCzWSQAAeIcvrWlcOZdRn/2sWrVKuXDhgsvjHDx4UKlYsaJqnn5+fsrWrVsdHscMPUNHkYu+mYspKSnKkiVLlOzsbN1zyM7OViZOnKi5zi9XrpySlJSke2x7yEfje3WuiI2N1bz+8fHxusazUm1UFPLRV/PRivWRXDQ2F832WRi1kXw0Qz5SG8nFHD179lSdo2XLlkpWVpZT4wwcOFA1TkBAgMO/t6mN5KOieD8fqY3kYn6mTp2qeW337t3rlvNRG8nHvHgqH61YGwEUPGxEABhswIABql/ahQsXVvbt25fvsdnZ2ZrHBwQEKHFxcQ7PwYhF3a1bt5SqVauqxqlTp45DC5Br164p9913n+r4qlWrKrdu3XJqLjNmzNBcDM2cOdOh46dNm6Z5/IwZM5yaB2BF1KTbjKhJzzzzjBIcHKyMHDlS+f333536H72MjAxl1qxZSvHixTXrUbVq1ZTU1FSHxwOsyFfqkSvnMvqDL9ZIgH7UJONqEmskwHW+UJOuXr2qNG7cWPO9PHr0aIfHMQrrJEA/apJxWCcBAOA9vrCmcYY7P/uJiYlRihQpojz99NPKtm3bnD7+1q1byocffqiUKFFCc10zatQop8fzds/QGeSi7+Xi1atXFRFR6tWrp8ycOVO5dOmSU/M4evSo5uaHOX+mT5/u9HNzFPnoe//wO4fVaqOikI++mI9WrY/kou/2DKmN5KMZ8pHaSC7m2L17t+Y/Gh0/frzDY3z55ZeaeTB06FCHx6A2ko+K4v18pDaSi/lp2rSp6twtWrRw2/mojeRjXjyVj1atjQAKFjYiAAx2+vRpJTQ0VPWLu1SpUsrGjRvtHpeWlqb0799f85f+c88959QcjFrULVmyRHM+DRs2VM6cOWP3uNOnTyuRkZGaxy5ZssTpeWRlZWku4Pz9/ZWPP/44z2M//PBDzf9Zbtq0qdM79wFWRE0yriY988wzNsdWrFhRGTFihPL5558rGzduVE6cOKFcuXJFycjIUK5fv66cOHFC+eWXX5SXX35Zueeee+z+j13p0qWVAwcOOP16AFbjS/Xo5s2bSmJiYp5/WrZsqTrXuHHj8j0uPT3d4XmwRgL0oyYZV5NYIwGus3pNun79utKiRQvNefTp00e5ePFivvUmvz9Xrlxx6vmwTgL0oyYZV5NYJwEA4D1WX9Pcyduf/cTExNiMW6lSJWX48OHKZ599pmzcuFGJj4/PXdOkpKQoFy5cUHbt2qXMnTtXGTZsmFKqVCm765omTZoo165dc/o18XbP0Bnkou/lYs5NwTl/AgIClA4dOiivvfaa8tNPPykHDhxQzp8/r9y8eVNJT09XLly4oOzevVv54osvlEceeUTzM4ecP85uzOEs8tH4Xp1eRm9EoCjWqo055yUffSsfrVofyUXf7hlSG8lHb+cjtZFcvNPTTz+t+do8++yzSkpKit3jMjMzlf/85z+a+VCyZEmnf2dTG8lHRfFuPlIbycW87NmzR/O1cveXRlAbyUctnsxHq9ZGAAULGxEAbvD1119r/gL38/NTunfvrixevFjZv3+/cvLkSWXjxo3KW2+9pURERGge06BBgzz/h06LkbtLDR8+XHN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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "rename_dict = {\n", + "\"DDPM_conditional\": \"C-DDPM\",\n", + "\"DDPM_guided\": \"G-DDPM\",\n", + "\"DDPM_conditional-CI\": \"C-DDPM-CI\",\n", + "\"DDPM_guided-CI\": \"G-DDPM-CI\",\n", + "\"Agg-LS\": \"GD-AGG\",\n", + "\"Agg-LS-CI\": \"GD-AGG-CI\",\n", + "\"EPO\": \"GD-EPO\",\n", + "\"EPO-CI\": \"GD-EPO-CI\",\n", + "}\n", + "\n", + "#rename indices of classes\n", + "scores = unconditional_scores.rename(index=rename_dict)\n", + "\n", + "classes = [\"Opt\", \"Opt\", \"TGM\", \"OAGM\", \"OAGM\", \"OAGM\", \"OAGM\", \"Data\", \"OAGM\", \"OAGM\", \"Opt\", \"Opt\", \"LLM\", \"TGM\", \"OAGM\", \"OAGM\"]\n", + "\n", + "\n", + "namedict = {\"Opt\": \"Optimization\",\n", + " \"TGM\": \"GenAI\",\n", + " \"OAGM\": \"GenAI+Opt.\",\n", + " \"LLM\": \"LLM\",\n", + " \"Data\": \"Dataset\"}\n", + "\n", + "classes = [namedict.get(cls, cls) for cls in classes]\n", + "\n", + "scores[\"class\"] = classes\n", + "\n", + "marker_map = {\"Optimization\": \"o\",\n", + " \"GenAI\": \"D\",\n", + " \"GenAI+Opt.\": \"v\",\n", + " \"LLM\": \"^\",\n", + " \"Dataset\": \"s\"}\n", + "\n", + "#drop scores that have Dataset\n", + "scores = scores[~scores.index.str.contains(\"Dataset\")]\n", + "\n", + "\n", + "scores[\"annotation_location\"] = \"top\"\n", + "scores.loc[scores.index == \"GD-EPO-CI\", \"annotation_location\"] = \"left\"\n", + "scores.loc[scores.index == \"G-DDPM-CI\", \"annotation_location\"] = \"left\"\n", + "# scores.loc[scores.index == \"Dataset\", \"annotation_location\"] = \"left\"\n", + "scores.loc[scores.index == \"O4-mini\", \"annotation_location\"] = \"right\"\n", + "display(scores)\n", + "\n", + "\n", + "plot_scores(scores, show_ci=\"CI\", zoom_hv=(0.13, 0.175), zoom_mmd=(0.0, 0.063), marker_map=marker_map) # to drop CI models\n", + "plot_scores(scores, show_ci=\"Base\", zoom_hv=(0.13, 0.175), zoom_mmd=(0.0, 0.063), marker_map=marker_map) # to include them with dotted lines\n", + "plot_scores(scores, show_ci=\"Both\", zoom_hv=(0.13, 0.175), zoom_mmd=(0.0, 0.063), marker_map=marker_map) # to include them with dotted lines" + ] + }, + { + "cell_type": "markdown", + "id": "56bda200", + "metadata": {}, + "source": [ + "Compare differences between models with and without constraint masking" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0a573b1d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.ticker as ticker\n", + "\n", + "def draw_lineplots(\n", + " triplets,\n", + " lb,\n", + " ub,\n", + " class_map,\n", + " color_map,\n", + " scaling=\"linear\",\n", + " direction=\"min\",\n", + " metric_name=\"\"\n", + "):\n", + " number = len(triplets) + 1\n", + "\n", + " if scaling == \"linear\":\n", + " plot_range = ub - lb\n", + " else:\n", + " plot_range = np.log(ub) - np.log(lb)\n", + "\n", + " fig, axes = plt.subplots(number, 1, figsize=(7, 0.3*number + 1), dpi=300)\n", + " arrowheight = 0.15\n", + "\n", + " for i in range(number):\n", + " ax = axes[i]\n", + " if i < number - 1:\n", + " name, start, finish = triplets[i]\n", + "\n", + " # lookup class & color\n", + " cls = class_map[name]\n", + " color = color_map.get(cls, \"gray\")\n", + "\n", + " # compute head_length & text_offset as before\n", + " if start > finish:\n", + " delta = start - finish\n", + " else:\n", + " delta = finish - start\n", + "\n", + " if scaling == \"linear\":\n", + " base_head = 0.01 * plot_range\n", + " text_offset = 0.01 * plot_range\n", + " else:\n", + " base_head = 0.01 * plot_range * (finish if start > finish else finish/(1+0.01*plot_range))\n", + " text_offset = 0.01 * plot_range * (start if start > finish else start/(1+0.01*plot_range))\n", + "\n", + " # clamp head to 80% of delta to avoid white gap\n", + " head_length = min(base_head, delta * 0.8)\n", + "\n", + " # draw arrow or tick\n", + " if start > finish:\n", + " adjusted_start = max(start, finish + head_length)\n", + " ax.arrow(\n", + " adjusted_start, arrowheight,\n", + " finish - adjusted_start, 0,\n", + " width=0.06, head_width=0.2, head_length=head_length,\n", + " fc=color, ec=color, length_includes_head=True\n", + " )\n", + " ax.text(adjusted_start + text_offset, 0.03,\n", + " f\"{(start - finish):.2f}\", fontsize=12,\n", + " ha='left')\n", + " elif start < finish:\n", + " adjusted_start = min(start, finish - head_length)\n", + " ax.arrow(\n", + " adjusted_start, arrowheight,\n", + " finish - adjusted_start, 0,\n", + " width=0.06, head_width=0.2, head_length=head_length,\n", + " fc=color, ec=color, length_includes_head=True\n", + " )\n", + " ax.text(adjusted_start - text_offset, 0.03,\n", + " f\"{(finish - start):.2f}\", fontsize=12,\n", + " ha='right')\n", + " else:\n", + " ax.plot(start, arrowheight, marker='|', markersize=40, color=color)\n", + " ax.text(start - text_offset, 0.03, \"0.00\",\n", + " fontsize=12, ha='right', va='bottom')\n", + "\n", + " # hide axes and label\n", + " ax.spines['bottom'].set_color('none')\n", + " ax.xaxis.set_major_locator(ticker.NullLocator())\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.text(-0.01, 0.1, name, fontsize=12,\n", + " transform=ax.transAxes, ha='right')\n", + "\n", + " else:\n", + " ax.xaxis.set_ticks_position('bottom')\n", + " ax.set_xlabel(metric_name, fontsize=12)\n", + "\n", + " ax.set_xscale(scaling)\n", + " ax.xaxis.set_major_formatter(ticker.ScalarFormatter())\n", + " ax.xaxis.get_major_formatter().set_scientific(False)\n", + " ax.xaxis.get_major_formatter().set_useOffset(False)\n", + " ax.spines['left'].set_color('none')\n", + " ax.spines['top'].set_color('none')\n", + " ax.spines['right'].set_color('none')\n", + " ax.yaxis.set_major_locator(ticker.NullLocator())\n", + " ax.set_xlim(lb, ub)\n", + " ax.set_ylim(0, 0.3)\n", + " ax.patch.set_alpha(0.0)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5333988c", + 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RQUGBRc2e3QokqUaNGurcubNFbc+ePTp06JDT+vu9xMREdevWTRcuXLCoR0REaPXq1XZ9/wAAAADAU1SsWFGDBg1ydxsAAAAA3OjG3b0l2ztj28PaWALNUpkyZVStWjV17dpVEyZMUHx8vD766COL4w9OnTqlbt26afv27W7sFLcrH3c3cKurWLGi/vrXv2rixInXa+np6Xrrrbc0depUl68fERGh4cOHa/jw4ZJ+S2wdPXpUW7du1bJly7Ru3Trl5+cbxl2+fFlDhgzR7t27rSa/ypcvb3W9lJQUVapUyaEeQ0JCitw9ICMjQ/Hx8Q7N+V9ms9nwlL+3t7dGjhxp9xwPP/yw1q9fb1GbOXOmpkyZUqyebElKSlK3bt108uRJi3rlypW1fv161apVy6nrAQAAAEBpaNiwoTp37qyNGzcWa/zgwYPl48OvJwAAAIDSVJIdBW5UpkwZQ83ZwYLf3zzHb0wmk5555hk1b95c99577/X3LSMjQ/fff79++uknm/f7gOLgJ3cneOmll/Txxx/r4sWL12vTp0/XmDFjVKdOnVLtxWQyqUmTJmrSpImeeOIJJSQkaOLEiVa32I+NjdXXX3+tIUOGGF6LiIiQyWQybC1z6dIlNWjQwKGeOnTooAMHDth8fdOmTYZdA+y1adMmQyihR48eqly5st1zDBo0SE8//bQyMjKu17744gv94x//cFoC7sqVK+rWrZvi4uIs6uHh4Vq/fr3D7ykAAAAAeJIOHTro4sWLNz12z5r69evL39/fBV0BAAAAKA1hYWGG2u/vuTjK2lhra+A3HTt21KRJk/TKK69cr509e1b/93//p7ffftuNneF2w1EIThAUFGSxY4Ek5ebmavz48W7q6H9q1KihWbNmafbs2VbTZzNmzLA6zs/PTzVr1jTU9+7d6/QeS2LmzJmGmr3HIPxXUFCQIVxx5coVLVu2rCStXZecnKzu3bsbjleoUKGC1q1bp6ZNmzplHQAAAABwF5PJpP79+zsU8gYAAABwe7D2c8DZs2eLPV9CQoJda+B/xowZYzhu+z//+Y/hAWKgJAgWOMljjz2m+vXrW9QWLVqkffv2uakjS6NGjdJjjz1mqG/dulW5ublWx7Ru3dpQ27Vrl9N7K66UlBQtXbrUUB82bJhMJpNDH3PmzDHMM2vWrBL3mJqaqu7du2v//v0W9dDQUK1Zs6bIIyIAAAAA4Fbi5+enYcOGKTAw0N2tAAAAAChFkZGRhtrp06eLPd+ZM2csPvf29rb6MCz+x9/fX126dLGoXbp0qVi7ygG2ECxwEh8fH7377rsWNbPZbLHtiLs9//zzhlpWVpbN1FinTp0Mta1bt6qwsNDpvRXHggULSnRGz82sXbvW8B8vR6SlpelPf/qTYZeHcuXK6YcffrAa3AAAAACAW1loaKiGDh3q1PNaAQAAAHi2hg0bGmrHjx8v1ly5ubmGHQvq1q0rHx9Od7+Z2rVrG2onTpwo/UZw2yJY4ERDhgzRXXfdZVFbt26d1q5d66aOLDVt2lRBQUGG+uXLl61e369fP0MtISFBq1evdnpvxWHtGARnKiwstLqTgT3S09PVo0cP7d6926IeHBysH374wfDnBAAAAABuF7Vr19a9997r7jYAAAAAlJKWLVvKy8vylmNsbKzy8/Mdnis2NlZ5eXkWtVatWpWovz+KgIAAQy09Pd0NneB2RbzHyf7xj38oJibGovbKK6+oW7du7mnoBuXKlVNmZqZFzVbKq3bt2oqJidGmTZss6h9//LF69erlqhbtcvDgQcMxExUqVFCNGjVKNO/Ro0ctjoaYPXu23njjDYeetsnIyNC9996rnTt3WtTLli2r77//Xm3atClRjwAAAADg6aKjo3XhwgXDsXAAAAAAbj9BQUFq2bKlxQ7OmZmZ2r9/v6Kjox2a68cffzTUOnbsWOIe/wiSkpIMtfDwcDd0gtsVwQIn69Spk3r16qXvvvvuem3//v1auHCh289/KSwstLo7QeXKlW2OGTNmjCFYsGrVKn333XduDRfMmDHDUHvnnXf05JNPlmje+++/X19++eX1z0+dOqUNGzaoa9eudo3PzMxUr169tH37dot6UFCQvvvuO91zzz0l6g8AAAAAbgUmk0m9evXS5cuXDduYAgAAALj93HvvvYajob/++muHgwVLliyxOjdu7sYHXiWpSpUqbugEtyuOQnCBv//974YtX8aPH2/xJLw7bN261bB9TFBQUJHBggEDBqht27aG+ujRo3Xx4kWn92iPnJwczZ8/36Lm5+enoUOHlnjuBx54wFCz98iFrKws9enTR1u3brWolylTRqtWrVKHDh1K3B8AAAAA3Cp8fHw0dOhQBQcHu7sVAAAAAC42fPhwQ2327NkO3Rvbv3+/9uzZY1G7++67FRkZWeL+bncHDhzQ0aNHLWohISFq3LixmzrC7YhggQs0b95cDz74oEXt5MmT+uSTTxya5+TJk1qxYoXT+nrvvfcMtXvvvdfmUQj/9Z///Ef+/v4WtaSkJHXv3l1nzpxxWn/2Wrp0qZKTky1qvXr1UoUKFUo897333mvYFuabb74xrHeja9euqX///obdHQIDA7VixQp16tSpxL0BAAAAwK2mbNmyGjZs2E1/7gQAAABwa2vatKnat29vUUtKStLkyZPtnuO1114z1J566qkS93a7Kygo0PPPP2+o9+nTR76+vm7oCLcrggUu8tZbbykgIMCiZm37lqJcvHhR/fr1U+vWrfXNN9+ooKCgWL2YzWaNHTtWP/zwg+E1awmyGzVr1kxTpkwx1A8dOqS7775b69atK1Zf/5WVleXQ9dZ2ELC200Bx+Pr66v7777eoXbt2zbBDwu/l5uZq0KBBhvfhv6GCLl26OKU3AAAAALgVVatWTX379nV3GwAAAABcbPz48YbaxIkTtW/fvpuO/eijj7R69WqLWmRkpEaMGGHX2jExMTKZTBYfc+bMsWusO02bNk1r164t9vicnByNHDnSsJu2yWTSs88+W9L2AAsEC1ykRo0aTvsXdt++fRo0aJAiIiL04osvKjY2VoWFhXaN3b59uzp37qwPP/zQ8FpMTIwGDx5s1zxPPvmkXn/9dUP9woUL6t69uzp16qTVq1crJyfHrvnMZrNiY2P1xBNPaODAgXaNkaRTp05pw4YNFrXQ0FD16dPH7jluxpHjEPLz8zV06FB9//33FvWAgAAtW7ZMXbt2dVpfAAAAAHCruuOOO9SuXTt3twEAAADAhXr06KH+/ftb1HJyctS5c2ebO3Tn5eXprbfesvrE/b/+9a/b/on7vXv36k9/+pPatm2rjz/+2O5jyPPy8rR06VI1b95cixcvNrz+2GOPqU2bNs5uF39w7EXoQq+99ppmzJihlJQUp8x34cIFTZ48WZMnT1ZwcLCio6MVFRWlKlWqKCwsTEFBQcrIyNClS5d0+PBhbd68WSdPnrQ6V9WqVTVjxgyH1n/77bcVEhKicePGGYINW7Zs0ZYtWxQYGKh27dopKipKFStWVFhYmIKDg5Wdna2MjAwlJCQoLi5OO3fu1Llz52yuVbNmTav1WbNmyWw2W9Tuu+8+w1ENJdGmTRvVr19fv/766/XagQMHtH//frVs2dLi2sWLF+vbb781zBEQEKCXX35ZL7/8crH7iIqKcvh7BAAAAACeqmvXrkpKStLx48fd3QoAAAAAF5kxY4b27t2rs2fPXq+lpaWpX79+ioqKUv/+/RUZGans7Gz9+uuvmj9/vhITEw3zPPvss059qPRmzp07p169etl8PTc311CLjY3VnXfeaXNMtWrV9N1339m1/s6dO7Vz504999xzaty4sVq2bKkmTZqoQoUKCg0NlclkUlpams6dO6cDBw5oy5YtunLlitW5OnfurH/+8592rQs4gmCBC5UvX16vvvqqXnnlFafPnZ6erg0bNhie3rdH7dq1tXbtWtWtW9fhsWPHjlV0dLRGjRql06dPG17Pzs7W+vXrtX79eofnlqQWLVroww8/VLdu3QyvFRYWWt22xlnHIPzeyJEjNWnSJIvazJkz9dFHH1nU8vLyrI5PSUkpcaAkNDS0ROMBAAAAwJN4eXlp0KBBmjFjhq5everudgAAAAC4QHh4uNasWaMuXbrowoULFq/FxsYqNjb2pnMMHTrU6hHdrpSbm6uDBw86NCYzM7PIMcW5T1RYWKjDhw/r8OHDDo+VpAEDBmjBggUKDAws1nigKByF4GLPP/+8IiIiijW2QYMGevXVV9WoUSOn9OLn56dx48bp559/Vr169Yo9T0xMjOLi4vTee++pSpUqJe7Ly8tL3bp108KFC7Vv3z6roQJJWrNmjRISEixqtWrVUocOHUrcw42shRXmz5+va9euOX0tAAAAAPijCAwM1LBhw+Tn5+fuVgAAAAC4SOPGjbVnzx6H79/4+vpq0qRJWrhwoby9vV3UnWcJCgpyyjw1atTQkiVL9M033xAqgMsQLHCxgIAA/e1vfyvW2AoVKujdd9/V0aNH9csvv+jf//63hg8frho1aji0focOHfTRRx/p7Nmzeu+995zyl1RAQIDGjRunM2fOaMmSJRoxYoRDIYOqVauqT58++uCDD3TmzBmtXbtWw4YNk5eX7T+SM2fONNRGjBghk8lUrK+hKHXr1lXbtm0taikpKVq6dKnT1wIAAACAP5KKFStq0KBB7m4DAAAAgAtFRERo8+bNWrx4sdq1a1fkvZyyZctq9OjROnTokCZOnFjkvaLbzb///W8dPnxYH374ofr27evQvbbKlStr2LBh+u6773Ty5EkNHjzYhZ0Cksl844H1uCWkpaXp119/VXx8vK5evar09HTl5OSobNmyCg4OVvny5dW4cWM1aNCgVFNdZ8+eVVxcnBISEnT16lVlZ2fL19dXoaGhCg0NVYUKFdSkSRNVr1691HoCAAAAAHierVu3asOGDRo3bpz8/f3d3Q4AAAAAF0pKStLu3bt14sQJpaWlycfHR+H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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.ticker as ticker\n", + "\n", + "\n", + "def prepare_ci_triplets(df, metric_col):\n", + " \"\"\"\n", + " Given a DataFrame df indexed by model name with column metric_col,\n", + " returns a list of (base_name, non_ci_value, ci_value) for every\n", + " model ending in '-CI' that has a corresponding base_name.\n", + " \"\"\"\n", + " # if the column is CSR percentages, convert\n", + " if df[metric_col].dtype == object and df[metric_col].str.endswith('%').all():\n", + " vals = df[metric_col].str.rstrip('%').astype(float)\n", + " else:\n", + " vals = df[metric_col].astype(float)\n", + "\n", + " triplets = []\n", + " for model in df.index:\n", + " if model.endswith('-CI'):\n", + " base = model[:-3]\n", + " if base in df.index:\n", + " start = vals.at[base]\n", + " finish = vals.at[model]\n", + " triplets.append((base, start, finish))\n", + "\n", + " triplets.sort(key=lambda x: x[1], reverse=True)\n", + " return triplets\n", + "\n", + "\n", + "metric = \"Hypervolume\" \n", + "\n", + "triplets = prepare_ci_triplets(scores, metric)\n", + "\n", + "\n", + "all_vals = [v for _, s, f in triplets for v in (s, f)]\n", + "lb, ub = 0, max(all_vals)\n", + "\n", + "class_map = scores['class'].to_dict()\n", + "color_map = {\n", + " \"Opt\": \"gray\", # blue\n", + " \"OAGM\": \"#46a2e8\", # purple\n", + "}\n", + "\n", + "draw_lineplots(\n", + " triplets,\n", + " lb, ub,\n", + " class_map=class_map,\n", + " color_map=color_map,\n", + " scaling=\"linear\",\n", + " metric_name=\"Optimality (Hypervolume)\",\n", + " # decimal_places=3\n", + ")\n", + "\n", + "metric = \"Constraint Satisfaction Rate\" \n", + "\n", + "triplets = prepare_ci_triplets(scores, metric)\n", + "\n", + "\n", + "all_vals = [v for _, s, f in triplets for v in (s, f)]\n", + "lb, ub = 0, 100\n", + "\n", + "draw_lineplots(\n", + " triplets,\n", + " lb, ub,\n", + " class_map=class_map,\n", + " color_map=color_map,\n", + " scaling=\"linear\",\n", + " metric_name=\"Constraint Satisfaction Rate\",\n", + " # decimal_places=1\n", + ")\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "26962025", + "metadata": {}, + "source": [ + "## Scorecards" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0416bba7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\Lyler\\Documents\\Bike-Bench-Temporary\\src\\bikebench\\benchmark_models\\../..\\bikebench\\design_evaluation\\score_report.py:99: UserWarning: No model_colors provided; using Matplotlib cycle.\n", + " warnings.warn(\"No model_colors provided; using Matplotlib cycle.\")\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[10], line 31\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m all_names:\n\u001b[0;32m 29\u001b[0m dashboard\u001b[38;5;241m.\u001b[39mshow_model(name)\n\u001b[1;32m---> 31\u001b[0m \u001b[43mcreate_scorecards_conditional\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcpu\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[1;32mIn[10], line 19\u001b[0m, in \u001b[0;36mcreate_scorecards_conditional\u001b[1;34m(device)\u001b[0m\n\u001b[0;32m 16\u001b[0m use_case \u001b[38;5;241m=\u001b[39m conditioning\u001b[38;5;241m.\u001b[39msample_use_case(num_samples, split, randomize)\n\u001b[0;32m 17\u001b[0m condition \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRider\u001b[39m\u001b[38;5;124m\"\u001b[39m: rider, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse Case\u001b[39m\u001b[38;5;124m\"\u001b[39m: use_case, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEmbedding\u001b[39m\u001b[38;5;124m\"\u001b[39m: emb}\n\u001b[1;32m---> 19\u001b[0m dashboard \u001b[38;5;241m=\u001b[39m \u001b[43mScoreReportDashboard\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 20\u001b[0m \u001b[43m\u001b[49m\u001b[43mdesign_batches\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mall_result_tens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 21\u001b[0m \u001b[43m\u001b[49m\u001b[43meval_funcs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mget_standard_evaluations\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 22\u001b[0m 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arr[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmean_obj_keys]\u001b[38;5;241m.\u001b[39mto_numpy(\u001b[38;5;28mfloat\u001b[39m)\n\u001b[0;32m 134\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_const_rates[name] \u001b[38;5;241m=\u001b[39m arr[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconst_violation_keys]\u001b[38;5;241m.\u001b[39mto_numpy(\u001b[38;5;28mfloat\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\Lyler\\Documents\\Bike-Bench-Temporary\\src\\bikebench\\benchmark_models\\../..\\bikebench\\design_evaluation\\scoring.py:232\u001b[0m, in \u001b[0;36mconstruct_scorer..scorer\u001b[1;34m(designs, condition)\u001b[0m\n\u001b[0;32m 230\u001b[0m designs_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(designs, columns\u001b[38;5;241m=\u001b[39mcolumn_names)\n\u001b[0;32m 231\u001b[0m designs_reverse_oh \u001b[38;5;241m=\u001b[39m 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2353\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21munique\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ArrayLike: \u001b[38;5;66;03m# pylint: disable=useless-parent-delegation\u001b[39;00m\n\u001b[0;32m 2354\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 2355\u001b[0m \u001b[38;5;124;03m Return unique values of Series object.\u001b[39;00m\n\u001b[0;32m 2356\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2414\u001b[0m \u001b[38;5;124;03m Categories (3, object): ['a' < 'b' < 'c']\u001b[39;00m\n\u001b[0;32m 2415\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2416\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munique\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File 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\u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 440\u001b[0m uniques \u001b[38;5;241m=\u001b[39m \u001b[43mtable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munique\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 441\u001b[0m uniques \u001b[38;5;241m=\u001b[39m _reconstruct_data(uniques, original\u001b[38;5;241m.\u001b[39mdtype, original)\n\u001b[0;32m 442\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m uniques\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "def create_scorecards_conditional(device=\"cuda\"):\n", + "\n", + " data = data_loading.load_bike_bench_train()\n", + " all_result_tens = []\n", + " result_dir = os.path.join(\"results\", \"conditional\")\n", + " all_names = os.listdir(result_dir)\n", + " for name in all_names:\n", + " if os.path.isdir(os.path.join(result_dir, name)):\n", + " result_tens = torch.load(os.path.join(result_dir, name, \"result_tens.pt\"))\n", + " all_result_tens.append(result_tens)\n", + " num_samples=10000\n", + " split = \"test\"\n", + " randomize = False\n", + " emb = conditioning.sample_image_embedding(num_samples, split, randomize)\n", + " rider = conditioning.sample_riders(num_samples, split, randomize)\n", + " use_case = conditioning.sample_use_case(num_samples, split, randomize)\n", + " condition = {\"Rider\": rider, \"Use Case\": use_case, \"Embedding\": emb}\n", + "\n", + " dashboard = ScoreReportDashboard(\n", + " design_batches = all_result_tens,\n", + " eval_funcs = get_standard_evaluations(device),\n", + " model_names = all_names,\n", + " condition = condition,\n", + " column_names = data.columns.tolist(),\n", + " device = device\n", + " )\n", + "\n", + " for name in all_names:\n", + " dashboard.show_model(name)\n", + " \n", + "create_scorecards_conditional(device=\"cpu\")" + ] + }, + { + "cell_type": "markdown", + "id": "ef7fd5b7", + "metadata": {}, + "source": [ + "Plot unconditional score cards for the first condition index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd2e6cc7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\Lyler\\Documents\\Repositories\\Bike-Bench-Temporary\\src\\bikebench\\benchmark_models\\../..\\bikebench\\design_evaluation\\score_report.py:99: UserWarning: No model_colors provided; using Matplotlib cycle.\n", + " warnings.warn(\"No model_colors provided; using Matplotlib cycle.\")\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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kuyYKpZiYGJowYQLp9XoR77Bhw6hDhw4EgC6++OJKn48sNy688ELq168fRUVFUXFxMbVq1YoaN25MN9xwQ5BCaePGjaTRaEij0dCoUaNo1KhRpFQqKTY2lo4ePUpERPfffz9dccUVdMcdd9D48eMpNjaWAIgBSqA8bNWqFd14442kVCoJAM2dO7fadxk4UfT5fJSSkkIAaPr06URUUaGUnp5OAGjMmDE0efJk6t+/Pw0ePFiUR6vVEuBXEk6ePJl69uwp6recpkKhoNGjR9PNN99Me/bsoauvvpoAUNu2bWnChAnUsmXLoHoil1GSJGrbti1NnDiRYmJiCADdddddRET0xx9/UK9evWjChAl01113UZcuXQgAde/evUKZNRoN3XTTTdSsWTMCQP379yciIovFIs41btyYJk2aRMOGDaMHH3yQfD4ftWrVigCIAeXcuXOD7m9I/vrrLxo1apT4fd9999EXX3whfpeWllLbtm0r3Ofz+Sg1NVXIlO+++67CBMnhcFBiYiKVlJQ0TObDUB+yccaMGRUUSitXrqR77rkn6NqOHTvo0ksvbaiinNPIbWbgwIE0ZcoUmjJlCt1zzz1C5vz+++/VtgtZLkqSRP3796dx48aRQqEgADRv3jwiInrwwQcJAKWnp9P48eMpMzOTAL8SiihYZsuKmhtvvFH0P4EKLVlmvf/+++Tz+ah///4EgFq3bk0TJ04UMvSJJ54goroplCRJoqysLBoyZAgBIK1WS8nJyTR+/HjS6XQEgD788EMi8k/kJUmi6OhoGjduHA0aNIgAUPPmzYXijImM6uRiIDabjbp27UqbN2+uUi5eccUV9M033xAR0XPPPRf0weRUU1c5+fHHH9MLL7xA48ePFwql5557jl544QVx//Dhw2nNmjUNVoZff/1VtKedO3eGDRNJOwVAXbp0oTFjxojfK1asICKifv36EQAaMmQI3X777TRkyBDq0KEDEfmVBdHR0QT4FT5Tpkyhn3/+OWh8kZKSQpMmTaIpU6YQEVHbtm3phhtuoLvuuku08ejoaPGxtzKFEgC66KKLxD1KpZKys7Pp559/Fu0+LS1NyNRwfPnll2K8dOmll9L06dPp999/D1LIVTfuef3116l9+/ZiXDplyhTRLwaOrbOzswkAGY1GuvXWW+nmm2+mrKwsevLJJ2nfvn10yy23iPBTpkyhJ598koj8H2rGjRtHd911F1111VWkUChIqVSKOvvGG2+I+4YOHUq33HILNW/enEpKSqp9H/K8Pi8vjxITE8X4W+4/JEmiZcuWBb0HADR8+HDq3bu3eFfh5kdEFRVKpaWlNHDgQAJA999/vwhX1Ri8qKhIKKEA0C233EJTpkyhtWvXnpHyfvny5WEVSseOHRNKVCKil19+mZ555hkiOs0KpQ0bNpBarRYPePr06VRQUEBWq5U+++wzMhgM4lpoxa7uqMmAlxVKzJlCx44dqaCgQPxOTk6uEObyyy8X1iBERB06dAi6h8j/pUK2diEieuihhyguLo6MRiO98847FeJcv349dezYsT6KEESgEmL48OGk0WhowIABpFAoguq7rFByu920cOFCeuqpp2jq1Kk0YMAAAkCdO3cWcebm5lJSUhJJkkQAqEePHkFfdAM7vcA8TJ06lYhOflGIi4sjp9NJ27ZtE/fIzzEShdKrr75KREQjR44kAOI5Lly4kABQVFRUpc8nUKE0b948MbABQE899ZSQE7JCaeLEiSINeWARaMVD5LcG+Oqrr2jmzJl0//33U7du3Qg4+dVK7hQVCgUdOXKEiPwDYgB099131+hdBtKjRw8CQJMnTyaiigql5ORkUigUNHfuXNq2bRs5nU7xBVEuT/fu3cXkjojE+5TT/M9//iOuHT58WDx7eSA3btw4AkAGg4G8Xq8oo0qlory8PCIi+uKLLwgA6XQ6kdbatWvpueeeo4ceeohuuukmEW9xcXFQ+nJH+e233wa9008++USkKw8YA/Mvfwy57rrrguqIPGFsSL755pug9/n8888HTQo2b95Mffv2peuvv56ysrJo6tSp5Ha7qaCgIEgWrFu3ji6//PKguBcsWBD2409DUh+yMVSh5Ha76ZJLLqGioqKgawsWLKCRI0fSFVdcQRdccAHNmjWrIYp0ThI45gl3LF++vNp2IcvFLl26iHhvu+02AkCDBw8mp9MpFPhjx46lKVOm0OTJk0UaJ06cCJLZsvWITOvWrQnwK7eOHj1KCoWCoqKiqLy8nNavXy8mi7KSXpYd0dHR5PP56qRQUqlUVFhYSCUlJeIeuV1eddVVQXJYnqz07t1byHvZmvWrr76q93d3PlCdXJTp3bs3GY1G8bGqKrm4a9cuatq0KaWlpVHbtm2FhdPpoC5ysqysjPr160dOpzNIofTzzz9T//79yeFw0NGjRykxMbFKS4668tlnn4m2IVsUhxJJO42LixPWt7LiWX7nPXv2FL83btxIVqs1yPpKlmeBFuyBCqVdu3YF5evQoUP02muv0X/+8x+aMmWKkFPfffcdEVWuUGrXrh15vV7y+XxkMpkIgFBShsqbyvB4PDR+/HgxNpaP9u3bi2dEVP24JzSPMoFj6507dxIAatasGS1atIj27dtHPp9PPLtwY2Yiv7LnnXfeoccee4ymTJkiPki+/PLLRETUokULAkAPPPBAULnkMVtV70NWKMkrGdq2bSsUpXL/IbdZuYzyx83AlVCBbSOQcMYp8vg8cHVHdWPw0GcpcybK+8oUSuvXrw8aF86bN0/I1UsuuYQyMzPpggsuoDfffLNO6asQId26dcOnn36KW265BXa7HU8//TSefvrpCuGmTp2Ke+65B/DX0ArXJ0yYgI8//hgA8Pbbb+OOO+6INCsMc1oJV69rEkaSJPH333//jffeew9//fUXAP9uiPv378exY8dgt9vRv39/DBo0SDjALyoqws033xzWl0B9cu+99+Knn37C8uXLMWrUqLA7MowePTpo3b5Mfn6++LtRo0aYNGkSnn32WQDAgw8+CLVaXW36spNo2cF/y5YtodFoEB0dLcJYLBYkJiZWuNfj8dQ43vbt2wOAiLemvldGjx6NtLQ0LFmyBBqNBrfddluQbyIAOHz4MADgn3/+Ef6WZPbt2we3240+ffpg8+bNFeIPfIaA/zk2adIEABAXFwcAEe/CQUQiTykpKWHDfPDBB3jkkUcwYcIEAIBOp8OUKVPw7LPPint79+4NheKk+73Q9xnojFK+BwD+97//BYWz2Ww4fvy4+J2YmCg2QJDfk8PhQGFhIT799FM89NBDYfOcn58vngkAdO/eHcDJ5yS/UzkvLVu2RKNGjSrk/5ZbbsHjjz+O77//Hrm5uVi6dCkMBkOla9Lrk+rkhNvtxrp16/DGG2+gc+fOuPnmm/HRRx+F9YsUeB/g94kwbty4es9zVdSHbAzlzTffxNixYyv4hHC73fjjjz+wZcsWJCcnY+jQoejRowcGDRoUecbPUwLHYA6HA3q9XlyrabuQZSlwsv0eOXIEBQUFsNvtABDW99++ffuEbANQwZntxIkTMW3aNHzxxRdo27YtfD4fxo4di+joaBw8eBAAEBMTg7S0tKC0zWZzWJ804aisz0hJSUFCQkLYcsp9hiyHZfmyZs0arFmzpkIZmcipqYxYvXo1zGYzxowZgx07doTt3+T73nrrLbz99tsYPnw43nzzTTzwwAMNPp6qjLrIyRkzZuDRRx+t4Otm6NChWLt2LS688EKkpaWhd+/eUKkinurVmMBnfejQoSA5IBNJO23fvj2ioqIAVBzrzJkzB/fccw8effRR+Hw+qFQqXH/99fjoo4+CxiSV5bNdu3bi95o1a9C/f/+w/opCx1+hdO3aVaQXGxuL8vLyiMdjSqUSc+fOxXPPPYfff/8dy5YtwyeffIJdu3bh1VdfxfPPP4+XXnqpxuOeqmjfvj1mzZqFV199VfgcTk5OxhtvvIGxY8eGvUf2QVZaWho2beCkzOvbt29QuSJBrhvt27cXbVSuG/I1mdCxHVCzcfCdd96JNWvWYMuWLfjtt99w/PhxtG7dOqIxeChnk7yvSo5+8cUXaNy4MYqLizF06FB07NgRl1xySa3Sicgpt8y4ceOwfft23HfffWjfvj0MBgM0Gg3S09Nx7bXXYuXKlXj55ZdrlSGGOZN57bXXhPOylJQUHDt2DABQUlIStLuhTFpamgjj8/lQXFwsJkQHDx7EzTffjG+//VYMWhcsWIA+ffpAr9cjPj4e/fr1w4YNGwAATqcTo0ePxrRp09CnT58GLeeQIUPQunVrAH7lUiilpaVCmfT111/D6/UKB+KBwkvuHHU6HQDgkUceCdtBhRI6AKqqkzIajSJPAIIcnNcl3uryd+eddwLwy0NZERKIPEm65pprQH5rUBARiouL8cILL2Dnzp2iI1uzZg18Ph9uv/12ABU7gEClTVWT7qp4+eWXkZeXBwBBGxYEMnjwYOzatQslJSVYtWoVVCoVnnvuORw5cgRNmzYF4FeC+nw+cU/oZEyr1VZ4BgCwc+fOoOeQnZ0ddL2wsBAFBQUiLOBXaCUmJgqn5/feey+cTmdQB17Zswp9TnL+Dxw4IJ5DYP4TExNx9dVXw263i11JR40aFaTEbCgC5QQAHD16FKmpqeJ3kyZN0KJFC2RlZUGhUGDkyJHYsmULEhMTUVxcLJ5B6H12ux2//fYbrrzyygYvQ33KxnCsW7cOzz//PJo1a4Y5c+ZgxowZ+PDDD9GkSRP06NED6enp0Gq1GD58OLZs2dIQRTwvqWm72LVrl/hbbr/p6elITEwU8v+nn34KkgH79+/HRRddFBRPoPwAgPHjx0OpVOLbb78Vu4/KOwQ1b94cgH9jhNzc3KB8GI3GsB8c5P4CqL7PCDcRr6zPkGXZI488ElTG48eP48EHHwx7D1M11cnFQKKjozFw4ED8/PPPVcrFL7/8EsOHDwfg75tXr17dwKUIpr7k5MaNG3H33XejWbNmmD9/PiZNmoSlS5cCAGbMmIEtW7Zg8eLFsNlsaNWqVYOVp2/fvkhKSgLg3xApcDxgs9lw9OjRiNppVWOdrl27YuPGjSgvL8e6devQpEkTfPLJJ+KDrNw2A8cnMqFyZd68eXC5XOjZsydKS0tht9sRExMDoHpFX1V5rCoPgezatQvHjx9HSkoKrrvuOnzwwQeiXprNZgCo0binJul5vV48+uijyMvLw/Hjx/Huu+8iPz8f//nPfyq958cff0RpaSnS09ORl5cHn88nlIVy2vKYKrAN+Xy+iPIm143du3cHPZvAazKVje2q4+WXX8aGDRvQrVs3FBUV4f777weAGo/BZeVhYDnOJnlflRxt3LgxACA+Ph5XX3011q9fX+t0aq22btmyJV599dVaJzx37lzMnTs34vsq22UtnABo1qxZ2PMzZ86ssMViVXGHy2tlcTPnNvfddx/uu+8+AP6BwaeffoouXbrgk08+wYgRIyqEHzFiBD755BOMHDkSixYtQp8+fSBJEkpLSzFy5Ei8+eab6Nixowifnp6OTz/9FA888ADcbjdWr16N2267DUSECRMmYODAgbjpppsavJySJGHRokU4duwYBg4cWOF6VFQUoqOjYTab8dJLL2Hx4sUVtqR3OBy49tprYbfb8d5772Ht2rX43//+h9tuuw3z5s2rt7x269YN//zzD1555RXk5uYKy8eG5t5778WFF16ITp06hb1+55134rPPPsO8efNQUlKCFi1a4NChQ1i5ciV+/vlntG7dGkqlEl6vF9OnT0diYiIWLlxYr3n85ptvsGnTJmzZskV0FI888oj40hNKVlYWmjZtimbNmsFsNsNms0GlUiE6Ohp33303Pv/8c6xfvx49evRA9+7dsXPnTjz00EOVKqgyMjJwxRVXYNGiRRgwYABGjBgBu92ODRs2IDU1NWg7WZ/Ph379+qFv37749ttvAfitIxQKhbAoWrhwIWw2G5YtWxbxsxg9ejSaNWuGQ4cOoVu3bhg2bBgKCgrQpk0bPP/88wCAO+64A1988QV++eUXAMDNN98ccTq1oWfPntixYweOHTsGk8mEn376KWgnktTUVCQlJeHgwYNo3rw5VqxYIb7o9erVC4sXLxayJnA73p9++gn9+vU7JUqx+pKNlfH555+Lv2fOnInExERMnDgRHo8HeXl5KCkpQUxMDFatWiUGhUz9UJN2sX37dgwYMAApKSnCEmnSpEnQarWYPHkyXn/9dVx77bXCqm7btm0oLS2t8BU6lNTUVAwdOhSLFy9GSUkJWrduLXbe7datG/r164c//vgD/fv3R79+/YTsuP/++8PWp7Zt28JoNMJiseDGG2+sN7l77733YsWKFXjppZewZ88eJCcnY9++ffjzzz+xb9++sFa+TNVUJxfLy8vhcDiQnJwMp9OJpUuXivdemVxMSEjA33//jV69emHZsmVo27btKS1TfcnJVatWiTATJkzAmDFjMHjwYHg8HpjNZsTFxeGvv/6C0+kMGmPWN3q9Hu+99x7Gjh2LefPmYdu2bejbty/KysqwcuVKPP/88xg/fnzE7TQcI0aMgNvtRqtWreD1enHixAkAJy1WmjZtigMHDuDxxx/HwoULMWnSpErjkscUO3fuxJQpU7Bjxw7YbLa6PAqRBwDYsGED7rzzTrRo0SLs7ubLli3D1KlT0adPH7Ru3RoWi0V8pB08eHBQHqsa98jpffLJJygtLcWAAQMwevTooDBHjhxBjx49cPHFF6NRo0ZC4V+VhZOcdm5uLqZOnYpjx45h//79QWEeeOAB3HPPPXjppZewc+dOpKWlYdWqVVi7di1iY2Nr9D5uuukmPPvss9i9ezf69++P1NRUzJs3D5IkCcVPfaBUKjFr1iwMHToUP/30kxiD1mQM3rRpUxw6dAh33XUX2rVrh4cffviskveNGzeGUqnEtm3b0KFDB3z55Zf43//+B4/Hg9LSUiQmJsLhcGDJkiVh62qNqdOCOYY5j7HZbDRy5Ehq2bIlXXzxxZSfn09ERD/88AM9/vjjROR3ojh58mRq0aIFde3alfbu3UtERE899RQZjUbq0qULdenShXr27ElE/vXHkyZNovbt21P79u2FP5g//viDJEkS4bt06VLljmS1oTK/OzJAsA+lRYsWUatWrUir1dKll15KTz/9NAGglJQUIiLh3HXYsGFERGQ2m8Wa6w8++CAozlAfSrJPn0C/RUTBa73le44cOUIDBgygqKgo6ty5s9h1LVC8yb/ltdyh/o7C+dUIJTQvoYTGSUS0Zs0aGjJkCCUnJ5Ner6dWrVrRbbfdRsePHyciog8++IDS0tJIr9fT1VdfTVOnTg1KI9yOGHI6sr+PcAT6RlGr1dSoUSMaMmQI/fDDD0HhQn0o3XfffdS6dWvS6/UUFRVFF1xwQZD/BXmXt0aNGpFWq6X27dtX2OUtcL08kX9HspkzZ1K7du1Ip9NRYmIi9e/fX+wWEljGV199lVJTUykuLo4mTZok1rrv3r2b+vTpQzqdjtq3by/8IQEQfhFC0w/3TnNzc+m2226j5s2bk0ajoSZNmtD7778flN+OHTsSAEpNTW3Q3XFC+eGHH6h169bUsmVLevfdd4mIaNiwYXTs2DEi8q+Dv+CCC6hTp0504403ksPhICKivXv3UteuXalFixY0efLkIP9W11xzzWlZz18X2VhWVkZpaWkUHR1NsbGxQXVfJtS/0k8//USdOnWijh07Cv9rTPVUt8tbYFuurF3IcvHaa6+l2267jaKjoyk9PZ1efPFFEcbtdtOrr75KmZmZFBUVRXFxcdSrVy+aM2cOEVXuw0Pmu+++E9flPlGmqKiI7r77bmrWrBnp9Xrq0KEDvfTSS8K5bTifJvPmzaOMjAyKiYmhK6+8km699dYgmRq4y5tMaP8XTg7/9NNP1K9fP4qPjyej0UgdOnSg+++/n51y14Gq5OLhw4epW7dulJmZSR07dhQOnokql4srVqygrKws6ty5M/Xr14/27dt3WspFVDc5GUigDyWLxSLGj5dccgllZ2efkrJs2LCBxo4dSykpKaRSqahRo0Y0evRo+ueff4iodu1UHp/I/oGeeeYZ6tChA0VFRZFOp6N27doFya6VK1dSmzZtxMYlCxYsCDuGIvKPS8aNG0dGo5EaNWpEb775ZgV5WJkPpcA2Hzpm9fl8dPPNNwvfSoG+5QLZtGkTXXfdddS8eXMyGAxkNBqpc+fO9N5774kwNRn3HD9+nC666CKxYYrsBDxwnFxUVERXXnklpaWlkUajodjYWBo0aJCYQ4STv16vl+666y6KjY2l+Ph4mj59ungfjz76qAj3zTffUK9evSgmJoaioqKoV69eYqfnmr6Pf/75h0aOHEkpKSlkMpmoV69e9OOPP4rr4fxEheujAgm3yxsRUd++fYN8JFU3BpfL2LRpU+HvSt5x+0yS94MHD6bExETS6/WUlpZG69atCxo/rlmzhjp06EAtWrQQz9FisVDXrl0pMzOTOnToQDNnzqxTHiQiNrNhGIZhTg8rVqzAgAEDkJGRUamV6Klk+vTpmDVrFh566CG88MILpzs7DHNGUFm7mDlzJp588kmMHz++VlbnDMMwDMOc3TScpzaGYRiGOUvYuXMnFi1ahI8++ggqlQp33XXX6c4Sw5x2uF0wDMMwDFMVtXLKzTAMwzDnEuvWrcN//vMfEBE+/PDDCg4hGeZ8hNsFwzAMwzBVEfGStwMHDuDpp5/GsmXLkJubC61Wi7i4OLRs2RKdO3fGs88+G7TlLFNzmjVrhpycHAA1206UYRgmHAW2Anyz9xuMbTMWSYak050dhmkQuJ4zDMOEh+UjwzCniogslA4cOIAePXrgo48+wuHDh+F2u2GxWHDkyBGsWLECr732GqxWa0PllWEYhqkBBfYCvL31bRTYC053VhimweB6zjAMEx6WjwzDnCoiUii98sorKC4uBgA89thjKCgogN1ux+7du8WWl0qlskEyWh31sd1jQ2O32093FhiGYRiGYRiGYRiGYepMRAqlvXv3ir+HDx+OxMRE6HQ6tG3bFjfddBMWLVqEuLg4EaakpAT/93//h86dOyMqKgp6vR6tWrXCHXfcERTv/v37MWnSJDRr1gwajQYmkwl9+vTB+++/H7T069ChQ5AkCZIkoX///vjxxx/RvXt36HS6IEeRq1atwujRo9GoUSNoNBokJyfj6quvxsaNGyuUqSZ5XLVqFUaOHImWLVsiJiYGKpUKiYmJGDRoEL7//vug+ObOnSvyOGPGDDz//PNo1aoVVCoVvv76awBAcXExJk2ahISEBERFRWHQoEHYtm1bJK+COY8wm81YsWIFzGZzRGHMZjMWLVqEDz74ALm5uRXCmc1mLFmyBEuWLKk07pqkXV9lOBs5U8tls9qC/j+fqMs7OR3vMzc3F3PnzhVtlKk553o9l+tjbm5ukNyuaR1tqLCh91XXj9SF0Phrm1515TtTZXldONvLdLbn/3SSm5uL7xd8DwBYs2ZN2GdY1+cbad8VOv6sLO1TIbcaOq76TofbwvlDbed8p5uIFEpNmzYVfw8ZMgRjx47Fyy+/jDVr1sDtdgeFPXToELp06YJnnnkG27dvh81mg8PhQHZ2Nr766isR7u+//8YFF1yADz/8EDk5OXC73TCbzVizZg1uu+02jBs3Lqw/oW3btmHkyJHYuHEjnE6nOP/222+jf//++P7775GXlwe3242CggJ899136N27N3788ceI87hp0yYsXLgQBw4cQHl5ObxeL4qKivDbb79h9OjR+PLLL8M+r7feeguPPvoosrOz4fV6AQAulwuDBw/Ghx9+iOLiYthsNvz222/o168fioqKInkdzFlKbm4uZs6cWeNO2GKxYOXKlbBYLBGFsVgs2LRpE44dO4bCwsIK4SwWC/7++2/8/ffflcZdk7TrqwxnI2dquWx2W9D/5xN1eSen430WFhYiJydHtNGaEKkMOVuItFznej2X62NhYWGQ3K5pHW2osKH3VdeP1IXQ+GubXnXlO1NleV0428tUm/yzbPRTWFiI/IJ8AMD27dvDPsO61o9I+67Q8WdlaZ8KudXQcdV3Omd7W2ZqTm3nfFVxKuRiRAql++67D1qtFgBgtVoxf/58PPjgg+jTpw9SU1Mxe/Zsofy57777cOTIEQBAr169sGnTJlitVuzcuRMPPvigiHPSpEnigUybNg2lpaXYuHEj0tPTAQDffPMN5s+fXyEvJSUlGDt2LI4cOYLy8nI89thjOHbsGKZOnQoiQteuXbFr1y44nU5s2LABSUlJcLvduO222+DxeCLKY//+/bFs2TKcOHECTqcTVqsVixYtEtdffPHFsM+rsLAQL7zwAoqLi5Gbm4tBgwbh888/F5ZSLVq0wLZt21BcXIwbbriBBcV5Qm5uLp588slzbsDDMMyp4VyVIedquRiGOTWcqzLkXC0XwzANz6mQH6pIAmdmZmLTpk2YMWMGfvnllyAFSFFRER577DFER0fj1ltvxc8//yyuffXVV8jIyAAAtG/fHo8//jgA/1K3nTt3AgASExPx1FNPQalUomvXrnjggQcwdepUAMDChQsxduzYoLyYTCb873//Q1RUFAAgOjoaH3zwgbBW2rRpE9q3b1+hDLm5udi6dSs6duxYozwCQJMmTfDxxx/j7rvvRk5OTgVfSHIZQhk4cCAeeuihoHNLly4Vf0+dOhWZmZkAgBdeeAHvv/++UHYx5z67du2qUbjS0lIRvjJhEC6MfA4ADh48CLfbHRQuNC/h4q5J2vVVhrORM7VcB/MP+v8/eBBGq/E05+bUUpd3cjre59GjRwGcbKM1oaay42ylpuU71+u5XB8PHvSXM/C51KSORlKfa1v3A/uZhmg3ofEHEkl61ZXvTJXldeFsL1Nt8s+y0Y/crwTeF/oM61o/Iu27wo0/65qv+qzjp6q91Cads70tMzWntnO+qjglcpFqicPhoNWrV9MzzzxDTZs2JQAEgPr27UtHjx4Vv6OjoyuN488//xThsrKygq4tWLBAXBs0aBARER08eFCc69GjR4X4nn76aXG9quOXX36pcR69Xi917ty52jhlPvroI3Hu4YcfrhDfZZddJq5///33QdcaNWpUIT7m3GPjxo01qqfykZqaSjNnzqTU1NSIwsjnZs6cSZmZmRXCBV6vLO6apF1fZTgbjzO1XE26NaFOcztRk25NTntezqZ3cjreZ2ZmZlAbjeTYuHHj6RZn9UqksvFcr+dyfZTrSKDcrkkdbaiw4e5rqHYTGn9t06uufGeqLK+PZ3e2lqku+T/fZWNmZiZNeWYKdZrbiaY8MyXsM6xr/Yi07wo3/qxrvuqzjp+q9lKbdM72tsxH/daP2taHhpSLEVkolZWVISYmBgCg1WrRu3dv9O7dGxdffDEuuugiAH5LpYSEBKhUKng8HpjNZhw+fDjI/5JMSkqK+Pvo0aPwer1il7hDhw6FDSdjMBiqjO/222/HO++8UyEMEUGSJDgcjhrlcfv27cJhdkpKCn777Te0b98eNpsNJpMp7HOqKo+JiYnib3m5HeBfQhiJDw3m7Oezzz4La0UXSmlpKf744w989tlniI2NrXEY+RwAPP3002jSpElQOADiemVx1yTtmlBf8ZxpnKnl2p6/HS/mvYinn34amcmZpzs7p5S6vJPT8T6PHj2KzZs3izZaE3bt2oUbb7yxgXN2+qipbDzX67lcH59++mls3rw5SG7XpI5GUp9rW/cD+5mGaDeh8QPV91tVxdPQfd2ZxNleptrkn2Wjn6NHj+L3f34Pui/0Gda1fkTad4Ubf9Y1X/VZx09Ve6lNOmd7W2ZqTm3nfFVxKuRiRAqle++9FwUFBbjhhhvQr18/NG7cGCUlJfjiiy9EmI4dO0Kn02H48OFYuHAhAOC6667Dm2++ibZt2+LIkSOYN28epk+fjlatWqF9+/bYtWsXCgsLMWPGDDz88MM4ePAgXnnlFRHnlVdeWaP8DRs2DFqtFk6nEx999BH69OmDkSNHQqPRYM+ePZg/fz6WLVuGNWvW1DiPKtXJR6RUKmE0GlFWVoZHH300kkcnGDx4sHD4/corr+CSSy5Beno6HnvsMV7udp7Rvn17dO3atdpwubm5+OOPP9C+fXukpqbWOIx8DgCaN2+OzMzMoHDAyYF5ZXHXJO2aUF/xnGmcqeWy7LEAef733rVt9XXsXKIu7+R0vE+1Wo3NmzeLNsrUXDae6/Vcro/NmzfH5s2bg+R2TepoJPW5tnU/sJ9piHYTGj9Qfb9VVTwN3dedSZztZTrb898Q1FQ2qtVq4J/g+0KfYV2fb6R9V7jxZ13zVZ915FTVt9qkw23h/KG2c77TTUQKJZ/Ph19++QW//PJL2Ot6vR7Tpk0DALz22mvYvHkzjhw5gtWrV+OCCy4Q4WJiYjB9+nQAwAcffIBBgwbBZrNh1qxZmDVrVlCcV111FcaMGVOj/KWlpWHOnDm466674HK5MH78+AphZD9JNc1ju3bt0KlTJ+zYsQPHjx9H8+bNAQBt2rSpUZ5CueGGG/Dmm29i48aNOHDgADp37gzAb81kMBhgs52bu9UwJ0lNTcWMGTPOGCHAMMzZxbkqQ87VcjEMc2o4V2XIuVouhmEanlMhPyJSKE2dOhUZGRn4448/xFaRbrcbjRo1Qr9+/fDoo48KBUlGRga2bNmCl156CQsXLkR2djaICGlpabj00ktFnH369MHmzZsxe/ZssZOaVqtFx44dMWHCBNx2222QJKnGebzjjjuQmZmJOXPm4K+//kJBQQFMJhPS0tLQt29fjB49WoStSR6VSiUWLVqEqVOnYuXKlfB6vRg0aBBeffXVGi9PCESj0WDp0qV4+OGHsWDBAjgcDvTq1QvPP/88xowZg5ycnIjjZM4uUlNTMXPmzBqHNxqNuOSSS2A0Vu50NlwYo9GIrl27Ii8vTyy1DA3Xq1cvcb62addXGc5GztRyGfSGoP/PJ+ryTk7H+0xMTERGRkbQcujqiFSGnC1EWq5zvZ7L9TExMTGoXta0jkZSn2tb941GY7X9SF0IF39t0quufGeqLK8LZ3uZapN/lo1+EhMTkZyUDMC/oVK4Z1jX+hFp3xWaXmVpnwq51dBx1Xc6Z3tbZmpObed8VXEq5KJERNSgKTAMwzCnlJ1FOzHux3H4esTX6JDQ4XRnh2EaBK7nDMMw4WH5yDDMqYIVSgzDMAzDMAzDMAzDMExEKE53BhiGYRiGYRiGYRiGYZizC1YoMQzDMAzDMAzDMAzDMBHBCiWGYRiGYRiGYRiGYRgmIlihxDAMU4/klOfgxp9uxIgFI3Ddj9chuzQ7bLgt+VswZuEYjFk4BqO+H4Un1zwJl9cFANhbshfjfx6PKxZcgdE/jMbM1TPFNQBYlL0IVy+8GqN/GI1bl9yKXEtupfnZXbwbd/12l/j9wIoHMHDeQGR+nAmb2ybOO71O3Pf7fRixYATGLByDO369A8csx8T1T3d+iiJ7kfj91pa38OL6F8Omuad4D+787c5qnhRzvlHTtgEA+bZ8XPfjdfCRDwAwe+1sDJk/BJkfZ2Jfyb6gsESEt7a8hRELRmD0D6Nxyy+3iGvf7/8eh8oOBf1+YMUDYdMstBfi+sXXw+Pz1KGUDMOcb7y95e2wsimQ7/Z9h8u/uxzDvh2GmatnBsmZlUdW4ooFV2D4d8MxdfnUoL65phw1H8U1i67BmIVjMPqH0XhgxQMoc5ZVGn754eX475r/AgBsbhtu//V29PuqH/p91a/Sex7/6/EKY4e3trwFt9ctfv/fn/+HL3Z9Efb+FUdW4Mk1T0ZYMoZhznRYocQwDFOP/HfNfzGmzRj8OPpH3NLpFjyx+omw4drGt8WXI77E/Cvn47uR36HEUYJv9n4DANAqtXjswsewaPQizL9iPixuCz7+52MAwIGyA3hl4yt4d9C7WDByAa5oeQWe+vupSvPz6qZXMbHTRPF7bJux+OaKb8KGHdNmDBaNWoT5V87HxU0uxpOrTw78Ptv5GYodxTV6Bm3j20IlqbD+xPoahWfOD2raNgDgvW3v4br210Eh+YcpgzIG4eNhH6NxVOMKYT/f9Tn2lezDgisXYMHIBXj+4ufFtR/2/4Cc8pwa5S9Rn4jMxEz8eODHCEvGMMz5ys6indhauBWpUamVhjlqPoo3Nr+Bj4d9jJ+u+glF9iJ8t+87AH5lzhOrn8CrA1/FT1f9hER9It7b9l7E+Ug2JOOTYZ9g/pXzsWDkAqQYUvDutncrDf/a5tcwKXMSAEClUOGWTrfg/cHvVxp+xZEVkCBVOP/21rfh9rkr3hCG/un98U/hPzhSfqRG4RmGOTtghRLDMEw9UWQvwq6iXRjRYgQA/yT4mPlYkKWPjF6lh1qhBgC4fW44vA4xWMswZaBtfFsAgFKhRKeETjhqOQoA2F+yH23j2yJRnwgAuKTJJfjz2J8odZRWSCPXkovs0mx0b9RdnOvduDcS9AkVwmqVWlzc5GJIkj8PXZK6iDTf3vo28u35eGDFAxizcAx2F+8GAOTb83HPsnsw8vuRmLRkUtDX0OEthmP+3vkRPD3mXCaStuH0OvHLoV8wKGOQONe9UXc0imoUNu6P/vkIU7tNhVrpb09JhiQAwLd7v8U/Rf9g9rrZGLNwDFYdXQUAsLqteGTlIxj9w2iM+3EcjphPTm6GtxiOb/d+Wz+FZhjmnMbldWHW2lmYfuH0sMoWmV9zfsXApgORqE+EJEkY23Ysfj74MwDgj2N/oGNCR7SIaQEAuLbdteIaAPx17C/c/PPNuGbRNbh+8fXYcGJD2DQ0Sg10Kh0AwOvzwuaxQVHJNG9j3kaYNCakGdPEvb1SeyFaEx02fKmjFG9vfRsP93g46Lxs4XTjzzdizMIxwor5QNkB3Lr0VoxYMAL3L78/yIJpcLPBWLB/QaXPimGYsw9WKDEMw9QTJ2wnkGRIgkqhAgBIkoTUqNRKl6QdsxzDmIVj0O+rfjCqjRjbZmyFMDa3Dd/u+xaXNLkEANAuvh12Fu3E4fLDAICF2QtBIBy3Hq9w74a8Deic1LlWZfl81+cizTu73IlkfTJe7v8y5l85H+3i2wEAthdsx6yLZuGHUT8gXhcvLKwAICspC2tz19YqbebcI5K2saNwB5pGN4Vepa82XovLghJHCZYdXoYbFt+AGxbfgF8O/gIAuLrN1eiY0BHTek4TVneAv97e3+1+LBi5AL1Se+HDHR+K+DokdMDu4t21WnLCMMz5xRtb3sCIFiPQJLpJleFyrblobDxpXZlmTEOu1S/7TlhPBF1rbGyMfFs+fOTDEfMRvL31bbx16VuYd8U8PNvvWTy66tFKLYLcXrd/TPF1PxwuP4w7utwRNtz6E+vRJblLjcs5a+0s3NXlrgoKpyd6+61MPxv2GeZfOV98rNpdvBtvDHwDP4z8AUX2Ivya86u4h8cGDHPuwQolhmGYeiT0KyWBKg2bZkzD/CvnY8U1K+DyuvDb4d+Crrt9bjy86mH0adwHA5sOBAA0NTXF470ex7Q/p+G6H6+D1WNFtDpaTNQDybPlCUumSHh/2/vIMefgvq73VRnuorSLEKONAeC3aAq09EjUJ6LIUVRjU3jm3KembSPPmhfWii4cHp9HWPh9fvnnePGSF/HC+heq9GVyQcoFYgIXWm/VCjWiNdEotBfWKH2GYc5PtuRvwT+F/+DattfWKHyg/COqfFwQyF/H/sIR8xFM+GUCxiwcI/y/nbCeCBterVRj/pXzsfKalWgW0wzz9s4LGy7PlodEXc3GBksOLYFaocYl6ZfUKDwAXNb0MuhUOr+FdWKnCmODE7bw+WcY5uyk4gyEYRiGqRWNDI2QZ8uDx+eBSqECEeGE9QRSjZX7VgAAg9qAYc2HYfGBxRjWfBgAvzLpoRUPIUmfhP/0/E9Q+MsyLsNlGZcB8DsS/mDbB0iPTq8Qr06pg8PjiKgMc3fMxW+Hf8P7g9+v1kJEq9SKvxWSIsjJqNPrhEqhEsv6mPObSNqGTqWD0+OsUbyxulgYVAaxlC7VmIqs5Cz8U/QPWse1DntPYL1VSkp4fd6g606vMygMwzBMKBvyNuBg2UEM/XYoAL+S5o5f78DMPjPRr0mwY+vUqNSg5b3HrceFz6VGUY2CLHaOW44j2ZAMhaQAgdC3cV880++ZCunPXjsbG/M2AgCe6fcM2sS1EdfUSjVGtRqFmatnBvlQlNEpdXB6ayZj159Yj7Un1mLI/CHi3KgfRuGNS98ISjMQjVIj/lZKSnjppIx1ep3QKXU1SpthmLMDtlBiGIapJxL0CWgX30449f0151ekGdOEn4JAjpQfEdY7bq8bv+X8JgZnHp8Hj6x8BDHaGMzoPUP4NZIpsBUA8PtJeGXjK7i23bVhlT9t4trgYNnBGuf/438+xk8Hf8J7g96DSWMKuhaliYLFbalxXAfKDlQ62GTOPyJpG23j2+Jgec3r7bDmw/DXsb8AAGXOMuwo3CHqnlFthNltrnFchfZCqBQqJBuSa3wPwzDnH7dm3orfr/kdS8YswZIxS5BiSME7g96poEwC/B+Bfj/8OwrthSAifLPnG/Hx6KK0i/BP0T84UHYAAPDV7q8wtLlfSdWncR/8dfyvIIvL7QXbAQDTLvQv5Z1/5Xy0iWuDXEuuWKrrIx+WHFpSaR8cydhgeq/pWDZ2mSgnAHw/8nsRd5Q6CmZXzWXswbKDwkckwzDnBmyhxDAMU4880fsJTP9rOj7Y/gGi1FGY1XeWuDZj9Qz0b9IfA5oOwPq89fh0+adQSAp4fV70TO2J27vcDgD45dAv+O2wX8E0dpHfr1JWcham95oOwL91b641F26fG/3S+mFK1ylh83JBygU4YT2BMmeZWJp277J7sbN4JwDgigVXoKmpKT4a+hFOWE/gxQ0voomxCSYt8e/8olFq8MXl/u1/b2h3Ax7/63HolDo8fdHT1T6Hv479FeRUmWGqahuBpBnTEKeNw/6S/WgV1woA8PTfT2P5keUoshdh8tLJMKgN+OmqnwAAU7pOweN/PY6v93wNAJiUOQkdEjoA8O9c+OKGFzF3x9xql3AC/no7sOnACkpchmGYSAjs79Oj03FX1l24+eeb4SMfLky9EKNbjwbgV8jM7D0TU36fAi950Tq2NWZd5JeNGaYMzL5oNmaungmH1wG3z4328e3xXNJzFdLbV7oPczbNAeBfUtc+vj2m9ZwWNm+XpF+Cd7e9C6/PC6VCCQC4ZtE1KLAXoNxVjku/uRQ9G/XE7H6zqy3n+A7jcevSW6FVavHuoMp3lZP589ifuKzpZdWGYxjm7EGimi7kZRiGYc46PtzxISRIuKXTLacsTbfXjWsXX4sPBn+AOF3cKUuXOXf45dAv2HBig1CinirG/zweM/rMEDsuMQzDnIs8/ffT6NGoB4Y0G1J94HqixFGCW5feiq8u/0rsyskwzNkPL3ljGIY5h7mx/Y012i2rPjlqOYopXaewMompNUObDUXzmObwke+UpVloL8Q1ba9hZRLDMOc8d2fdDZfXdUrTPGI+gsd7Pc7KJIY5x2ALJYZhGIZhGIZhGIZhGCYi2EKJYRiGYRiGYRiGYRiGiQhWKDEMwzAMwzAMwzAMwzARwQolhmEYhmEYhmEYhmEYJiJYocQwDMMwDMMwDMMwDMNEBCuUGIZhGIZhGIZhGIZhmIhghRLDMAzDMAzDMAzDMAwTEaxQYhiGYRiGYRiGYRiGYSKCFUoMwzAMwzAMwzAMwzBMRLBCiWEYhmEYhmEYhmEYhokIVigxDMMwDMMwDMMwDMMwEcEKpTOEZs2aQafTVXo9Li4OjRs3BgB88803kCQJ27Ztq3CNYRimOnbt2gVJkvDXX3/V+B5Z7hQUFDRgzhimYZEkCc8999zpzkadsdvtUCgUmDNnTqVhJEnCPffc0yDpx8bGok+fPg0SN1M/tG7dGlFRUac7G5WiUqmQmZl5urNRL8TFxaF79+6VXtfpdGjWrFmDpH3ZZZfBaDQ2SNwMI8N9Z/1wrvadrFCKAEmScPXVVwedC1XuNBQlJSU4fvx4tdfqIz/vv/8+tFotJEkSR9u2bWsdH8OcSlQqVVDdVSgUSElJiUh5Uh/cc889QfmQjzOBoUOHIjk5GX379gXgV2hLkoQLLrggKJxCocDFF18MABg7diyio6Nx6aWXnvL8Mg1H6KTus88+gyRJSEpKgtfrPS15+vbbbyFJEgwGw2lJvzJMJlPYNr1q1apTnpfBgwdDq9Xi/vvvP+VpA/5xwpo1a1jBfJqoTBkTOOnbt28frFZrndLxer2intvt9jrFVZ9cfPHFYdviyJEjT3lePvnkE5SWlmLp0qWnPG0A+Omnn2C1WjF79uzTkj4TnpiYGEiShLfffvuUpcl9Z/Vw39kwsEKJqcBtt92G5ORk5OTkwGaz4bXXXkPr1q3rPR2LxVLvcTIMAHTq1AlEBI/Hg9deew1msxkXXXQRVqxYETZ8Q9ZFIgo6IqW+82axWHD48GFMmzatwrUtW7YgOzu70ntvvvlmbN++vV7zw5w5vPTSS7jpppuQkZGBgoICKJXK05KPBx54AID/S+K6detOSx4qIzExsUKblpWugXi93goTcLvdHrGSrrL2v3r16tMyeZYZO3YslEolrrvuutOWB6bhuffee8Xft9xyy2nMSUUkSarQFn/44YewYcO1o0j71srCP/LII0hJSUF8fHxE8dUXGo0GGRkZ54T1yLlCdnY2ysvLAQBPPvlklWHrc4zHfedJuO88tbBCqZ657LLLgrSvLVq0AOAXLjqdTpxXqVR4/vnng+4lIqHRliQJo0ePFtdMJhOSkpLCphl47ZprrgEAdOnSBZIk4bLLLoNCocAll1wSdI9Go0G7du0qxPX3338DAN588000bdoUer0e9957L3788UcRZteuXUhISBD5VCqVWLhwIQBgxYoVMBgM4lqTJk1Eo5YtNrp37w5JkhATEwMAmDx5MpRKpbAmGTt2bA2fNsNUjVKpxD333IP8/HxIkoTrr78eQOV10Wg0Blk23XXXXUHxZWVlievp6elBFjyRsHfvXsTGxoq44uLicPDgQQAnrQz79esHSZIQHR0NAHjqqaegVqvFPYmJiSK+p59+Ouha7969K037v//9LwBU+Dqj1WqhVCpx2WWXVXrvrFmzAOCUfnFjTg2PPPIIHnroIXTp0gWHDh0S51UqFdLS0qDRaES7ePnll8X17OzsoP4gOjoaO3bsENc/++yzIIvXli1bwuVyVZoPl8uFw4cPC+uLW2+9Nei6JElo166d6DNUKpXofwDg9ddfF9fUajVSU1OhUqkqTW/o0KFQKBSiL3vkkUdq/MxCkSQJrVu3hlKphEqlwttvvy0s/xQKBQwGA7Kzs/Hkk0+KPEqShCFDhog4mjVrBq1Wi/j4ePG8Qvn111/h8/lEW5afW9OmTUWcXbt2jais27Ztg16vF/fLsq4qmjZtitWrV9fmUTGngFBXCpIkoXPnzuIdGwyGoLYajk8++QRarRbR0dH4/vvvg66ZTCbExsYGjfkCl4lEWqci6ceqw2QyISYmRuTtoosugkqlQmpqqkhj0qRJtRqzhpKXl1fBcnfkyJEiztjY2Aofk6oqq8vlQnp6elBbrm4JzqhRo1BWVlbbx8XUMzfddBMAoHfv3sjLywt6NxdffDEUCgUyMjKENbAs9+Pi4sR7v+uuuzB69OigulkV3Hdy33laIabGAKCrrroq6Ny8efMIAG3dupUOHDhAAOi+++4jIqKtW7fSE088QUREO3fupKFDh9KBAwcoNzeXEhMTCQB5PB4iIsrIyCAA1KNHDzKbzXTNNdcQAPr000+JiCg6OpoSExMrpFndNSKirKwsUiqV4vfixYsJAC1durRCGT0eDwEglUpFw4cPp/nz51cIo9VqSa1W0y+//EJOp5OeeeYZWrNmDRERqVQqMhgMtH//fpo/fz5JkkStWrUiIqK7776bAFBsbCzl5ORQTk4OvfjiiwSAbr/9dnI6nTR16lQCQO+9916kr4dhiIhIqVRSp06dKpxv0qQJKRQKIgpfF4mILrroItq+fTuVlpZSu3btCADt2bOHiIgmT55MAGj69OlUVFRE6enpBID69esXNh9yGuEwmUykUqlo7dq1tGbNGlIqlRQTE0NEJ9uwVqulrVu3Uk5OjmizXbt2pdzcXMrJyaEpU6YQEdGyZcsIAA0fPpxsNhu98sorBIAmT54cNu2uXbsGyQMiv/zRarX0yCOPEAD6888/iYhIkqQK5ZMkiQYPHhw2bubsQ6lUkkajIQA0YMCAsNcB0DPPPENms5liYmKC6o9eryeDwSD6QIPBQFFRUUREdPToUQJAnTp1oqKiIvruu+9IkiTq06dPpfkZP348AaA1a9ZQ06ZNSZKkoOsASJIk+vzzzyk3N5c0Gg1FR0cTEVFpaSkBoGbNmlFRURE99thjBCAovwDo2WefJSKi66+/ngDQU089RU6nk0aOHEkAaO3atWHzFtjXhgOA6L9sNhsVFRURAFIoFPTLL79Qfn4+bdiwgQBQly5dqLS0lO655x4CQA8++CARnRwL9OvXj8xms5BNgcj5DqRv374EgN555x3Kycmh6OhoAkB33313jcoaFRVFGo2Gtm/fTkuXLiWFQlGp/JK58sorqw3DNAyV9XOB9VuW64HX5Lq4detWUqlUou2EY+vWrQSAxowZQ3feeScBoOXLl4vrch27/fbbyWazUYsWLYLqQ3V1KrAMkfZj/fr1qyAbApHzNn78eHI6nZSTkyNk2fTp08nj8dDRo0cjHrOGkpOTQwDo888/F+c++ugjAkDXXHMNmc1m6t69OwGgjIyMGpW1T58+BIA++OADOnr0KJlMpqC2HI61a9dWOq5nTj0KhYKSk5Np//79BIBGjhwprvXr148AUPPmzamoqIhycnKE3B8xYgTZbDZq0qQJASCj0UgHDhyg1157TcjvyuC+k/vO08m5VZoGpqYKpR49etDOnTurjEvuqBcvXkxEJxtCIGq1mlq0aEFEdVMobd68mQDQV199RURErVq1Io1GU2neli1bRsnJySRJkmjkEydOJCKi5cuXEwD67rvvKtwnT3qXLVsmzl122WWiXHLnHJi3+Ph4SkpKCorHYDBQ8+bNK80fw1RFZQPtbt26VVkXwxHYWZlMJoqPjxfX5A64OoVS4JGenk5Op5MA0GOPPSbCPvzwwwSAnE6naMNyx01E1KZNG1KpVGHTadeuXdCkgYgoLS2NTCZT2PAtW7as0P4DJx5arVbIk3AKJYVCQV27dg0bN3P2IU+yKhsMKpVKSk1NFb8ffPBB0Y7+/PNPAkAbNmwQ1+XJlM1mo8svv7zCoLZXr16V1mUiv4JKrouffvopARAfZoj8bbJ79+7i96WXXirSmDJlCgEgs9ksrsfHx1c6KNZoNJSZmRmUvkKhCKtYIzo5SQ09AuMOlT2h44ZLL720Ql8fExMjFMoZGRlVTpRDyyyjUqmobdu24rfcV8uD4qrKarPZCADNnDlTXLv88surHfBOnDjxnBsUny0EttvQoyqF0vDhw8VvedLodDrDptG1a1cCQKWlpeJjY7t27cT16Oho0uv14rc8Bly7dm2N6lRgXx1pPyZPykOPjz76SOQtND6lUhk0qa3NmDUUOQ75oyoRUfPmzSv0sYEKperKqlKpgp6zLGerUijJ45HXX3+90jDMqeH1118nAPTcc88R0UmFg4xcdwPbXUZGhvjgGRjHO++8I84pFArq3bt3pely38l95+mEl7xFiNPpDPotOzyMjo5G8+bNMXnyZGzduhUdOnSASqUSS2aOHTsmzPAkSUKXLl0AIMjcWK1WB8UdFRWFkpKSOuc5KysLBoMBjz76KABg//79GDp0aKXhBw4ciLy8PPh8PmzYsAHJycn48MMPsXHjRixbtgwAgpbjychOjwcOHCjOhdv1onPnzuJvi8WCgoKCoGWCNpsNpaWltSorw1RGXl4eFIpgkRdYF10uF5o0aVLBgba8/MdutwctM6vM/D0UClgvfvjwYWHmOmjQIBFGbo/yklMAQUvPCgoKKt3FJT8/H06nMyjfx44dg8PhCBs+JiamyrXor7zyCgoLC/HNN99UWp7A58Cc/bRt2xYajQa9evUKqoMyCQkJ4u/ApdeyE1p5SYgkScLPyvLly5GdnQ0iCqqbf//9d6X1b926dbDb7bj88ssBADfeeCOUSiVee+21oHBNmzYVf8fExIjlJHv27AGAoLZSVV11u93Yvn17UP58Ph+OHj1a6T3h/EAEEs7MPtDZ/ZEjRyr09cnJyUE+I0Kvh5KSklIhXY/Hg+bNm4vf/fv3D7peVVnXrl0LAEFL4+UxSlUUFhZWG4ZpOGRfgZXVxXBkZWWJv+VlWpUtvdi8eTMSExMRExMDpVKJJk2aYPfu3UFhAneRk5fk5OTkRFynIu3HgPA+lCZMmCCuh+szU1JSxN+1GbOG0qlTJwDAgQMHxLnS0tIKu+sFtunqyurxeIJ2hJM3z6gKWfbxBjqnn6eeegqSJIllUddddx1cLhd++uknEUaSJGg0mqD7AuuIXE8Dl0JKkgSz2Rw2Te47/XDfefpghVIEqFQq7N+/P+jc8uXLAUBUxvfeew9OpxOlpaXo0KED3n77bbhcLlx88cUoKyvDokWLQETYunUrAARVbLfbHRS3zWZDXFxcxHkMx/XXX4+cnBwh4D766KMaxdetWzf88ccfAPw7ScgDkMB1tzJypxfo+HjTpk1Vxm8wGJCamlpByBQXF9cofwxTEywWC44dOxY0mAxl2LBhOHbsGD744AN4PB7RNuX/9Xp9UCdQW0eK8nahv/76qzi3ZMkSAECvXr3EucCOMSkpqdL0EhISoNfrK7ShUOW3zKWXXlqlQunOO++EyWTCxIkTK1wrKysDEWHUqFGVF5A561Cr1cjNzYVGo0GfPn1qvCOiPIjKycmpUP+GDx+OjIwMKBSKCtd8Pl/Y+CZNmgQA+O6778TAzev1orS0FIcPH642P/JkKrCtVDVwU6lU6N69e4X87d27t0blD0c4R+aBbTk9Pb1CX19QUAC9Xi9+V+d/4eabbwaAIAf6KpVK+GEDUGH3nKrKeuGFFwIAVq5cKcLLY5Sq2L59e1C+mTOfLVu2iL/l8Wu4Layfe+45EBEKCwtFW5Qniw899FC16URapyLtx2pCuHYUeK42Y9ZQ5An64sWLxbnY2NgKu+sFtvnqyqpSqYL82NVEHn/xxRcAgj9UMaeesrIy5OfnB31I+eCDDwAA9913X4Oly32nH+47Tx+sUIqA3r17Y8+ePZg9eza8Xi++/fZbfPbZZ0KLu2LFCowePRp79+6F0WgMsmCw2WyQJAkdO3ZEdnZ2BQ1oYBoWiwXXX389XC4XHn/88Yjy2LNnTwCosNPFO++8AwB48cUXkZiYWOluFFu2bEF6ejreffdd2O12HD58WDg9u/HGG9G/f39otVqMHTsWv/76K1wuF2bPno1169Zh+PDhUKlUGDFiBA4ePIgFCxbg119/Dat1lnnkkUeQm5uLu+66C3a7HQUFBXj00UfZ6S9Tb7z77rviq4Q86AqHbBWXlZWF4uLioC8WgN/hfXFxMZ588kmUlZWJL5ORotFoEB0djeeffx4bN27EunXr8PLLLyMmJqbCFyuZl156CR6PBz169EBBQQEOHz4snGrPmTMHdrsdI0eORFlZGcrKyvDcc89hxowZYeN64oknAPgdMFbG559/DovFUuFLzvTp0wH4lU7MuUV8fDzy8vKg0WjQr1+/Gm3nO3DgQOh0OmRmZgrLpt9//11YsL755pvw+Xzo1q0bTpw4Abvdjvfff79S57L//PMP4uPjsWzZMnHMnz8fwMmBYFXIu+l07twZZWVlmDFjRpUfJ6666ips2LABTz31FLxeLw4fPozJkycHbUJR37z44osA/B9rLBYL7r//fpSWloZV4FbGkCFDIElS0PigR48e2LNnD95//30cO3YMV1xxRdA9VZVVr9cjKioKzzzzDHbt2oXff/8dP//8c7X5OHLkSJASnDnz+eWXX7BkyRJs27YNzz33HIxGY9h+54UXXoAkSUFtcdmyZVCpVHj//ferTSfSOhVpP1Yf1GbMGo7k5GRhvQ8A06ZNg8vlwvXXXw+LxVLBuXh1Ze3Rowd2796NTz75BMeOHRNWJ1Xxww8/wGQyRZRvpv6R+6l33nknqN2kp6cjOzs74p3Kagr3nTWD+84GpLZr5c5HPB4PdenSJWj9Z6NGjYTjr6VLlwata1coFHTnnXcSkd8vkUqlEtcGDBhQYa27RqMRzvfwr3M2mZr6UCIiatasmYjjsssuq3BeXtcbjgMHDlRY66pSqYLW4W7fvp1iY2ODyrlo0SJRTp1OJ66lpqZSaWkpEVXupPj2228XTszk9AL9xzBMJITzLZGcnEwrV64UYcLVxQMHDpBWqxX3yE65A9dwZ2ZmiutNmjSp0kF1VU65d+7cGdTWY2JihPPvcH7QiPz+LgLLFtjmn3nmmSD5olAoKnVmSkSUnp5OKSkp4neorw0iotTUVEKIjyiTyRTWPxVz9hLqc6y0tFTI8GXLllW4/uyzzwbV6z179lBycnJQe2vSpIm4/umnnwb1CZIk0ZAhQyrkY/r06QQEO7eVSUlJEb4cQtvkVVddFZSfF198UfQnKpWKkpKSSK1Wi+uB/S4RCT9Pcv40Gk1YH4FElfuBeOaZZ8LmLVx6clkD+7yBAweKa+HaYjj69u1LOp1O/HY6nZSWlibizMrKquB3paqybt68Oeg9dezYMei5tmrVKug5zp8/nwBQbm5utXll6p/aOuUO7MN0Ol1Y/0C5ubkEgC6++OIK18aNG0eAf7OK0LGn7Bt03rx5RFR9nQotQyT9WGU+lDp37kxE4Z0Ah3tmtRmzhvLBBx8QAHEfEdHw4cNFnCaTiTQajfChVF1ZQ9vyBRdcQADokUceIaKT/m5kZL+MVTlsZk4NgY6uA1mzZg0Bfgf24RzKh7bVcOPAyto8953hn2FlcN/ZMEhENVh0zZwT9O/fH6tWrap0uQHDMDWnuLgYCQkJmDp1atA26mcDu3btQocOHfDnn3/WyD8DAHz77bcYM2YM8vPzg/zoMMyZjLxl97m2pbbdbkdUVBReffVV3HvvvfUe/6BBg/Dbb79V6pcnNjYW7du3x5o1a+o9baZhkCQJzz77rPCneaqprk6dzcTFxaFly5bYsGFDvce9ZMkSDB06FIsWLcKIESMqXB80aBBWr15dYZkdw9QF7jtrx/nad4Z3uMOcc2RnZ+OPP/4490zsGOYUMmrUKLz55ptwuVxi7bS8DOxson379hEP6q+++upzciLAnFvcf//9GDhwIIYOHYp77rkHZWVlGD9+/OnOVr2j1+vr9ePQ3LlzkZOTg+nTp+PTTz/FsmXL0Lhx40rD88YZTHVEWqfOZupjAx0Zi8WC8ePH48MPP8TBgwcxevRoKBSKsMokINgfI8PUFu47awf3nX7Yh9J5wKefforOnTtjxIgRQY7DGIaJDNkPWlZWFtq1a4fNmzdX6o+MaRj6fNEH1y2+Dk+ueRJPrnkSU36fgsyPM7GneM/pzhpzBnDBBRfgzjvvRGxsLJYvX45XX30Vc+fOPd3ZOuPJysrCl19+CZPJhMceewx33HFHnRysno1kfpyJzI8zccWCK5D5cSZ+OvBT9TedZcg+MU8FXKdqh1arxbFjx5Ceno4BAwagf//+FTYEYpj6hvvO2sFyzg8veatHvF4vFApFtV7mGYZhzlfqKid7fdELLWJaoG28f1eSYkcxfj/8O+aNmIf2Ce3rM6v1isvlqtTpOsMwp5/MuZmABLSIaYEDZQcwu99sjGgR3iqEYZhTi9frDbsTWGXY3DbMXDMT92bdi3RTegPmjGEYtlCqJ7xeL/Lz889ZUzaGYZi64na7kZ+fj6Kiolovnwu9T4JfMeWjM9s3XFU7pjAMc/qJdkcDOClT+Hsrw5w55OXlwel01jj8npI9+Pngz1h8cHED5ophGIAVSvUCEcHlcoGIYLfbeRDCMAwTBlk+ulwulJSUwOPxRBwHgfzWTV4AjrNHocQwzJmNkvzWDwrJPzRmmcIwZxaR+L4pshcBAMqc55ZTaYY5E2GFUh2x2Ww4ceJE0O4KLpfrNOaIYRjmzMTlckGv1yMuLg5ut1tYdJaXl6O0tLRGyngf+fxKJDsAF6Bw+7sxL3kbMOcMw5zr+CT/ZFVejssKJYY5e2FFEsOcOlihVEdsNpvYVlGj0UCpVEZkkskwDHO+4Ha7oVarodfrYTKZ4HK5YDabYbFYYLPZYLPZqo2DiAAf/AcAhYetCRiGqTuytaNsoURga3OGOZOIZAWIxW0BADi9PCdjmIaGFUp1xO12Q6fTwe12C0WSw+E4zbliGIY5s/D5fCAi4VRTdlBdVlYGnU4HnU5XI9npg08sTYEGkHwSQKxQYhimbkjkVyjxMlqGOfOQJCkihZLN7f9A5fDwnIxhGhpWKNUBeYJkMBigVCrFV/aioqJa+QZhGIY5V5FlokqlAgAolUr4fD7Y7XYYDAZotVrhi64qiAgK379dl8o/+VOQgid/DMPUHfJPXCVILFMY5izG5vlXoeRlhRLDNDSsUKoDXq/fZ4dSqUR0dDRiYmKQnJwsdjKyWCwoKChgJ90Mw5z3yPLS4/GgrKwMRCSU8lqtFmq1GkRUrTLeRz4oSAEoASj8kz8lKeEDT/4Yhqk7EiRIEiuUGOZMorYWSk4PL3ljmIaGFUrVUGQvwsa8jWGFmDxBUigUkCQJUVFRiIqKQnx8PMrLy1FcXAy32w23232qs80wDHNG4fV6oVAoYLFYYLVa4XA44PP5hPyULZeqkpdE5N/lzSf5e69/ezAFKSLa/eV0wB8WGObMR1YmsYUSw5zdyJZJbKHEMA0PK5SqwO1148afb8SEXybgxwM/Vrju9Xr9X8f/XbqhUPgfZ0xMDFwul5gY8fI3hmHOd2SFksfjgc/ng8Vigc/nE18dFQoFlEqlUNSHQ3aSqyCF6L0khQQllLzLG8MwdUYK+MdOuRnmzCFSCyW7xw6AnXIzzKmAFUpVsOzwMhw1H0W0Jhqf7PykwvXAyRARia1mDQYDFAqFmEBVNUFiGIY5Hwhc8maz2XD8+HHYbDa43e6g5cNVKeB95INE/sme6L2UfgUTT/4YhqkLGp/mpA8lXvLGMGcckSiUZGfcLq+robLDMMy/sEKpCn7N+RUJugS0j2+P3cW7UWgvDLouf1WXl1rIFkpKpRJxcXEwmUzVfnFnGIY5H5DloMvlgk6ng9PpFIfVagXgd9hdlbyU/ScFKZQkv0LJ42NLUIZhao/Wq/U7+YeCl7wxzBmG/NG+pshOudlCiWEaHlYoVYKPfFh9fDWSDclI1CcCALbmbw0O86+FUqhCCQCMRiOUSqVYDscwDHM+I8tBj8cDnU4Hk8mEmJgYqNVqmM1mAH6FUnUWSkpSAhJOLnlT+nd5Yx9FDMPUFYkkYaHEMoVhzhwibZOyhZLbx35sGaahYYVSJewv3Q+L24IkQxIMagMMKgP+KfonKIxsoSQLuECFkmy5FGjBxDAMcz4i7+gmSRK8Xi80Gg20Wi2ICEajEVarFR6PRyjgQ2Wm2+1GWVkZvD7vSYXSvx8r5a+Wbi8PGhmGqT0ajwZKUvotIAH2y8YwZzFCocRjA4ZpcFihVAlbC7ZCgoR4XTwAIEYbg93Fu4PChCqMAhVKgUvd+CsXwzDnMz6fT8hB2d+cSqWCQqEQu7tZLBbxt8fjgd1uh9lsBhHBarXCarXC7rAHOeQG/BZKAHhpMcMwdULj00DhU4hd3mril43lDsOcGiK1UJKXurl87EOJYRoaVihVwj+F/yBWFwuVwj/BidZEY1/JvqAw8sRIVigFru8NVC6xhRLDMOcz4WSkUqmEXq+Hx+OBWq2G3W4X110uF0pKSmA2m2G321FWVoaSkhI4XU6/hVKgQunfezxe9qHEMEztMXgMUJMaCsmvVPL6qlYWWSwW5OXlwW63n6IcMsz5S3FxcUS7Zju9Tigk9q/IMKcCVihVwo7CHYjRxIjfJo0JJ2wnxDaUQLCFkrzmXoYVSgzDMH7kr/ihfudMJhO8Xq9Y6ma326FUKmGxWCBJEjQaDaxWK8xmMzweD6xWa0ULJQVbKDEMU3eUpITap4b07z8fKh+7EREsFgsAwGaznaosMsx5i9frhctVc2sjp9cJlULFPpQY5hTACqUwuH1uZJdlI1YbK84ZNUYAwOHyw+KcbKEk+1IKJPA3EfGyN4ZhzltkBVLgzpiSJEGhUECtVsNmswnlkUKhgMVigU6nQ1RUlFju5vF4xAQOypNxS5IEn+SL6MslwzBMKD74oPKpauSU2+l0wufzQa/Xw+3mCSvDnAoiXfKmVqhZocQwpwBWKIXhYNlBeHwexGhPWihFq6MBAIfNJxVKgRZKVSmU5LAMwzDnI7IVksfjEY65LRYLSktLQURwOp1wuVzw+Xyw2WxwuVwwGAzQaDTIz89HWVkZfD4fysvLAQCkODmolOBXKLGFEsMwdYFAUHvVUEABCVKVTrldLpdYtuvzsfxhmIYmcBOk6iAiuL1uqBVqeHwe/qjPMA0MK5TCsLdkLwAEKZQ0Sg00Co2wUJKtjuTlG6EKpMDlbwArlBiGOX+RZaTH44FCoRBLROLi4qBSqYR1ksvlQllZGYhInCMiGAwGmEwmlJeVQ+1VB8lXSfI7zz3TfSjxgJZhzmy8Si9UPpXwoeSjysdtLpcLGo1GbCTACiWGaVgiccrtIQ988EGtUIvfDMM0HKxQCsPekr0wqo3QKDXinCRJiNZE44j5CICTk4PKLJTk5RwyrFBiGOZ8JdRCyW63Q6/XQ6/XQ6vVQqPRwGg0wmw2Iz8/Hz6fD263G1arFXq9HiqVCiaTCW63G1qPFiqbCkHuTRQ8oWMYpm4ofUqoSS12eavMQomI4Ha7oVKpYLfbQUS85JZhGphA/4vV4fL6fS3JCiW3l5e9MUxDojrdGTgT2VeyD9Ga6Arn9Sq9WPIWuGuRbKkUSuA5/jrNMMz5is/ng1KphNfrhUKhgNPpFA65dTodfD4ffD4foqOj4XQ6UV5ejgMHDgiFkyxLNToNtD4tVA4VYAVgBCD5l8BVtyMTwzBMVRDI70OJ/AqlysZtXq8XRASHwwG32w2Px8MKbYZpYCJZ8ubwOABA7NTNfpQYpmFhC6Uw7CneA5PGVOG8QW3AkXK/hZKsUKrMQkm+JsMWSgzDnK94vV6hfPd4PDCbzXC5XCgpKYFGo4FSqURZWRl0Oh2Sk5NhtVphsVhgs9ngdrsRHx/v97Hk9UEBBUhJAAFw+uMn+BVKLGcZhqktClJA69X693iTKrdQkhVK8o5T7EOJYRqeWlkoKf+1UGKFEsM0KKxQCsHsMqPAXgCTtqJCyag2It+eD7fPLbTklflQAk5q02WlE8MwzPkGEQn55/V6YbPZIEkSkpOT4XK54Ha7oVQqYbVaoVar4fF4EBsbi0aNGolJW3S032LU7rDDAw8kleS3r3UB8AGSwu/vhCd1DMPUGgmIUcQgThnnd/ZfiQ8lj8cjlElqtVpYWDIM03BEMpdyev1fm3jJG8OcGlihFEJ2aTYAIEYTU+FalDoKPvLhhPVEjS2UAneCYxiGOd/w+XxiWTARwWazwWg0IioqCjqdDmazGZIkwePx+H0kabWIi4tDWVkZjEajkJ/yLnBOldPvP8kNwAbADkABVigxDFMn3JIbcZo4qJ1+P0qVLaN1u91wOBxiSS7APtwYpqGJxCl3qELJ5XOx6xGGaUBYoRTCvtJ9kCCF9aEUpY4CABw1HxWCKdBSKRTZeikSM02GYZhziUDZ53a7gyyOoqOj4fF44HQ6hZWSyWRCSkoKfD4f1Go11Go1ysrKYDabodVr4YUXKrsK8ADQACgHdG4d3E433G7+CskwTO2wK+3+UbHf/UqlFkpWqxWAX34plcogK0yGYRqGulgoLT20FP2+7ger29pg+WOY8xlWKIWwv2Q/TFoTlAplhWsGtQESJBy1HBWWR4G7vYUSuOSNNeMMw5yPyANA2YktEcFk8i8pVqvViIuLE799Ph+MRiMcDgckSYLFYgEA5OfnQ6FQIMoUBQ1poPQqAS2AGAAqwOA1wOv0ih3izlS4H2CYMxeX0r+MDV7/jm+V+VCy2WzQ6/VQq9VQKBTCwpJhmIajLgql+Xvno8xZhkNlhxoqewxzXsMKpRD2luyFUW0Me00hKWBUG3HMfEws4Qhc+lYhPC95YxjmPEdeCuLz+eB0OqFSqWAwGMR1vV4Pg8EAr9cLjUYDj8cDj8cDk8kEn8+HlJQUREdHw2AwgBQErU8Ln8oHSACUAKIAj8oDGP1plZeXn56CMgxzVuNRePxyxQuofeqwCiVZMS7LMKVSCYVCAa+XNwVgmIYkko/zslNuldK/y5vZZQYAFDuKGyZzDHOewwqlAIgI+0r2IUZb0X+SjEFtwBHzkRpbKPGSN4ZhzmdkWenxeOBwOKDVaqHT6YLC2Gw2cc5utwvZKiuWDAYDNBoNXG4XfPABavgdcgOABlCSEl6fF3q9XuwMxzAMEwkexb9WRuRXKIVb8uZwOODz+aDX6wGcVCjxTm8M07DI44KaKJUcXv+61UAfSgBQ7uIPTgzTEJw3CiUiQm5JLgYPHgyLxYJLL70URqMRDz30kAhT5ChCmausgkPuPz/7U/ytcqjw+YOf18hCSfarxAolhmHOV2SFkryjm8FggFJ5ckmxw+GAxWLBlClTUFZWhuHDh6Nly5Z45plnEBUVhfLycjidTowdOxYP3PYAfn/2d2xbvs2vUCLgz6/+9C858Xpw4sQJDB8+HGazuUI+hg0bhuLi0/d10mazYciQIeJ3bm4uTCYT3njjDQBAaWkp3nvvPXF9xYoVGDNmTIV4PB4PBgwYwEozhomQgwcPQq1WIzs7G2q13/F2o0aNxHWvwusfFXv/VVJ7/Aqi559/XoQ5fvw47r77buGMW6FQCIUSj/MYpuEIdcpttVoxePBgLF++HFlZWeJQKpXYtX0XvFYvdi/eDcC/BM6yy4Kn7nyqQrzcpzJM3TkvFEpEhPuX349u93ZDWp80qNVqzJgxAy+88EJQuL3FewGggoXSX5//Jf6Oi4+DL8aHv//+O8gCKZxTblnJdDY5bPT5fPB4PGdNfhmGObORFUp2u11YHAVisVgwb948jBo1CpIkYerUqZg+fTpUKpVQKMXGxmLlypX4cPGH6PNwH6z9bi1spTbA7JfPHpUHXpdXpOVwOMS23nIebrjhBrz77runuviCL7/8EldffbX4/Z///AeDBg0Sv0MVSpWhUqkwaNAgzJ8/v0HyyTDnKqNGjULXrl1hMplw1113oVu3bkHXvZLXv+SNAAkSvC4vSktL8dxzzyEvLw8nTpyA1+tFSkoKVq9eDcA/yVWr1WyhxDCnAI/HI5RK//vf/zBmzBgMGDAAW7ZswZYtW/Dtt9+iadOmSGuTBq/Nix2LdwTfTxV9nXGfyjB157xQKP1++Hf8fuR3WNdZsT5hPUo8Jbj44ouFybLMlmNbkPNSDj6Z+AneuvEt7P97P5a9swwOiwNvj38bi19cDKPaCH0XPb785ktheRTOOgkItlqqqZnm6cDr9cJisaCwsBAnTpxAfn4+Tpw4gdLS0jM2zwzDnB14vV4oFAqUl5dDoVAEKZRcLhdcLhe+//57jBgxAnFxcbj44ouh0+mg1Wqh1WphtVqFnyVJIcHtdoN8BNIRls1dBofZgbX/txYL31oIl8sFr9eLqVOnolOnTrjmmmtgNpuRn5+Pnj174quvvjptz2HBggW48sorAQCrVq2CyWRCZmamuP5///d/2LlzJ7KysjBr1iwAQFlZGUaNGoU2bdrggQceEGGvvPLK01oWhjkb2bFjB2bPno2kpCS8+uqrQb7cAMDtcOOL17/AW9PewvL/W46Dfx3EE088gbKyMlx22WX4v//7P5SUlOCiiy7CZ599Jiwa5J3eWKHEMA1HWVkZysrK4HT6HW5/8cUXok+V+frrr3HNNdfA6XUi/7t8FB4qxP7H9yN/YT4AwFJu4T6VYRoA1enOwKngy91fIkGdgMPlh6GJ1WDBvgW4M+vOCuGWLFkCg8mAO9+9E0QEp82JVr1aYcMPG3Dnx/7wZc4y6DP0+PuDv4OcbocSaOEjWy/5fL6gpR6nG5/PB4vFIrbA1el0iI2NhUqlgsvlgtlshsfjQUJCQlgLLIZhmOrw+XxQq9UoKyuDTqcLUuRbrVb4fD7k5eUhLS0NZWVl8Hq90Gq1QUtJAL8Fzy3Db8Gh7EO4dOKliIqLwqUTLsWGxRsw6IlB6JDQAU6nE7t27cKnn36KxMREjBkzBr/99hsGDhwItVoNq9WKsrIyxMRU7ievISguLkZ+fj4aNWoEj8eDJ554At999x1ee+01EWbWrFnYs2cPNmzYAMC/5G3z5s3YtWsXYmJi0LFjR9x///1o2rQpOnTogI0bN57SMjDM2UxxcTF8Ph8GDhxYaRjbdhv0Rj1ufvBm7HTvRDIlY+Loifj888+xdu1amM1mFBYWon///vj4449RXl6OuLg4ochmy26GaTiKiorEEnmlUonc3NygJasAMG/ePHz44YfY7t2OtLFpKC4uRsK0BACAZZcFR3YfwR+L/uA+lWHqmXPeQqnIXoR1J9YhyZcEfbQejY2N8eOBH8Na3pTGlaJ8TzmWvrkUR3cchS5KVyGMUW2EKlqFgvyCCgolj8eD0tJSnDhxQlj6FBcXo7y8/IwbbHi9XhQWFsJqtcJoNCIlJQVxcXHC+a3RaERCQgLcbndYfyQMwzA1wefzweVywWazwWg0Qq32O8n0er1wOBxwOByIjY2FQqFAWVkZfD6fcNDt8Xig0+ngcrkQGxuLd39+F72e7oVdK3fBkm/x+1GSAJ/SBy+8sNvtaNWqFTp37oykpCR07dpVKJBiY2MRFxeHQ4cOnfJnsH79emGZ9eabb2Ls2LGIj4+v9r7evXsjKSkJGo0GnTp1Qk5ODoCTzknZ5wPD1Iz169dX+2HMlGrCkb1HsPTrpbDvtCPKEYXCwkJ4vV7k5eWhvLwcVqsV6enpyM/Ph9PpxNGjR1FYWIjy8nJ4PP7lNBaLBWazmS28GaYekf2eWSwWFBQUIDY2VlwjIuzduxdWqxVdu3aF3WOHSqpoM9GkUxPuUxmmATjnLZRWHl0JAGgS1wQelwdpxjSsPr4aB8oOBIUzu8woji7GyDdHwrHDgZ/n/Iwuw7rgwjEXBoVTKpQwKoyQVH6/SV6vF0qlUgwgFAoFDAaDmDTZ7Xa4XC5YrVZotVokJiZWukTuVOF2u1FcXAxJkpCUlASVKnw10Gg0iI6ORnl5OfR6vSgTwzBMTfH5fLBarSAiREVFCXljtVohSRLi4uLgcDiEdVJ0dLSQkU6nEzExMXA6nfD5fHCTG8oYJZKbJSNnSw46DugISAApCB7JI6xAbTYb9Hp90G5ySqUSHo8HXq9XbKpwqkhMTBQ+ndatW4c///wTL7zwAkpLS6FUKmEwGMJaTsiOf+X8By6p8Xq9lcpuhmGCSUxMrFbBE50cjVtn3IoDew/grwV/wdrDiqt6XwWfz4cDBw4gNTUVbrcbLpdLLMM9duwYiEhYe+v1epSX+3eSIqIKPuMYhqkdGRkZKC0thd1uh16vF0vfCgsLYTab8dVXX2HcuHEAAIfHAaVCCUiASqGCx/ev76SARSLcpzJM/XHOt5yVR1ciUZ+I2PhYeJweJGmSoFaoseLICigDJMv2wu1wl7iRnJqM+GHxUCgUOLjpIAC/5trn9UGh9E9ypEIJpjSTmKDYbDaoVCoYjUZER0cHTVSio6PFpEaeVMXGxgZNFE4lDocDJSUlUKvViI+Pr1a5FRUVBbvdjrKyMiQmJp6iXDIMcy4gWyc5nU4oFAoYjUZx3mazQafTCYWTQqFAXFxc0BJhr9eLuLg47Nu3z++jhLxwOV04vPMwevbvCUh++eyVvHDDv4McEaG0tBRlZWVwOBx+n0v/KpBKSkqQnJwMh8NRwYdeQ9KtWzc4HA54PB58/vnn4vzMmTORmJiIiRMnoqioqMbWoKWlpUhOTualyAxTQ2QH3BaLRcihUIosRdAka5A1Kgu57lzk7s6F0+mESqWC1WpFbm4uNBoNtmzZgvbt20OSJMTHx0OtVqOkpATFxcViWa9KpYLFYkFUVNQZ5eqAYc5WioqKYLVahULJbrfD7XYjLy8PHo8HX3/9tfCD5PA6oNFr4LK5guII55Qb4D6VYerKOa1QcnvdWHN8DVrEtAAAZFyQgdw9uUhKSMKDIx6EwqyA2+3GV199hbvn3g3fcR++ev0rSEoJao0aVz7md/bWZXgXvHXTW2jetTkuf+hy2PfYYWxvhN1ux9GjR6HRaJCYmAiv14vy8nKoVCqoVCoolUohnOSlHkSEoqKisMqnhsZms6GsrAxarTZo4lYVkiTBZDKhqKhICHGGYc4vZOVO4OYC8u6WCoWi0gmTvKzN4/EISxzAL4tkZTwR4ZJLLkF2djZat26NHj16IC8vT8jmjRs3oqioCNdffz0sHgsKLAXoP7I/GjVvBHj98nn5tOXIyczByEdHAgCSkpLgcDiEn7iCggLk5OSgZ8+e0Ov1sFgsp1yW9erVC+vWrUPfvn3DXk9ISEDXrl2RmZmJa6+9ttJwALBy5UoMHTq0obLKMOckRqMR//3vf/H888+LJS6AX5YtW7YMBfkF+N9b/4NCrYBb6Uaf+/qgUaNGGDduHKZMmYJ27dphzpw5+Oabb9CiRQts3boVjRs3hsFggFarRWFhIUwmE9LS0oTPNqvVylZKDFOP5ObmwufzoUePHvjtt9+QnJyMnJwcOBwOZGRk+JVO5XaYDCaktknF7sd2w9TThOg20XD7wi9pC9enEpGwgtJqtaxsYpgqkOgcXuS9Nnctbl16Ky5teinidHE4suMItv68FR1v64hNeZuw/JrlSND7nbVN+HkCcq256JPWp+pIvcB7d72Hjjd2xPSLpwMuICUlBXq9Xky4ZL9K8lI4WZGkVCphMplgt9ths9mg0WiQkJAAvV4PjUZTI2ElO38EIBRX1d1HRGLtf1RUFEwmU8SCsbi4GG63mzX4DFNHZOVMqIIGOKmkUSqVVSpqGjp/slWRy+UK2qa3MiRJEkp0tVotDrvdjn379sHj8eDEiRPo2rUrNBoN8vLy4HQ6oVarERsbi61bt+Lbb7/FjBkzAACpqakwm81QqVSIi4uD0+lEUVERVhSuwOtrX8fQ5KF+03UngCRgbeFaNEETTO8+HWVlZWjWrBmio6MBQGwwMH36dFx++eUYNGgQioqKEB0dLcKcChYtWoSffvoJb7/9dp3juvbaa/Hkk0+ibdu29ZAzhjk/mDZtGt59910UFxeHvZ71URau0F0BlUGFHY4dkAwSPhzwIcxmM4qLi7F79240btwY9913H2bOnAm1Wo3k5GTEx8fjxIkTyM3NRVJSEtq3b4+4uDiUl5fDZrMhOTn5tLs6YJizHavVin379uHAgQPQ6XTIzs7GypUrcf/998NoNMLlciEqKgpGoxFv7nwTewr2oGtaV3x37Dv4yIdoTTQaRzXG/CvnV4g7tE/1er1i3gNArOpga0OGCc85baG08uhKRKmiEKuNBQCkd0pH4aFCNI5qjE3YhN+P/I6xbcbC6rZia8FWdErsBLvHjgOlB9AitgX0qoAv2B4ALsBeaEfPy3pCq9LiQP4BNI9pDpvNBpfLJSaDslJJXlfv8/mg1WqFY26FQgFJklBWVoaSkhJERUVBr9cjKioqaIkc4Pd3JH/lslqtQpkk74CkUqlgMBhgNBqh0+mChJ1sHSDnISYmBlFRURE/RyKC0WhEQUEBysvLT/kOSQxzNiI7eAw8aqKcAfxKHdmJv9zOZSWNVquFWq2utwmK1+uFy+USSiTZsaxKpYJGo4FerxcKLlkZJudRVox5PB5hiSQrvb1eL0pLS8VObm63GwqFAg6HA16vFzExMUhKSoJWq8Vll12Go0ePQqfToaioCPn5+SgtLUVCQgK0Wq0os7XMimhfNOwOOw4oD6CFpwX0pXroXDq4FC4hi48fP464uDh4vV7o9XrExcWhQ4cO6Nq1q/DTZDab4Xa7ERMTU0FuB/4t/x9okVVTZX4g3bp1Q35+frXhCmwF+GbvNxjbZiySDEkVrns8HgwbNoyVSQwTIbNnz8aff/5Z6XWlTwmH0oEcRw7sajtUPhWioqJgsVjgcDjQrFkzlJaWYvz48bjssstgtVpRWFgISZKEfJaXumm1WkRFRcFqtYoNCSJB7j+cTifcbneQHJL7A61Wyz5fmPOGqKgoNG/eHOXl5dBqtWjUqBHKy8vRpEkT5Ofno6ysDBqNBgBglaxwa9yw2+xQeBTwKXwwqAywuC0V4g3tU30+n9gVMjExEZIkobi42L9jXEICK4cZJgznbE9ERPgt5zekRKUEDfovGHEBACDFkIKF+xdibJuxWHFkBTzkQaoxFQ6PA7uKd6FxVGPooffvIuT49/ACep8eXTK7YH/ZfuRJeeiZ1BPJycnCuZvc6cuTLKVSCbvdDrVaDYvFArvdLvKiVCrhcrnE+l9Z0aRQKKDVaqFUKsXkRpIkaLVaGAwG6HQ6Ya4tf7nPy8sT1gEajSYoLoPBgOjoaDHwkCe0gdYR8t9yvuX/5b8B/9eBoqIixMfHQ6PRiEGNRqOp1wkuw5xtyAqTUOURADHZ0Gg0MBgMFayP5LYmO3uVJw+BCpvAIzROWeESqGhSKpVBS24DkZU+8hFOgRS4c6WsZAp0XikTqGSR5ZIkScLKqbS0FJIkCYvMsrIyWK1WqFQqxMTECGf/kiThzjvvDLLy1Gg0cLvdOHz4sFDEWYusiHHF+OW0Yxca6xtD79JD69HCqXJCkiSUl5ejsLAQRUVFwqG30WjEmDFjYLPZUFJSIiyTjh8/jmPHjiEmJqbSiZlcRiBYZkqShNTU1IjqyS233FJtmAJ7Ad7e+jb6p/cPq1BSqVQYP358ROkyDOPnjz/+qPSawWPwyxb3LrTytIJP5R/PabVaWCwWtGzZEl26dEFmZiZKSkoQGxsLk8kEt9stfKDJimyz2Qy9Xg+9Xi+sw2tqhW6324XiXaFQCEWVLFvdbjfsdjuICGq1GgaDAQaDga3HmXOeqKgopKSkQKvVQqfTYejQoTh69KgYT9ntdkiSBGe5EyaXCQ6VAx6lB0anEUnaJJTby7F792643W4olUrh82zEiBGw2+3QarUoLS2F1+tFQkKC2IwoPj4eRUVFKCsrQ1xc3Ol8BAxzRnLOKpT+KfoHudZc9EvrF/Z6s5hm+Dv3b2zN24qvd3yNNFUaotxRcDn+deBWCsD77+FD0JOS1BLISNhfsl8ohcS1gAmWWq1GVFSUcCwLADqdDjqdDm63G4WFhbBarcKXiPwlSp44BlogRUVFQaPRiAmaUqkMUugoFArY7XaUl5fD7XaLr1hyGPkeecAR6vhWPmSFmBy/vIxFFtYulwuFhYWIiooSCid5kiVPSOUvdXJ6dRnkyBPUUH8t4f5nmEBkxYRcrwORLfzkOhVJHa2J8kir1Qq/afJEQG4vcni5rcsKClkhLCuGAtuPbEHj9XrFUjSn0wmn0wmr1YqysjL4fL6gdi+3fYVCIZ5BoC+kQEsbpVIp/A0Fygg5DtnJrEqlEs8u8DkHHnI6+fn5Yhc32Y+Iw+GAy+WCWq1GYWGhCCNbPwEQ1gAGgwEqlUpMxHw+H7x6L+xqu18eewBI/kPv0cPmtkGpVIolcnLe5cmey+WCSqWC2+1GQUEBYmJikJycDJvNBgCIiYkRsjJU7oS+f/n9MQxz7uBSugA1ADegd/jdGOzcuVP4YysvL4fH44HJZILVakV+fj60Wq04LyvAiQhJSUlQKBTQaDSw2WzCd2a4vidUiaRUKsUulbLFRShEBIfDITZNkRVYgTtpMsy5hrwBktxW5FUXssKntLQUDocDVEQwwSTmbjqfDlGIQqGrEBqNRtzndDphNpuhVCphNpvF+KVx48ZBO1vLy/OLi4uhVqsjtjhkmHOdc7bXWbBvAaJUUWG/8IKANG0aUhWpeHjRw/C4POgc2xkoAiDPEcrhn6yoASjg99Xh/vdvCYhTx+Gg+SDcKrfovC0WC3bt2oWkpCRs3rwZvXr1QnR0tJgEejwe4Tgu0PJIrVajcePG8Pl8OHToEFq2bAmn04kjR44gJiYG+fn5YqJjt9uRl5eHxo0bQ6PRiHCSJKF58+bC2TcAoag6fPgwUlNTodFogpZwABCT0EDLguPHj6Np06ZC8SUv0wP8wtdut8NoNApFFhHBbrfj0KFDaNKkCbRabdBkNZyCSY4vdOIWOokLtaKS8xxOQSArvgKVYPJv/nJ37hNoeSMremqKPMgPPQKVUoEWe3IdlRVGsvJDtiqUlQ5yuw9c6ia3DXm5ghxHTZSi8n2BEJFQMMn+2axWK0pKSnDw4EGkpKRAp9MJpdbx48eFrABOWkoePHgQbdq0QXR0tGg7ct5lhVZNKSoqQnFxMfR6PcxmMxQKBRISEoRlUnR0tFgmZ7fbUVpaigMHDiA2NhZ79+4VfuXUajXS09Nx4sQJpKWlwXbIBh3p/BajgF8ua+DfsdPp9/Xm9XqFol2SJKSnp4vdl0wmk7AeKC8vh91uF+W3WCxISUmByWSq0k+CPEmsbKJXGYsWLcIVV1yB6OhobNy4Ed26dYvIh5PZbA57X+h5+Xfbtm2xZ8+eiNOpjzzUNp7TSXV5OhPzzNQveo8ekMWcEnC73Ni5cyfMZjOOHTuG2NhYuN1uREdHo23btlCpVPB4PEJ+x8XFwWKxYO/evTh69CgaNWqElJQUuN1uHD9+HLm5uejYsSMMBoPor2T5Kluwyofdbsdff/2F7t27V6hvgXUxPj4eXq9XLK2zWq1iuR07EmbOJWSla2FhIbZu3QoAyMjIAACsWbMGarUaPp/Pv/OiXQ0ynRyztI5uDRVUaGJugjVr10Cv1SMrKwvNmjVDcXEx1q9fj7S0NPHhadeuXbjwwguRnJws4tDpdDAajSgvLxcf/wDuGxgGiEChVFJSUsGnRCiByoLqrEkaspMrshfhh+wf0CKmBRSSwj9AcMM/CbEDcAKSS0IPVw8U2YugV+sR64j1K5DkOZ0u4G83/F/DFQA0/msxihi4bW6szF6JgekD4fP5UFJSgg0bNqBDhw44ceIEDh8+jJSUFJSUlKCkpAQqlSroy7k8EJB3T5MHIomJiXA6nTh48CAuvPBCHDt2DB07doTRaMSJEydw5MgRJCYmCn8kubm5APxf2OUldfK7sNvtOHDggLByCnT6K1tEBH4xKy8vR3Z2Nho1aiQmoYETbL1eD4PBAIVCISae8uRZ3qUpJiYmaBIuWwAEWj0FTtgDnRPLSjaZQMVTYLnk/IbWw0ClUzi/J3L9C/xbPgJ/h6bDg7IzC3lJVegyMQBCgSlv1xz4TgFUsKSRvyy73W6xzbx8hLPmCazDcj4CrRQBBNUpeaIQaA0UrjyBy72q+j8w73L5ZUVXYH6VSiUOHjyI1q1bIynJr1gvKipCdnY22rZtK5Z5KZVKFBUVYceOHejYsSNiYmKC2oXczkLbgMvlEksuZOUNEaGsrAwOhwNJSUkoLi6G2WwWS251Oh0MBoN4Xnq9HjExMdBqtViyZAkGDBiAsrIylJWViXTatm2L3bt3Iz09Hb4TPihTQ56fAiA9odBdiOTkZJSWlsJsNkOtViM/Px8lJSVQKpUoLi6G1WoVeTUajUGOx0tKSnDixAmhlJePwGV5deHEiRMoKCiAJElYuXIl2rZtG9Hg02KxhL0v9Lz8OyEhoVbp1EceahvP6aS6PJ2JeWbqF6/C6/+IaAegBhRmBXbt2oXk5GQQEUpKSgD460KnTp2Qnp4Og8EAq9WKgoICeDweNGnSBEVFRcjNzcWBAwdQUlKCtLQ0OJ1O/PPPP4iOjkZcXJzoB+R+IVDRDfi3MV+1apVY4iPLWZVKheLiYqxcuTLoA4DJZEJ0dLT4qFBcXCx21tTpdEHWFoGEuj0I5xIh1LI99MMfj4+YU8GOHTugUChgNptx8OBBAP4lcGazGUVFRSKcXq+HneyIQpQwEjCYDVB71UixpWD/3v0A/LvB2mw2SJKEDRs2IDExEQkJCSgtLcWmTZuQkpIirJ7lOUh0dDScTicKCgoQHx8PSZJQUFCAlStXIi0tTYyVQudYDHOuU2OFkmzJIluZhGsgoZMe2aFgoO+NwA4qcLIHVLRWCbVMCVyWJR+Bfn9cLhccDgfm/zMfrQpboaWtJXAEJ5euyboHhb/kGqUGqdpU/25Bcn/ohf93OU4udQtUMun8YTWSBomGRPx65Ff0a9oPWtVJqwG5vEQEjUaD2NhYMRhISUkRSzJcLhcUCgVsNpv4wiQ/a9nxtXxOXtsrO+xOSEiAyWRCQUGBSNdoNIovX/IzC9yhQP5aFToIkJ+hnLb8v2yJAUAsx5MnX06nU/h1UqvVcDj8JgN5eXnCCXhgXZDfUaCVRuiSm1CrkHCDlNC6IccVGmdoPQ1c4hc4QQ9VFoUOmAL/DjVVD510h1NOyZ1KZeFrs+SqMmTLN5nQ9hPuXE2uVfYeqvo73ECzsnPhBrGBisFwS8XkuiRbCQUqK2Ulj1wnAxXg8nuXzwdaG8kySa/Xw2QyBVnXBeYzsC6Fe36BYQOVVi6Xq1LlUGWEtqPQ+ivXJ1mBFjjpKCwsBAA0a9YMqampwhcI4B9IJScnC5kqT25k3xyBhCqW5K/gHo9HKLXkcD6fD6WlpeKcvOmAbG0kWzYG5hOAeFfhkGWe2LVN7wIMACzw/68BJJ+Ecks5tEYtUvWpcLlcwleT/F48Hg/y8/NFuvIkK1CpbjabxQcAWYZotVro9XqxYUKgzyq9Xh8+0wzDnHWUakoBE4Byv2sDx7+mkKFWoYB/TFVWVibGQo0aNRJLhrt06YLmzZtjz549yM/Ph8Vy0hGw1WoVivTY2FhhqSoruuX+InADFqfTCZvNJvosWbG1f/9+lJaWhh1neL1e2Gw22O120V8Gjo8CLbgDx2G1GYuEUzSFUzo1xLVTSU3HV6F/VzauDPyfqZ6ysjIolcqg8YIkSTAYDEHhDCYDvIVeGJwGeODxK4l9gNYa3I6zs7MhSZL4wFVQUCA+sAHA0aNHceLEiQqW5AqFQlgmRkVFiTFTUVFR2HohK5YC4wnngqS6Oh/uIzfXH+ZMoUYKJfnLidxo5AlQ6ORPviavNw9UJgR+fQ/8Gh+qIJInIvIyDjlM4PKVwPQCFRVOjxPFzmJYXBakqlLhs/ngUDiEAgkq+JVFKvgVSD74l0wQ/BZIAJySE169F06PEw6l46QySomTlkrw39PI2wi7S3bj1eWvYnjL4dB79GKnI4fDIdb6qlQqlJeXw+l0wmQyobS0NCj/Pp9PWPE4HA5hIeBwOITwlCeN8vPV6XRi2ZssXFu1aoVGjRoFTcJzc3Oxdu1apKamCouoQL8ygcovwK84cjgckCQpaDKm1WrFumNZUWi1WmGxWMTAxeFwIC8vTzj1DRV4gQrAwPcuEygsA6045Am9PNgKrGvhJuiBE39ZQRWoyJLLG5qvwPyGWmQEWjuF5jfwCCyvTLgld7IjzUDCKbCSkpLQpk2barcq9Xq9yMnJCXutPhQ+gVT2u7rBlrzmvbKwoXIjVLbIdTK0Uw6VDYF1JFRRJZctsH6GKgHrk0BljKwIC6foDK1rgf8H5jNwYBK6vDMQWQY5HA4cPnwYxcXFcLlcKCoqgsPhwL59+1BSUiLaVnFxMRwOB8rLy8XXcvmQd2mT45QnUPIXb1kGy8v75Hx5vV7ExsYiIyNDTJ4C8yf7nQL8JuMOhwNWq7WCckn2qST/LzkkOFVOeO1eOBVOOMgBpVcJl8OFQnshMmIy0KJFC7FUWN7tUnbQ7XQ6QURiJyZZ0aXX6+F2u1FeXi7ktbxTXV5eHgAImUjk9xnXqlWrGrdPh8OBdevWITo6Gg6HAytXrjy562aAeDjmOgav3YtVK1fhgOaAqANy+VetWhXkv0E+v3r1asTExKC8vBwOhwNbt26Fw+HAmjVrYDKZqsxfTZHjDo2zsvORxnM6qS5PlV0PlRmSJAXt1FpXZKvgyixMAqmtQuBcxmKx1LiNeh1euKwueO1e+FQ+OB0nx0Ohckmv18NoNAprSrfbjZKSEpjNZhw/fhzR0dFo0aIFDAYD8vLyRP0pLCxEcXGx6AcCFUryR0F5t0uHw4H8/HzhQFih8G+yIo/7ZHcKgbttysqo0A8VgX1rIPI5ue6EKqdkBbz8f+gHoMA4wlk3VfXBKFxckRJa3wPbY3XjnNBxXiRp1ufHhKoUC1X9X1Vc1Z2rzflAqntfNXmf8geqmrbPpKQk4Z9Rbo+NGzeGy+XC5s2bRViP3oMcRQ7iDfFwR7nhLfbCmeSEVqmFt+SkgYPRaER8fDyOHz8u2prdbhfjkLKyMmi1WqFwkpWywMn2JPtldDgcyM7ORl5eXtBHTuDk8wz0mRnO3YdcH+X2GE6RHUo45Wtl4cL9Xdm56uYGlZ2XJElsKlVZWlXlo6prldWpcOdrK0/qg5q0n+rC1EccdSHw+dW0DwXVgD///JPgV7vwwQcfp/j4888/uY3ywccZenD75IOPM/vgNsoHH2fuwe2TDz7O7KMmbbRGFkry8qtFixahZcuWNbmFYZg6kp2djSuuuEK0v6rgNsowpxZunwxzZsNtlGHOXLh9MsyZTSRttEYKJdnMqWXLlmjfvn3dcscwTERUa2YIbqMMc7rg9skwZzbcRhnmzIXbJ8Oc2dSkjbLreYZhGIZhGIZhGIZhGCYiWKHEMAzDMAzDMAzDMAzDRESNFEqJiYnIyMhAYmJiQ+eHYZh/iaTdcRtlmFMLt0+GObPhNsowZy7cPhnmzCaSdicRnca99RiGYRiGYRiGYRiGYZizDl7yxjAMwzAMwzAMwzAMw0QEK5QYhmEYhmEYhmEYhmGYiGCFEsMwDMMwDMMwDMMwDBMRESuUrr32WkiSBEmS0KdPn4bIE8Ocleh0OkiSBL1eH/b6448/DoVCAUmS0Lx5c3H+s88+g1KphCRJiIuLg9frrXUeuH0yTOUsXLgQKpUKkiRBoVBgxIgRAIChQ4dCoVBAoVDAYDCgoKCgwr2B9ykUtf8Ww22UYcITrg+trN9UqVSiLUqShKSkpHrJA7dPhqmccG20sv7zxIkTMJlMoj098MAD9ZIHbqMME55wY9zDhw9Dq9WKc1lZWSJ8y5YtRVuqKxGNii0WC77++mssXrwYe/bswZo1a7Bx48Y6Z4JhzgUmTpyIa665ptLrzzzzDF544QWYzWYcPnwYc+bMAQBMnjwZY8eOBRHBbrfjhhtuqFX63D4Zpmr0ej1mzZoFIsKKFSuwePFiHDx4EP/f3n3HOVH0fwD/bHpyvVd67yBFQOkoRaSpjw2xYHnsgorYKCogKGLv5VEs2LAgCChNQYqgFKVzx8E1riaXXuf3R347JLnkLrmau/u+ed2Lu2yymdnZ2Zn97uzsxo0bsXv3brhcLgDAjBkz/H5+5cqVcLlc/H2hojpKSGD+2tBA7abD4eB1USqV4tprr63191P9JKRqvnXU6XQGbD8HDhyIlJQUMMag1Wpxyy231Pr7qY4SEpi/Pq7T6cSdd94JxhhOnz6NgwcP4pNPPgHgrqubNm2qmy9nIXj00UeZUqnkfycmJrLLL788lFUQ0qzde++9TKVSVXp9w4YNTBAE/nfPnj1Zx44dmcPhYACYw+FgjDE2depUFhsbW6PvpvpJSGgEQWA//PADA8B++OEHZjKZmEKhYPfee2+l90qlUrZy5cpafR/VUUKq5tmGBmo3Pa1bt44BYFartdbfTfWTkOp51lGxD+uv/QTACgoK6vS7qY4SEjyxj+tJoVCwBx980Ou1EMNBfoU0Quno0aOIiIjgfycmJuLs2bN1E9kipBnbvXs35HI5/7tNmzYoKyvDn3/+CUEQIJVKAQC9e/eGyWSq0XdQ/SQkeIsWLQIATJ48GZMnT8aUKVOg0Wggk8nw+uuv+/3MnDlzIJFI0L9//xp9J9VRQoIXqN309NhjjyEhIQEKhaLW30f1k5DQSKVSv+3noUOHAAA9evSARCJBdHQ0Tpw4UevvozpKSHA8+7iiH3/8ETabDQ8++GCdf19IASV3EMtbXdx3R0hz5+8WGUEQajVfki+qn4QEZ+/evVi4cCEeeeQR6HQ6/Pzzz1izZg0P5o4ZM6bSZ9atWweXy4U///wTBw4cwP333x/y91IdJSR4gdpNT0eOHKmTW2kAqp+EhCpQ+6nX6wEA48aNg8vlQnR0NEaPHl3r76M6Skj1PPu4osLCQkybNg2TJk3ymo+wroQUUOrevTuMRiP/u6SkBBkZGXWeKEKam6FDh8Jut/O/c3JyEBcXh8GDB4MxxgNLhw4dCjipd3WofhJSveLiYgwdOhSjRo3C8uXL8dJLL0EQBEybNg1qtRqjRo3C33//Xelz48aNAwD0798fHTt2xK+//hryd1MdJSR4gdpN0Y8//giXy4Vly5bVyfdR/SQkNIHaz8GDBwMAPv/8cwDArFmz/D7sIlRURwmpmm8fF3DPdda+fXu0bdsWa9eurZfvDSmgNH/+fFitVqxfvx4nTpxASUkJFi9eXC8JI6Q5GTduHARBwIoVK2AwGHDkyBHcfffdkEqlUCqVfCLun3/+mZ+4horqJyFVczqdaNu2LTIzM7FlyxYA7pNWm82GAwcOAAB27tyJzMxMr88ZDAbs3r0bgPsqT1ZWFgYMGBDy91MdJSR4gdpN0bx585CcnMxvGa8tqp+EhCZQ+ymVSqFSqfDYY48BAL766iuvYHBNUR0lJDB/fVwAaNWqFWQyGU6fPl1/Xx7qpEtXX301A8AAsEGDBtV6EidCmgu5XM7rBgA2f/58plAo2IYNGxhjjM2bN48JgsAAsDZt2vDPffTRR/z1mJiYWk0uSvWTkMBmz57NADBBEPjPihUrWL9+/fjrGo2GTyQaHR3NFi5cyLKysrw+07Zt2xqngeooIf75a0MDtZuMMSaRSNgTTzzh9VqrVq3Y1VdfXeM0UP0kJDB/dTRQ+/nZZ58xiUTCBEFgCoWC7du3jzFGdZSQ+uKvj/vggw9Weu3mm29mjDHWpk0br/rcu3dvxljN6qjAmJ8bUgkhhBBCCCGEEEIICSCkW94IIYQQQgghhBBCCKGAEiGEEEIIIYQQQggJCQWUCCGEEEIIIYQQQkhIKKBECCGEEEIIIYQQQkJCASVCCCGEEEIIIYQQEpJmEVASBAEvvfRSo33/Qw89BEEQGu37m6u2bdtCpVLV2fquuuqqKsvJd7lEIsF1111XZ99PSKjMZjMkEglefvllALWrE19//TUEQUBxcXEdppA0J8OHD4dE0iy6BfVGEAQsW7YMANCpUydEREQ0cooIIbUlCAKuuuoqAMDYsWMhlUobOUWEhKY57be+fd3GPs8n1WvQnuOcOXOgUCggCAIEQYBEIkHXrl1RVlZWq/UyxjBnzpw6SqU3mUzG0yumOSUlBTt37uTvefnll8EYq3Zd9913X1gHnjw7yjUxfPhwr20l/kyZMqUOU9lwXC4XVq9e3djJIA1IrO933HGH1+vdunWDIAhISkpq0PRcfvnlUCqVeOihh2q9rmuuuQZRUVEYM2ZM7RNGmizfNk0mk2HBggWNnayA8vLy0KpVK680q9VqPPvss42dNJw8eRJGo7Gxk1HJiRMnEB8f77XNNBpNYyerxfDXDxJ/FApFYycvLJjNZnTv3h0SicRr29x5552NnTT8+uuvcDqdjZ2MSgwGAzIzM732J6VSiRMnTjR20poN3/ZR/GlMwV7oqe1+W1/n6HWhPs/za+Omm26CVCrl20wqleLGG29s7GQ1igYLKF133XVYuXIlunTpgl27doExhrfeegv5+fnYunVrQyWjRnr27AnGGBwOB1599VXo9Xpceuml2LZtW2MnLewIggDGmNfPDz/80NjJIiQkn3/+udffx48fb5R0/PHHH3UakJ05cyYOHz5cZ+sjTZPYphUUFCA+Ph7PPPNMYycJBoOh0ms6nQ5t2rRBQUEBnn32WWi1WmRlZeHSSy/Fu+++2wipbBoGDhwIo9GIrVu3gjGGtWvXokePHnX+PWazOSxPvBubZ/9HKpXy+sYYg81m4+9rStvPX/2sjYSEBBw7dgz33nsvioqKUFpaiunTp+Obb76p0+9pTvr06YOCggKsXr0ajDFs374dAwcOrPPvcTqdMJvNdb7epsKzvoo/4a629bMpn6M3lk8//RSffvoppk6dCqvVioKCAtxzzz31cuG5ro+/9YI1AIfDwQCw9u3bV/m+hQsXMolEwgAwAOzyyy/ny5599lmvZQqFgi8DwJ5//nnGGGNt2rRhCoWCxcfH8/eOHz/eKy29e/fmy6RSKXvzzTcDpkkqlbKePXt6vabX65kgCCwtLY0xxti9997LPDflmDFj+PoBsHbt2rE9e/Z4vQaAPfjgg2zNmjVMJpN55eurr77i64qKimIxMTFMrVbz99x77718eWlpKWvVqhVfJggCe/bZZxljjGm1Wq9lCoWCbdiwwW8+PbctANaxY8dqy8TXsGHDmCAIAZdHRUWx6OhoplKpeFpXrlzJBgwYwNd/0UUX8feLZRkdHc2XT506lS+vrizXrl3Lt60gCKxjx45e5VTdcgBs+vTpXnlr164d/z7P/cJqtbLMzEy+rF+/fpXKioQ/qVTK2rRpwwCww4cPM8YYe+KJJxgAFhUVxRITE/l7IyIivOrd3XffzZetXr3aq15LJBKWk5PDGGOsR48eXnVtyJAhftOyadMmBoAdP36cv9amTRumVCr535mZmUwQBLZp0ybGGGOTJk3i642JiWEKhYK1adOGv1+r1TIAVR7zSPPm26atXLmSAWB79uypdAz33Vf79u3Ll3311VcMABs1ahRfHhUVxbRaLWOMsVOnTjGlUul1fF62bBn/vHh8j4uLYwBYcnJypbQOHz6cAWAHDx4MmB+tVsvS09P596jVarZjxw6+3PN4DIBFRESwffv28TZVEAT29ttve72/Q4cOTBAEBoDJ5XKvdtO3v+FZH1NTU7221xVXXMGXif2EIUOG8OWpqaleebntttu82luxHWaMsdtvv50vEwSBXX311QG3iSAIbNSoUQGXW61Wr7YTAJs5c2bQ27Nv3758+xw/fpytWrWKKRQK/pn27dszq9Ua8PtbEt/65m/71aae7du3z6ueCYLAvvnmGzZlyhSv/gxjF9qygoICxljV+5TYXxPrSZ8+farsh2/fvp1pNBq+LDExkRUVFfndJvfddx8DwFatWhVwuzkcDtatWze+Prlczj777DOv7dixY0eeHrlczvbt28ePJwDYo48+6lUOKSkpTCqV8jbZsx30198T1aZ8GGPsmWee8eoPJCQk8GXPPvus17LBgwcH3CYKhYK1a9cu4HLGGLv88sv5vgWADRs2rEbbc8WKFSGVaXPh75zP0+bNm736flKplOXm5la7rKptWVXbsGTJEq99DwBbs2aN3/bTd789cuSI17mwRCJhP/zwQ6U81cU5upie2NhYvvzuu+9mU6dO5X9nZGTw94tpzcjI4Mt9z/8821bPdnfJkiW8HgNgGo2G7dmzx6sM09PTmVwu58e2FStWBLVdTp065bUsMjKSnwv4EvvbVfnoo4+82kbPPAWzPT3LN9T4RUNrkIDS+++/X23jcfDgQd5oabVa3uA8/PDDjDF3wKNDhw7MarWyoqIi9sADD/DP+nbwxIIxmUxsxIgRvNFmjLGLLrqICYLAPvroI6bX63nhlJaW+k1XoINLZmYmk0gkjDHvgFJWVhYDwNN38OBBNn/+/ErvE3311VdsxowZrKCggB05coSpVComk8n48qioKAaA3XXXXcxkMrH27dt7rSM5OZlJJBL20UcfMYfDwd599132zTffMMbcnVuZTMY2bdrESktLeWMaiOd2DKZMfAUTUALcgTS9Xs8iIyN5RSktLWW33XYbA8C2bt3KGLtQlgMHDmR6vZ795z//8dqPqitLqVTKIiMjWU5ODnv77bd5JRRVt9y3gwGAde3alWm1Wl6WYlqGDh3KALD333+f5ebm8iAYBZSaFrG+azQaNmDAAMYYY7Gxsaxr166VAkqXXnopO3z4MNNqtaxr165exxmNRsNiY2OZVqtlWq2WPfroo6y0tJQfC8UT2K1bt7IXX3zRb1puuOGGSscLsZHV6/UsOjqaSaVS3tiJ6545cybT6/Vs8ODBDIBXQIkx98lmVYFh0rx5tmk5OTksMTGR72e+x/DJkyezH374gTkcDnb33Xd7Hf/FEym1Ws2OHDnCNmzY4HUCc+TIETZ+/HiWlZXFCgoK+Pc4HA7G2IXj+7Bhw5her+cBV08ajYZFRERUmZ+2bdsyQRDYmjVr2KlTp5harfZqQwEwmUzG9uzZw9atW8c7mJ7tkO8FKrGO5uTksMjIyErrCxRQGjNmDNu+fTszmUxs3LhxDABbvXo1Y+xC+5+YmMgKCgrYm2++6dVGiCcOkydPZlqtlh0+fJjNmzePMcbYiy++yPsBVquVzZ49mwFg7777rt9tInbqBw0axF588UW+zUViW/nMM88wh8PB1qxZwzumwWxPiUTCNmzYwIqKinifp2fPnqy0tJStWbOGCYLAhg4dWmW5tRT+Akqe289qtdaqnmVkZDCFQsFycnKYyWRizz//PNu3bx8rLS2t1F+Lj49nMTExjLHq9ymxv3bzzTczq9XKcnJyAvbDHQ4Hk0gkLDk5meXk5LA9e/YwmUzGWrVq5XebZGRkVNkXZYzxvvuKFStYQUEBS05OZgCYXq/n21HcTw8ePMiDKJMmTWImk4m1atXK61gmnoDOnTuXlZaW8hNZz/UFCijVpnzEY85FF13ECgoKWE5ODnvwwQcZY+4ABAA2ceJEZjKZeHD/jjvu8LtNOnXqxOvawoULedpFYh9ZLNPt27ezJUuWBL09xfI3mUysqKgopDJtLqoKKFmtViaRSFhUVBT7+++/mV6vZ7Nnz+b1ONCy6upHdW2Dv3Mrf+2n7/uUSiW/IGK1WtmSJUvYrl27KuWrLs7RxfSI9U+8wB4ZGcmysrLYq6++ygDwAQ/iOVVGRgYrLS1lc+fO9Tp3riqgtGzZMjZ79mym1WrZ1q1bmVQq5cc1sQwBsCVLljC9Xs9iYmK8jjdVbRe1Ws00Gg07ePAgy8rKqrIPsnr1agaAxcXFsZkzZ7J9+/Z5LRfbxoyMDJaVlcVKS0v5hedgt6dn+YYav2hoDRJQevjhhxlw4aqIP+KoHk8xMTF8JxF3mLVr11b6rG8HTwz0MHYh8vrMM88wxtwnU1dddVWlz4sHeF+BDi79+/fn6fUXUBo4cCA7cuSI12f8BZR8Pf/88wwAv7oXFRXF1Go1Xy42Tnv27OF58wyu+ebb84pwQUEBA+A3Qs1Y5YBSdWXiSzxA+P589NFHfvMinjB7nkgAYLfffjtj7EKF8iSXy3kUvaqy/OGHHxgAtn37dr6sc+fOfH3VLRfX5RtQ8v2uyZMnM8YYk8lkrGvXrnzZjh07KKDUBIn1XRwpkJubywCwzZs3Vwoo+fJsDCIjI5lSqeT7vmjVqlV8vxGvXAUyZswYv50ImUzGFAoFU6lUXlcL27Vr53VizJi7jvgGlCQSideVINKyeF7ZA9xXqsWgZnUXBZRKJevQoQNj7MKJlOfonpiYGK+r757EDtS6desYY+59uarvYsx9XBVHAgcCeI9CFk/oxFFFYpBVpFar/bZDnuvr0qUL/3vr1q0MAO97VBVQ8iUIAhszZgxj7EL779neyWQy1q1bN8YYY3FxcSw6OtrveuLj41lSUpLXaxqNJuBohaKiItatWzevq589evTwyuOIESP8fjaY7Sm2i4wxdsUVV1Qqx8GDB3sFoVoyfwElz+3nTyj1rHXr1kwikXj19URxcXEsNjaWMXZhdKrY161un4qKiqq0bwfqh8+fP9+r38oY4xcI/RFH3VdFJpOxzp0787+LiooYcGHUEQB+0Ycx9wVUz77/a6+95nWyJZVKWVxcHF8ubo/Zs2fz9QUKKPkKpXw6d+4csC507dq10jbOyMgIeBywWq1syJAhXiOa0tLSmMlkYoy5+8dt27b1+9lgtqfnfhpqmTYXvu0jAF5+4gg/f323qpZVty2raxsCBZR8X/N8n9hurVmzpto818U5uu+5t1j/POuFRCLhI/LFcyrPkXyebWBVASVf06dP90qbVCr16jeI+WOs6u0inrd5BoY++ugjBoDXMV/PPvus16g0mUzGR/6NHz+eAah0QYex4Lanb/mGGr9oaA0yh1KXLl0AuCcMC+TcuXOQy+VeryUnJ/P7eP/3v//B6XTiyiuvhCAI6NevX8B1ea5HnPH+/PnzAADGGL799ttKk60dPXo0pDydP3/e7yRp7dq1wx133IGDBw+ie/fukMlkuOeeewKuZ+fOnYiIiOBpmTdvHgB4TbLn+RSZzMxMAEBOTg6fGHzSpEmV1rt7924AwGOPPcbXnZaWBgDYsWNHUHmsrkz88TeH0i233OI3L3FxcQCA1q1be63DcwI43++PiIhAeXk5gKrLcs+ePQDck9mJ2rVrx3+vbnmgvPn+LabF4XCgbdu2fNkll1xS5bpIeHvrrbfgcrlw8cUXQ6FQYPTo0V7LbTZbpckxAeDMmTMAgHXr1kGj0eDWW2+FIAho27YtbDYbZsyYgYkTJ2L9+vXIzMyEXC4POBF+SkqK33v3HQ4HbDYbli5d6nWvtlarrfTEKZlMVunzjDEkJiaGtD1I8+I7p8vDDz/s931jx471mjTXarWioqLC6z1Dhgzhv8vlcj5HTF5entfE0H369AEA/PPPP17vr4pCoaj0ff5cdNFF/Pdx48YB8G7nunfvzn+XyWR+2yFPnm3ByJEjAQC7du2qNh29evXy2l6MMRQVFXm9x7O9k0qlMJlMAACj0Yjk5GS/6zUYDCguLvY63phMJmi1Wr/vT0pKwpEjR+B0OlFUVITRo0fj33//xW233cbfc/HFFwfMR3Xb07P/dfr0aTDGvNK2e/fuJjM3UGPw7b/Wpp5t2bIFKSkpvK+XlJSEvLw8AMBdd90FrVaL4uJizJo1CwCwYsUKAMHtU5GRkV5pCNQP379/PwBAqVTydX344YcB8x8REeE1l5Q/DocD7du353+Lbd2///7LX/OsS0ql0ut4kpKSAgDIzc2ttA4AiImJARBc37825VNcXFxpO4qKiopgtVq9yiAvLw8Wi8Xv+xUKBf744w/Y7Xbo9XrccMMNKCgo4Mcou92Ozp07+/1sMNuzQ4cO/PdQy7Q58Z1DyW63AwAOHDgAAMjIyKj0maqWBbstA7UNgVTVfm7evBkAMG3atCrXAdTNObpvesT651kvBEGAXq/3WodYDwEgMTExqLm7PvnkE6hUKr4t16xZU+k9CQkJ/HfPel/Vdtm0aRMAYMCAAXzdt956KwAEnEfqqaeegsFgAGMMq1atAuCeqBsAsrOzIZfL/T55L9TtCdRd/KK+NEhASezELFy4MOB7WrVqxSutqLi4GGq1GgAwY8YM6PV6OBwOzJ49GwcOHKjxU15mzpxZKeixcePGoD9vMBiQl5fHK4yvd999F1arFVqtFt27d8dbb70Fm83md6eaOHEinE4nduzYAcYYnn/+eQDuJ4xVRwxarFu3rtIycaK+t99+u1Jely9fHlQ+qyuThuD7/SaTyesEIFBZip3l3377jb9XPNkHUO3yUMlkMq/Pez4FkDQ9CoUCrVq1Ql5eHiZMmFBp+YQJE5CXl4f3338fDoeDB37E/4cPH46ysjIwxrBy5Urk5OTgmmuuAeCur3a7Hbm5uUhISMDjjz/uNw0zZ84E4D5h86RUKjF06FDMnj0br732Gn89Nja20hOnHA6H1986nQ6MMUydOjWErUFaou+++w6bN2/GrFmzoNfrwRiDUqkMeoLS4cOHQ6fTYe3atWCM4eDBgwDg9fnqnp7Tv39/GI1GryCUP3/99Rf/fcuWLQCASy+9NKh0+pOdnc1/F9sIz46xP3PmzME///yDRYsWwWq18iBLsNsrIiKiUvBJpNFokJaWVqmtC+bpO0lJSdi8eTMP9Ij+/PPPgJ+pbnt6dnTbtGkDiURSKW3B9GFaKs/tV9t61qFDB+Tn54MxhtWrV6O0tBSXXXYZAGDp0qUAgNtvvx3r1q1Dq1ateD80mH3Kt34G6of36tULACqtK1Aepk2bBqfTWenhF55kMhmysrL432K6ajO5fHFxMf9dp9MBcD/BtSq1LZ+kpKSAE+omJCRArVZX2mZWq7Xa9UZGRuKzzz6DSqXifQS5XI6TJ0/6fX8w29PzHCXUMm0J+vbtCwA8YBvsstpuy0BPeKuq/RSf5vvjjz9Wu/66OEevKbEeAkBJSUlQ65s1axY0Gg0OHjwIxhimT58e9PdVtV1GjBgBwD1gw7ecJk6cWO26Z8yYgenTp/O2r127drDb7X4vrgSzPf2Vb23jF/WpQQJKUqkU1157LU6fPo2+ffti7969AIAPPvgAMTEx+Pbbb/Hiiy8CcHcgDQYDHnroIWi1Wr6jjxw5Etu2bYNUKuVXD5VKZchp6dOnDz799FO89957ANyRvauvvjrox26+8847fOSAv8Zw27ZtmDZtGk6cOIHIyEiv6GvPnj0BeAcb7HY7fwrI3r17Q3p8s1QqRWJiIl577TV88skncDqdeO+99/Dtt99CoVAgOTkZDzzwAK84e/fu9Xty7OmPP/7gv1dXJg1lyJAhMBgMuOGGG2Cz2fD0008DqLosJ0+eDKlUiiuvvBJ5eXl47733vJ7UVd3yUA0cOBDHjh3DJ598gry8PFxxxRW1yzRpdN999x1uv/12fPzxx5WWiVdx+/bti7Kyskqj26644gp89913cDqd/KqgXC7He++9h1mzZvHRG1U1nuPGjYMgCHx/97Rz504MGzYMDzzwAL/i/Pjjj8Nms+G2226DwWDApZdeWqnD8tRTTwEA7r777uA3BGmRxI5x165doVAocOONNwZ1oiMymUwQBAE9evTA6dOn+VX0UKxduxZSqRR9+/bF0qVL+cWcK664gl/Nbdu2LTZu3Igff/wRp0+fxpVXXgmZTMZH1tTE8ePH8d577yEvLw+TJk2CVCr1OxLYkzgKumfPnrDZbH7rX1XmzJmDiooKTJs2DQaDAf/88w8PNs+dO5c/QcZsNqO4uBiPPfYY3nrrLb/rateuHebOnYu8vDyYzWZcc801YIzxPPTs2RNbt27FsmXL4HQ68d133/F1hbo933jjDbhcLvTv3x+FhYUwm8147733cN999wWd95astvXsuuuuwwcffACbzcbbIc+Rqe3atcP69ethsVjw3HPP8ddD3aeAwP3w+fPnQyKRoFWrVjhx4gScTie+/fZbfqXe1+uvvw61Wo0ZM2bgoYceQnFxMXQ6HW6++WY+smDo0KE4ceIEXn/9dRQXF/PAx/z584PeNr7Ky8vx5JNPoqysjJ/kV/eEy9qWz4oVK+BwODBw4EAUFxfj7NmzeOihhwAAL7/8MsxmM6ZMmQKdTgedTodly5YFPA/o1asX7rjjDpw+fRpOpxOPPPIILBYL+vfvDwC48sorkZ2djfvvvx82mw2//fYbDyqGuj1DLdOWYMGCBRAEAd27d8ehQ4dgMBgwZ84cFBcXV7msttuybdu2YIz5DVYFMnLkSCiVSlxzzTX45Zdf+Kh28fzbU12co9dUr169UFZWhieffBLl5eW48cYbq/2My+WCSqVC165d8eOPP+L7778P+vuq2i6jR4+GSqVCr169+MWXLVu2BBzl9dBDD6F///48oLNlyxZ8//33/ILB66+/DsB9DM7OzkZZWRm/Y6km27O28Yt6F+y9cXVh9uzZfNZ1wD2hXteuXfk9zk899ZTXPf+jR4/mnxUncBZ/+vXrx5ehmjkN4DGXjcPh4BNbietSq9VeT1Ly5O9+2uTkZK+5dzznRtq0aZPXZyQSidfTn2JiYviy2bNnszfffNMrz4MGDWLAhafa+M7bIs5DIT4JrqioyOuJLIIg8En4SktL+STe4k+ge7MZY15PGOjUqVO1ZeIr0BxKvXv39psXf3NKweM+dn9PeZs0aRJ/b3Vl6fkEPUEQWIcOHby+r7rlnmnxdw+zIAh84kWr1er1tALxqUJz584NuL1I+KlqQkbP/TcrK8vryTripNzi/pKWluZVB1q1asUcDgdbsWKFV33yffKVr0suuYSpVCr+t+/xTXyyjFjnxXu2AfdT3uRyudeToqKjo6t8gglp/qrax32Pc55PCY2MjGRqtZrXAXHuEM8nsCUmJrKoqCjGmHvCWc+5PsR9Ndj5h0Q5OTlex1YATKVS8ck9S0tLvZ6uplKp+IMdGKs890J17RDg/ZQ3mUzmNWdMoDxYrVav9j05OdlrW/tr75RKpdccZzNmzPA6PnjOeXLXXXd5LZPJZAHnlPBMv9i+jR07li+3Wq1eT3wC3JMv12R7MuaeG058eqv4fePGjfObtpbG3xxKvtuvNvWsZ8+eXuUYFxfnNUmr+HnP+U1EVe1T/uYMrKofvn37dj6Rt7gP9OnTJ+B2MZlMrGvXrl77qVwuZ3fddRdjzN2/69Kli9eyjz/+2Gs7es5F5Xs88d1u/p7ytnLlSr/rq8vjIGPu+XU8zws8t+uSJUsqPRE20KTcgwYNqvRE5l69enm9Z8SIEV7bVJwrLdTtyVjoZdoc+DvnA8CfIrZhwwavp257PsmtqmVVbcvq2gatVuvV3xSf8ubbfvrut4cPH/Z66ppEIvE7D7GoNufo1dU/cduKx0J/T3nzfHpiVXMozZ49m+/jgiDwpzD6+x7GLsxNHMx2OX78OJ+wXvzJzMz0u71WrlzpVS7i8WHz5s38Pe+++67XU948+/OhbE/GQo9fNDSBsRY8fpGQerJx40aMHz8ea9eurfbKNiGBmM1mRERE4JVXXsH9998f8ucFQcDo0aOxefNmfPvtt7j66qtRVFTkdU85IeQCQRAwffp0fPvtt42dFEJIHZHJZOjWrRsOHz7c2EkhpMUbPnw4duzYQbdGNyMNcssbIc2dwWDAVVddBZ1OhwMHDmDatGmQSCQUTCK1olar4XK5gg4mXXvttThx4gSKi4v5RMSLFy8GAFx11VVgjFEwiRBCCCGEEFInKKBESB1wOp34/vvvERsbi379+lU74SQh9eHXX39Fly5dkJycjOPHj+Ouu+7C4MGDGztZhBBCCCGEkGaIbnkjhBDS6PQ2PYZ+MRS9E3vjUMkhvD32bVyScUljJ4sQQgghhBASQEgjlFwuV6XHUhNCSHXMZjNsNltjJ4OEMRdz30svESRefzcXJpOpsZNACGkiTCYTf9w0XfclpOVgjMFgMFC9J02KrPq3XKDT6WA2m6FWqyGR0N1yhJDglJeXAwDS09MbOSUkXDlcDgDNN6Ck1Wqh0WgaOxmEkDDndDqh1Wq9XouPj4dKpWqcBBFCGozVakVFRQUEQUBERERjJ4eQoIQ8QglwR09LS0tRVlZWL4kihDQf4lVWgK60ksCczL2fiAElB3M0ZnLqBe3/hJDqeLaZgLvvXV5eTscPQloAh8Pd9/E9DhASzmo8zMhqtcJisdRlWgghzZDnY0HtdnsjpoSEM6fL3XmSClIA7hFKNqetWY1UohNCQkh1fE8kjUYjKioqqM9NSAsg1n8xsERIU1CjgBJ1igkhwfLsHFMDSQLxHaHkdDkx5fspeP/w+42ZrDpFbSchpDqebWZ0dDSkUimcTiesVmsjpooQ0hDE+u95MZaQcBdSQEnsDFOnmBASLLFRlEqlFFAiAfkGlCxOC3INuRRQIoS0KJ4BJalUCsYYGGMUUCKkBRD7yRRQIk0JzaxNCKlXLpcLEokEMpmM7gknAfGA0v8/8EFn1QEAzA5zo6WprlFAiRBSHafTCZVKheTkZEgkEkilUqjVajgcDnrSMiHNnNPphFQqpYASaVLoljdCSL0SA0qAey4IOn4Qf3znUNLb9I2ZHEIIqXeMsUpzC4onlFKpFBaLBTKZDCqVCgqFAmZz8wmwE0K8OZ1OMMagUCjgcrmov0yajBrd8lbda4QQIhIDShaLBTqdDjabrbGTRMKQ7y1vRnvzuxJP7SUhxJNWq0VxcbHX7eAOhwNSqRTl5eUwGo3QaDQA3KM36bZxQpovsX4rFAoAdNsbaTpqPUKJrpYQQqoiBpTEeSCoQ0z8EUcoCf//z+QwNXKK6h4FlAghIsYY70OL8yOJoxIkEgmsVitkMhliY2MhkUjgcrn4DyGkeWGMwWQyQRAEyOVyABRQIk1HnUzKTTs8ISQQ34ASHS+IPw7mDjRKBAkkggQme/MLKBFCiMhzTkHxtjfxNbG9jI2NhUwmg1wu520ntaGEND8VFRUwm81QKpWQSt23/lNdJ01FrSfldjgcKCwshMViqYv0EEKaGc+Aksvloom5iV9eI5QEGqFECGnexNG6SqWSB5TEE0jxWCGTyQAAcrmcHidOSDNmsVig0WgQHx/PRySWlJTAZGp+fSHS/NT6ljexYfOdVJAQQoALASXxWEG3vBF/xDmUBEGABJJm9XQ3EQWUCCEih8MBiUQCpVJZ6VHh4sTc4gMt5HI5vyhDASVCmhen08mf7gi4+0FWq5XPPUp1noS7OrnlDXDv/IQQ4knsAAuCwH9ohBLxx3NSbkEQ6JY3Qkiz5nA4IJPJIJPJwBiD0+nk7aXL5eK3vQDgc6o4HA46uSSkmREHZYj1HPB+QjLNV0zCXY1GKHk2ZuLvFFAihPjynfNBJpPRCCXil8Pl3i8ECJAINEKJENK8iU9zE29rE4NFEokETqeTvw64206pVMqDToSQ5kMcregZRHY6nZDL5VCpVHTbGwl7tR6h5DmBICGEeHK5XDCbzSgrK4NEIqGAEglInENJIkgggQQWx4V5+ZpD+yIIQrPIByGkbohBI5lMBkEQvAJK4uglT+JIJgooEdK8+NZ38dxaKpXyOdao3pNwRgElQki9cblcsNlskEgk/OqL+PQaQjyJT3kTJ+X2HKFkd9EcfYSQ5kO8xU0ckSCTybxOGn1veQNAT34ipJnyDSg5HA73fJL/P8caANhstsZKHiHVqrNb3gghxJd4fBDvA5dKpfSkN+IXv+VNqHzLm83Z9DtSNEKJECIS20DxJFIcves56bbvCCXxggz1uwlpXnwDSna7HVKpFIIgQCqVQiqVUkCJhLWgA0qeowo8O8V6vb7Sa4QQArgDSowxyOVyJCQkUIeYBOR5y5sAARbnhVvemsMIJQooEUJE4q3fniOUxICSeJyggBIhzZ84L5pvQEkul/PAs0KhoIASCWs1GqFEt7wRQoIhdnzVajVUKhV1iElA4lPexFvePDWHgBJA7SQhxM3pdPLRB4A7eORyubzmUfI9Doqv0TyEhDQfYn32DCjZbDaoVCreX1YoFLDb7dSHIGErpBFKvr9LpVIoFIpKywkhBHB3mhljlW55o4AS8SXe8iYRKp9INZdb3gghBKh8i4v4uHDxpNF3dBLgbj/FCbsJIc2DOF+SGFzW6/VwOp2IiIgA4D4mKBQKMMZolBIJW7UKKEkkEn5iSAElQogv30ehiveEU0CJ+LK77Hx0ksSnabK5mn4nymKxUDtJCAHgPkn0DBqJbaQYUPKdkFt8jzhCiY4lhDQPYnBZEAQ4nU7o9XpEREQgIiICgiDwY4VEIqGAEglbNQooiSeDNCcEIaQqYkBJHKEk/k8BJeLL4XJAIrj3j0q3vDmb/i1ver2eRhYQQgC420ZxVBLgPuaJJ5S+86mIxFvePOdZIoQ0bTabjdd3i8UCQRAQFRUFQRD43Eni71artZFTS4h/tRqh5DnSgBo3Qogv36G8np1mQjx5BZQgeP3fHOZQov2eEAKAz5PkGVAC3COQxNFH/kYoCYLA51qiizKENH1OpxN2ux1KpRIAYLVaIZfL+cVXpVIJm80GxhjUajVsNhtdmCJhKeSAkkQi8Qoo+XvyGyGEABcefUojlEh1HKzyCCW55P/nFWkmASXa7wlp2RhjsFqtfMQBYwwWiwU6nQ46nQ5arZYHjvyhgBIhzYc44kipVPJjg0ql4ssVCgVcLhfsdjtUKhUkEgnMZnNjJZeQgPy3WH74jkoSZ5632+1eQSZCCAHcQSOn0wm5XM4DBOIIJeoME1+eI5TEOZTkEjlsLluzmZSbRigR0nKVlZXxE0iFQgG9Xg+j0chHJCmVSpjNZuh0OmRkZPhdh0wm47fFEULCB2MMTqeTz3UWDHFEklQqhdVqBWOMj1YCwPvP4sTc4i1whISbkAJKYmUxmUwwmUxwOp0wGAxITk6mgBIhBIB7rhi73Y6oqCivJ1UANEKJBOZvDiWZxN1ENZeAEu33hLRMDocDFouFnyxKpVIYDAZERUVBrVZDJpPBaDTi7NmzMJvN/MTUF41QIiT8OJ1OlJWV8UEWsbGxXiONAhEn3K6oqIDVaoVMJqs0t5pMJoPd7h6lLQaiGWP05FgSVkK65U2v1yMvLw96vR4A+H2dhBAi0uv1sFgssNvtleaJEBtA6gwTX3aXPXBAqRk85U0qldJ+T0gLJd7mFh8fj7i4OFgsFkRERECtVvMLtgaDAXa7HXa7HWVlZX7XI5PJ+B0ChJDGxxhDWVkZXC4X4uPjoVAoUF5eXu1IIsYYDzSLdd/zAqxILpd7BZTEzxESTkIKKFmtVthsNt6QOZ1O3rBRYIm0ZEajEZdffjkMBgPGjBmDyMhIPPLII3y5Xq/HwIED0bdvX/Tq1QvvvfceX7Z8+XL++5kzZzBgwAC/36FQKLB///76y0Qd8GxAtVotHA4HFAoFf0184htdYW05gq0by65bhoOPHcT+H/bzwFLeT3kA3E95q6puTJgwIeAJWEPIzs6GXC7H6dOn+RD11NRUr/dIJJJKt7x51v3y8nJMmDChQdJLSEsSTP30py7rpzgSQRAEmM1mHkQqKipCQUEBDh48iOPHj0Ov18Nms6GwsBBarbbSesRRS3T7LGlOwqGO1pTZbIbdbkdcXBxUKhXi4uIgl8tRXl7Op36wWq2V6qzD4YDD4YDVakVUVBSSk5P9BpQUCgXsdjsYY163wBESToIOKIkNmzjcVhySKzaKFFAiLdkHH3yAq6++GnK5HAsWLMALL7zgtVyj0WD79u04cOAA9uzZg6VLl6K0tBSAd4NYlREjRuC6666r87TXJXF+CKfTidLSUj4vhF6v51dwxBPrYDvEdrsdpaWlKC0tpasyTVCwdeP+Vfej73N98fuq3+HQu8s554ccANWPULrpppvwzjvv1E8GgjB16lRcdNFFiI6Oxj333IP+/ftXeo+/uQY9635cXBxatWqFXbt21Xt6CWlJgqmf/tRl/XQ4HHyibTGgZDQaoVKpYDQaYTQa4XQ6kZiYiLi4OFitVhQVFVU6cRTnZ6G2kDQn4VBHa0IcWahWq/nFU0EQEBcXB6fTibNnz+L8+fMoLS3F+fPneZDJ5XLh7NmzOHv2LIqKiqDT6QJ+hzjK326381vgaB4lEm6CDih5Nmri/ZyeV1kooERass8//xyTJ0+GUqnE8OHDoVarvZZLpVJoNBoAgMVi4cHYJ598ElqtFn379sW9994LwF3Xbr75ZnTr1g0ajYYHXl599VWcPn26YTMWIqvVCoVCwQPQaWlpsNvt0Ov1cDgc0Ol0PJgUTEBJDEyJV3nKysroWNPEBFs37C47YAeYk0ECCc5/cx4OowOnnj6FN+a/AcC7blx77bV8X5g0aRK++uqrBs+b6J9//sHSpUuRlJSEV155hdd10enTp3HLLbdg4sSJ6NWrFzZu3Oi37k+ePBmrV69ujCwQ0mwFUz8VCgV69uyJnj17YsOGDXVePx0OB791RRyVpNPpkJ+fD5PJhMTERGg0GiiVSh40Ki4uRl5eHsrLy6HVauF0OiGTySCRSGiEAmlWgqmj48ePR69evcKqDTWbzXA4HIiMjKy0zOFwQK/XQ6lUIjk5GbGxsbBarcjNzcWJEydQWloKpVIJmUwGq9WKkpISv/VarPPiBVvPW+AICRdBB5TMZjMfneR0OvkTa+RyOd3yRlo0m82GgoKCaofnarVa9OnTB5mZmZg7dy4SExOxePFixMbG4sCBA3jjDfdJ89GjR/H444/jyJEjsNlsePjhhwEA3bp1A2MMJ06cqPc81YTL5eLzqrlcLkRHR0Mmk0Gv1yMyMhJJSUlQKBQwGo0wmUzV3vLmcrlQVlYGiUSChIQEfsXHYDA0UI5IbYVSN9646Q3svmc3LplxCdSxaqRcnQJ5hBxdn+uKmU/NBOBdN86fP48dO3YAAKKjo2E2m/n8fg1JHHk3evTogO+ZM2cO4uLi8MMPP+DQoUMYMmSI37p/0UUXYefOnQ2VdEKavWDrp1wux6ZNm7Bx40Z06tQJTz31VJ3VT/ECiniLm8FgQGRkJFwuF0wmE+Lj4yEIAqRSKZ+gWyKRwGKx4MyZMzAajbBYLLw9lEqlNEKBNBvB1tGEhAQcPnw4bNpQcXSSSqXymivU4XCgpKQESqUSaWlpvK4qFAo4nU7k5uZCq9UiNTUVycnJiIuLg1Kp5H1e34utgiBAqVTCYrHw9Yi3wBESLoIOKLlcLkRFRUEikcBms8FoNMJqtaK0tJTPOE87N2mJSkpKEBsbW+37YmNjcfDgQWRnZ+Pzzz/H+fPn/b6vS5cu6Nq1K5/A89ChQ3yZRCLBH3/8UVdJr1NarZZPRBgZGQmZTAaTyQSpVIqoqCgIgoCEhARoNBro9Xq/80N4EkczxcXFQSKRQC6XIyIiAgaDgYb7NxGh1I2bP7gZQ18fisObDsOuu3D1TSqRwup0X5nzrBv9+vXDmTNn+PsSEhJQWFhY11mo1p9//lnt01YmT56MvXv3Yvny5di9ezeio6P9vi8pKQkFBQX1kUxCWqRg66fJZMJLL72E06dPIz4+HuXl5ZX6tTWtn1qtlt/ykpWVxecjFQQBHTp0QNu2bREVFYXExETExsYiKioKCoUCrVq1gkKhgNVqRXx8PBwOB0wmE79QI45YIKQpC7aO/v7775g7d27YtKHi6KSoqCj+ms1mQ35+Pr+45XK5oNVqcejQIRw+fBhFRUWIjIyESqVCdnY28vPzYbVavaaR8Xf7m0qlgt1u5yMdAdAoJRJWQgooyWQySKVSOBwOOJ1OuFwuWK1W3vDSJIGkJVKpVCF17FJSUtC7d2/89ttvfpeLjxUG3AEk3+BJfHx8zRJazyoqKsAYg1KpRGZmJr/iEhcXxzsLYlBJrVbzp8H5YzKZYDabERMT43XlRwxqV1RUNEieSO2EUjfsLjtUsSqkdExB2b8XJtiWChcCSp51QyqVerU5Vqs1qMf01rXExMRqL6bMmjULv/76Kzp27IgHH3wQr7/+ut/3NVYeCGmugq2fe/bsQe/evfHII4/gs88+Q1xcHBhj/JZroGb102AwQKvVQqlUwuFwoLy8HHa7HRqNBhkZGUhISOAnkuLFl+TkZKjVatjtdsTExKCoqAgVFRVQqVQwGAyIiIiAy+WC0Wis8XYhJFwEW0f//vtv9OzZMyzaUH+jkwwGA7Kzs2E0GhEVFcUDvwaDgQ++EC+OiqOMtFotLBYLrFYrcnJyUFhYiNzcXOTl5XkFjFQqFSQSCYxGI5/cn0YpknAiC/aNnpXdaDRCoVDwW93ECcboqU2kJYqPj+dXKsRJN32dP38earUa0dHRqKiowG+//Ya7774bwIUTY/HpLVVxuVwYP358naa/LohPgYyKikJCQgJsNhtsNhuioqK8AkKA+35wlUrFG1vfxt/pdKKiogIajabSfDuCICA6Ohrl5eWwWCx08h3mQqkbVqcVLrMLOQdy0H90fxhggEQqgRRSWB3VB6VKSkqQkZFR11moljh5qHgbiz8bN25Ejx49MGnSJLRq1Qrbt28HULnunzp1Ct27d2+YhBPSAgRbP/v06YNBgwZBKpVi69atUKvVkMvlsNlsKCkpQUJCQsj1U5w/UKlUQqfTQafTISkpCd27d/eaxFe8fUW8nU2hUKB169bIysriQfPs7GxkZGSAMQa1Wh3yhSxCwlUodXTSpEmwWCzYsWMHrFZro7WhYr9GDDwXFhaiuLgYKpUKbdq0gcvlQmFhISwWCxITE9G2bVsUFRXBZrMhOjoaCQkJiIyMRElJCTQaDRISEiCXy3nguLS0FE6nk49YFAQBGo0GJpOJ96tphBIJJyEHlHQ6HbRaLWJjYyGTyeB0OvktcDRCibRUI0aMwN69ezF06FD06NEDBQUFsNvtWL16Nfbt24e8vDzMmjWLD6G/77770Lt3bwDAzTffjF69emHUqFF49NFHA37H8uXLvTqh4cRut8PpdCIiIoJfhVWpVJU6Bw6Hg79XHMJrtVq9Rp6IE3irVCowxvjoJsYYTCYTrFYr7HY7ysvLkZyczDsSjDE4HA5IpVJIJEEPviT1LNi6cbbiLBxOB4b/Zzhi28citzgXHcZ2wIHHDoANZpj64tSA33Hw4EEMGjSo0co9MjISzzzzDJYvX+71NDdBELB582Z88cUX+PPPP+FwOBAbG4uPPvoIgHfdf+ONN7B9+/ZGeewxIc1ZMPVzwoQJ6N27N9RqNT744AMA7vo5duxYDB48GEuXLsWWLVuCrp+MMZSXl0MQBJSUlKC4uBgpKSlITU1FTEyM13ttNhtPlzjywOl0IiYmBiUlJZDL5dBqtbBarTyYJD7pqapgPSFNRTB1dPbs2ZBKpVCpVFixYgVKS0tx9dVXo0ePHhg9ejTefPPNBmlDPUcnOZ1OnDlzBmazGYmJiYiJiUF5eTnKy8v5bauCIMBoNCIpKQkymQwWiwXFxcVwOBz8NYfDgdatW6OkpAQWiwUWi4XPXWy1WhEbG4uIiAgYjUaYzWYoFAqYzeZ6zSchoRBYkBMfHTx4EJGRkTh+/Di0Wi0SExOhVCr51eWMjAx07tw5YHSZkOZs9+7d+OSTT/Dmm2/W23ekpqbiyiuvxHvvvVdv31FTBoMBWVlZSExMhCAIkMvliI+P5yf4jDFotVp+VaegoAAKhQJRUVFQKpVISEiA3W5HRUUFioqKEBERgaioKEilUn6/uVarhc1m45MTlpeXQ6PR8FsAxc61OIopIiKiMTcJ+X/B1o3rf7oeBrsBA1IH4JT2FA4UHcDFqRfjlPYURrYaiYVDFwb87KOPPopx48Zh7NixdZz64Dz++ON45513UFZWFvA9NpsNJ06cQEpKCpKSkvy+Z8yYMfjmm28QFxdXX0klpMUJpn5WRXzC6PTp0/HNN98gJSWlyvczxpCfn4+ysjJIpVKcPXsWqampSE1NhUajqTT/izhywWg0QqPR8IskgiBAq9XyuZPE22MYY4iOjkZ0dDQyMjKQmppa7Rw0hISzmtRRu90Ok8kEk8kEQRAQGRmJKVOm1HsbajQaodPpoFareWAoMTERLpcLFRUVcLlciImJQXR0NB9sERkZicjISAiCAIvFguzsbNjtdsTGxiIjI4PfCiuXy2G1WlFRUYHy8nJERETwpz/GxMTwi7DiCMXk5GQKKJOwENJeaLPZIJPJoNFoEBUVBcYYIiIi+ERhdMsbaakGDx6Mo0eP1ut3dOjQISyDSU6nE3l5eSgrK4NSqURSUhJiY2N5MEkcsSSOzlAqlbBarTCZTHA4HCgqKkJxcTEkEglMJhMAQK1W8zmZioqKIAgCYmNjkZKSwm+31Wg0KC4uxtmzZ/mxKCYmxmveC8/JEknjCLZumJ1mSAX3iVSKJgWR8kjEq+MhrZDC7Kj6SlzXrl0bLZgEAEuXLuVPnAtELpdDo9GgoqKCdxI9lZeX495776VgEiF1LJj6WRVx1OusWbPgcrmqvd26qKgIpaWliIyMhFarRXx8PFq3bu31OfE2cZ1Oh6KiIkgkEn4xxPNJb4mJifyJUS6XC/n5+cjJyUFBQQGsViufbiI9PZ2CSqTJqkkdlcvliImJQWRkJPR6Pc6dO4cbb7zRa8R7XXM6ndDr9ZBKpSgtLeUPoRFH1qvVan5+rNPpoFAoEBcXV2nqh6ioKBgMBhiNRhiNRsTFxcFoNPKnGEdHR0MQBJSVlcFsNoMxhpycHP5gLIVCgejoaKhUqrCdV5W0LCGNUBKH+Z0/fx4dO3aEw+FAaWkprFYroqOj0bt3b+oMExKEYlMxvj7xNa7pfA2SNP5HK4QLcZ40cSJ+xhjsdjssFgtMJhPOnz/PH2eclpYGlUoFpVIJtVrNG1+JRMJvk7Xb7Thz5gzKysqQmJgIq9UKuVyO6OhoGAwGPm+S2WyG2WyG3W6HRCJBREQE4uPjERcXx4f8i4FsQRB4mpxOJ4xGI0wmExITE5GSkkJXcJqAcd+MQ6QiEp3iOiFLm4X2se2hlqmxM28nusR1wWtjXmvsJNbK8789jzFJYxAtcY+eS09Pp1szCQkjbx54s8o2WbyNzWq1IiYmplJQWFyem5vLJ9UWRxOJ7WFMTAzMZjMPBonzCIoBZ39PxTSbzXy0QkxMDMrKyrB3717odDo+Grhbt2585JT4kByJRMJvH7JYLJBIJFAoFBR4Ik1SdfXT4XCgoqICFouF9ynrOrgkBngqKipQUVGByMhIKBQKyGQyyOVyyGQy/hCryMhIfozwnG+4uLiYT/0gPuhKnCNJfKqjeOwwm818qgfxFjilUskDTyqVCl27dkVCQkKd5pOQUAV9liVGSMUTM6lUCqPRCKlUyu/xNJvNFFAiJAjF5mK8dfAtjGw1MuwCSk6nkz91Qmz0RGIQyel0wmq18ie1iU+gsNvtXg2g2JlOTk7mnxNHOgqCwJ9oU1ZWBq1WC7VajZiYGN5Au1wumM1mGAwGGAwGnDt3DufOnYNKpeJzSYhXhCIjIxETE8ODTKWlpSgrK4PD4UBaWlpYzj1FLjA7zIhVxcLisOBo2VGkR6ZDLVNDJpHBYDc0dvJq7bPsz9DW0RYdozuioqKC37JCCAkP1bXJgiAgLi6OzyUqBpYkEgksFguKior4iFqJRMJvUzEYDDCZTHzeUblcjoiICB7YiYmJgU6nC3jyq1ar4XK5oNPpIJFIEB8fj0GDBuHYsWMoKChAYWEhtFotUlJS+GgIuVwOQRDgcrn4ZN8A+IiJYB4CQkg4qa5+ymQyxMfHw2azoaKiAqWlpVAqlYiMjKx1YEkMFut0OpSUlKC8vJxP8SJe1BQvuCqVSkilUq/+ssvlgsPhgE6nQ2lpKTQaDR9dJJPJYDabUVJSwoNR4ghFuVyOqKgoREREgDEGvV7PR+RLpVIYDAacOHEC6enpyMzMpHpNGk3QASW9Xg+n0wmlUsnvARWDTFKpFCaTCXq9nlca8eoL4H7UMz2NiZDwZrVaYTQaYbFYIAgCFAoFH3rvcrlgMBigUCj48F7GGJKSknjAyeVyeQWUpVIpGGOw2WzQ6/XQ6XS80fUMGGk0Gj4JoTi6SbwyIz75RrzSFBERAZPJxEcu2Ww2GAwGFBUV8StSkZGRfOLGiIgIlJSUQK/XIz09HRqNhneuxVsMSHgwOoyQS+SVXpdL5NDb9I2Qorqn1qh5p3Lfvn1ISUmBSqWCQqFAREQE3/99O4XilU2pVApBEPgE9GL7S51IQuqHODpXfKAGYwwqlYrPl1JSUsKfBGe1WiGTyRAdHQ2bzQapVIrCwkIwxqBQKJCYmMhPBEtKSvicKeKNAnK5HE6nk7dLgiDw38UTyoqKCjgcDsTExKBz585wuVwwGo1wOBwoLCyE0WhETEwM4uPjeV9dJpMhMTERGo2GnxDHx8dXug2nLojbSESjMElDE+uaxWKBXq9HaWkpn65FHN0eDHHuMrPZjLKyMh4MMpvNiIiIgEwm431UwN3nFS+CAt71V7zIabFYkJCQgISEBP4kSXEkoUQigdVqhc1m46PtxWCV2GeNiopCSUkJCgsL4XA4oNFoeNpycnKQnJyM5ORkv09YJqQ+BR1QcjgcUCgUyMrKgtFoRH5+PsrLy/m8JoWFhXC5XHykUnZ2Njp37gy5XI5Tp06hd+/eyMzMrPPhh3q9Hvv370f//v1pvhQSVg4VH8Lms5uRpc1CubUcNqcNMokMMcoYPlfMp0c/RZuoNmgd3Ro9E3oiMyoTgiDA7DDDaDciQhoBtUJdb2kUAzcGgwEOhwNyuRyxsbH86RWeywRBgEwmQ2lpKRwOBxISEvgjkVUqFaKiovi93OL8EMXFxdi/fz9KS0vRtWtX5Ofno02bNnwepfz8fFgsFmi1Wpw5cwZ2ux2CIKBLly68wyt2UMUG+eDBg+jcuTOkUimfZ8nlcnkNIRZvuROfFFdaWorc3Fw+uaF4G4B4BUi8P71v376Ii4vjw5fFRt4z8OR5zNFoNPyEQ2z8PW8NFP83GAw4cuQIunXr5nVl2pPna6H87nvXsuff4lODPH88Oy9iJ6WxA2w2pw02pw0KaeVRZHKJHDqrrhFSVfeUciVOHT0Fq9UKjUbD50YpLS1FUlISzp8/DwBo3bo1YmNjYbVacfjwYQwcOBBJSUkwGAw4deoUunXrxts7o9GIY8eOoV+/fvy2UrPZjAMHDmDAgAE1ahera1ep3SXNVYWuAoX2Qj43qGeAxPN/8aJqQUEBSktLIZVK+a1t//zzDzIzM3lbIo4SqqiogE6n43OlMMag0WhQWlqKs2fP8mCxTCZD27ZtkZubi+joaBw6dAh9+vSBwWBA27ZtedslHrPtdjtSUlJgMplgs9n4CWZcXBy/tVar1cJoNPJbywsKChAfHw+1Wu3VPgTL4XBAq9Vi//796N69O+8z+M6lKq7XYrHg6NGjXm0s3YpOQuV565hnkNeX2NeJjY3lk3eXl5fz+iXeXSOOlnc6nXwwhNVqhVarxcmTJ9G2bVs+EshgMMDpdCI1NRUpKSmIioriddazv+cZRJVIJDz4bLFYEBcXB4lEgn379iE5ORl//vknnE4nkpKS0KdPH6jVapjNZpw8eRKtWrXC2bNn0bFjRyiVSh5sFh9kk5WVhezsbH5bndPpxKlTp/gFqtjYWMTHxyMyMhJqtRo2mw3//vsvevbsyfuh4nby/V/83WQy4e+//8aAAQNoRDWpUtBHc/EJSrm5uV6vl5WVIT4+HuXl5VAqlWjdujWio6Nx/PhxDBgwAHa7HYcOHUJycjKMRiNUKhV//KF4L7e484ZKHP63fft2dO7cmTq2JCycqziHZ/c8i135u6CRaRCjjIFcIodUkMIFF0rNpTA53JNPb83ZCiecfNJhpVQJmUQGo90IAFBL1dg7Y2+dp9HhcMBsNsNoNMLlcvF6KXb8SkpK4HA4eMAFACoqKmAymaBQKPitZYIgIDk5GeXl5bBYLMjNzYXZbIbFYuEdzhMnTgAAzp8/j+zsbGRkZMBisfB7ycUgTE5ODk9fmzZt+HvE44PY8S0tLUVMTAy6d+8Oi8UCo9HIOwFiniwWC+8AeObZYDBAIpFAo9EgJiaGj4YqLi7GgQMHIJPJ+Em52NEA4NXYarVabN++HVKplHcOxCtIYifFM2AjbtP9+/ejQ4cOlebI8BcQ8hcs8nyMrufvvicCvr97drrETpNnh8yTmGbfQJPna/UReCqzuJ/sopBUDigpZUqUV5R7bZemqqKsAqdPnwYA9OvXD4A7IHTu3DlIpVLk5eUBuPCUKKPRiJKSEvz111+Ii4uD1WpFVlYWf6qMUqmETqfDX3/9xfdHtVoNo9GI3377DZGRkUhPT4dCoYBSqeRzPIhlGYjBYMD27dvRpUsXv+1qdcsJaarO5JyBoBH4sVs83oknneLIWJvNBq1WC7vdjtTUVMTHx/OHTOTk5CAxMZE/gYoxxkfOMsZgNBr5iFudTgedToesrCyvdMTExODIkSNo164dTCYTCgoKcObMGcTHxyMqKgo2mw0A+FOhCgsLkZ6ezkcrWa1WnDlzBjk5OfwJUy6Xi99u53Q6UVhYCIVCwedw8syzJ/HOA7H9EEdsabVa7N69G1FRUfzx557by/NEu7i4GHv37uWPVRffI96e5zkHjZgOoHJ7Vts2IMhpY+tsXf7a5ppePKpuff7W39ycO3sOslL3qavn/J6egSbP/o3n/4C7H2i3270+JxL3fZlMBpvNhuPHj/M+ohhcFts7rVYLnU7HA1SefUDPW0ydTid0Oh1sNhsEQeDzPP3111/o1asXSktL+fpat24Nl8uF8vJyHDx4EBKJBIcOHUJUVBR/4IyYXplMhoSEBBw9ehStWrXiT5QzmUyw2+18JKI4Wl+cm/TQoUM80CZeJFar1TyfvkG6srIy/P7771AoFHzUo9g/Fi+6+rtQKW6PQMHqYPbvQPwF+H2Xe96C6Bl89LdfVHVR1t8FWd//PfvGnn3k2h6vqju+1PT4U1+CDigZjcaAifccbp+dnc0rnMlk4iejMTEx/F5yMSjlefIllUq9GhMA/Kq/2Ih57uRiQel07ivX4n3rvoXu78dXoJ3J38mc53f727H8fbf4Gc/P+/udNG0WhwWfHf0Mbx58EwqJAkPShiA90v+TV8ot5dh8djMuTr8Ycao4WJ1WlJnLoLe7byVTyVQ4XHwYNpct5HR47r/iwVJsRMXAi81m4/dqy+VyftuYZ2fR5XLBZrN5zYckDukVO9YAcOLECVitVqjVat4Yy+Vy/lQaX3FxcUhJSUFCQgIKCgqQkpICp9OJ3bt38/e0a9fOK2glpkUcSix2CDxvkRPzLAZOxM68yWTiQSbPEwGxsRUnS/XcfuIEpv4C3haLBQD4dgwmwKLVagFceDx0qKo6jnieAHg2Zv4aes+/xavDYhmJnTDP2zt8O1ye3+F58uG5jTw7EZ7bRvxbbOw9y+xsyVnInXJomAYu2//vM/+/mSKdkYAVyC3ORbTS/xWyQMf2qsolmI55oNfEv0MdUl5cUsx/NxqNUCgUPOgp7iOA+/YW8WpgSUmJ11VU8b3irakOh4O/T3xksbiPHTt2DPn5+V6j7TyvzioUCq/XxfZYbFcLCgp4PRfzLJFI+KOdxZEZ4rLqTmh8R3v4/u5vHb6vBWpbxf2vqnQEs4/U5DO+rwWzjrpcTupOZkYm2se156NexR+r1crbI8B9vBSf3pacnIyKigrYbDbExMTg8OHD6Ny5M29b4uLi+NNJy8rK+GfFUbcxMTH4+++/vdIhTrIrztMi9qvT0tKQnJzM22px8u28vDzodDrEx8dDr9fDZrPxYFN5eTm0Wq3X8V28W8Dz5NszkOPJ89ZazwsnYnss5sn3xMqzjfBsu81mM+/Xiz/iJMTiCA/xeOQZYPJti4Otc4Hqa1UnZL7LfNuYUII3nuct/tIRjJq0b4G2gb9tGOh46/vZ6pb7BiP9Ha89f0Kdd9fusFfqQ4nf6Xkxz3N/8ayznueU4jyh4sh4cblYZwCguLgYERERfC4m8VY1cf/2vfAorke8cCc+lCYyMhIqlQrR0dG8zfa9Yyc2NhZJSUk8P+IxIC0tDQkJCV51XkwnAN4Pjo2N5XOYiqMVxZGW4q16AHDmzBmoVCqvOiD2ZzyDwuLccACQn58PvV7P3yt+zt8od99t7xlg891n/J2vVDXyzN/r/gJMVdXtYGIDvunzXW+gYFSg7/G3bQAEDD75Lvdcn2f6/L0e6DgVqA4G+hHTkJiYGDCPnoIKKIlPalIqlXzn8iTudDKZDMXFxSguLobFYsGhQ4cQExPDJyYTbzVRqVT8xFYcISGe4IqVUMy0vyvj4s7sOYzw3LlzqKioAOBueCUSCeLi4gJ2YH0F0/iIxHX4i37WVKDvD6VjKpPJaFL0BmS0G/H+ofdxsPggsnXZMDvdB2uVVIV28e1gMVqQZczy+1mTwwSn2YncklyUycr461K4D7oOmwNWo5U3HNXNkeJ0OrFv3z4+SsczGi/eAy521MR6JQgCr38AvBoFxhi/hUwMPHkGIcTJRaVSKRQKBTQaDRISEvjk3IB7X7VarfyYoVKp+H3h4ugjxhhOnjzp9T7AHXjxDPIAFwI9FosFBQUFXg2fOH+S78FUrVZDrVYjISGBb0ubzQar1QqDwQCdTsefoGOxWJCTk+N1pUY84WaM8SupJpOJT1ountSLB9+2bdvy7/YM1Ihpj46O5p0Ez8CKb+c20Gu+P77f4XkM9b0KE+iKXTA8P+c5Z1ag9ARLLKvDJYfRqrgVTCYTDDDAKXOi5FQJrLDC4XQg3ZyODds3oE1Mm5DSXNV3hnq8FvcHz05gfHw8v/2yKk6nE06zEzK1jO/narUa0dHRkMlkyMrKQnx8PAoLCwGAz4sijvYzm82Qy+V8bgWxvRRvSxVHFYpzu4j7aF5eHoqKiip1gny3ke/+5nA4YLFYsG7dOr95Ex+d/uOPP3p14v3x7KCIt6P6Iz4txzdtvr+Horp2HQCv11V91vdv8SS/pmkJZVlNPh/oveJtWfUVwKpNH6g+1gO49+VQ6ujxY8dhUBh4GsT6KvZFxZOu5ORkftEjNzcXBoMBERERMBgMvD6mpaXx7S2e0DHGkJCQAJPJhIqKCj4K2LdvLdYxsb0U//fsF4vEE9zi4mIUFBQgIiICERERiIyM5LetiyfRYj0Xgzqe20lsJ4ALZeA5mtGzfRWfRmWxWJCfn8+DSv549gXOnj0LjUbj1Wf2bac8T/x9A81SqRQJCQmIiIjwWr+4XNzedXH88BTMiaPv7yJxfjzf8wTf85NA3+Xvff7W5fu6b1lW91nPZdWdzPtLt+d3+vuMv/0j1DY0Qh3BR4b7jg5xuVz81k5xHl+x7yVeFBX7wZ6BkJKSEp5G8WKluL+mpqYiLS0NcXFxSEhIqDKdRUVF/EEw4vrEeUfFuwDEB1qJFy096704b6l4PBCXi0+A9FceFosFaWlpUCqVfBCHOBrZbrd73Vork8mQn5/Pz8PFeiIGnsQ7CzzLVOxzFBQU8JGFvhchPfcZz/1fbFeDmeNREATev/JMm+f3eb4XQKV+h+e5jWcQR+T5XsaY1y18/vrfvt8HuM9jPJ/cJ/4fqK/tr9/sb3RcVceXYM8RPJcx5r4I4Hmc9JfPQN/n+R3FxcVB1VGwIOzYsYMBoB/6oZ9G+NmxYwfVUfqhnzD9ofpJP/QT3j9UR+mHfsL3h+on/dBPeP8EU0eDGqEUExMDAFi7di06dOgQzEcIIbV0+vRpXHnllbz+VYXqKCENi+onIeGN6igh4YvqJyHhLZQ6GlRASRzm1KFDB3Tr1q12qSOEhCSY4aJURwlpHFQ/CQlvVEcJCV9UPwkJb8HU0dAfrUYIIYQQQgghhBBCWjQKKBFCCCGEEEIIIYSQkAQVUEpMTESbNm2CfnQcIaT2Qql3VEcJaVhUPwkJb1RHCQlfVD8JCW+h1DuBsTp8PishhBBCCCGEEEIIafboljdCCCGEEEIIIYQQEhIKKBFCCCGEEEIIIYSQkFBAiRBCCCGEEEIIIYSEJOSA0nXXXQdBECAIAoYOHVofaSIk7FVXDxISEiAIAqRSqdfrhYWFiI6O5p+dM2eO1/LU1FQIglBv6SKkpTpx4gSkUikkEgkkEgmGDBkCIHBd9SSTySAIAv9sbVAdJcQ/lUoFQRCgVqv9Lh8/fjyvgxqNBsXFxQCofhLSUAK1o54Ctan//e9/+efkcjn2799fozRQHSXEvx9//NGrPZw0aVKl9zz99NOQSCQQBAHt2rXjr3fo0IHXq5oIqeU1GAz48ssvsW7dOhw/fhy7du2q8QGBkKYqmHpw66234plnnqn02YEDByIlJQWMMWi1Wtxyyy182euvvw6DwVCv6SKkpWrVqhWys7Phcrlw5swZ7N69G3v37g1YV32tXLkSLpcLLperxmmgOkpIYLfddhv+85//+F3mdDqxceNG7N69m9fBGTNm8OVUPwmpf4HaUU+B2tR3330X77zzDlwuF5KSknDdddeF/P1URwkJTK1WY/HixWCMYdu2bVi3bh2ys7O93rNkyRK88MIL0Ov1OHv2LF5++WUA7vZ006ZNNf7ukAJKzzzzDJRKJSZOnIjOnTsjMTERTzzxRI2/nJCmKJh68OKLL6Jr166VPpubm4vff/8dABATE4PevXvzZQ8//DBWrVpVr+kipKVSq9Vo3bo1APCRDS6XK2BdrQ9URwkJ7M0330RSUlKV7yksLITZbIbT6USnTp3q9PupfhJStUDtqKeq2tT8/HwAgNVqRVpaWsjfT3WUkMAuu+wyPPbYYwCA4cOHQxAEHD58mC/fuHEjGGN4+OGHERkZie7du+ONN94AACxatAiXXXZZjb87pIDS0aNHERERwf9OTEzE2bNna/zlhDRFNa0Hhw4dAgD06NEDEokE0dHROHHiBABgwoQJSEtLw7Rp0xo8XYS0FIcOHYJEIsGAAQPQr18/DB48OOjPzpkzBxKJBP3796/x91MdJaRmpFIpJk+ejClTpkCj0UAmk+H111/ny6l+EtIwatqOzpkzBwsXLoQgCNDpdNi4cWPI3011lJDgLFq0CAAwefJk/tru3bshl8v5323atEFZWVmdfF9IASXGWKXXajPfCyFNUU3rgV6vBwCMGzcOLpcL0dHRGD16NLKzs7Fp0ybs3LmzUdJFSEvRu3dvuFwubN26FQcPHsS2bduC+ty6devgcrnw559/4sCBA7j//vtr9P1URwmpGZ1Oh59//hlr1qyByWQCAIwZMwYA1U9CGlJN29E33ngDS5YsAWMMSUlJ6NmzZ8jfTXWUkOrt3bsXCxcuxCOPPOL1ur9bwuuq/oQUUOrevTuMRiP/u6SkBBkZGXWSEEKaiprWA/Eqzueffw4AmDVrFoqLi/Hdd9/B5XIhMzOTV+yaTCxK9ZOQ4IwcORIxMTF48cUXg3r/uHHjAAD9+/dHx44d8euvv9boe6mOElIzL730EgRBwLRp06BWqzFq1Cj8/fffAKh+EtIYQmlHd+7cCavViscffxwAcOeddyI3Nzfk76Q6SkjViouLMXToUIwaNQrLly/3WjZ06FDY7Xb+d05ODuLi4urke0M6a50/fz6sVivWr1+PEydOoKSkBIsXL66ThBDSVNS0HkilUqhUKn5/61dffYW4uDjMmTMHjDH+A/iPItdXughpCbZt24ajR48CcA+b12q1GDVqVLWfMxgM2L17NwD3/C1ZWVkYMGBAjdJAdZSQmhk6dChsNhsOHDgAwH2CmpmZSfWTkAZU03a0T58+YIzhyy+/BAB88cUXiImJCfn7qY4SEpjT6UTbtm2RmZmJLVu2VFo+btw4CIKAFStWwGAw4MiRI7j77rvr5stZiK6++moGgAFggwYNCvXjhDQL/uqBQqFgGzZsYIwxFh0dzZcDYFOnTmWMMfbZZ58xiUTCBEFgCoWC7du3r9K6a1Atq0wXIYSxZ555hgmCwH/69+/PGAtcV6Ojo9nChQtZVlaW1+fatm1bq3RQHSXEP7lc7lUX58+f79Wu9uvXjwFggiAwjUbDCgoKqH4S0oACtaPB9H8nTZrEP6dQKNiOHTtqlAaqo4T4N3v2bN5Gij8rVqzwqp/z5s1jgiAwAKxNmzb8s23atPGqt7179w7puwXG/NyQSgghhBBCCCGEEEJIAKFP1EIIIYQQQgghhBBCWjQKKBFCCCGEEEIIIYSQkFBAiRBCCCGEEEIIIYSEhAJKhBBCCCGEEEIIISQkFFAihBBCCCGEEEIIISGhgBIhhBBCCCGEEEIICQkFlAghhBBCCCGEEEJISCigRAghhBBCCCGEEEJCEhYBpR07dmDChAmIi4tDbGws+vTpg+XLl8Nms+HOO+9Ely5dIJFI8PLLLzd2UmssUB5PnDiBadOmITU1FbGxsbjkkkuwc+fOxk5ujQXKp9VqxciRI5GcnIzo6Gh07doV7777bmMnt8aq2mdF//zzDxQKBaZOndp4Ca2FqvLYtm1bqNVqREZGIjIyErGxsY2d3BqrKp+MMSxduhRt27ZFREQEOnfujD179jR2kmskUD63b9/Oy1H8kUgkeOCBBxo7ySGrqix37NiBwYMHIyYmBhkZGZg7dy5cLldjJ7lGqsrnL7/8gosuughRUVHo3r07NmzY0NjJDVpt+gL5+fmYOHEiIiIi0Lp1a7z33nsNn4Eg1CaPTak/VNN8NrU+UU3z2ZT6RHXRR28K/aHa5LOp9Ilqk8em1B+qaT5///33JtUfqk15NpU+UW3y2JT6Q7WJE4RD/6fRA0o//fQTJkyYgHHjxuHkyZPQarX48ssvceTIERQUFKBPnz548803MWjQoMZOao1Vl8cJEybg8OHDKC0txS233IKJEyeipKSksZMdsqryWVhYiNdeew35+fmoqKjAmjVr8PTTT+P3339v7GSHrLryBACXy4U77rgDQ4YMaeTU1kwwefziiy9gMBhgMBig1WobN8E1VF0+n3zySaxbtw6//vorDAYDfvnlF7Ru3bqxkx2yqvLZtm1bXo4GgwGnT5+GVCrFdddd19jJDkl1ZTllyhRMmTIFZWVl2LlzJ77++uuwPYGrSlX53LVrF6ZNm4ZFixZBp9Nh+fLluOqqq5CVldXYya5WbfsC119/PVJTU1FUVISvv/4ajz76KLZv397AuahabfPYVPpDtcmnVqttMn2i2uRTJpM1iT5RXfTRm0J/qC7yGe59otrmsan0h2qTz2HDhjWZ/lBt8ul0OptEn6g2eczKymoy/aHaxgnCov/DGpHL5WLt2rVjzz77bLXvHTFiBFu5cmX9J6qOhZJHUVxcHNu8eXM9pqruhZrPI0eOsJSUFPbhhx/Wc8rqVrD5fPnll9nNN9/MFixYwKZMmdIwiasjweSxTZs27Lvvvmu4RNWD6vJZWlrKlEolO378eAOnrG6FWjeXLVvGunXrVs+pqlvBlCUAlpeXx1+7/fbb2b333ttQSawT1eXzjTfeYMOGDfN6beTIkWzBggUNkLqaq21f4NSpU0wikbDCwkL+2j333MNmzpxZ10mtsbrs74Rzf6g++nXh2Ceq63yGY5+orvIY7v2hushnuPeJapvHptIfqut6Ga79obooz3DvE9U2j02lP1TbOEG49H8adYTSyZMnkZ2djeuvv74xk1GvQs3j4cOHodfr0b1793pOWd0KNp+TJk2CSqVC9+7dkZKSgmnTpjVQCutGMPk8e/YsXn75Zbz44osNmLK6E2xZ3nXXXUhMTMSQIUOwfv36Bkpd3akun7t374ZSqcS6deuQkZGBdu3aYd68ebDb7Q2c0toJ9Rj04YcfYtasWfWcqrpVXR7j4+Nx22234YMPPoDdbsfp06fx66+/YsKECQ2c0tqpLp8ulwuMsUqvHTp0qCGSV2O17QscOnQIaWlpSElJ4a/17ds3rPLdEvo7QN3nM1z7RHWVz3DuE9VFHptCf6iuyjKc+0S1zWNT6Q/V9fEnXPtDtc1nU+gT1TaPTaU/VNs4Qbj0fxo1oFRcXAwAyMjIaMxk1KtQ8lheXo7rrrsOTzzxBFJTU+s7aXUq2Hz+9NNPMBqN2LZtG6666iqo1eqGSF6dCSaf//3vf7Fw4UIkJiY2VLLqVDB5XLVqFbKzs5GXl4f7778fV111Ff7888+GSmKdqC6fZWVlqKiowP79+3H8+HFs374d69evx/LlyxsymbUWyjHo999/R1ZWFmbOnFnfyapTweTxmmuuwbvvvgu1Wo2OHTti0qRJuOKKKxoqiXWiunxefvnl2LdvH77//ns4HA58//332LlzJyoqKhoymSGrbV/AYDBUmrMkNjYWer2+tkmrMy2hvwPUbT7DuU9UV/kM5z5RXeSxKfSH6iKf4d4nqm0em0p/qC6PP+HcH6qLfIZ7n6i2eWwq/aHaxgnCpf/TqAElsYHJy8trzGTUq2DzqNPpMH78eFx66aVYuHBhA6SsboVSllKpFCNGjMD58+fxwgsv1HfS6lR1+fz8889hsVhw8803N2Sy6lQwZTls2DBoNBoolUrccMMNuPLKK/Htt982VBLrRHX5jIyMBAAsWrQIkZGRaN26NR588EH88MMPDZbGuhBK3fzggw8wefJkJCUl1Xey6lR1eTx+/DimTp2KlStXwmKxID8/H0ePHsXjjz/ekMmstery2blzZ3z99dd45plnkJycjA8++ADXXXcdEhISGjKZIattXyAyMhI6nc7rNZ1Oh6ioqFqnra60hP4OUHf5DPc+UV2WZ7j2iWqbx6bSH6qLsgz3PlFdHGOB8O8P1WW9DOf+UG3z2RT6RLXNY1PpD9U2ThAu/Z9GDSh17twZbdu2xerVqxszGfUqmDxWVFRg3Lhx6NGjB95++20IgtCAKawbNSlLu92OkydP1mOq6l51+dy0aRP279+P1NRUpKam4sUXX8SGDRuQmZnZwCmtuZqUpUTS6PP7h6y6fPbp0wcAmmR99BRseVZUVODrr7/G7bff3kApqzvV5fHw4cPIzMzE1VdfDZlMhrS0NNx8881Yu3ZtA6e0doIpy0mTJuGvv/5CWVkZ1q5di5MnT2LEiBENmMrQ1bYv0Lt3b+Tn56OoqIi/duDAAfTq1auuklhrLaG/A9RNPptCn6g+yjPc+kS1zWNT6Q/VR1mGW5+otnlsKv2huirLcO8P1TafTaFPVBdl2RT6Q7WNE4RN/6dBZ2zyY+3atSwyMpK9+uqrrKSkhDHG2PHjx9ltt93Gzpw5w6xWKzObzWzYsGHshRdeYGazmdnt9kZOdWiqy+PgwYPZTTfdxJxOZyOntHaqyue2bdvYpk2bmMlkYna7nf30009Mo9Gwzz77rJFTHbrqyrOgoID/PPzww2z8+PFek6U1BdXlcfv27cxisTCbzca+/PJLplKp2K5duxo51aGrLp9jx45lM2fOZEajkeXl5bE+ffqw5557rpFTHbrq8skYY2+//TZr1apVkz0OVXf8UavV7LvvvmNOp5MVFRWxyy67jM2YMaORUx266sryzz//ZHa7nVVUVLBFixaxjh07MoPB0Miprl5t+wLDhg1js2bNYkajke3Zs4fFxsaybdu2NVZ2/KptHptKf6g2+dTpdE2mT1SbfP79999Nok9Umzxqtdom0x+qTT5zcnKaRJ+otsefptIfqovzyqbQH6pNPrOysppEn6i2ZdlU+kO1jROEQ/+n0QNKjDH2+++/s3HjxrGYmBgWExPDevXqxZYvX86sVisbMWIEA+D1E24ztAcjUB7/97//MQBMo9GwiIgI/vPpp582dpJrJFA+9+zZwwYMGMCioqJYdHQ06927N3v77bcbO7k1VtU+6ylcn2oSjEB5PHjwIOvTpw+LiIhgMTExbODAgezHH39s7OTWWFVlef78eTZlyhQWGRnJ0tPT2dy5c5nNZmvsJNdIdfvswIED2fz58xs5lbVTVR5/+OEH1q9fPxYdHc2Sk5PZjTfeyIqLixs7yTVSVT7Hjh3Lj7NXXXUVO3fuXGMnN2i16Qvk5uay8ePHM41GwzIzM9m7777beBmpQm3y2JT6QzXNZ1PrE9U0n3/++WeT6RPVVR893PtDNc3nv//+22T6RLUpy6bUH6rtPttU+kO1yWdT6RPVJo9NqT9UmzhBOPR/BMZ8pkAnhBBCCCGEEEIIIaQK4XWTLyGEEEIIIYQQQggJe7Jg39hvVb/6TEdY+Pumv9Hnkz6NnYx6dXDmQXx5/MvGTka9u7bLtcipyGnsZNSrNtFtGjsJDSbXkNvYSah3mZHhNVEpIeSCYnNxYyehXiWpk2B1Whs7GfVOKVU2dhIazJI9Sxo7CfXqiYufwElt+ExiXl86xXZq7CQ0mJZwnG0pCowFjZ2EepUWkdbYSQgrNEKJEEIIIYQQQgghhISEAkqEEEIIIYQQQgghJCQUUCKEEEIIIYQQQgghIaGAEiGEEEIIIYQQQggJCQWUCCGEEEIIIYQQQkhIKKBECCGEEEIIIYQQQkJCASVCCCGEEEIIIYQQEhIKKBFCCCGEEEIIIYSQkFBAiRBCCCGEEEIIIYSEhAJKhBBCCCGEEEIIISQkjRJQ0v2tw9G5R3Hk0SMo3VZaaXnZH2U49vgxHHv8GIrWFfHXXTYXct7NwdG5R3H0saMwHDc0ZLJDVnGgAsceO4Zjc4/5zWf5H+U4/sRxHH/8OIrWX8in4YgBJ54+gRNPnUDW8iw4DI6GTHbI9m/dj9njZ+OhcQ9hy9dbKi3/8JkPcefQO/HEVU94vb7gxgV4bOpjeGzqY7hjyB34eMnHDZXkkG3esBmj+4/GyH4jsfrj1ZWWH9h/AJddfBlG9B2BV5a9wl9/YNYDGN1/NC4ffDmWLVzWgCmumZ9++gldunRBp06d8P7771davnfvXvTo0QMdO3bEM888w18/ffo0BgwYgI4dO+K///0vGGMNmeyQ/PrzrxjRbwSG9R2GL/73RaXlf+/7G2MGjsGlfS7Fy8+/zF+/77b7MKLfCIwZNAbPL3i+AVNcMy2hLIGWkc+WkEeg5eRz0/pNGNJnCC7udTE+/ejTSsv/+vMvDOs/DIN6DsKLS17kr7/0/Evo17kfurbq2pDJrZH1P61Hr+690KNrD3z4wYeVlv+590/0690P3bt0x+JnF/PXT58+jaEXD0X3Lt1x3z33hX1ZtpR99tiOY1j5n5V46eqX8OcPf1Za/v497+O1Ga/hletfwZYPLvQDv1rwFV6b8RpevfFV/LDsB7hcroZMdki2btiKcQPH4bL+l+GrT76qtPzg/oOYOGQixl40Fq8vf52//sidj+DKS6/EpKGTsGDOgrDOI9By9tmWcJxtKWX5y8+/4NJ+l+KSPpfgs/99Vmn53/v+xsgBIzG091C8tPQl/vrLy17GgK4D0KN1j4ZMbo015fJs8IASczLkf56PDvM6oMszXXB+3XmvgIlD70Dht4Xo+FRHdFncBYZjBlgKLACA8z+ehypVhW7Lu6Hr4q5QZaoaOvlB4/l8rAM6LeqE4vXFlfJ5fs15dHiyAzov7gzjMSPPZ/5n+Wh9T2t0fq4z1G3UKNta1ljZqJbT4cSq51fh6Y+fxtJvl+LH93+EQesd6Ltk0iWY9+68Sp9d9NkiLPt+GZZ9vwzp7dIxYMyAhkp2SBwOB5574jl8vvZz/PTbT3j75behLdN6vWf+w/Px6gevYvO+zdj882YcP3IcADD9+unYsn8L1u9Yj7/3/Y0/tv/RCDkIjsPhwJw5c7Blyxb89ddfWLZsGcrKvPe9e++9F1988QWOHTuGtWvX4p9//gEAzJ07FwsXLsSpU6dw/vx5rFu3rjGyUC2Hw4FnHn8Gq9etxs+//4w3X34T5WXlXu956uGn8PpHr2Pb/m345edfcOzIMQDAVddfhe1/b8fGPzbirz//ws7tOxsjC0FpCWUJtIx8toQ8Ai0rn/Pnzcea9Wuw+Y/NeO2l1yodg+bNnoe3//c2/jjwBzat34Sj/x4FAIwaOwobtm9ojGSHxOFwYO6jc7Hhlw3Y/edurHhhRaWyfOiBh/DJp5/g0L+HsH7devz7z78AgCfnPYmnnn4KR44fQdH5Ivy87ufGyEJQWso+63Q4sf6V9bjt9dtw78f34vdVv8OkM3m9Z8YLM3D/p/fj/k/vx4ldJ5B/PB8AMHnuZNz/6f144LMHYK4w4+hvRxsjC9VyOBxY+tRSfPzDx/hu23d475X3oC3Xer1n0aOL8NJ7L2HD3g3YsmELThw5AQBY+OJCrN2xFj/98RO0Wi1+Xf9rI+QgOC1ln20px9mWUpaLHl+Er9d9jY07NuLNlZX77U/MeQJv/u9N/PbXb+5++7/ufvvIsSOxblv45s1TUy/PBg8ombJMUGWqoIhXQKqWIrpPNPSH9Xy5tcgKVboKsggZBImAyK6R0O3TAXCPXEoanwQAEGQCZBGyhk5+0ExZJqgyVJDHyyFVSxHVO8orn7YiG5TpSp7PiK4RqNhfwZe7LO4rHC6rC7LY8M3nqUOn0KpTK8SnxEMdqUbf4X1xcMdBr/d0uagLImMjA66j7HwZinKL0G1gt/pObo0c3H8Qnbp1Qmp6KiKjIjHy8pHYvmU7X36+4DwcDge69ewGmUyGyddMxq8/uzsUI8eOBADIZDJ07d4VhQWFjZGFoIiR74yMDERFRWHixInYuHEjX56fnw+Hw4HevXtDJpPhhhtuwNq1a8EYw65du3DFFVcAAGbOnIm1a9c2VjaqdGDfAXTu1hlp6WmIjIrE6MtHY/vmC2VZWFAIp8PJy3LqNVN553DUZaMA/H9Z9uiKwnwqy8bWEvLZEvIItJx8/rXvL3Tp1gVpGe5j0NhxY7H11618eWF+IRwOB3r06gGZTIbp107HpvWbAAD9BvRDSlpKYyU9aH/u/RPdu3fnZTl+wnj8sukXvlwsy169e0Emk+Ha667Fup/WgTGGPbv3YMIVEwAAN950Y1if6LSUfTb3SC6S2ycjJjkGygglOg/tjJN7Tnq9RxXhvsDrtDvhtDsBwed1hxN2qx2CIDRo2oN1aP8hdOzakffzRlw2Ajs27+DLzxech9PhRNeeXSGTyXDl1Vdiywb3SKzIaHf/1uFwwGq2hm0egZazz7aE42xLKcu/9/2Nzl29++3bft3GlxcWuMuye8/ukMlkmPafafjlZ3d707d/X6Skhn9ZAk2/PBs8oGQvt0MeJ+d/K+IUsJfb+d/KFCXMuWbYymxw2V2oOFQBe7kdDqMDgkRA/up8HH/6OM6+dxZOs7Ohkx80u9Y7n/J4ORzlF0YoKVIUsORaYC+zw2V3QX9Qz7dDxs0ZyH4xG0cePALzOTPiLolr8PQHq7yoHHHJF9KXkJqAsvOhjajavWE3Lr78Ykgk4Tml1/mC80hNS+V/p6Wn4Xz+ee/l6T7LC857rUNfoceWjVsw+NLB9Z/gGsrPz0dGRgb/OzMzE3l5edUuLy0tRXx8PO9E+X4unJwvrFxWnoGhSmWdkVYpCKiv0GPzxs0YMmxI/Se4hlpCWQItI58tIY9Ay8nn+YLzSEtP43+nZaShIL+A/11YUOi1PD0j3Wt5U1BQUID0jHT+d0ZGBvLz8i8szy9AerrH8swM5Ofno7S0FHHxcbwsfT8XblrKPqsv0SM6KZr/HZMcg4riikrve+eOd7Bk4hJ0GNgB6Z0vlO/nj3+OpROXQqFRoOuw8LyNqKiwyCuIkJqe6tWPq275/Tffj6Gdh0ITocGYCWMaJtE10FL22ZZwnG1JZenVb/fpl1cq6/S0JleWQNMvz/A4g/cI5ssiZciYkYHsV7Jx+vnTUKYpIUgFwOke1RPVOwpdnu0CWawMRT8VBV5nY/N3+6JPPtNvTMeZV88g6/ksqNJVECTuN5RsLEH7ue3R/ZXuiOgYgaK1YZxPP0K9OrPr510YMjF8T8793Yvqmcdglj9y9yO46fabkJ6ZXum94aKm+azuc+GkLspyzn/nYObtM6ksw0BLyGdLyCNA+Qx2eVNAZVn18maRT1RO713v3YV5a+eh4GQBzp++EGy5YekNmLduHsCArD+z6jWtNeV3zhGh6uWeZfbax69hx7Ed7tEC23fVRxLrRIveZ+k4S2UZxpp6eTZ4QEkeJ/cakWQrt0EeI/d6T2z/WHRZ1AWdnu4EeZwcihQFpFFSSNQSxPSN4e8xnzU3aNpD4ZtPe5m90q1rMf1j0GlhJ3R8uiNksTIoUhRwVDhgybdA3Ubtfs/AGJhOed+rHk7ikuNQXnThXtbSwlLEJsUG/fmSghKUnS9D536d6yF1dSM1PdUrGl6QX4Dk1GTv5fk+y1MuLF/69FLExsXijvvvaJgE11BGRoZXVDs3NxdpaWnVLk9MTERZWRk/qPl+LpykpvkpK9+y9CzrPO+yXPzUYsTGxeKuB+5qmATXUEsoS6Bl5LMl5BFoOflMTU/1unpakFfgNSTf9+pqfl5+kxmyL0pPT/caWZSXl+c18jM9Ix35+R7Lc/OQmpqKxMRElJeV87L0/Vy4aSn7bHRStNeIJF2RDlGJUX7fq4xQokP/Djix64TX6zK5DN2Gd8OR347Ua1prKiUtxWvEUWF+oVfb7295UkqS1zoUCgXGXjEWv6z7BeGqpeyzLeE425LK0qvfnle53+5V1vkFTa4sgaZfng0eUNK01/Bb2pxmJyoOViCql3fDZK9wB2LsWju0e7SIG+weAh3VMwrGk0YAgOGoAcp0ZUMnP2ia9hpY8ty3tDnNTugP6Svl01HhvgXOrrVDt1eH2MGxkEZI4dA7YCu2AXA/8U2ZGr757Ni7I86dPIey82UwG8w48NsB9Lm0T9Cf3/3zbgweNziso8l9+vfBiSMnUJhfCIPegG2btmH4mOF8eUpaCqRSKY7+cxQOhwNrv1nLhzx/+sGnOHL4CJ5b+VxjJT9ogwYNwj///IO8vDzo9XqsX78e48aN48vT09MhlUpx6NAhOBwOfPHFF7jyyishCAIGDx7M57r45JNPcOWVVzZWNqrUd0BfHD96HAX5BTDoDdiyaQtGjhnJl6empUIilfCy/OGbHzB24lgAwKoPVuHI4SNY8vKSRtUdo2kAABqXSURBVEp98FpCWQItI58tIY9Ay8nnRQMuwrEjx1CQ5z4G/brxV4waO4ovT01PhVQqxb+H/4XD4cB3X32Hyyde3ogpDt3AQQPx77//8rLc8PMGXHb5ZXy5WJaHDx2Gw+HAV6u/whWTroAgCBh08SA+Efdnqz7j80KEo5ayz2Z2z0TR6SLoinSwGq048ccJdBrciS+3GC0wlLkfxuKwOXBy70kktkmE0+FEeb77gqPL6cLxP44jqU2S3+9obL3798bJoyd5P2/7L9tx6ZhL+XKxn3fsn2NwOBz46dufMHr8aDgcDuSezQUAOJ1ObNu0De07t2+sbFSrpeyzLeE421LKst+AftX226VSKY78cwQOhwPff/09LptwWeAVhqmmXp4NHlASpAIyrs/A6aWncfzp40iemAxZlAynXzzNR/TkfpyLo/OO4vTy00i/Lh2ySPfInvRr05G/Oh/HnjwGw3EDUq4M3wikIBWQdl0aTj9/Gifnn0TShCTIImXIXpHN85n3SR6OP34c2S9kI+26NMgiZe7tc3MGsldm48RTJ2A8bkTylcnVfFvjkcqkmPHYDDwz8xnMmz4Pk26bhKi4KDx/5/N8LqV3nnoH86+fj5wTObhnxD3Y+8te/vldG3Zh8ITwnVcIcE/C/OTiJ3H9pOtxxbArcOcDdyIuPg63XH0Lv2K16MVFeGDWAxjdfzRGXj4SXXu45wlY8OgC5J7NxeRRkzHh0gn46tPKj6INFzKZDCtWrMCoUaPQr18/PProo0hISMDEiRP51eTXX38d119/Pbp06YKJEyeiV69eAIBly5ZhwYIF6NChA5KSksL2JEAmk+HpxU/j2onXYvyl4/HfB/6LuIQ4zLxqJh+Z9NyLz+G+W+/DiItGYPTlo9Gth3uy+Kcffhrnzp7DpBGTMG7oOHy56svGzEqVWkJZAi0jny0hj0DLyueipYswbcI0jB4yGvc+dC/iE+Jx/dTr+VXYpSuX4r+3/BdD+gzBmHFj0L1ndwDA8ueWo0/HPtCWa9GnYx+89+Z7jZmVgGQyGZa9sAzjxo7DxQMuxuyHZyMhIQFTJk3hZbnylZWYOWMmenXvhXETxqFnr54AgMVLF+PZZ55Ft87dkJiUyCfoDkctZZ+VyqSY8MAEfHDvB3j95tdx6YxLoYnR4OPZH6OiuAIWgwUfz/kYr974Kt645Q206dMG3YZ1A3MxfDn/S7x646t47abXoFArMGj6oMbOjl8ymQzznp2HmZNnYuqIqZh1/yzExcfh9mtu5/28+cvnY84dczBu4DiMuGwEuvToApfThTm3z8GkoZMw+dLJiIiIwPW3Xt/IuQmspeyzLeU421LKcv6S+bhm4jW4/JLLcfeDdyM+IR4zps/g/fbFKxbjnlvuwbB+w9z99p7ufvuLi19E/879odPq0L9zf7z/5vuNmZUqNfXyFJjfG4cr67eqX32npdH9fdPf6PNJ8KNrmqKDMw/iy+PheyJcV67tci1yKnIaOxn1qk10m8ZOQoPJNeQ2dhLqXWZkZmMngRASQLG5uLGTUK+S1EmwOq2NnYx6p5SG74jvurZkT/iPpq2NJy5+Aie1J6t/YxPXKbZT9W9qJlrCcbalKDA2vYmxQ5EWEb63CTaG8JiUmxBCCCGEEEIIIYQ0GRRQIoQQQgghhBBCCCEhoYASIYQQQgghhBBCCAkJBZQIIYQQQgghhBBCSEgooEQIIYQQQgghhBBCQkIBJUIIIYQQQgghhBASEgooEUIIIYQQQgghhJCQNGhASfe3DmU7ynD2vbM4fO9hFP9SzJflfpqLk4tP4viC4yjfXQ4A0B/Vw1Jg4b/nfZFXaZ057+TAaXU2TAaCVHGgAuU7ynHuvXP4975/UfJLCV927r1zOLnwJE4vPY2i9UUAAMNRA6yFVv57/hf5ldZ57t1zcFldDZOBIO3fuh+/ff8b3nr8Ldwx5A5s+HSD13KXy4WHr3iYv/7vnn+Rn53Pf1+1bFWldb752JuwmCz1n/ga2LxhM7794lu899p7mH7ZdNw09SacLzgPAPj6s69hs9kAACuXrsTmDZu9PmsymjDnv3MaPM018dNPP+GTTz7BrbfeiqSkJLz++usB33vgwAHs3bsXAGA0GnHzzTc3VDJD9uvPv+Kbz7/BnP/OQZ+2ffC/d/7Hly2YuwBXj78aV4y4Aj9++yMAYNfvu5B1Mov//uwTz1Za5+w7Z8NkNDVI+muiuZalL8pnZU01ny0hj5vWb8KXn32J/Nx83HT1TZg6bipeWPwCAGD1qtW8LVn+3HJsWr/J67NGoxH33XFfg6c5WOt/Wo9PV32KO2bdgczUTLz1xltey10uF/r26stf375tO06eOMl/n/fovErrnHXrLBiNxvpPfA0153322I5j+Hv93/j22W+xePxi7Pp6F1/msDnw/fPf44N7P8Cncz8FAGTtz0LJ2RL++8+v/lxpnd888w1sZlvDZCAIWzdsxXerv8O8e+fh4o4XY9W7F/qmj93zGKaPno4Zk2bg/VffBwDs2bEH2aey+e/PP/18pXXOvXsu9Q0aiXh8feDOB9CtdTd88NYHfNnxo8cxacwkTBg5Adu3bAcA7PxtJ06fPM1/X/D4gkrrvO+O+8L2GNScy/KXn3/B159/jYf++xB6tumJD9/+kC87cfQEplw2BVeOvhK/bf0NAPDHb3/wsvzjtz+w6IlFldb54J0PUt2sB7KG/LLS7aVoe19bRPWMQkTXCLgsFwIkGddlQJAJcFqcOPncScQNjoPhqAGadhqo0lQB1xk7IBblO8uRODqxIbIQlLLtZWh9b2tE9oyslE8AaHV7K6gyL+TJcMydT2WqMuA6owdEo/yPciSMSqi3dIdq6zdb8dDKh9BraC90G9itUiDoj3V/IDHtQrkc2XsE7Xu2R3q79IDrHHT5IOz4cQfGXje23tJdU6s/WY2FyxbikbsfwbebvsXBvw7i1eWvYvHKxfjm828wccpEKBQKv5/VRGgQGxeLUydOoWPnjg2c8tC8//77+Oqrr3DZZZdhxIgRMBgMAd974MABGAwGDBo0CBEREYiPj8exY8fQtWvXBkxxcL74+Au89clbGDZ6GAZfOtirQXlq8VOQy+UwGoyYfvl0TL5qMnb9vgu9+/VG+07tA65z/OTxWPPlGsy4bUZDZCFkzbUsfVE+K2uq+WwJefz0f5/i/U/fx/133I/lryxHWkYaX7b609WYNG1SwLYkIiICcXFxOHn8JDp16dRQSQ7aRx9+hM9Wf4YxY8dg2PBhMBq8T8K+XP0lWrVqxf/+bftv6N+/Pzp1DpyXqdOm4vPPPscdd95Rb+mujea8z+77cR+uW3wdOgzqgHb92sFqtvJlu77ehS5Du6Db8G78tey/spHRLQOJrQP3ybuP7I4DGw5g0LRB9Zr2YH216iu88tEruGTkJRh0yaBK++zzrz+Pzt0787/37NiDXn17oV3HdgHXefmVl+PHr37EdbdeV2/pro3mvM+Kx9eRo0di6LChXuW5eMFivPrOq0hKTsK1U67FiNEjsPO3neh7UV906NQh4DqvmHwFvvniG9x8e/gFYJpzWX7+8ed455N3MHz0cAy5dIhXWS5dtBQr31qJpOQk3DDtBgwfNRx//P4H+lzUp8qynDB5Ar5d/S1umnVTQ2QhZE21PBtshJLD6ACzMUhkEshj5ZWWCzIBAOCyuqBKV8Flc6FsRxkKvi7A2ffOAgAsuRZkrczCsaeOwXzODACI7B6Jir8rGiob1XIanXDZXAHzCQC5H+Yia3kWzGfNcNlcKN9RjsKvC3HuvXMAAEueBdkrs3Hi6RMX8tktvPJprDDCZrFBppAhLjmu0nKX04XdG3Zj8ITBAACbxYbt323H6pdW463H3Vcmc0/m4oW7X8BjUx/D2ePuMu5xcQ/s37q/4TISJJ1WB4vZgqLzRejcrTMEQUDPPj2xb/c+7N+7H0cOH8EtV9+CD99yR89//OZHzJw+E/+Z8B+YTe4yHDZqGH5Z90tjZqNaWq0WZrMZCoUCaWlplZbfeuutGDZsGIYPH44zZ87grbfewiuvvIIJEyYAAC677DL88MMPDZ3saum0OlgsFigUCqSkplRaLpe766rJZEKnrp1gNpvx9Wdf4/mFz/ORZcePHset/7kV44aOw9F/jwIALhlxSdiWaXMtS1+UT7fmkM+WkEfxWCQIAs7mnMWCxxdg+oTp2Lt7L/7c8yf+OfQPrp96Pd59410AwJqv1+Dayddi8mWTYTK5g+AjRo/Azz9VHvnR2KorP6fTiTXfrMFV11wFADCbzVj1ySo8/dTTuGOWO1j077//4qqpV2FQ/0H45/A/AICRo0Zi3dp1DZaPUDTnfdasN8NutUMmlyE6MbrS8pO7TuLMwTN4/+73sfe7vbBb7Phr3V/Y9NYmfPvstwCA81nnseqRVXjtptdQeKoQANChfwcc/f1og+YlkApdBawWKxQKBZJTkystFwQBTz30FG6ZdguOHj4Ki9mC7z7/DiueWYF597pH0508ehL/vf6/mDxsMo7/exwAMHjYYGz+eXOl9YWD5rzPevX10ir39YoKi9C+Y3tERUchPj4eebl5+PLTL7F4wWI8cOcDAIBj/x7DTVffhFEXj8KRf44AAIaNHIaN6zY2aF6C0ezL0hy43+5ZlnHxccjLzcNXn32FpQuW4qH/PgQAOH7kOG7+z80YO2Qsjv7jPuZcOuLSSiN/w0VTLs8GG6FkLbRCnuA/wCLKeSsH+n/1SPtPGiQKCeIvjYemnQYx/WKgP6oHczJ0eLQD9P/oUfZbGTJuzIBULYVD72igXFTPWmiFIsH/lUUASLs+DbJIGSz5FuS+n4uO8zsi7tI4aNppEN03GoajBsAJtHukHfT/6FH+eznUN6jd+awIn3wWnClAYnrgK1A71u7A4PGD4bC706xQKTBi2gi079ke/Uf1x797/oXD4cDj7z+OQzsPYduabZj5+EyoI9WoKAufwJko+3Q2MjIz0KZdGxz86yCsVit2/b4LOq0O/Qf1R/de3fHhlx8iIjICK5euRPtO7fHgYw/ihWdewI5tO3DZxMvQqm0rrF2ztrGzUqUTJ06gdevWfpfZ7XYcPXoUu3btgiAIcLlcuPvuu2EwGHDffe5bMNq3b48vv/yyIZMclKxTWcjIzKjyPffPuh87tu7AvEXzoFarcc2N16B3v94YO2Esdv2+Cw67A59+9yl+2/Ibvlr1FRY8vwCRUZEoLS1toFyEprmWpS/KZ/PJZ0vI4+mTp5GZmYmykjIc/ecoPvj0A0hlUsy8ZiY2/r4RPXv3xKffforIyEgsf245OnbqiEc+egRLFizBb1t+w/hJ49GmXRt8/+33jZ2VSk6eOIlWrVsFXP7F519g+tXTYbfbAQBqtRo3zbwJ/fv3x8RJE7F923bY7XasXb8Wv/7yKz753ydYvmI5oqKiUFJcEnC9jak577MlZ0sQmxIbcLmuSIeLr7oYl999OT68/0N0uaQLLrriImR0y0DXS7sia38WnA4nbnn5Fpzacwp//fQXJj40EcoIJYza8Lh9KPtUNtIyK5+4iR579jHExcfh9InTmHfvPHz9y9eYdsM09OrbC6PGj8KeHXtgt9vxwTcfYOfWnfj282/xxOInEBkVibLSsgbMSfCa8z4rHl8DYYzx36NiomAxW3DtjGvR96K+uHzi5dj52044HA58+eOX2LZ5G75Y9QWeXfasu69XEn59veZcllmnspDRKnC/3bMso6OjYTFb8J8b/4M+F/XBZRMuwx+//QGH3YHPv/8c27dsx5effomFzy8M27IEmnZ5NugcShJ51V/X5u426LqsK4rWFoG5WKXl6tZqAIA8Xg6nKbzmTfIkyIWAy2SR7hieKt19y5u/fKpau5fJ4+VwGsM3n3KF/wChy+nCrp93YejEoVV+vm3XtgCAhLQEGCvCo3NRFaVKifiEeNx42424adpN2PbLNrTv6P92qB69ewAA0jLSoNPq3C9WLuqwpFL5v8VULpfjgQcewG233YaHHnqIXy335HmADzdKVeBbSgHgtQ9ew9b9W/HGijfgclWer6x7r+4AgPSM9AtlGuaaa1n6onw2n3y2hDwqVUpEx0ajfcf2SM9MR0pqCqQyKRyOyheNevXpBQBIz0yHVqsFEN75DFR+TqcT33z1Df5z7X+q/HyfPn0AAK1atUK5trzO01cfmvM+K1MEvu6silShw8AOkMqkaN2rNZ87yVNaJ3ewJiYlBma9ud7SWRtKZeC+QVy8ewR+h87uW2iczsp98m693Lf8pWakokIbfhdE/WnO+2xVfT2J5MJ5aIW2ArFxsZXeI/bfMzIzoCsP/75esy7LKuqmV1nqqi7L9IwL7We4a6rl2WABJWWqErbiwJPwuezuEziJQgKJWgJBIrhvg/M8r/OI04gbzWlxQhbVoFNBVam6fDrN7sbIUeGAy+5y51Mq+A0seX3O4oQsOnzymdY2DUW5RX6XaUu00JXosOyuZVj30Tr88sUvyPonCzK5zDuffsrTYrQgOr7y0OrG1q5DO5zLcd+SeM2N1+Cr9V9h3KRxGDrcHTSTy+ReHQ1BuJA5MW/ncs6F/fxJnTt3RnZ2tt9lTqcT11xzDT766CMkJydjzZo1kMu9852dnY1u3br5/Xxjat+xPc6dORdwudXqnhdCrVEjMioSEokEMrms2jI1GoxISAifec08Ndey9EX5bD75bAl57NCpA86eOQu1Wo2Y2BhU6CpgNBpht9khk8kgk8vgcl7o+Hged8SLEmfPnEXnLp0Rbjp17oQz2Wf8LissLERRURGmXjkVr6x8Be+8/Q7+2v+XuwxdVR9nDQYDEpPCZ55MT815n01snYjygsBBvTa926DgRAEAoPBkIeLS4iCRSbwuyPgrT6vJiojYiHpKdWjadWyH3JzcgMsNFe75S0qLS2Gz2iCVSt39vWr2WaPBiPiE+HpKde00531WPL4GkpSShKxTWdBX6FFeXo6ExIRK+fMqT1w4BiUkhl9frzmXZfuO7fl5lz/BlKXneabYfhoNxrAsS6Bpl2eDRShkETJAArhsLhR+X4iKvyvAXAy2IhsybsxAzls5cBgcgBNImeK+VzKqWxTyv8qH4ZgB0Rf5DzIY/jUgum/4BCCkEVJAcOfz/Pfn3fMeMcBWZEP6jek4+/ZZ96gjF5B+vXty6sjukSj8shDGY0ZE9wuQzyMGRPcJn3xGREdAIpHAZrXh29e/xf6t++FyunD+3Hnc/PjNWPLtEgDAtjXbYDFZ0L5ne/fQwxWf48jeI+g/ur/f9f6z+x/0G9mvIbMSlJjYGEgkElgsFjxy9yMoKylDRusMPPui+8lfYyeOxb233ItJ0yYFXMfvW3/HDbfc0FBJrpHY2Fiez0WLFuHHH3+E0+nE6dOnsWDBAkyZMgUulwuCIGD16tWwWCyYOXMm9u3bh1WrVuGXX37BnXfe2djZqCQmNgaCRIDFYsHLS1/GL+t/gdPpxJnsM1j4/ELcf9v9KC8rh8PuwANz3ffRXzL8EiyZvwS7d+zG5Vdc7ne9O7btwJgJYxoyK0FrrmXpi/LZfPLZEvLo2ZY8vvBx3HjVjXDYHZg33z0fy/grxuP2m27HlOlTAq5j+5btuOm28JtQ1LP8nnvmOaz7aR2cTieyTmfhhZdewB97/gAAfPLxJzAajLio/0Ww2Wx48vEn8fv233HFlVf4Xe/WLVsx8YqJDZmVoDXnfVYdpYYgCLBb7djywRYc+/0YXC4XyvLKcMVDV2D4TcPxzbPfYNNbm9BpSCfEZ8Sjw4AO2PjGRpz56wy6DvM/MezpfafR9ZLwmAQ4OiYaEokEVosVry17DVs2bIHT6cS5M+fwxJIn8Mhdj0BbroXL5cK859x1dPDwwXhh4QvYu3NvwPZ/12+7MGrcqIbMStCa8z7reXx9ccmL2LhuI+/rPbv8WTy56Ek8cNcDcDqdeOzpxwAAl468FM8+9Sz+2PEHxl8x3u96f9/6Oy6bcFlDZiUozb0sxX77S0tfwqZ1m+B0OpGTnYNFyxbh8QWPY/bds+FyuvDIU48AcM9ruvjpxe5++0T//fbft/2OsePD78FPQNMuT4EFOT6q36ran+TrDujg0DuQMKzuIoM57+Qg8+ZMSFXSWq/r75v+Rp9P+tR6PRUHKuDQOxA/rO6uTpx79xzSZ6bXOp8HZx7El8fr5v7Kv7b9hYqyCoycPrJO1gcAbz72Jm6bfxtUEYGf7BeMa7tci5yKnDpKlduWjVtQWlKKa268JuTPmowmPDXnKbz0zkt1lp420W3qbF2e1q1bh+LiYtxyyy0hfc5oNOLuu+/GJ598UudpyjUEvoIYrM0bNqO0pBT/mVH1LRehmH3nbDz30nOIiKz91dbMyMD3/ddUOJZlfaB8Vq0p5TNc81hsLq6zdf3y8y8oLSnFdTeF/gQoo9GIuQ/OxRvvv1Fn6QGAJHUSrE5r9W+sxs/rfkZxSTFm3jyzDlLlNuvWWXjltVcQGRlZ63UppVXf+lwT4brPLtmzpNbrOLbzGEzlJlw06aI6SJHbN898gysfuRJKTe3K4omLn8BJ7clap2frxq0oLy3H9Bum13pdorl3z8WCFxbUSd+gU2zdP80xXPfZujjO1ub4Gsh9d9yH51c+X+tjUJI6qY5SdEG4lmWBsaDW6/h1w68oLSnFtTOurYMUuT1454NY8tKSWtfNtIjAc6/VRriWZ3UaNKAU7uoqoBTO6jKgFM7qI6AUbuoroBSO6iKgFO7qI6BECKkbdRlQCkd1FVAKd/URUApXdRFQCmd1FVAKd/URUApXLeE421LURUApnNVXQKmpatBJuQkhhBBCCCGEEEJI00cBJUIIIYQQQgghhBASEgooEUIIIYQQQgghhJCQBD2HEiGEEEIIIYQQQgghAI1QIoQQQgghhBBCCCEhooASIYQQQgghhBBCCAkJBZQIIYQQQgghhBBCSEgooEQIIYQQQgghhBBCQkIBJUIIIYQQQgghhBASEgooEUIIIYQQQgghhJCQUECJEEIIIYQQQgghhISEAkqEEEIIIYQQQgghJCQUUCKEEEIIIYQQQgghIfk/vkVUbaBoyXoAAAAASUVORK5CYII=", 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", 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def create_scorecards_unconditional(device=\"cuda\", cond_idx=0):\n", + "\n", + " data = data_loading.load_bike_bench_train()\n", + " all_result_tens = []\n", + " result_dir = os.path.join(\"results\", \"unconditional\", f\"cond_{cond_idx}\")\n", + " all_names = os.listdir(result_dir)\n", + " for idx, name in enumerate(all_names):\n", + " if os.path.isdir(os.path.join(result_dir, name)):\n", + " result_tens = torch.load(os.path.join(result_dir, name, \"result_tens.pt\"))\n", + " all_result_tens.append(result_tens)\n", + "\n", + "\n", + " num_samples=10\n", + " split = \"test\"\n", + " randomize = False\n", + " emb = conditioning.sample_image_embedding(num_samples, split, randomize)\n", + " rider = conditioning.sample_riders(num_samples, split, randomize)\n", + " use_case = conditioning.sample_use_case(num_samples, split, randomize)\n", + " condition = {\"Rider\": rider[cond_idx], \"Use Case\": use_case[cond_idx], \"Embedding\": emb[cond_idx]}\n", + "\n", + " \n", + " dashboard = ScoreReportDashboard(\n", + " design_batches = all_result_tens,\n", + " eval_funcs = get_standard_evaluations(device),\n", + " model_names = all_names,\n", + " condition = condition,\n", + " column_names = data.columns.tolist(),\n", + " device = device,\n", + " )\n", + "\n", + " for name in all_names:\n", + " dashboard.show_model(name, truncate_tails_magnitude=0.0)\n", + " \n", + "create_scorecards_unconditional(device=\"cuda\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "063241cc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bike-bench-cuda", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/bike_bench_internal/benchmark_models/sdv.ipynb b/bike_bench_internal/benchmark_models/sdv.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f76fdcd7efbca243ba4486c0304e9b836a6f4a72 --- /dev/null +++ b/bike_bench_internal/benchmark_models/sdv.ipynb @@ -0,0 +1,315 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a795b027", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mileva/mambaforge/envs/bike-bench-cuda/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import torch\n", + "\n", + "from sdv.single_table import TVAESynthesizer, CTGANSynthesizer\n", + "\n", + "# from bikebench.design_evaluation.design_evaluation import *\n", + "from bikebench.resource_utils import datasets_path\n", + "from bikebench.data_loading import data_loading\n", + "from bikebench.conditioning import conditioning\n", + "# from bikebench.benchmarking.scoring import *\n", + "from bikebench.transformation.one_hot_encoding import encode_to_continuous, ONE_HOT_ENCODED_BIKEBENCH_COLUMNS, BOOLEAN_COLUMNS\n", + "from bikebench.benchmarking import benchmarking_utils" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "99dc27d5", + "metadata": {}, + "outputs": [], + "source": [ + "data = data_loading.load_bike_bench_mixed_modality_train()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1517f250", + "metadata": {}, + "outputs": [], + "source": [ + "import sdv\n", + "from sdv.metadata import Metadata \n", + "metadata = Metadata.detect_from_dataframe(\n", + " data=data,\n", + " table_name='my_table'\n", + ")\n", + "\n", + "categorical_cols = ONE_HOT_ENCODED_BIKEBENCH_COLUMNS\n", + "boolean_cols = BOOLEAN_COLUMNS\n", + "continuous_cols = data.columns.difference(categorical_cols + boolean_cols).tolist()\n", + "data[continuous_cols] = data[continuous_cols].astype(np.float32)" + ] + }, + { + "cell_type": "markdown", + "id": "6ae57bf0", + "metadata": {}, + "source": [ + "### CTGAN" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "14b02ea0", + "metadata": {}, + "outputs": [], + "source": [ + "# CTGAN = CTGANSynthesizer(metadata, verbose=True, epochs=500)\n", + "# CTGAN.fit(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d05f1a98", + "metadata": {}, + "outputs": [], + "source": [ + "# CTGAN.save(\"results/models/CTGAN.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "39185eb7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mileva/mambaforge/envs/bike-bench-cuda/lib/python3.10/site-packages/sdv/_utils.py:500: FutureWarning: The 'load' function will be deprecated in future versions of SDV. Please use 'utils.load_synthesizer' instead.\n", + " warnings.warn(\n", + "100%|██████████| 100/100 [00:08<00:00, 11.62it/s]\n" + ] + } + ], + "source": [ + "CTGAN = CTGANSynthesizer.load(filepath=\"results/models/CTGAN.pkl\")\n", + "synthetic_collapsed = CTGAN.sample(num_rows=10000)\n", + "synthetic_cont = encode_to_continuous(synthetic_collapsed)\n", + "CTGAN_tens = torch.tensor(synthetic_cont.values, dtype=torch.float32)\n", + "\n", + "CTGAN_main_scores, CTGAN_detailed_scores, CTGAN_all_evaluation_scores = benchmarking_utils.evaluate(CTGAN_tens, device=\"cpu\", evaluate_as_aggregate = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a90caefc", + "metadata": {}, + "outputs": [], + "source": [ + "# torch.save(CTGAN_tens, \"results/designs/CTGAN.pt\")" + ] + }, + { + "cell_type": "markdown", + "id": "bcff537d", + "metadata": {}, + "source": [ + "### TVAE" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0d69133f", + "metadata": {}, + "outputs": [], + "source": [ + "# TVAE = TVAESynthesizer(metadata, verbose=True, epochs=500)\n", + "# TVAE.fit(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a71ed1ff", + "metadata": {}, + "outputs": [], + "source": [ + "# TVAE.save(\"results/models/TVAE.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "72fc7e98", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mileva/mambaforge/envs/bike-bench-cuda/lib/python3.10/site-packages/sdv/_utils.py:500: FutureWarning: The 'load' function will be deprecated in future versions of SDV. Please use 'utils.load_synthesizer' instead.\n", + " warnings.warn(\n", + "100%|██████████| 100/100 [00:08<00:00, 11.68it/s]\n" + ] + } + ], + "source": [ + "TVAE = TVAESynthesizer.load(filepath=\"results/models/TVAE.pkl\")\n", + "synthetic_collapsed = TVAE.sample(num_rows=10000)\n", + "synthetic_cont = encode_to_continuous(synthetic_collapsed)\n", + "TVAE_tens = torch.tensor(synthetic_cont.values, dtype=torch.float32)\n", + "TVAE_main_scores, TVAE_detailed_scores, TVAE_all_evaluation_scores = benchmarking_utils.evaluate(TVAE_tens, device=\"cpu\", evaluate_as_aggregate = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1d17c883", + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(TVAE_tens, \"results/designs/TVAE.pt\")" + ] + }, + { + "cell_type": "markdown", + "id": "3252ce31", + "metadata": {}, + "source": [ + "### Compare to Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f57b2e7f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:08<00:00, 11.40it/s]\n" + ] + } + ], + "source": [ + "dataset_tens = torch.load(\"results/designs/dataset.pt\")\n", + "dataset_main_scores, dataset_detailed_scores, dataset_all_evaluation_scores = benchmarking_utils.evaluate(dataset_tens, device=\"cpu\", evaluate_as_aggregate = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cfcdd30e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mileva/Documents/Lyle/Bike-Bench-Internal/src/bikebench/benchmarking/score_report.py:147: UserWarning: No model_colors provided; using Matplotlib cycle.\n", + " warnings.warn(\"No model_colors provided; using Matplotlib cycle.\")\n" + ] + }, + { + "data": { + "image/png": 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f8x+Sdvhz57/v/DF/rV6Hffv9foXD4RUL+wcAABhXhHIPxp9zf9ZtW26TZVsd++4e3F3nH3++np14dse+6J5t28rn822DhrZnmqai0ai8Xu8KVzYYlmUpnU63fJ61s0AgoHA4TDgxxoZt20qlUm2D5qSt4w/RaLQ/RQEAAADAgBWLRWWz2ZbtTqdTiUSC8YExYVmWZmZmZFkLj1k6HA5NTU3x/QYAAACAMUYoNwAAPdZoNDQ7O9tyAZVpmpqammIx1AjLZrMqFost271eryYmmJiN4de0mrr8sct10d0X6Yn8E4MuZ0E+p09nH3S2Xn/Q6xVwLS+kBwAAAAAAAEB/2LatLVu2tJyk3ovA7E6vsZKLw7sJ3QyHwwoGgyvy+v2Sy+VaBnOw+B4AgB0NYh7BpsImffGOL+qKx65Y9LlBV1AHxg/UnqE9WTi4SJVGRXfM3KGnCk911X+P4B76wgu+oAPiB6xwZRgF8wHdjUZDjUZD9Xp928dSmaapSCSyIqH/AAAA44pQ7sFJV9K68akbVWqUOvZ1Gk6967B36Q0HvUEO09GH6laPSqWiTCbT8hnLzsYxjLpWqymdTrd91jPPMAxFo1HuuzCWLMtSKpXqODYRDAYVDrPxHAAAAIDx1iknRJLi8bg8Hk8fq8JKKxQKyuVyLdsjkYgCATIOAAAAAGBcEcoNAECPzc3NqVKptGyPRqPy+/19rAi91k3YRiwWY9IlhpZt27pu03X619v/VQ9nHu7JNQ0Z8jg8cppOGTJkyVK9WVfNqvXk+nFvXO89/L06Y90ZMg02NQAAAAAAAACGWb1e1+zs7IJtpmlqenq6J8EN7UKjDcPQ9PT0imySmk6nVS6XW7a73W5NTk72/HX7rVKpaG5ubsE2h8Oh6enpPlcEAMBwqlarSqVSLdtN01QikZDD0ZsAsWw1q6/+4av67v3fVd1aXJBvwBXQgfEDtVdor7EK0uo327b15/yfdefMnWpYjY79PQ6PPn70x3XGujP6UB1GkWVZqtVqqlQqqlarXYXC7czn8ykSiazIPRAAAMC4IZR7sKrNqm55+hZtKW3pqv9zp5+rTx/3aa0Nrl3hylaXZrOpTCajarXaVX+Xy6VYLCan07nCla28YrGoXC7XNmBrntPp1MTExFh83kArzWZTyWSy43jEOGxKDAAAAACt2LatVCqlWq31uvhAIKBIJNLHqtAPtm1rZmam5X2xaZqampriWTwAAAAAjClCuQEA6KF24QTS+IQwoPP3msF1DKuH0g/pvFvP001P37Tka/icPiV8CcW8MUU9UQXdQXkd3gUXbVu2pWK9qHwtr3QlrVQlpWQ5Kcu2lvTaz0k8Rx8/+uN61sSzllw/AAAAAAAAgJVVLBaVzWYXbPN4PIrH4z15nWazqS1bWod2rMTC8E7PBwzDUCKRGItwBsuytGXLlpahFFNTU2PxeQIAsBy2bWt2dlaNRutg5l5t3l5tVvXd+76rr97zVeVr+UWd63f6dUD8AO0d3ptNkHuoWC/qlqdvUarSOpR9e2c96yx98C8+KJfDtcKVYZTZtq1araZSqaRKpdJVSNw8h8OhiYkJuVz8jAEAALRDKPfg2bate1P36r65+7rqH3QF9ZGjPqLT9juNTaZ6yLZtFYtF5fP5ru49DMNQJBLpyTjHINi2rUwm03bj1e35fD5Fo1F+5rAqNBoNJZNJWVb7dR69GusEAAAAgGGTz+eVz7eei+FwODQ1NcU4wZgqlUrKZDIt20OhkEKhUP8KAgAAAAD0DaHcAAD0SDcLLScnJ+V2u/tYFVZSOp1uOyHT5/MpFov1sSKgtUKtoH+/69/13fu/q6a98E6t7UQ8Ee0R3EO7BXdT2B1e1kPDptXUltIWbSps0qbCJjWs1r83F+I0nHrDwW/Q25/zdrkd/E4FAAAAAAAAhk0mk1GpVFqwrdcT09uN1fd6EUSz2dTs7GzbxeiRSESBQKAnrzcMksmkarXagm3j9rkCALAUuVxOhUKhZXsvNiSxbVu/3PhLXXDHBdpU2LSocz0Oj9ZPrNd+kf3kMB3LqgMLs2xL96Xu6zpI7vCpw/WFF3xBcV9vNqrBeGs2myoWiyqVSh1DseaNekgeAABAPxDKPTw2Fzfrls23qNZceBx6ZyftdZI+fvTHuafqsVqtpnQ6rWazuznmPp9PkUhEpjk6G381Gg3Nzc21Xe8zzzAMhcNhnoFg1anX60qlUm3HIAzDUCwWk9fr7WNlAAAAwODlajk9nn1cm0ubla6kVWlUZMuWx+FR2B3WlH9Ke4b21JSf0OZRVK/XlUwm225aFo/H5fF4+lgV+sm2bSWTSdXr9QXbDcPQ1NSUHA7m3gAAAADAuCGUGwCAHikUCsrlci3b/X6/otFo/wrCirMsSzMzM20nnE1MTDDZDANl27auevwq/cst/6LZ8uyiznWaTu0T3kf7RPZR1BNdkfoaVkNP5p/Uw5mHlalmFnXuuug6nXvcuTowfuCK1AYAAAAAAABgaWZnZ1tOTO/1woRqtapUKtWyPRqN9iyMbm5uTpVKpWV7L0I3h00+n1c+n1+wjc1JAQCrXa1WUzKZbNluGIYSiYScTueSX+POmTt1/q3n657kPYs6z2k69azYs7R/bH85zaW/ftfsBT52Pr59X+10bCHGdn+2+ru507EBminN6Oanb1a1We3Yd01gjb54whd1QPyAPlSGcWBZlgqFgorFYtuF4NsLBAIKh5e36ToAAMC4IpR7uJTrZd2y+Zau5zlHPVF95KiP6NR9TuX9bg9ZlqVsNttyI9SdOZ1OxWIxuVyuFa5s+crlsjKZTFf3Uw6HQ7FYTG63uw+VAcOnWq1qbm6u7b8XwzAUj8f5dwIAAICxliwn9bsnf6ebnr5Jf5j9Q9cbaIfdYR08ebCOmD5Cx+52rA6MH8j4xZCzbVuzs7NtN/IKBAKKRCJ9rAqDUKlUNDc317KdnwMAAAAAGE+EcgMA0APNZlMzMzMtJx2ZpqmpqSmZptnnyrDSyuWy0ul0y3aHw6FEIsH3HgOxubhZ5958rn77xG8XdZ7f6df+sf21T2Qfucz+TJS2bVuz5VndP3e/ZkozXZ/nNJx612Hv0hsOeoMcJrvLAgAAAAAAAINm27Y2b97c8pnJmjVrej5m3i4E3Ol0KpFILHthS7FYVDabbdlumqYSiYQcjvEap2wXem6aptasWdPnigAAGA62bSuZTLZ8DyJJ4XBYwWBwSdd/Iv+E/vX2f9VVj1+1qPNMw9QzIs/Q+vh6eRw92AjF+t8Pe7s/t/+YPzYMtg/p3v5Pc7v/XmGVRkU3PX2TkuXWYe3zfE6fzj3uXJ2y9ykrXxjGRqPRUC6Xa7tZ0Pa8Xq9isRgL/QEAAHZCKPfwsW1b983dp/tS98nu8kbzhD1P0EeP+qimA9MrXN3qUiqVlM1muwqwNgxDoVBoyeMfK822beVyORWLxa76ezwexWIx1n5g1esUQiZtfU44OTm5rA0JAQAAgGFTaVR05eNX6icP/0S3bb6t6zGKdtYE1ujUfU7V6c84XfvH9u9Blei1bDbbduygV3NQMRqSyaRqtdqCbYZhKJFIcC8MAAAAAGOGUG4AAHognU6rXC63bI9EIgoEAn2sCP00NzfXdrGb3+9XNBrtX0FY9Wzb1qUPXarP3/Z5FeqFrs/zO/1aP7Fe+0T2kWkMbjLxbGlWf0z+UanKwkEvCzlyzZH6zPM+oyn/1ApWBgAAAAAAAKCTer2u2dnZBdscDoemp3sfjlEqlZTJZFq2x2Ix+Xy+JV+/0Whodna2bQBFNBqV3+9f8msMq04h64lEQi5XfzZ3BABgmOTzeeXz+Zbtbrdb8Xh80Ysyc7Wcvnr3V/Wd+7+jhtVY1Ll7hfbSQZMHKeBaxPwUa6cPe6e/j5P5kG6H/i+o26Geh3VbtqU/Jv+oB9MPdtX/3Ye9W2959ltYwItFKZfLymazsiyrY9//n707j5PrOuhE/7tb3bq39q27tcuSLUvyJtuxHSe2YzuJHbKRBTKEOMEQIMkQlk/IkAQ+H8LAewxMMsAASSDDY1gHSCYEXjKQzPCyeJVt2bFs2bJsWZK1dnft+3KX8/4od6tbXVtv1bX8vp/P/VSrz6nbR9XVVXXP8jsejwfRaJShckREREQLMJR7cKWqKTx+4XFU7EpP9f2aH794wy/ifVe+b0PnPo8a27aRzWY7bka20CCGWTuOg2w22zZE6VJz4eK8Pidq6jb+CjTHfuPx+MhtWkxERERE4ydfz+Nvjv4N/v6Fv0eunlu3n3PDxA340P4P4a7td7EfY0DU63Wk0+3XskuShFgsBo/H08dW0UZqNBpIpdpvxO71ehGNsi+ZiIiIiIholDCUm4iIaJW6dbZrmoZ4PM7JeSPMcRwkk8mOC92i0Si8Xm8fW0Xj6kLpAj77yGfx6IVHe76PR/ZgX2wfdoV2QZEHY0KkEAJnS2fxTPIZVO32mx4sFPVG8Tu3/w5u3XzrOreOiIiIiIiIiIiI2um0QHu9JqMLITA7OwvHcVqWr2asRgiBVCrVMXhi1CfZp9Np1Ov1lmXBYBB+v7/PLSIiItpYlmUhlUq13bRCkiQkEgmoqtrzOW3Xxldf/Cq++PQXl73Id8KcwDXxaxDxRlpXuDR4e+FBzVBu5ZJjDab4nC2exRPTT8ARrT+jLvT2XW/Hf3zdf4RH4UJe6p3jOMjlcm0/qy+kqipisRhDsoiIiIhexVDuwWY5Fp5OPo1XCq/0fJ9rE9fi11/767gyeuU6tmy8CCFQKBRQLpd7qq8oCsLhMHRdX+eWdVev15HNZnvayEiWZUQikYFoN9Gg6bYxIdDsc4jH4wMVyk9EREREtFyv/R+vRdnq7fp3LewO7cZHD3wU9+y4h+HcG8h1XSSTybbzToHmJl6BQKCPraJBkMlkUKvV2pbH43EGtRMREREREY0QhnITERGtghACyWQStm23rcNO1fHQKWQEaE4yTSQSnGhG60YIgX9++Z/xu4//LkpWqaf7SJCwO7wb+2P7B3Zxr+3aOJo+ihezL0Kg+6WLBAkfv/7j+OlrfpqD0URERERERERERBsgn8+3DWhYzwUK5XIZ+Xy+bXk4HIZpmss+b6FQQKnUvs91HPr/S6USCoVCyzJd1xGLxfrcIiIioo3Ty4Ydy/3M89C5h/C5Jz6HE/kTy2pLyBPCNYlrMOWban5jYdi2s+BrzhBdPhnNcG711dsVftTL1/N49PyjPY3h3zBxA/7grj9oH65O1IIQAqVSqWtAFsBgbiIiIqKFGMo9HM4Vz+HJ2SfRcBo91VckBe/f+378+wP/HgEPw5LWSq1WQy6X6yngGgD8fj8CgcCKNkpdreVcIwGAx+NBJBLhdRJRB53Gfud4PB7EYrEN+bsnIiIiIloLl/YV9cv+2H78yk2/ghsnb9yQnz/ucrkcKpVK23Je64wv27aRTCbRLpLN4/EgHo/3uVVERERERES0XhjKTUREtAqdQggAwDRNhMPh/jWINlQ6nUa9Xm9bbhgGIhEunqS1l66m8R8f/Y/47pnv9nyfhJHA9RPXI6gH17FlaydXy+HQzCHk6rme6t+97W789u2/DZ/mW9+GERERERERERER0SKpVAqNRuuAjGg0Cq/Xuy4/VwiB2dlZOI7TslxRFExMTCxrgUStVkMmk+lYJxaLQdf1ZbV12FiWhWQy2bJMkiRMTk6OdCg5ERHRQt3miWiahng83tNnjpdzL+Nzhz6Hh889vKw2eGUvroldg+3mdkhCuhjAzZmg60dCM6B77ljGmtuG08DBCwcxW5ntWndbYBu++MYvYmdo58raSWOrVqshm822XRQ8h8HcRERERE0M5R4eNbuGH8z+AOdK53q+T8wbwyde8wm8fdfbIUvsu14LjuMgm822Hf+5lMfjQTgchqqq69yyi1zXRTab7bieYyGfz4dgMMhgLaIeZLNZVKvVjnV0XUc0GuXfFBERERENpY0K5Z7ztl1vwydf80nEDYb89ku1WkU2m21bLkkSEolEX/s2aLB0C21fz/nQRERERERE1F8M5SYiIlohx3GQTCbhum7LclmWkUgkuIhpjHR7TgBAOByGaZp9bBWNuu+f+T5+/ZFfR6bWORhmjkfx4LrEddge2D50Ex5d4eJo+iheyLwA0cOK8t2h3fiju/8I24Lb+tA6IiIiIiIiIiIiEkJgenq6bRDc5OTkuo6blMtl5PP5tuWBQACBQKCnc9m2jVQq1bHP3+fzIRQKLbudw2hmZqZt4HkkEoFhGH1uERERUf/Zto1kMtn2s44kSYjH49A0reN5crUcvnj4i/jKsa/AEa3fX+cJQBYyVKHCAw8uD16Onb6dUGUu/NxQyqvHXEh3F65w8UzyGRzPHe9aN6SH8F/v+q+4cfLG1baSxoxlWchkMm0/t8/RNA2xWIwb6xAREdFYYyj3cBFC4GzpLH4w+wM0nN5CoQHg2vi1+NTNn8K1iWvXsXXjQwiBUqmEYrHYU31ZlhEKhfoyftBoNJDNZrteDwHN/ptwOMxxDaJlEEIgk8l0Db03DAORSKRPrSIiIiIiWju9hHJLkBD0BOH3+GGoBjS5OS/AFjbqdh0lq4RCvQBb2CtqQ9ATxKdu/hTesesdQ7f2e9j0Mjc0FArB5/P1sVU0aBzHwezsbNs5QpqmIR6P8++ViIiIiIhoBDCUm4iIaIW67W4YDAbh9/v72CIaBJVKBblcrm05w9pprVSsCj5/6PP46otf7fk+2wPbcd3EddAVfR1btv7S1TQeu/AYKnb71+A5IT2E37/z93HT1E19aBkREREREREREdF4sywLyWSyZZksy5iamlrXny+EQDKZhG23XtjSa1Cm67pIp9OwLKttnXGbUN9pXMw0TYTD4f42iIiIaAOkUik0Gu3Dv/x+P4LBYNtyy7XwlWNfwRef/iIKjULLOrKQobgKVKFCdVUoojm3YLNvM3aFd8Grelf3n1gvcx+J5AX/Xnigxe3Cr9vNYhULysQl37v02CgSmsHcGppB3R0+Hp7IncAPZn/QdRNqTdbw27f9Nt5y2VvWsKE0DhzHQTqdbntNNMfj8SAWi43N9QwRERHRpRjKPZzqdh2Hk4dxunh6Wfd726634Rev/0Vs8m9ap5aNl3q9jlwu11MANtAcQwiFQut2/TEXFN7LEllVVRGNRqGq3OyMaLl6GT8FuveREhERERENonah3F7Fi62Brdjk24SYEeu6ebYQAvlGHjPlGZwvnUe6ll52W+7edjc++7rPsq9qnQghkE6nO8790HUdsVisj62iQVUsFjtuUBcOh2GaZh9bREREREREROuBodxEREQr0Gg0kEql2parqopEIsGFS2Mqk8mgVqu1LedgDK3Wc6nn8OkHP41ThVM91fcqXtw4eeNITSZvOA08OfMkzpXOda2ryip+49bfwA9f/sN9aBkREREREREREdH4qlaryGazLcv61TfeqQ1A9zBtIQQymQzq9Xrbc/Qa7j1KarUaMplMyzJZljE5OclxMSIiGmnlchn5fL5tebd5Ig+efRCfO/Q5nMyfvPhNASjiYgC3KlRIYvH9o94orghfgYAeWJP/x7JJaAZtL7y99OuFwdsbZS6Y221xO3f0y1xAt4qWj8tsZRaPnn8Ults5wAgAPnHjJ3D/VffzcxYtS68hWV6vF5FIhM8vIiIiGksM5R5u0+VpPDXzFCp2640kW9EVHfftuw8fvubDCHg26Bp7hLiui1wu13HNxEKqqiISiazpuMpy22AYBsLhMK+BiFbBdV2kUqmum4EFg0H4/f4+tYqIiIiIaPVahXLfuulWbPJvgizJLe7Rm7JVxsn8SZzIn0DDaR8Cfam4Ecd/uv0/4bWbXrvin02tFQoFlEqltuWyLCORSEBRlD62igaV67qYnZ2F67aedKIoCiYmJtjfRERERERENOQYyk1ERLRMQgikUqmOC5disRh0Xe9jq2iQOI6DZDLZtoMdAEKhEHw+Xx9bRaPAcR389+f+O77wgy/AFp0nMs7ZEdyB6xLXwaN41rl1/SeEwPHccTyTfAYC3S9rPnLtR/BzB36Og1tERERERERERETrpNOCBb/fj2Aw2Jd2pFIpNBrtF7GYpolwOLzk+0IIZLPZrgEO4XAYpmmutplDxXVdzMzMoN0Uk3g8Do9n9PqhiYiIgOYcgNnZ2bbvg0D798IT+RP43BOfw0PnHgIEoAoVmqtBdVUoov0iTlM1cUXkCsSN9puJrIm5gO25Q7rk61EZWr00oNtBf8K6tVcPBYsey2KjiIfOPYSyVe56ih+78sfw6Zs/DUXmol/qXa/B3D6fD6FQqE+tIiIiIhocDOUefrZr42j6KF7MvtjTHOI5YT2Mn77mp/Fje38MusL1HqtVLpdRKBQ69pnMkSQJwWBwTdZQWJaFbDbbNRh4rX8uETX7SlOpFBzH6VhvHMdTiYiIiGh4XdpXdNuW2zDlm1qz89uujVP5U3gh8wJqTm+bS0mQ8LHrPoaPXPeRVQWD00X1eh3pdLpjnUgkAsMw+tQiGgblchn5fL5tOTemIiIiIiIiGn4M5SYiIlqmbh2nhmEgEon0sUU0iGq1GjKZTNtySZKQSCSgqmofW0XD7ELpAn71oV/FoZlDPdXXFR03Tt6Izf7N69yyjZeqpvDo+UdRd+pd675z9zvxG6/7DWiy1oeWERERERERERERjZd0Oo16vXU/XT8XK1iWhVQq1TEE4tKQcNd1kc1m27Z/TrtA73HQ6ffLMD8iIhplnd4Dgdbvg/l6Hl96+kv42tGvQXKkriHcc1RZxa7QLmwNbF3bhbVzodAKloZwjzOBiwHdDgD71e+tNQmLA7oB1J06Hjn3CNK1zot+AeDubXfjd+/4XXhV7zo0jkZVr8HcoVCI4XREREQ0dhjKPToK9QJ+MPsDJKvJZd1vyjeFj177Ufzw5T8MVeZ8/tVYTkA2AHi9XoTDYcjyyvo9KpUK8vl8T0HgiqIgEolwU1GiNWZZFtLpNFy3/Y53kiQhEonA62V/FhERERENvvUO5Z5juzZeyr6EFzIvwBGdN7qZc/uW2/E7d/wOgp5g98rUluu6SCaTHTcYGue5odSeEAKzs7NtnzuyLGNiYmLFfV1ERERERES08RjKTUREtAyu62J2drbtxCFJkjAxMQFF6b6QkkZfLpdDpVJpW65pGuLxOCRp3Fe5UjffOvUt/Oajv4lio9hT/c2+zbhh8oaxWpBbtap45PwjyNazXeu+fvPr8V/u/C/waVxUSkREREREREREtJamp6fbjqFMTEz0daPKQqGAUqnUsY6u6/D5fLBtG6VSqePCcYD9+p02rlUUBZOTk31uERER0fqrVCrI5XJtyxVFwcTEBCRJghAClVoF//TCP+GvnvkrVOrt5wtcSoKELYEt2BXaBY+yiqAoGYuDt+e+pt7NBXQvPNaSgvmAbkc4ODRzCGeKZ7re7brEdfjju/8YYW94jRtEo8xxHKTT6Y7heJIkIRqNQtf1PraMiIiIaGMxlHu0CCFwtnQWzySfQdWuLuu+2wPb8dHrPoq3XvZWKDLXgKyUEAL5fL7j2omFVhKWvdyfsdrwbyLqrNFoIJ1OdwzIlyQJsViMwfhERERENPD6Fco9p2JVcDh5GOdK53qqvyO4A3909x/hstBl69amUZfJZFCr1dqWq6qKRCIxtnNDqbNqtYpstn1+gd/vRzDI4HwiIiIiIqJhxVBuIiKiZegWshwMBuH3+/vYIhpkveyayk526qRslfHbj/02/t+X/9+e6iuSggMTB7AzuHMsB/4ct/cFy1fFrsIX3/RFLqIgIiIiIiIiIiJaI7ZtY3Z2tmWZLMuYmlq/RSqtCCGQSqVgWdaanE9RFMTj8bHemNVxHMzMzLQtj8fjXFRPREQjxXEcJJPJjht3hEIhAEC9XseT557EXz73l3il8Mqyfk7MG8MVkSvg9yxjvomEi6HbC0O4x2+YeP0JAPaCYy1n3GqA0ASO5I7gWOZY1+o7gzvxJ2/+E2zxb1nDRtCos20bqVSq42uZLMuIx+N93UiJiIiIaCMxlHs02a6NY5ljOJY9Bld03oTzUjuDO/HR6z6Kt+x8C8O5V6FarSKfz3fdBHVOIBCA3+/vOu/dtm1ks9mex3y4roeoP2q1GrLZbMdgblmWEYvFoGlaH1tGRERERLQ8/Q7lnnOueA5PzT6FulPvWjegBfB7d/0eXrvpteverlFTKpVQKBTalkuShHg8zusW6iiZTLbtm5IkCRMTE2M9v5iIiIiIiGiYMZSbiIioR5ZlIZlMti3nDpjUSr1eRzqd7lgnFotB1/U+tYiGxeHkYXz6gU/jbOlsT/Wj3ihunrp5eQu1R5AQAkczR/F8+vmudXcGd+LLb/4yNvk39aFlREREREREREREo61arSKbzbYs03UdsViszy3qLXyuFwyouyiVSqHRaLQs8/l888GkREREoyCTyaBWqy36nhAClmWh0WhAlmWYponZyiz+5vm/wWPTjy3r/D7Nh8sjlyNhJDpXlHAxeHthCDdtDBeAhWZAd/s92pdHAU5VTuHJzJMQUucpvXEjji++8YvYF9u3Rj+cxoFlWUilUh1DsjRNQzwe59w3IiIiGgsM5R5tFauCI6kjOF08vez77gzuxE9f89N46663QpMZxLQStm0jl8u1HUu4lMfjQSQSaRtaVKlUkM/nO17PzFEUBeFwmGsziPqoUqkgl8t1rMPNj4mIiIho0G1UKDcA1O06Ds0cwoXyha51VUnFZ1/3Wbzr8netf8NGRKPRQDqd7tivEAqF4PP5+tgqGkbdMkNM00Q4HO5fg4iIiIiIiGjNMJSbiIioR51CBgAgGo3C6/X2sUU0LAqFAkqlUttyWZaRSCQ4wYwAALZr478989/wp8/8KRzR2wrefdF92BfbB1niyus5rxRewaHpQxDofLkzaU7iy2/+MnaFd/WpZURERERERERERKOpU1+43+9HMBjsc4ua6vU6MplMT2ENrciyjFgsBk1j+AYAlMtl5PP5lmWyLGNycpIhfkRENBIWbjjiui4ajcb8IYSAoigwAgb++eV/xjdPfBOWa/V8blVWsSu0C1sDW1uP8SqXHBwGHlwumuHcc8cqJatJHMocQkWudAznNlUTv3/X7+N1m1+3+h9KY6NWqyGTyXSsw4XCRERENC4Yyj0eMrUMnkk+g1Q1tez7bvZtxv1X3493X/5ueFWuEVkuIQSKxWLHNRQLybKMcDi8aD2OEAL5fB6VSqWnc3QL9yai9VMqlVAoFDrWUVUV8XgcsszOTiIiIiIaPBsZyg00r4GP547jmeQzXddEA8DHD3wcP3vtz3KeXheO4yCVSsFx2q/V93q9iEbZL0i9SafTqNfrbcsnJiagqmofW0RERERERERrgaHcREREPahUKsjlcm3L2eFOnQghkEqlYFntF+Hquo5YLNbHVtEgOlM8g888+BkcTh7uqb5P8+GmqZsQN+Lr3LLhNFuZxaPnH+26AD6iR/Anb/4T7I/t71PLiIiIiIiIiIiIRk+nyeaRSASGYfS5RRfVajVks9llB3MrioJoNMpA7gUcx8HMzEzb8o3+XRMREa0F13Vx/vx51Go1NBqNlmP9z5Wfwz8c/wdkap0DbheSIGGLfwt2hXfBo3jmvrk0hJvrZoeTQDOY28KqArrz9TwOJw+jLMqoKTU4cusFwqqk4rdu+y28fdfbV/7DaOz0EpIVDodhmmafWkRERES0MRjKPT6EEJguT+PZ1LMoNDp/Fm4l6o3iA/s+gH935b9DSA+tQwtHW71eRy6X6xh+tZBpmggGg3AcB9lsFrbd2wW23+9HIBBgGNkaEUKgaleRr+dRtIqoWBVUrAqqThUNpwHLteC4Dlzhzt9HlmSosgpVVuFRPNAVHV7FC1Mz4dN88Gt+BDyBi31iNHI6beA8R9M0xGIxBnMTERER0cDZ6FDuOelqGgcvHETVrnat+2NX/hg+c8tnWm8EThBCIJPJdAxQVhQFiUSC1yjUs0ajgVSq/QaAzJwhIiIiIiIaTgzlJiIi6sJ1XczOzsJ13ZblkiQhkUhw10LqyLIspFKpjoEfwWAQfr+/j62iQSGEwD8d/yf8zuO/g4pd6ek+2wPbcf3E9dAUhsF0kq/n8dC5h7oOQvs1P77wxi/ghskb+tQyIiIiIiIiIiKi0TI9Pd12LGViYmLDx1EajQay2WzPwQ9erxfhcJgLLlroFMDOTUiJiGiY2baNarWKmZkZlMvllnXOls7in0/9M46Wjy7r3FFvFFdErkBADwAqmuHbKgB+1BhNLi4GdPf28XORilXB07NPo2JXYMs2akoNttw6iOwTN34C9191P4PHqGe5XA6VSvu5KZIkIR6Pc3MiIiIiGmkM5R4/QgicLp7G8+nnUbZaX/N3YqgG3n35u3Hf/vuwLbBtHVo4uhzHQS6X6xiCtVCj0YAsyz2NK8myjHA4DK/Xu9pmjg1XuEhWkjhTPIPz5fM4XzqPmcoMZiuzSFaSSNfSyNaysNylm9StBa/iRVAPIqyHEdEjiHqjiBpRxLwxxI04YkYMCSOBhJlA1BtluNyQyWazqFY7r9vQdR3RaJR9WUREREQ0UAYllBsAanYNj55/FOlaumvdt172Vvxft/1f0GSO612ql42DYrEYdF3vU4toVHS79o3H4/B4uCkZERERERHRMGEoNxERURf5fL7tYksACAQCCAQCfWwRDatyuYx8Pt+2XJIkxGIxdrSPmWwti9989Dfxb6f/raf6mqzhhokbsC3ICd29qjQqeOjcQyg2ihB49fKnxRxOr+LFH979h7h18639bSAREREREREREdGQcxwHMzMzLcskScLU1NRALKx2XRfFYhGVSqXtJpqqqiIYDDLAoYNqtYpsNtu2fBBC2ImIiHo1F8Rdq9VgWRbq9ToKhcKSeiWrhG+d+hYOzRxCWS1DSL1Nu9Q1HXviezARmICkSQzhHkcugAaaAd3LmK3bsBs4nDqMfL05z8SRHNSUGizZWjLe/YF9H8Cv3PQrDKqingghkEqlYFntA9ZUVUU8HucmRURERDSyGMo9vlzh4lT+FI5mjqJqdw6ObUWWZNy17S7ct+8+3Dh540CMfQyLUqmEYrHYdnxGCIFSqYRarQYAME0Tpmm2fYw1TUMkEuF4RBuO6+B08TReyr6E47njOJE/gZP5kzhdOI2aU9vo5vVElVTEzTgmzAlMmpOY8k0tuU0YCSiystFNpVcJIZDJZLqG8BuGgUgk0qdWERERERF1N0ih3EDzmu6p2afwSuGVrnXv3HonPn/n56ErDJeeU6vVkMlkOtYJBoPw+/19ahGNEtu2kUwm2/ZxeTwexOPxPreKiIiIiIiIVoOh3ERERB1YloVUKtW2U1RRFExMTHAyJfUsk8nMTxRthYvaxsuDZx/Erz/y60hVUz3Vjxtx3DR1E3yab51bNqRcAM6Cw8X8ouaG08DTyadRqDcX0AtJwIULR3LgSA5s2YYjOfAoHvzenb+HN2x7w8b8H4iIiIiIiIiIiIZQp0UMgzjB3HXd+eBNx3EgSRJUVYWu69w4swdCCMzMzMB13ZblPp8PoVCoz60iIiLqnW3bqNVqqFari0JpXddFNptd9B7nCAePnHsE/9+Z/w91p46qUoUt223PLSBgyzaEKnB57HLsju5mMBA1CTTHsRsA2j+FFrFdG0dSRxbNKXAlF3WljrpcXxTOfc+Oe/Dbt/82F1tTT2zbRiqVavuZHmgG4IXD4f41ioiIiKiPGMpNjuvgZOEkjmWOrSicGwD2Rvfix/f+OH7osh+CV+VGn71oNBrI5XKw7cUXxrZto1gsLvm+qqoIBAJLgrd9Ph+CwSDX8bzKcR28nH8Zz6Wew3Pp53A0fRQvZl8cmvDt1VAkBQkzgSlzanFot28Sk+YkJswJxI04VJnh7f0ihEA6nUaj0ehYj+OJRERERDRIBi2UG2h+tn4h8wKeSz/Xte6tm27Ff737v8JQjT60bLB1ywYBAK/Xi2iUfYG0cvl8HuVyuW15NBqF18v+QiIiIiIiomHBUG4iIqIOUqlUx4lAkUgEhsEBCuqd67pIJpNwHKdtHcMwEIlE+tgq6reqXcV/OfRf8A/H/qGn+hIk7I/tx97oXk4evpS94Gi/VrRZ1bXxTPIZZGrtdzi2ZAuu4uK33vBbuHfXvWvZUiIiIiIiIiIiopFVLBZRLBZblnFB9WgqFAoolUotyyRJwuTkJDcgJSKigTK3KUelUmk7D6RQKKBer8//+1j2GL758jeRqjUDkW3JRlVdGtTlSA4s2YIlW3AkBztCO3BN/BoGclF7LgALzYDuLjN4XeHiWPoYzpXPLfq+kARqSm1ROPcNEzfgD+/+Q4R0fv6m7jptrjSHc+OIiIhoVDGUm+Y4roNThVM4ljmGil1Z0TnCehjvvuLd+NE9P4ptgW1r3MLR47ou8vk8qtVmH0u1WkW5XG4bliVJEkzThGmakGUZ4XB47EONio0inp59Gj+Y/QEOJw/jSOrIip+/40CWZMS8MUyYE0iYCUwYE4ibcSSMBBJGAnEjjpgRQ8wbg6ZoG93ckeC6LlKp1JKg/UsFAgEEAoE+tYqIiIiIqL1BDOWecyp/Ck/OPAnRZWD5pqmb8Md3/zFMzexTywZPLxkOqqoiHo9zbiOtiuM4mJ2dbdufpaoqEokEMyGIiIiIiIiGBEO5iYiI2qhUKsjlcm3LdV1HLBbrX4NoZDQaDaRSqY51QqEQfD5fn1pE/XQkdQSfefAzOFU41VN9v+bHzZtu5mT/hZaxQPlSjuvgSOoIktVkx3qyJOMXb/5FvG3v26Dr+kpbSkRERERERERENBYymQxqtVrLsnA4DNMc34Ueo8q2bczOzrYt5yJ6IiIaBEII1Ot1VKtV1Gq1tovhgGY47dwmI6lqCt848Q28mH3x4rkgUFbLEJKAgIAt22jIDdiyDSE1zxv1RnFg4gDHdql3As0NqBsA2q8LhhACJ/MncSJ/YmnZJeHcu0O78aU3fQmb/JvWq9U0QjpttgMAsiwjkUhAUZQ+toqIiIho/S0rlFu8eriX/LvVJaa04AAA+ZJ/08ByhYvThdM4ljmGotV6E9JuJEh4/ZbX40f3/Cju2HoHVFld41aOlmKxiHPnzi3aIK0Tn8+H7du3w+PxrHPLBk++nseTM0/iiekncGjmEI5ljnUNY6OVCXgCiHljiHqjiHqjiHgjCOvhJbchPYSwHoZf8zNgqw3HcZBKpTqG4QEcSyYiIiKiwTDIodwAcKF8AQfPH4QjOn++vnHyRnzxjV8cy2BuIQTS6XTbTdqB5sZf8XgcmsYNmWj1isXi/DyjVni9S0RERERENDwYyk1ERNSC67qYnZ2F67otyyVJQiKRgKpyoiStTLeOdg7sjB7LtfBnz/wZ/vSZP+068DnnstBluC5xHSdlz3HQXIxsre40rnDxfPp5TJenO9aTJRkfu+5jeONlb4TP54NhGJw0S0RERERERERE1MLMzEzbBdWJRIJ93SOqUxi7JEmYmJhgeB8REW0Iy7JQqVRQrVbbzvtYyHVdZLNZVBoVfOfMd/DwuYfhYvH9ykoZVa0KS7ZgS/aiQDVDNXBN/BpsC2zjeCKtXA/j4WeLZ/FC5oWWZQvDuSfMCXzxTV/EldEr16WpNDp6WZyu6zpisVgfW0VERES0/lqGcnuizc/lLpq3c0Hca7XqTm5xKGBg94ARQuB86TxeyL6AbC274vNMmBN41+XvwnuueA+2+LesYQtHQ61WQy6Xg2VZKBaLsKzOk8NN04RpmlAUBaFQCIZh9KmlG6Pu1PHUzFM4eOEgDl44iKPpowzhHlCqpCKoB+dDuuduI3rk4tfeCCLeyHzId0ALjE0fom3bSKVSXftoo9EovF5vn1pFRERERLTUoIdyA83NxR8+9zAst/M19LgGc+dyOVQqlY51GJJMa6lbHo2iKJiYmBibPgAiIiIiIqJhxlBuIiKiFvL5PMrlcttyv9+PYDDYxxbRqOllUZuiKEgkEpBluY8to/VwKn8Kv/rQr+LZ1LM91dcVHTdO3ojN/s3r3LIh4QKoY9Vh3AsJIfBC5gWcK53rWE+ChI9c9xHcte0uqKqKQCAw8hO5iYiIiIiIiIiIlsN1XUxPt94AT5IkTE1NcVL5iGo0GkilUm3LfT4fQqFQH1tERETjzHVdVKtVVCqVrkFOl8rmsjh49iC+ferbKFmli+eEC0u2UFWqKOpLN92WJRl7InuwN7qXGy3T2nHRDOduM50kWUniSOpI283A58K5VV3F7931e3jd5tetW1NpNDiOg2Qy2TEgKxQKwefz9bFVREREROvrmr+4BopQoLkaVKHi1slbEfJsUF+mhGY498KDXeobTgiBVDWFF7Mv4kL5wqrOdcumW/Cuy9+FN21/E7zqeIfOCiFQKBQWrdURQsz36Vy6zFWWZQSDwSWbvxqGgVAoNDLrLIQQOJE/gYfPPYxHzj+CQzOHUHfqG9omj+yBR/FAUzRosgZVUqHIChRJgSzJkCUZEiRAwvzvTQgBV7hwhANHOLBdG7awYTs2LNeaP8adJmuIGTHEvXHEjTjiZhwT5gSmzClM+iaxybcJm3ybRub1otFoIJ1OL/n7XkiSJESjUei63seWERERERFdNAyh3ACQq+Xw4LkHu14z3jR1E77wxi/AUMdjLXS5XEY+n+9Yh3MZaT10e+4Fg0H4/f4+toiIiIiIiIhWgqHcREREl7AsC8lksm05g5JprfSyqM3r9SIajfaxVbSWhBD46otfxecPfR5Vu9rTfTb5NuHGyRtHZhLlqgg0w7jbZ9ev7vRC4KXsSzhdPN217s9c+zN40/Y3AQA8Hg+CwSA8Hs/6NIyIiIiIiIiIiGiI1Ot1pNPplmWapiGRSPS5RdRPqVSq4wakiURiSVgGERHRWmo0GqhUKqhWqx2DXdo5cuEIvnrkq/Ob+c4FcduyDVdyISSBglaAkBafe4t/C65NXAufxpBaWicCF8O5L3lq52o5HE4e7hhk5UouLNXCZ277DN59xbvXs6U0AqrVKrLZbNtySZKQSCSgqtyAgIiIiEbD7V++fdG/b5q6CSF9gEJ55sK5VTCkewAUG0W8lH0JrxReabtBUi/8mh/37rwX79z9Tlw/cf3YbWhqWRay2Sxs225Zbts2isXifLmmaQgGg23X7SiKglAoBK93OOfcl60yDl44iAfPPoiHzz+M6XLrDXDXg67o8Gt++DQffJoPpmbCUA0YqgFd0aEr+ro9P4UQaLgN1J06Gk4DdbuOmlNDza5dvLVrqNpV1JzaurRhWMSNOLb6t2J7cDu2B7ZjR2gHdoV2YWdwJzzKcK1jqNVqyGazHftvZVlGLBbjuCIRERERbYhhCeUGgEK9gAfOPtD1munWTbfij974R9CV0d78pts4JwDouo5oNDp2fTG0/oQQSCaTbfu7ZFnGxMQEc2mIiIiIiIgGHEO5iYiILtEtPCASicAwxmNnUFp/tVoNmUymYx3ugjmcUtUUPvvIZ/HA2Qd6qq9ICq5LXIfLQpdxYA8ALAA1LFlcvCKXPpwLzimEwMv5l3Eqf6rraT589Ydxz8575v/t8/kQCAQ4GEZERERERERERGOtVCqhUCi0LDNNE+FwuL8Nor7qFMoONEMz4vE4+72JiGhNua6LarWKSqUCy2ofStxJuprG/zj6P3D4lcNLgrgXKqtlWMrFnxH0BHFg4gAmzIlV/R+IeibQHD9vAFjw9CxbZTw9+3TXDcJdycX7D7wfP/ean+NnMuoom82iWm3/fPJ4PIjH431sEREREdH6GfhQ7kupCw5OWd0wdaeOk/mTeDn3ctdrsW62+LfgrZe9FW/f9XbsCu9aoxYOrlKphGKx2HVDNSEEyuUyZFmGYRg9XceaptkxvHtQCCFwqnAKD5x9AA+eexBPzjwJ220d2LRWdEVHSA81D08IAU8AQU8QmjIcoceucFG366jYFVTt6vxRsSuoWs3bml2DWJMFB8NDkRTsCO7AFZErsDe6F/ui+7A/th8Rb2Sjm9ZRpVJBLpfrWEeWZcTjcW4KRkRERER9N0yh3EBzA7EHzj7QtX/ijq134A/u/IOhuQ5crkajgXQ63bG/QVVVxOPxge83oOHVLRje7/cjGAz2sUVERERERES0XAzlJiIiWqDbJB9d1xGLxfrXIBoLxWIRxWKxY51YLAZdH+3daEfJd09/F5995LPI1jvvrjsn6o3ipqmbEPAE1rllQ8BFM4x7pXOMZTQXHSivfi1jaSj33M9xATiAsAVOZE7gZO5k19Pff9X9+KHLfmj+34qiIBQKwev1rrDBREREREREREREw61TcFsoFILP5+tzi6jfMpkMarVa23Kfz4dQaIADbYhoTQghYFkWbNuG4ziwbRtCCLiuC9ddHHIsSRJkWYYsy1AUBYqiQFXV+VuidizLQrlcRrVa7Rri1PYcroVvvPwNfP341+HYDgTEkiDuOQ25gYpWAQBosoar4ldhV2gXZImLNWmDWADqmA/nrtt1HE4eRqHRepOche7YcQc+c/tn4De5KTy15roukskkHMdpW4fXeERERDQqhi6UeyEFzXmyGhjQvUFc4eJC6QKO544jWU2u+nx7o3vxQ5f9EO7deS+2+LesQQsHh+M4yOVyqNfrPdVXFGV+s9dcLtfx+uTS+w3ifO6G08Ch6UP4/tnv48FzD+JM8cy6/SxN1hD1RhH1RhHxRhDRI/Cq3pHfoEsIgZpdmw/ungvsXhjiXbWrYxHcvcW/BdcmrsWBxAHcMHkDrghfAUVWNrpZi3Ta7HmOoiiIxWIcKyAiIiKivhq2UG4AKDVK+P7Z73cN5n7zjjfjP9/xn6HKo/UZ27IspNPpJXOSFuLGP9QvyWQSlmW1LJMkCRMTE1CUwbpGJyIiIiIioosYyk1ERPQq13UxOzvbtvNdkiQkEgl2vNOaE0Igk8l0nGyqKAri8Tg73Adcxargc4c+h//54v/sqb4ECfti+7A3upcLt4HmAuIasOw5rzKaiwtWs8BAAC+mXsTx5HF4HE/Hqh/a/yG8bdfbFn3P5/MhGAyO/MRlIiIiIiIiIiKiS83OzsK2W++yF4/H4fF07m+j4WfbNpLJZMdw1EgkAsMw+tgqIlpvruuiXq+j0Wig0Wi0XVi0XLIsQ9M0aJoGj8cDXdc5/jLmhBCo1Wool8toNBqrOs/jM4/jL479BU7XTkN1VHjd9kFNQhLIa3lAAnaHdmN/fD90hRtp04BYEM5tuzaeTT6LdC3d9W5Xxa7Cp1/3aWyJb4GmaeveTBo+9Xod6XT75xLnzxEREdGoGOpQ7oXWYv4srUqhXsCJ/Am8UngFlrv6/rGrY1fj3p334k073oStga1r0MKNU61Wkc/nO4ZjLWQYBkKhEGS5+WR2XReFQgGVSqXnn2maJoLB4Pw5NsJMeQYPnnsQD5x9AAcvHOwajrZSPs2HhJFAzIghZsQQ0ALsR21DCIG6U58P6K7ZNdScWvP21a/rTh01uwZH9BYEPwwCngBunLwRt0zdgls334pdoV0D8RwpFAoolUod63D9FBERERH12zCGcgO9B3O/c/c78Vuv/62RWcfuOA5SqVTXzbyi0ejAbeBFo6nbOLtpmvMb0REREREREdHgYSg3ERHRq/L5PMrlcttyv9+PYDDYxxbROHFdF8lksuMAkMfjQSwWG4iJcLTU8+nn8akHPoVThVM91Q9oAdy06SZEvdH1bdgwEGiGcS93LroKwPPq7Rp5MfMinkk+A93VoTs6ZNF6kPmD+z+It+96+6LvaZqGcDjMxctERERERERERDQ2XNfF9PR02/JNmzaxT3tMFItFFIvFtuWSJCEajULXGWZKNMxc10W1WkWtVkOj0egYxr9WJEmCpmnQdR1er5fjMGPEcRxUKhVUKpWuCym7OV87jz99/k9xMHUQkADFVRCwAh3vU9JKiPgiODBxYDiD2Wg8vBrO7ToujqWP4Vz5XNe7bPZtxqdu/hR2JXYhEAgw3IiW6DaHTtd1xGKxPraIiIiIaO0tO5RbQjP0Wnr1wILbS4lLjt7yiFdPwcWAbnbL953t2jhbPIsT+RPI1DJrcs690b24e/vduHvb3dgT2TM04y2u6yKfz6Na7S2MWpIkhEIhmKbZsrxWqyGXy/Uc7q0oCkKhUN9Ct2zXxjPJZ/DguQfx4NkHcSx7bF1+jk/zYcKYQMJMIGEkYGjcCHU92K6NulNH3a43bxccDaeBhtNA3b34dcNpQGA4lmdP+aZw25bb8Iatb8BrN70WXnXjgulyuVzXwH1VVRGLxdh3RURERER9Mayh3ABQbBTx/TPfR82pdaz3Y1f+GH71ll8dmv6FdlzXRSqVgm3bHeuFQiH4fL4+tYoISKfTqNfrbcsTiQTnvREREREREQ0ohnITEREBsCwLyWSybbmiKJiYmBj6gQYabI1GA+l0uuPidZ/Ph1CIC34HiStc/PXzf40/eOoPYLudB/Hm7A7txjWJa6DKa5gmPawcAFUsb9GBCkBHcwHBOngp+xIOJw8DAvC4HhiOAUksff3/wL4P4J2737noe5IkIRwOwzA40ZmIiIiIiIiIiEZfvV5HOp1uWaZpGhKJRJ9bRBtFCIFUKgXLar/7oiRJiMVi8Hg8fWwZEa2Fer2OSqWCWq3WlyDuTlRVhdfrhWEYXKg0oizLQqlUWvXzTVEUWIqFPz/25/jay1+DK14dkBRA0Aq23ZwXAGSPjD2b92CLfwvnidBwsABREziVPYWX8y93re73+PHJGz+J/fH98Pv98Pv9fK7TPCEEZmdnO26IEA6H2wbmEREREQ2DtqHcCprh23O3lwZxr9RcOPfc4Sz4ej2oADyv3lLf5et5nMqfwivFV9BwGmtyzi3+Lbhz2524Y+sdeM3ka+BRBrOffbkB2pqmIRKJQFU7P1kdx0E+n0et1jlgbCHTNBEMBiHL7fuAVmqmPINHzj+CB889iIPnD6Jotd+0dKU8sgcT5gQmfZOYMCfg0xgkNoiEELBdGw23sSi4u+Fc/Pel4d51p31AV78YqoHXb3493rTjTbhz2519f34JIZDNZrv+TTOYm4iIiIj6ZZhDuQGgUC/g+2e/3/V642eu+Rn8wg2/0KdWrT3XdZFOpzvOUQQAv9+PYDDYp1YRNXXLq/F6vYhGo31sEREREREREfWKodxEREQAUqkUGo32Ex4jkQgDVqkvSqUSCoVCxzpc2DY40tU0fu2hX8PD5x/uqb5X8eI1U68ZqsHYdWWhGcjdKwWAF+sWxr3Q8dxxPD37dPMfAvA6Xngd75J6P3blj+HdV7x7yff9fj8CgQAXLhMRERERERER0Ujr1KdtmibC4XB/G0QbyrIspFKpjgGqsiwjGo0ymJtoCAghUK1WUSqVYNu9bUzbb5qmwTRNGIaxLuE61F+1Wg2lUqnj3I1uJEmC1+uF5tXw9RNfx5ee+RKKjcWhSKZlwuO2fh9SJAXbw9uxc9NOBr3Q8BEAbOB8+jyOJo9CoPPUYEVW8DPX/Azu2nYXFEVBIBCAYRgc4yYAnTdgApqf6ycmJvj+S0REREPr9i/fDiEJ2JINS7Zw2/bbEPVtQCCKQDOge+Gxlqv8ZAAamgHd/Kjfd65wcaF0AacKpzBdnu56ndYrQzVwy6ZbcPuW2/H6La/HFv+WNTnvariui0KhgEql0vN9VjLXulKpoFAo9Bz6rSgKwuEwdF3v+We0UrWreHLmSTx6/lE8cv4RHM8dX9X52ol6o5jyTWHKnELEG+E1+ogSQqDu1FGza6g61eatXUXFqqBiV1C1qijb5YubDK4zj+zBHVvvwNt2vQ13bL2jb6H/QghkMhnU651DAzVNQywWYx8EEREREa2rYQ/lBoBcPYfvn/k+LLdzYPUv3/jLuP/q+/vTqDXUayC3YRiIRCJ9ahXRYtlsFtVq++CEeDzOubNEREREREQDiKHcREQ09srlMvL5fNtyXdcRi8X62CIad5lMBrVarW25JEmIxWLsdN9gj114DJ9+8NNIVVM91d/i34IbJm+ArqxuUu/IqAHodT29hGYYt7Z+zWnl5dzL+MHsD+b/LQsZpm1CddVF9d535fvw3iveu+T+uq4jEolwAigREREREREREY2sThPIg8Eg/H5/n1tEG61arSKbzXasI8syYrEYNK3Pnb5E1JO5MO5isQjHcTa6OT2ZC2I2TXPVATvUX0IIVCoVlMvlVYW/q6oK0zRhmiYeufAI/vMT/xkn8yeX1NMcDT7b1/Ick75JXB6+HEbQANSWVYiGgwCS+SSOnDsCx+3+Ov72XW/HB/Z9ALIkQ9M0BINBvpYSACCXy3UM1ONGTERERDTMrvuL6+BKF8NO795+N6LeDQjlbsUFYC841or26sFr3g1Rt+s4XTyN04XTyNY796Ev1/bAdty6+VbcsukW3DR5E8Le8Jqev5t6vY5cLtdzX+Jqg7Idx0Eul+sa5LuQaZoIBoM9z+m2HAtH0kfw2IXH8Pj043h69umuoWYroSv6fAj3pG+yb2HINPiEEKjZNZSsEkpWCcVGEcVGEYVGAWWrvG4/N+AJ4K2XvRXvuvxduCp21boHwzOYm4iIiIgGxSiEcgNAppbBA2cfgO127lT6jVt/A+/ds3RN9KByXReZTKbrJu+6riMajXKTK9owtm0jmUyiXZSbx+NBPB7vc6uIiIiIiIioG4ZyExHRWHMcB8lkEq7rtiyXJAmJRAKqytmn1D+u6yKVSnVcdKwoChKJBCeVbQDHdfCnz/wp/uTwn0Cg+0dpVVJxYOIAdgR3cCAPAASAKnpfKKCiGci9QU/1E7kTeGr2qUXf8zgemLa56HvvveK9+NE9P7rkd6yqKmKxGBRFWfe2EhERERERERER9dvs7GzbvuxYLMYwvzFVKBRQKpU61mEwN9FgqtVqKBQKqwpH3miapsHn88EwDI7NDTDHcebDuNvN1+jm0jD2E/kT+NwTn8ND5x5qWV8WMgKNACQsfl4EPAHsiexBxBsBdDQPohFQqBVw6MwhODVnyfP+UgcmDuAXrv8F+LRmaL3X60UwGOR8qTHnui6SyWTHYD1e9xEREdGwujRoaaBCuRcSABw059xar/57tWQAHjQDutl1siGKjSJOF07jTPEMSlbnvvTlkiBhT2QPXjP1Gtw4eSOun7gecWN9gn5c10WhUOi4mc+lDMNAKBRakzUQlUoFhUKh574lWZYRCoVgGMaSsppdw5HUETw58yQOzRzC4eRhVO3Wm9KuVkSPYMo3hU3+TYjoEfZh0rLZro18PY9cPYdsLYtsPYtCvdDT2pbl2BPZg/de8V68Y/c7EPAE1vTcC/UarufxeBCNRrmGioiIiIjWxaiEcgNAspLEQ+cegiPaj/HJkozPv+HzePOON/exZSvjui7S6TQsq/NmWdzMhwZFPp9Hudx+Q61oNAqv19vHFhEREREREVE3DOUmIqKxls1mUa22nyzn9/sRDAb72CKiJsuykEql2u6ECXDH1o2QqWXwqQc+hYMXDvZUP+qN4uapm+H3+Ne5ZUPCBVB59bYXBpqT/jfYyfxJPDnz5KLvya4Mn+2DIi6Gbf/w7h/G+/e+f8nfpKIoiEajDJchIiIiIiIiIqKRIoTAhQsX2pZPTU1xgcMY67awAGAwN9EgsW0b+Xwe9Xp9VedRVRWapkFVVSiKAkVRIMvykvcDIQRc14XjOHAcB7Ztw7ZtWJbVcXx0OWRZhs/ng8/n4/vRALEsC+VyGdVqdcW/a1mWYZomfD4fFEVBvp7Hnxz+E/z9C38PW7QJlBdAwAosGtvzKB7sDu/GZt/m5vieAsAEA8lopNTsGh499ygq5Qp0p3Nw8mbfZnzypk9ii3/L/Pd8Ph8CgQBfR8dYrVZDJpNpW66qKhKJBOcuERER0dAZmlDuSzlohnPb6H0ubjsSLoZz8yP/hhBCIFfP4UzxDM4Wz6Ji9x5uvRzbAttwIHEA1yauxTWJa7AnvAeasrp++Vqthnw+33ETn4U6BWKvhuM4yOVyy+rX1HUdFbWC57LP4ZnkMzicPIzn08/Ddtdno0JVVjFpTmKTbxOmfFPwqgxdorVnuzaytSxS1dT80SmAbzkM1cBbL3srfnzfj2NPZM+anPNSvYbsMZibiIiIiNbLKIVyA8B0eRoPn3u44+Y9mqzhC2/8Am7dfGsfW7Y8juMgnU7Dtjtfs6uqing8zmsFGgiO42B2drbtvCiOsRMREREREQ0ehnITEdHYqtfrSKfTbcsVRcHExAQ7NGnDVKtVZLPZjnV8Ph9CoVCfWjTeDicP4xPf+wRmK7M91d8b3Yv9sf2QJQ7iAWguBKgAHcZwL1LQDOQeoIfuVP4UDs0cWvxNARiOsWjx8lsveys+tP9DS947ZFlGJBKBrnde6ExERERERERERDQsGo0GUqlUyzJVVTExMdHnFtGg6bY5LtAcj4vH41AUpWM9IlofQgiUSiWUSqUVBSQrigJd1+H1euHxeFa9uE0IAcdx0Gg00Gg0UK/Xew72aUeSpPlwbr7WbJxGo4FSqYRarbbic3g8HpimCcMwIEkSLNfCV459BV86/CXk6/mO9zXsi2N6EiRsC27DZcHLFgdQ+TFQ45NEa8VxHRyaOYRzhXMwbAOa2z54zVRNfPz6j+PGyRvnvyfLMgKBAEzT5ByqMZXJZDq+fgcCAQQCgT62iIiIiGj1hjaUe6G5gG4Lvc3N7URDM6CbXScbZi6g+2zpLM4Vz6FkldbtZ3lkD/ZE9mB/bD/2xvZib2Qvdod3w9TMrvd1HAf5fH5ZfTxerxehUGhd++YqlQry+fySPk4hBFK1FE7lT+Fk/iRO5E/gRP4E8vU8qmoVdWV1mxS2E/AEMOWbwibfJsSNONcTUN+5wkWmlsFsZRbT5Wlkau033FqOm6duxgf3fxB3bL1jzZ/XDOYmIiIioo00aqHcAHC2eBYHLxzsWMdQDfw/9/w/uCZxTcd6G8G2bWQymZ4CuWOxGOcE0UApFosoFotty8PhMEyze18cERERERER9QdDuYmIaCwJITA7O9txAW80GoXX6+1jq4iWKhQKKJU6T6plx/v6EkLgqy9+Ff/p8f8E2+08eAc0ByFvnroZCTPRh9YNCRvNQO5eeADoAAZwLe8rhVfwxPQTS76vORp8tm/+32/e8WZ8+OoPL1mQLEkSwuEwDMNY97YSERERERERERGtt3K5jHy+dQCmYRiIRCJ9bhENGiEEcrlc12BuTdMQj8cZ8kjUZ5ZlIZfLdQ24uJQkSTAMA4Zh9GUzUsuyUK/XUa1Wl93WhSRJgmma8Pv9XIjXR7VaDaVSCY1GY0X3n3u+maYJj8cDoPn+8uC5B/H5Q5/HyfzJrudYOJYXM2LYE9kDn+ZbXMlAM4CMaEQJIXA0cxTPp5+H4iowbROKaP9a+CN7fgQ/csWPLPp8pmkagsEgN6IeQ47jYHZ2tu0GHpIkIZFIQFXVPreMiIiIaOVGIpR7jkBznq716u1qKGjO4eVHuw0lhEChUcD50nmcL59HtpZd958pQcLWwFbsDu/G7tBu7AztxM7gTmwPbkdEj0CSJFQqFRQKBbiu29M5ZVlGMBjsyzoH27VxtnAWL55/Ea9kXsG50jmcLZ7FmeIZVOz2k9gdyUFZLcOVe/s/taNIChJmAlO+KUyZU/B7/Ks6H9Faqzt1zJRncL50HhfKF+CI1W2GuTO4Ez9x1U/gHbvfAV1Zu74ix3GQTqe7hu5pmoZYLMZgbiIiIiJaM6MYyg0AJ/Mn8eTMkx3rhPQQ/vItf4nd4d19alV3jUYDmUymax+EoiiIx+OcB0QDx3VdzM7Otn0OK4qCiYkJzpklIiIiIiIaEAzlJiKisdQt6JhhETQohBDIZDKo1+tt60iShHg8Dk3jKuG11nAa+O3Hfhtfe+lrPdXf5NuE10y9Zk0nFg49C0DnvJWLhmCx++nCaTw+/fiS7yuuAp/tgyyaEzvfsPUN+Oh1H4UsLZ3oySB9IiIiIiIiIiIaBblcDpVK6yCDYDAIv5+BA9Qc58hms6jVah3rmaaJcDjcn0YREUqlEorFYttwzVYURYHf74dhGBsWdGHbNmq1GiqVStdQjnYYzr3+hBDzYdwrDVKXZRk+nw+maS76Pb2YfRGff+LzePTCo72dx5URtILwaT5cEbkCcSO+tJKG5jgl0Rg4UzyDJ6afgCtcaI4GwzHmx7gvdWDiAH7++p+HX1v8ud7r9SIUCvE1dMx02pQJAHRdRywW62OLiIiIiFZnpEK5F3LRnLdrvfr1SskAPGheMzMXZsPV7BoulC9gujyNmcoMbHe16evLE1SC2OHZgSl9CnEzjpg3hqg3iog3gpAeQkgPwaf6FoUI6bqOcDi8JteOjusg38gjXU0jWU1itjKLmfIMLpQv4EL5As6VzuFc6dz84+JxPDBsA9Iynrw1pYaaUlvW8z3oCWLSnMSkbxIJIwFF5nUyDQfHdTBTmcGZ4hmcL51fVUB33IjjQ/s/hPdd+b6lGyGutH0M5iYiIiKiDTCqodwAcCxzDM+mnu1YZ8KcwF//0F9js39zn1rVXrVaRS6X6zqnSVVVxGIxjlvTwOo2xh4IBBAIBPrYIiIiIiIiImqHodxERDR2LMtCKpVq2xkvyzISiQQ74WlguK6LVCrVcVKZoihIJBKcULaGUtUUfum7v4TDycNd60qQcG3iWlwevpy7ki7UANA5Z6VJQnOhu7q+zVkrZ4tn8diFxyCw+H1EEhL8lh+KaL5/3Lr5Vnz8wMehykv/YwzmJiIiIiIiIiKiYZdMJtsGbcZiMeg6Ny+kpl6DuaPRKLxeb59aRTSeXNdFNpvtuCHwpVRVRSAQgNfrHahxsEajgUqlgmq1uqxw8TmSJMHn88Hv93OMdY0IIVCtVlEqlVYcmq6q6nwY98LnW6qawhee/gL+8aV/hCt6TBUTQNSJ4vLg5dga2NpyM13IAHxgwBiNlWwti0fOP4KqXQUEoLs6vLa3ZVhZwkjgEzd+ArvCuxZ9X5Ik+P1++P3+gXpvoPUjhEAqleq42UIkEoFhcJcDIiIiGg4jG8q9kI3mPN7V5DdLaIZze8Br5wHhChepagoz5RnMVGaQq+fW74cJwOt44XW699srkgK/xw9TM6GbOrymF4ZqwFANeFUvNFmDJmtQJGX+OtIVLhzhwHZtWK6Fml1D1a6iYlVQskooNoooNArI1/NL5mx3IwkJpm1Cc7We7+NKLipqBbbc+o9GV3RMmBOYNCcxYU7A1DgPnIaf7dq4UL6AVwqvYLo8veLzBD1B3Lf/Pnxg3wcQ9ARX3S7HcZBKpeA4nQPDGcxNRERERGtllEO5AeDZ5LM4lj3Wsc7O4E785Q/95Yb1kQkhUCwWUSqVutZlIDcNAyEEkslk2zlUkiRhYmKCz2MiIiIiIqIBwFBuIiIaK0IIpNNpNBqNtnVCoRB8Pl8fW0XUXbcweQDQdR3RaJQLHtfAC5kX8PPf+fmeJhaaqolbNt2CmBHrQ8uGSP3VoxsZgPnq7RA5VzyHgxcOLp3kLQCf7ZufxH39xPX4xI2fgEfxLDkH32+IiIiIiIiIiGhYCSEwPT3dts96amqKi59pESEEMplMxyBgbkBKtL4ajQay2WzXEIs5siwjEAgsCUceNK7rolKpoFwu9/x/W0iWZfj9fvh8voH+fw6y1f4OAMDj8cDv9y/ZnKFqV/FXz/0V/vzIn6NiV5Z1zn3GPlzhv6LlON08HwCubaMxVLNrePT8o0jX0gCaYWVexwvdWbqxjiqr+ND+D+GeHfcseZ1UFAXBYJBBzGOi0WgglUq1LVcUBRMTE3w/JSIioqEwFqHcc1wAFpoB3atZQTgXzs3u24FSt+uYrc4iWUlitjKLktU9uKoXmqPBcAzIovdfuC3bKKtlCGlwlqpqjgbTMSGJ3q9TLNlCRa1AVVQkjAQSZgIT5gSCniCvd2ikVe0qXim8gpP5kyhb5RWdI6AF8MH9H8QH9q8+nHs5wdzRaJQhZkRERES0KktCuTffhiljqtmX4qJ5O3fg1duFl4jSq4fc4usBIITAU7NP4WT+ZMd6+6L78Of3/jn8Hn+fWtbkui6y2WzHuYVzuDkPDZNqtYpsNtu23DRNhMPh/jWIiIiIiIiIWmIoNxERjZVSqYRCodC23OPxIB6P97FFRL3r1vEOAD6fD6FQqE8tGk3fO/M9/MoDv4KqXe1ad8o3hZunbu68kHsc9RrIrQIwMDADy8t1oXQBj154FK5wFxcIwLRNeNzm82JfbB8+9ZpPwdCWLkQOBoPw+/s7QE1ERERERERERLRalmUhmUy2LFMUBZOTk31uEQ0D13WRTqdhWVbbOhznIFoflUoF+Xy+4wbAC/l8PgQCgaFawCaEQK1WQ7lc7rhJdzuKosDv9w98CPkgcV0X5XIZ5XIZrut2v8MlJEmC1+uF3++HpmmLyhzXwTdPfBN/+IM/xGxldlnnnTQncV3oOgRFl9AXA4DWuQrRKHNcB08nn1608Fp2ZRiOMb8J9UK3bLoFH7n2I/BpSzee9ng8CIVCS/6WafTk83mUy+2DuQKBAAKBQB9bRERERLQyYxXKPUcAsNEM517ZnlpNKgAd3ORqQFXtKlLVFFKVFNK1NHL13LLuLwsZpm1CddWe7yMkgapSRUNZfp9cP0hCgmEb8/O629EVHWE93DyMMPx+PySPNLTz3IlWSgiB2cosXs6/jPOl8ys6R8ATwP1X3Y/79t0HUzNX3BbHcZBOp2Hbdsd6qqoiFosxmJuIiIiIVuzAfz8AVahQXRWKUHBD4gbEjTXIm5gL51Yuud2Aa00hBA5eOIhzpXMd6900dRO+9KYvQVeWbui8Hur1OnK5XE+bwOu6jkgkMlTzmYhSqVTHuWyJRIJzLYiIiIiIiDYYQ7mJiGhs2LaNZDLZdqGxJEmIx+PstKSBVigUUCqVOtYJh8MwzZVPXBtnf3v0b/G7j/8uBLp/RN4f24990X1clH+pGpoT9rvRAHgx9BOVZ8ozeOT8I3DE0gFfr+2F1/ECAC4LXYbP3PwZhPSlYTKhUAg+39KFy0RERERERERERIOqUqkgl8u1LPN6vYhGRzy8hFasl/G6RCIBVe098IOI2hNCoFAodAzPXEjTNITD4aEfM280GigWi6jXe9lFdjFVVREMBuH1etehZaPBcRyUSiVUKpWeg94XkiQJpmnC7/e3DEl55Nwj+L0nfw/HsseWdV6/5se1iWuxSd8EqdplEFJDM5SbiHAidwI/mP3BonkSqqvCtE3IYvFi5oSRwC/c8AvYE9nT8lymaSIYDHIR9AhzXRezs7NtN2Pg53kiIiIaFmMZyr3QXDh354zTzhQAHnDDqwFnuRYytQwy1QyytSwytQxqTm1pRQF4nYvznns+v2yholYgpMFfnrrwWleWZAQ8AQQ9QYT0EEJ6CIbaorNIRnO+Oy9xaExVrApezr2ME/kTsNz2m+62E/VG8eGrP4x/t/ffrTjQr9dgbkVREIvF2CdBRERERCty+5dvX/TvAxMH1iaUux3lkqNPw6uO6+Dh8w933Rz9rm134ffu/D2o8vp9vhZCoFgsds1MmGMYBsLhMNf009BpNBpIpVJty3VdRywW62OLiIiIiIiI6FIM5SYiorEghEA6ne64i6Df70cwGOxjq4iWTwiBTCbTcQG5JEmIxWLweDx9bNlwc4WLzx/6PP76+b/uWleVVdw8dTM2+zf3oWVDptdAbg+aE5RHRLKSxMPnHoYtlk701G0dhtOcpL3Jtwm/esuvYsKcWFKPYfpERERERERERDRM8vl824DXQCCAQCDQ5xbRMOkU6g4w2J1orQghkM1mUau1CNlpwe/3IxAIjNTitUajgVKp1PNjsJDH40EwGOSY6wK2baNUKqFara4ojFuWZfh8Pvh8vpaBvUfTR/H7T/4+Hr3w6LLOq8ka9sf2Y3d4dzNAuAx03INZBuDD0G8eTLSW0tU0Dl44iKpdvfhNAeiuDq/thbTgD0aRFLxnz3vw7svfDUVaGqwvyzICgQBM0xyp9xS6iJ/niYiIaBSMfSj3HBfNeb+9zP1tR8bFcG5eAgyFql1FrpZDrp5DrpZDtVKFU3cgid5/gUISqCgVWMryQ3r7TZM1hPQQwnoYYU8YETmCgAhAlpaReKYB0NG3kDSiQWO7Nk4VTuGl7EsoW71tArrQlG8KP3fg5/COXe+AIi/tT+qm12BuWZYRi8WGfuNRIiIiIuq/vodyX0pGM5xbxbqHdFuuhQfOPIBsPdux3jt3vxO/9frfWt71c48ajQby+Twsq7d+BWaA0LDLZrOoVqtty6PRKLzeEQpeICIiIiIiGjIM5SYiorFQLpeRz+fblquqikQiwcVgNBRc10Uqleo4oUxRFMTjcSjK8iesjZuG08CvPfRr+Napb3WtG9ACeN2W1yHgYaDMEr0GcuuvHiMmU83gwXMPwnKXDgLrjg7DbgZzh/UwPnPLZ7AzuHNJPQZzExERERERERHRsEgmk20XRHByOPUinU533IA0Ho8zCJdoFRzHQSaT6WnxmqIoCIfD0PURHMB5lWVZKBaLKwrnNgwDwWBwrMddLcuaD+NeCUVR4PP5YJpmyzDus8Wz+OOn/xj/68T/WtZ5JUjYHd6N/bH98CieZhB3Gc1AsU78YIASUQs1u4bHpx/HbGV20fclIcGwDXjcxZ/N9kT24OcO/BymfFMtz6dpGoLB4Ei/v4yzVCqFRqP9JJFYLMbfPREREQ00hnJfQuBiOPdKVxlKuBjOzevu4WCjOf/bBRzXQcWqoGSVULErqNgVVK0qKnYFtrt4zUJdqaOqVAcuhN1QDfg1PwKewPwR9ARhqMbSdUIL/u89k9CcA88AehpjQgicLZ3Fscwx5Oq5Zd//8vDl+KUbfgl3bL1j2ev3lhPMHY1GOc5IRERERMuy4aHcl1KwOKR7ja9D604d3zvzPRQbxY717tt3H37lpl9Zs/wN13VRKBRQqVR6qi9JEkKhENdd09CzbRvJZBLtIt6YdUNERERERLSxGMpNREQjr1snJcCF/TR8LMtCKpXq+Lz2eDyIxWLsgO+gbJXxi9/9RTx24bGudSfNSdyy6Zbmgm5arNdAbi+aE+5HVK6ew4NnH0TdWRok43E8MO3mwK+hGvjlG38Z1ySuWVIvEonAMIx1bysREREREREREdFKCSEwPT3dtn96cnJyrINLqTfdxu+8Xi+i0TEOwSFaBdu2kU6n4ThO17q6riMSibQMSh5FjUYDxWKx46YArUiSBJ/PB7/fPzaPFbDyx2uOqqrw+/0wjBbBRwDS1TS+/MyX8ZUXv7Ik3Kmbzb7NuCZxzcXNlAWAKpqBSp2YaC5aJaKWhBB4Pv08jmaOLilTXAWmbUIRFz/r64qOD+77IN60401t56Z4vV6EQiFeI4yYRqOBVCrVtpyLhomIiGjQMZS7DYHmtXUdywsrvpSGZnjx+HSjDBcHzbnf3bsPAQCWY6FqV1FFFSW5hIpbQdWuoubUULfrqDvNQ6w40b0zWZKhKzp0RYdX8cKremGoBgzVgKmZ8Gk+mKoJRV7mdedcGP1yu75kNOfEs4+JxpgQAtPlaRzNHEWmlln2/W+cvLHteopOXNdFOp3uuiGpJEmIRCLcSJqIiIiIejZwodyXUhcca9TfUrEq+N6Z76Fidw7I/vcH/j0+dt3HVvWzhBAol8solUpw3d46nRRFQTQahaZpq/rZRIOiUCigVCq1LQ+FQvD5fH1sEREREREREc1hKDcREY28dDrdcZGoz+dDKBTqY4uI1ka1WkU2m+1Yh8/v9rK1LD72bx/Dc+nnutbdHdqN6yaugyxxdvgSvQZyG2hOsh9xxUYRD559sOVA9MJgbkVS8JFrP4I3bHvDojqcAEpERERERERERIPOsiwkk8mWZYqiYHJyss8tomGVz+dRLpfblicSCS6qIVomy7KQTqd7WsAWCATg9/vHMiyzXq+jUCh0De64lCzLCAQCME1zpB+3Wq2GUqmERqOXQcClNE2D3++H1+tt+TgVG0X85XN/ib96/q9QtavLOndEj+DaxLVImInFBVUA3X6d+qsHEXU1U57B49OPt92Q2nAMSOLi3/e18Wvxs9f9LBJGYkl9oDkO7vf7x/Z9Z1TlcjlUKu0X6QeDQfj9/j62iIiIiKh3DOXugY3m/ODl7aO1mArAA4YXDwoHzQDq5f5OJTT7VDztqwghYAsblmPBci3Yrg3bteEIB45w4AoXrnAhhICAgITmtaEkSZAlGbIkQ5EUKJICVVahyio0WYNH8UCRlPW9lnTRnA+/3MdFRTOcm8sLaIwJITBbmcXz6eeRrqWXff+37HwLfvGGX8TWwNae77OcYO5wOAzDMJbdLiIiIiIaPwMfyr2QjIsB3QqAVVwyFxtF8Z3rxQABAABJREFUfO/M91qOCy/0qZs+hfv237fs8wshUK1WUSwW4Tg97g4GQNd1RCIRyDIvuml0uK6L2dnZtvP6ZFnGxMQEn/dEREREREQbgKHcREQ00srlMvL5fNtyVVWRSCS44IuGVrddMQEgHA7DNM0+tWg4zFZm8bP/+2fxcv7lrnWvS1yHKyJX9KFVQ4iB3C1VrAoePPcgio3ikrKFwdwA8J4r3oP37XnfovchSZIQjUah60wFICIiIiIiIiKiwVOpVJDL5VqWeb1eRKMMLqHeuK6LmZkZtJu2YpomwuFwfxtFNMQajQYymUzXQG5uEHrR3MI/215e4o6qqggGgyP1GAoh5sO4lxtWPsfj8cyHcbdStav4uxf+Dn9+5M+Rr7efx9GKqZq4On41tgW2LZ3fUX/16EQFwCFzomWp2lU8fuFxJKstNuQRgNfxwutc/Hv3Kl68f+/7ce/Oe9vOw1IUBcFgkGFII6LbomFJkjAxMQFFUfrcMiIiIqLuGMq9DA6ac4VX1l3QpOBiODeXbfSfi2bfyUp+hxqawdPj8Huz0Zwb332/w8U8aIaWj8NjRNSGEAIzlRk8l3oO2Xp2WffVZA0f2PcB/My1P4OgJ9jTfVzXRSaT6WljSW4aRkRERES9aBnKbcabAdgymtd8C4+FxCWHu+C2HxQsDulepmwti++f/T5st/Pcmd983W/i3Ve8u6dzuq6LcrmMcrncdR7TQpIkIRgMwufz9XwfomHSLfvG7/cjGOzt2piIiIiIiIjWDkO5iYhoZNm2jWQy2XYhPwDEYjGGntJQE0Igm82iVqu1rSNJEmKxGDweTx9bNrjOl87jw9/+MM6WznasJ0sybtl0C7b4t/SpZUOmit4mZ49ZIPeculPHw+ceRqaWWVKmOzoM++Ii41s334qPXfcx6MrF9yNJkhCPx6FpY/jgERERERERERHRQMvn8yiXyy3LAoEAAoFAn1tEw6zT80mSJExOTkKW5T63imj41Ot1ZDKZjmPjQDMMNRqNcvxhASEEKpUKisXishYCAs0Q6mAwONTjsEIIVKtVlEqlZYeTz/F6vfD7/W0fh4bTwFdf/Cr+7Nk/Q6qaWta5NVnD3uheXB6+HIrcYvWohea4ZScyAB8YjES0AkIIHMsew3Op5yCw9D1GFjIM24DmXnxf2RPZg5+99mexLbCt7Xl1XUcwGOT70QjotmiYG+0QEY0X27UxU5nB+dJ5XChfwEx5BslqEplaBrlaDvlGHsVGEVW7iqpdheVYsMXF6xBVUqEpGryKF6ZmIuAJIKSHENWjiJtxTJqT2OTbhK2BrdgR3AGfxmAWWjmGcq+Ai2Y4d/f80/YkNMOLNfA6vR9WE8atoBnGPW577Ag0n+PdNoC7FJ/bRACafUkXyhdwJHUEhUZhWfcN62F89LqP4n1Xvg+a3L3PqJe1VHNM00QoFGq7kRwRERER0W3/7TbYkg1btmFLNm7deium/FOrO+lcMPfc4bx6rGfCk4SL4dxzRw+SlSQePPcgXNF+3owsyfjdO34Xb9n5lpblc5vBV6tV1Ov1rnOYLqVpGsLhMMeQaaQJIZBMJtvO0ZIkCYlEAqqq9rllRERERERE442h3EQ09hzHgWVZcF13/pAkCZIkQZZlKIoCVVWhKOM2m2y4CSGQTqc77nrv8/kQCoX62Cqi9eG6LlKpVMdF0oqiIB6Pj/1r2ZnCGXz4f38YF8oXOtbzyB68fsvrETNifWrZkGEgd09s18bBCwcxXZ5eUqbbOgznYjD3ZaHL8B9e8x8WPedkWUY8HufgGRERERERERERDZRkMgnLat1BGI1G4fV6+9wiGmaO42B2drbtIpxgMAi/39/nVhENl14DuTVNQzQaHfvxwnZc10WpVEK5XF72wkDDMBAIBIZqTGcujLxUKsFxnBWdwzAM+P3+tgsiLcfC149/HV9+5suYqcws69yyJGN3eDf2Rvcu2th2ERtApcuJJDQDubm/A9GqZGoZPH7hcZSsUsty1VVh2iZk0fxjUyQFb9/1drznivfAq7a/PvD5fAgEAtyEZYgJIZBKpdpeIwJAIpHg4nkiohFjORZO5E/gxeyLOJ47jhO5EzhVOIWzpbOw3ZVt9rMSE8YEdod3Y09kD/bF9uHq+NXYHtjOsEXqCUO5V2EutLiBlYdISWjOL/aA1+zrwUEzVHolL8kSmmHc4/4R3gVQw/IfQxkXw7mJxpgQAqeLp/Fc6jlU7G6duIvtDO7EL7/ml/GGrW/o+rlOCIFcLodqtdvOjc3NJSORCD8rEhEREVFLl/YV3bblNkz5VhnK3c5cQLf96u3y9o9fvrmQbnnBbQvnS+fx6PlHW27WPH8qScXv3/X7uHPbnRBCwLZtNBoN1Ov1FQVxA80Q4kAgAJ/Px8/rNBZqtRoymUzbcq/Xi2iUfdVERERERET9xFBuIho7ruuiVquhVquh0WjAdXvrqZZlGR6PB7quw+v1cqHqgCsWiygWi23LFUVBIpHgoi4aGbZtI5VKdXxN83g8iMViYzsodbpwGj/17Z/quuDbUA3cvuV2BPVgn1o2ZBjIvSyucPHUzFM4VTi1pMxre+F1Li5ADukh/NINv4T9sf3z32OgPhERERERERERDRIhBKanp9sunpicnGRfFi1bNpttu1heVVVMTEz0uUVEw6PRaCCdTndd1KbrOiKRCMfHe+A4DorFIiqV5QWFSJIEn88Hv98/0I+z67ool8sol8s9z5dZSJIkmKYJn8/XNoR8Loz7z579s66bJbeyPbAdV8Wvgk/zta/USyA30Azk5kcTojVhuzaeST6DE/kTrSsIQHd1eG0vJDTnpcSMGD60/0O4ZeqWtnNVZFlGIBCAaZpjO59l2NXrdaTT6bbluq4jFou1LSciosHmuA5O5E/gmeQzeDb1LJ5PP4+Xci/1NXx7OUJ6CAcSB/Caydfg5k03Y290L2RpcK/RaOMwlHsNCDSvzxtoBkitlPbqMTx7nQ2uud/HSl+idTSD0nlpdpGFZjj3clffKmg+nnxe05ib+yx5NH0UDbexrPveMnULPnnTJ7E3urdr3Xw+j3K53LUeNy8lIiIionb6Gsp9qYUh3TZWvgnacihoXv/LCw4Ap0un8cTME4uqSkKCBAmykCELGbqs4z/e8h9xVeSqFYVwL6TrOkKhUNv5J0SjKp1Oo16vty2PxWLQdb2PLSIiIiIiIhpvDOUmorFRq9VQLpc7dk4th8fjgWEYMAxjoBdUjqNGo4FUKtWxDjsiaRR12xkTAEzTRDgc7k+DBsiZwhnc/+37MVuZ7VgvoAVw+9bbYWpmn1o2RASak4oZyL1sQggczRzF8+nnl5QZtgHdufh+pEgKfnzfj+Ntl71tfsGxpmmIxWL8vEFERERERERERBvOsiwkk8mWZYqiYHJyss8tGh6u68JxHLiuCyEEhBBLwlAlSYIkSZBlef5WUZSRDyfsNrbHcT2i1hqNBjKZTNdgZa/Xi0gkMvKvJWvNsiwUCoVlzzEZ1HBZx3FQLpdRqVRWHMbt8/ng8/naBpY0nAa+/tLX8WdH/gzT5ell/4wp3xSujl+NsB7uXLHXQG4TDD4iWgfT5Wk8OfMkqnbrTVUkIcFwDHgcz/z3ropdhZ+46iewI7ij7Xk1TUMoFILH42lbhwZXJpNBrVZrWx6NRuH1etuWExHR4LAcC0fSR3Bo+hCenHkSh5OHUbJKG92sFQvrYbxu8+vwhq1vwG1bb0PQE9zoJtGAYCj3GlttGDTQDH+aC+jmdNneCTTndjfQDPFaCc+rBx/31gSaj+9KlqKpaIZzM/+XxpzlWHgh+wJeyr4EV/T+YiVBwrsufxd+/vqfR8JMdKxbLBZRLBa7nlNRFESjUWgaF7wQERER0UUbGsp9KRfNa/25oO4+O1M8g2OZYx3r6IqOz9z8GeyL7VvRz9A0DYFAgOOHNLY6zcUGAFVVkUgkBmruGRERERER0ShjKDcRjTQhBKrVKkqlEmx7fXqdJUmCaZrw+XzchXEAuK6LVCrV8fft8/kQCoX62Cqi/ullIlkoFILP5+tTizbe2eJZ/OS3f7Lrwu+wHsZtW26DV+Ug3hIM5F4TpwuncWjm0JKJpKZtLlqQDAA3T92Mj173Ufi05t+qx+NBLBbjABoREREREREREW2oSqWCXC7Xsszr9SIaZWiJbduwbRuWZc1/PRfGvVKKokBRFKiqClVVoWkaVFVtG4o6jJLJJCyrdSf0uG44StSJZVlIp9NdX1sMw0A4HOb4wirUajUUCoVlzzlRVRWBQACGYaxTy3pj2zZKpRKq1SpWMk1QluX5MO52G8hWrAq+9tLX8BdH/gKz1c6bJLcS88ZwdfzqrqEqAJqLTss9nNSLZpgUEa2LhtPAM8lncKpwqm0dxVVg2iYU0fzMKksy7tx2J963532IeCNt72cYBoLB4Eh91h0Htm0jmUy2fa/homEiosElhMDx3HE8cv4RPHrhUTw181TbzTeGnSqpuGXTLbhn5z144/Y3IqRzLvk4Yyj3OnHRDC7uZb5xJyqa1/UKAH6EbM1FMyjaQnOe90poaAZGM4y7N6t5fvOxJgLQ7Et+Lv0cXim8sqz7GaqBn7z6J/ET+38Cpma2P3+lgnw+37UvXJIkhMPhDe+/JyIiIqLBMVCh3AsJXAzntrHyDbmW6VT+FI7njnes41W8+LXX/hr2RPb0fN5BmUtDNAhyuRwqlUrb8nHLAyEiIiIiItpIDOUmopFVr9dRKBTaLt5ea5IkwTAM+P1+hnNvoG6dj1zgQ+Mgk8mgVqu1LZckCdFoFLqu97FVG+NC6QLu/9b9OF8+37Fe1BvFbVtug0fhyuwlBIAqettR2URzIjy1laqm8Mj5R9BwGhe/KQCf7YPmLk4zTxgJ/MINvzA/KO31ehGJRPgeRkREREREREREG6bTOEwgEEAgEOhzizaeZVloNBrzh+M4ffvZsizD4/HMH5qmDW3/YblcRj6fb1kmyzImJyeH9v9GtNZ6DeQ2TROhUIh/O2tACIFKpYJisbjsTRY8Hg+CwSA8nv6OQzYaDZRKpY7jxp0oijIfxt3uOZSv5/H3L/w9/vbo3yJbzy77Z4T1MK6KXYUp31Rvz1MbQPvpIBd50AzlJqJ1N12exlMzT6Fit//j9DgeGI4BSTT/znVFx1sveyvesfsd85tUX0qSJPj9fvj9fr6PDZFCoYBSqdS2PBgMwu/397FFRETUTsWq4OCFg3jg7AN46NxDmKnMbHST+k6TNdyx9Q68c/c7cfvW26HJWvc70UhhKPc6c9EMLm5g5YHRQDOQW3v14L49zcfSRvNxXc1QhIpm3wkDolfGAVDDyn4HDOcmAgBka1k8k3wGyWpyWfebMCbw8es/jnfuficUufUbQ71eRyaT6WmTynEd3yYiIiKipQY2lPtSLi4GdDtYXb9LFy/nXsbJ/MmOdUzVxK/e8qu4InJFx3oejwc+nw9er5fjv0Svcl0Xs7OzbeeiybKMiYkJyDI7koiIiIiIiNYbQ7mJaOS4rot8Po9qtbohP1+SJPh8Pvj9fnZw9Vm1WkU2236hpyRJiMfj0DROnKbR5rouUqkUbLt9irIsy0gkElCU0Z2hnKwkcf+37sfp4umO9WJGDLdtuY2LKlphIPe6KFtlPHLuEeQbCwJmBOC3/VDdxQ+iLMl49+Xvxnv2vAeqpMI0TYTD4f42mIiIiIiIiIiI6FXJZLLthrjRaBRe7+gnYAoh0Gg0UKvVUKvV+hrC3Y0kSfB4PNB1HV6vd6g2EnZdFzMzM20XyEciERiG0edWEQ0ex3GQSqW6vvZwPGF9uK6LUqmEcrncU6DHQoZhIBAIrOtrsxAC9XodpVIJjUaj+x1aUFUVfr8fhmG0XQg5XZ7G3zz/N/jqi1/tGMTbTsATwFWxq7DFv6X3xZYM5CYaWLZr47n0cziePQ7RbtW3AAzHgO5c3Dzep/nwjl3vwFsuewsMtfXnPFVVEQwGx+I6YxRw0TAR0WBLV9P43pnv4TtnvoOD5w+i4a7smmEtyZIMCVLzukAALly4YnkbIa2FmDeGd13+LvzInh/B1sDWvv982hgM5e4TgYvh3Kv985bRDDRWMX4B3XNhWxZWF7alohkIPW6P33qx0AznXsnvhOHcRBBC4EL5Ap5NPouiVVzWfa+MXIlP3PgJvG7L61qWW5aFTCbT0ziu1+tFOBxmfwURrYgQAo5wICCgSApkia8lRETDamhCuS/lYHFI9xoSQuCl7Etd1+j7VB9+7bW/ht3h3Yu+L8syvF4vTNPs+0b2RMOiVCqhUCi0LeccQCIiIiIiov5gKDcRjZRarYZcLtd2YUc/KYqCQCAA0zQ3uiljwbZtJJPJjgtfg8Eg/H5/H1tFtHFs20Yqler4eqhpGuLx+EjuKpur5fCT3/5JHM8d71gvbsRx25bboMrDE0zSNwzkXle2a+OJ6SdwrnTu4jcFELACUMTS2e6XhS7Dx677GHYEd8Dv9yMYDPaxtURERERERERERM2Atenp6bblU1NTI71YudFooFqtolqtDsRYZC8URYHX64VhGEOxsCWTyaBWq7Us83q9iEYZikPjrZeNeYHmYpxQKDSSY4CDwnEcFItFVCrLD6Q2TROBQGBNN08WQqBSqaBcLnd9frSjaRr8fj+8Xm/b586xzDH81fN/hX85+S+w3eX/HL/mx/7YfmwLbFve87PXQG4NAPdvINowuXoOT808hUwt07aO7MowHXPRZtUBTwBvu+xtuHfnvTC11vPsdF1HKBQaqk1nxlW5XEY+n29b7vP5EAqF+tgiIqLxlqqm8G+v/Bv+9yv/G0/OPNmXwGtDNeDTfPBpPhiqAUM14FW90BUdHsUDj+yBKqtQJKXldcFcoJnt2mi4DTScBmp2DVW7iopdQdkqo9QooWSV1vz/I0HCG7a+Afftvw83T93M6+oRx1DuDTAXzr0WAVEymvOW5wK6R/HPda2CuAEGQK8ngebzuoGV/Z48rx783dAYc4WLE7kTeD79/LI3jrl10634xGs+gb3RvUvKHMdBJpNpu+H0QpqmIRKJsO+JiOZZjoVXCq/gVOEUzhbP4nz5PGbKM0jVUsjVcig2iqjYFdSd+qL7qZIKQzUQ8AQQ0kOIGlFMmpPY5NuEbYFt2BncictCl7XtCycioo0ztKHcCwk0+13mgrrXoA9GCIEXMi8sXgfdwlww9xXRK6DrOrxeb8f5J0TUJIRAMpnsON8rkUhA07Q+toqIiIiIiGj8MJSbiEaCEAKFQgHlcnnZ95UkCZqmQdM0yLIMWZYhhGhO6nUc2LYNy7I6hj13wkVB608IgVQq1XGijMfjQSwWY+c9jZVarYZMpv1iRwAwDAORSKRPLeqPslXGT3/7p3EkfaRjPQZyd8BA7r4QQuBY9hiOpC4+VyUhIWAFIIuls6sVScE7d78T793zXsTCMW40QUREREREREREfVWv15FOp1uWqaqKiYmJPrdo/bmui2q1uqqQ00ExDAHd1WoV2Wy2ZZkkSZiamuJYH40tIQTS6TQajc6BFIZhIBwO82+lTyzLQqFQQL1e7155AUmSYJom/H7/qsK5HceZD+Ne6YYRHo9nPoy7FVe4ePjcw/jr5/8aj154dEU/w6/5sS+2D9sC2yBLy0wYstAct+xGRTOQm099og0lhMArhVfwbOrZJWEkC6muCtM2F42Lm6qJe3bcgx/a9UMI6+El95l77QwEAiO9GdCw62UeHxcNExGtr3w9j//zyv/Bt05+C0/MPLFuQdyGaiCshxHSQwh5QgjoAfg1f9/mY7rCRdkqI1fPIVvLIlvLIlPLwBFrkfYL7I3uxf1X3Y97d97LOaYjiqHcG8hGM8B4rbrcJSwO6B7WywWBi0HcNlYfxA0w8LmfBIA6ms/tldDQ/F2t3T5+REOn4TTwQuYFHM8dX/Zn2Lftehs+fuDj2BrYuuj7rusim8321IcvyzJCoRAMgzs/Eo0by7HwQuYFPJN6Bs+lnsPRzFGcyp+CLdZvjsj2wHbsje7FNfFrcGDiAK6KXQVNYZ8pEdFGGolQ7ksJAC4uBnXPfb3c0wiB59PP40L5wtIyScCRHNiSDa/uxR/f88e4buK61bWbaMx0mpsNNOd2xePxPraIiIiIiIho/DCUm4iGnuM4yGazXRefLiTLMgzDgNfrhcfj6bogVQgBy7JQq9VQrVbhOMvrcZYkCcFgED6fb1n3o97kcjlUKpW25bIsI5FIrGoxK9GwKpVKKBQKHesEg8GRCfetO3X83L/9HB6bfqxjvZg3htu33s7FEq0IABX0NrjKQO41MVuZxWMXHptflCwLGQErAEm0/nwy5ZvC/Vfdj7uvvJuTPomIiIiIiIiIqG869TeP2gaQtm2jXC6jUqmseOPeQaaqKkzThGEYAzV+JoTAzMxM22DZSCTCPlEaS0IIZDKZrqERXq8XkUiEgdwboFaroVAoLHsDB0mS4PP54Pf7lxUwa1kWyuUyqtXqqjaY9/v90HW9ZXnFquAbL38Df/vC3+Jk/uSKfsaqwriBZpBRL3nnDOQmGjiWY+Fo5iheyr4E0S5JTgC6o8NwFn++U2UVt2+5HW/d9VZsD2xfcjdZlhEMBmGa5no0ndZAt0XDuq4jFov1sUVERKOv4TTwwNkH8I2Xv4EHzj0A213b4DBVVhHzxhD1RucPXW19LbGRXOEiV8shWU1ipjKDVDW16lDyrf6t+Olrfhrv3P1OBqSNGIZyDwAXzc24rFe/XisyLgZ0D3JIt0BzrraNi+FYa0FCM9xZw+D+30eZi2Z/Vvt9ijpT0fz9cX4+jbFSo4RnU8/iXOncsu6nyiret+d9+JlrfwZx42JYmRACxWIRpVKpp/P4fD4Eg0GOtRCNMMd1cCR9BAfPH8QT00/gcPIwak5tQ9vkVbw4MHEAr930Wty25Tbsiezh6xARUZ+NZCh3O+6CQyy4nTsuJTU/Vx9OH8a58jk4kgNXcuePhXyaD3/ypj/BgYkD6/t/IBoxmUwGtVr7z6ScO0tERERERLS+GMpNREPNsixkMpmeQ7JVVYXP54NpmqsalKzX6yiXyx07tlrRdR3hcHigFrcPu0qlglwu17EOOxlp3GWzWVSr1Y51YrFY2wXXw8JxHXzy+5/Ev53+t471InoEd2y9gwskWmEg94ap2TU8Pv04ZiuzAADZfTWYu0N6wA2TN+Bjr/0Y9k7u7VcziYiIiIiIiIhojHWa9B0KhQZyc1ohBPL1PDK1DAqNAip2BZZjwRHNTlBN1qArOnyaDwFPAD7FB6kuLXsMsBeyLENRFCiKAkmSIMvykvFKIQSEEHBdd/5wHGfdgsElSYKu6/D5fAMzRtBpTGPUwt+JetVtk2qgORchGo1ycfYGEkKgWq2iWCwue6N3WZbh8/ng8/nahnMLIVCr1VAul5e1af2lDMOA3++HprUeK32l8Ar+/oW/xz8f/2cUreKKfkbQE8S+6D5sDWxd2XNSAKiht/AiBnITDbReQpQkIcGwDXhcz5Kyq2JX4d6d9+LGqRuhSosnKGiahmAwODCfY2mxbouGo9EovF5vH1tERDR6hBB4Lv0c/un4P+FfT/4rCo3Wm+mthCZrSBgJJMwE4kYcYT08lNebtmtjtjKL86XzOF86j4a78mupLf4t+Mi1H8E7dr8DqsyJk6OAodwDxsbFgO61JuFiQPdcSHe/w6rnAq7mwrfnvl5LCi6GOQ/fS/bocdAM517pPhlzv08ud6AxlqqmcDh5GNladln3M1QD9+27Dz9x1U8gpIfmv1+pVJDP53sad/V4PIhEIlz/STRCio0iHjz7IL539nt45PwjyNfzG92kjqZ8U7hz6514844348bJG6HIfD0iIlpvYxXKvUKucPH4hcdxtnS2Yz1DNfCFN34BN03d1KeWEQ0/27aRTCbbXrMqioJEItF2XhkRERERERGtDkO5iWho1et1ZLNZuK7bta4sy/D7/fD5fGs6Kdi2bRSLxa5ht5e2JRwOc1HJGrAsC6lUquOEGJ/Ph1Ao1LacaBwIIZBKpWBZ7Wcqy7KMeDwOVR3OxQJCCPzWwd/CV1/8asd6IU8Ib9j2BniUpYs5xx4DuTecEALHssfwXOo5CAiorgq/5e94H1mScdfeu/CR6z+CTf5NfWopERERERERERGNo+np6bbjcolEom24Zz/U7BqOZY/hWOYYXsq+hFcKr+BM8QxmKjOw3O4pHoqrwOt4obkaVFlF1BtF1BvFpDmJCd8ENpmbsNm/GZt8m+BVO4/xSZIETdPmD1VVoarqqibDO44D27bnD8uyYFnWmoZ1r9XmxqtVrVaRzbZeXC/LMiYnJ4cyBIpopYrFIorFzsHIHo8HsViMfxsDQgiBUqmEUqm07NdpSZLmw7nnwj5c10WlUkG5XF522PfC85qmCZ/P13I82HZtfP/s9/GVY1/BI+cfWdHPAICIN4J90X3Y5Nu08uejC6CK3sYsNQBeMGiKaAikq2k8m3oWqWqqbR3FVWDaJhSxNFwkokdw57Y7cdf2uzBpTi4q83q9CAaDQzvfZVR1WzSsqioSiQQ/vxARrUCulsM3TnwD//jSP+J47vianTfmjWHKN4VJcxIRb2TkXqNd4SJZSeJ08TTOlc7BdleWkrozuBMfv/7juGfHPSP3GI0bhnIPKIGL4dxrHVp9qYUB3dKC24VHrwQuBm9fejt3rJe54GbmNA6m1YZzy2j+fj1gHxiNJSEEzhTP4EjqCCp2581LLxXQAvjQVR/Cffvug9/TXJPRaDSQzWZ76muXZRmhUAiGYayo7US08fL1PL5z+jv49ivfxmMXHlvxdeBGi3ljeMtlb8Hbd70dV8Wu4rUoEdE6YSh3b1zh4rELj3XclBkAdEXHH9z1B7hty219ahnR8Os2T9Dv9yMYDPaxRUREREREROODodxENJRqtRqy2WxPCxi9Xi9CodC67k5uWRYKhQLq9XrP9/H5fAgGgxwEXSHXdZFMJjtOhNE0DfF4nI8xEZqL3VKpVMeNDIb5b+ZLh7+ELz79xY51/Jofd267s2tgylhaTiC3D5y4vc6ytSyemH4ChUYBmqPBZ/s61heSQNVTxQ/v+WH85NU/iW2BbX1qKRERERERERERjQvbtjE7O9uyTJIkTE1N9bVvuWJVcGjmEA5eOIinZp7Cscwx2GL5CyhlV4bhGNDc3gLFJUiYMCewLbANO0M7sSOwAztDO7EltAVerxe6rkPTtL48FkKI+XDuRqOBer3e02bG3ciyPB/cup7jq+0IITA9Pd12HDgWi0HX9T63imhjdAqpn6OqKuLx+KqC/2l9OI4zv8n7SsK5595PGo3GijdhkGV5PuS71XPkfOk8/vGlf8TXX/o6Zqut3+d7MWlO4srolUgYqwxXtdEM5O7lv6sBYBYK0VARQmCmMoPnUs8hW2/z/iYAj+uB4RiQROvXk6tiV+GOrXfglk23wFCbLwRzmw8EAgG+Jw6QfD6PcrnctjwUCsHn6zwfgoiImoQQODRzCF998av4t1f+radN6LrRZA1Tvils8m3ClG8KHsWzBi0dDrZr41zpHE7mT3bcNKSTa+PX4pM3fRLXT1y/xq2jfmEo9xBwcTGgez1DrTu5NJxbwsV+i0tv+0lBM6RZBYOah8Vqw7kBBrDTWHNcB8dzx/FC5oVlfxYOeoL40P4P4cf3/TgCngAcx0E2m0Wj0ejp/qZpIhgMss+JaEjUnTq+d+Z7+OaJb+Khcw8NbRB3O7tDu/HuK96Nd+5+JyLeyEY3h4hopDCUu3eucPH4hcdxtnS2Yz1VVvG7t/8u7tl5T59aRjTchBCYnZ1tm58jSRLi8Tg0rbf5zkRERERERNQ7hnIT0dDpNZBbkiSEQiGYptmnljUXxRYKhZ52TAeaAbiRSASqqq5zy0aLEALpdLrjBBhZlhGPx/nY0kgQQswfABYtZJYkqeeFzfV6Hel0umMdwzAQiQzXpIyvvfg1/Majv9GxjqEauGvbXTC1/r0nDA0XzUDubhPWJQAmOJG3TxzXwfPp5/Fi9kVojgbT7vzcdSUXRa0ISZbwxu1vxAf2fQA3TNwwlCH7REREREREREQ0eDoFw3o8HsTj8XVvw3R5Gt85/R1898x3cWjm0KoWT0pCgtfxQndWHu5syzYacgOWbMGv+7Evug/7Y/txVewq7I/tx7bAtr72z82FdNfrddTr9Z4XkrczF2zo9/v7Hs6dyWRQq9Valvl8PoRCob62h2gjNBoNpNPpjvMiFEVBPB7fkAB96p1t2/Ph3N0IIVCv11GtVmHbNiRJgq7rMAxjWXMfVFWFz+eDaZpL3osaTgPfOfMdfP2lr+PR849CrDA5SoKEbYFt2BPdg7AeXtE55gk0Q4l6fevyAOA+zERDSwiB6fI0ns88j2ytfTi34RgdP697FA9umLgBr9/8ehyYOACP4oEsy/D7/fD5fBwrHwCu62J2drbt5kGyLGNiYoKhVkREHRQaBXzj5W/gH479A07mT676fF7Fi83+zdji34KEmYAsXfIaLF493AVfX3qgxW07UovbVoeMpcG3fVKoF/By7mWcKpyCI3qb/7/QvTvvxSdu/AQ2+zevQ+toPTGUe8g4aIZz29i4gO6NpqAZwq2h+bpJw2ktwrkZyk5jrO7U8UL6BbycfxmuWN4bQsATwI/v/XF8YN8HENbDyOfzqFQqPd1XURREIhF4POOzkQ3RMBFC4EjqCP7p+D/hX0/+K4pWsW8/W5GU+WtrAQHHdVY89rYcmqzhnp334P17349r49eyP5yIaA0wlHt5XOHi8enHcbbYOZhblmR89tbP4j1XvKdPLSMabp3magP9m69NREREREQ0bhjKTURDpddAblVVEYlENmSXN9d1USwWUS6Xe6ovyzLC4TC8Xq4Y7FU+n+/6+EajUT6mNJCEEHBdF47jwHGc+a9d111yLAzi7mQumFuSJMiyDEVR5m/nDlVVoSgKSqUSCoVCx/MFg0H4/f61+i+vqwfOPoBf+M4vdFwM4ZE9uHP7nQh6gn1s2ZBwAZTR2+IYBnJviGwti6dmnkK1XIXX6fy+5kgOilpxfoL15eHL8e7L34237nor4gYH2YiIiIiIiIiIaOU6jc34/X4Eg+vT/5qv5/Gtk9/Cv5z8Fzw1+9TqTygA3dFhOMaK7u5KLupKHQ25ASF17lgNaAHsizWDuvdF92FfbB92BHcsDTtaJ47joFaroVqtriqgeyPCuSuVCnK5XMsyRVEwOTnZl3YQbRTbtpFKpdoGWALNeQaxWGxD5kTQyliWhWKx2HLTAdu2UavVUK/X2/7eNU2DaZrQNK3twnqPxwOfzwev17uozlwYwT+//M/415P/ikKj83hxJ5qs4bLQZbg8fPnabIjsAKii90AtL5qhQ0Q09IQQmKnM4FjmGJLVZMs6sivDdEyobueNCbyKFzdO3oibNt2EA/ED8Hub1yiGsbLP/bR2yuUy8vl823JuukNE1NqxzDH83Qt/h385+S+o2t03+OlEV3Rs9W/F1sBWxI04JCE1P387WBzAPXe7kRaGdM8FdSsL/r2OGk4DJ/Mn8VL2JdSc1pvFteNVvPipa34KP3X1T0FXVr4JIPUXQ7mHmIuLAd3Lz9IfLgziHl1rEc4tofnc0MA5/jR2ylYZz6Wew+ni6WXf11ANvPeK9+JD+z+EsBJGLpfrad0W0BwXDwQCDL8lGhCFRgHffPmb+J8v/U+8lH1pzc9vqiaCniD8Hj/8mh+mZsKreuFVvPAoHiiS0vL1wBUuGk4DdaeOml1Dxa6gZJVQapRQaBRQbKxtaPg18Wvwwf0fxJt3vBmq3Psmv0REtBhDuZfPFS4OTR/q6XP5L93wS/ipq3+Kn6WJepBKpTrO+Q2HwzDNNZizRURERERERPMYyk1EQ6PXQG5d1xGJRCDLGzvrrF6vI5fLwXF6m+kXCATg9/vZmdxFt4U6wPqGP4wTIcR8cLRt20vCo+cCoxcecxY+jxeGRV96OxcePXcoijJff1gtDNxudcw9dhtBkiSoqopyuQzbtqGqKlRVbfl6OQzB9s+lnsNPfvsnOy46USQFb9j2Bk6Ub8UBUAEDuYeAEAIv51/GiekTkK3On29s2UZJLc0HcwPNv4NbNt2CN+94M+7cdicDuomIiIiIiIiIaNlmZ2dh262TAda6P9kVLg5eOIivvfg1fPfMd2G51pqcV3VVmLYJWSx/DNGSLdSVOmx5NekIzUXeV0auxJXR5rEnsgdXhK9Ym1DTDhzHQbVaRbVahWWt7PGcC+cOBALrPg7rui6mp6fblk9MTEBVuZiURpPrukilUm1fc4Hm32M0GoWuM+hrGDUajflw7nq9jlqttqzXZkVRYBjGfPC2JEnwer3w+XzweBanVZ8tnsW/nPwXfPPEN3Eyf3JV7fZrflwevhw7QjugyWsQBi/QDB5azr4RJppBVEQ0cjK1DF7MvIhzpXMQLSYxaI4GwzF6+iyvyiquil2F6yeux81bbsaVm69c8vpI/SOEQDKZbPvZRpIkJBIJfr4nIgLguA6+e+a7+Nujf4tDM4dWdS5VVrHV3Irtvu2I63HIrtwMse11M5xBNRfOrWDdwrod18Hp4mm8kHkBZav1JoXtbPVvxa+99tdw25bb1rZRtC4Yyj0iBC6Gc9sY/tc5Cc2+j7ljeJdUUK9cNPvIVjsUpuBiQDefNzRGcvUcjqSOYLrcflyxHVVScc/Oe/CBKz+AKWmq47jMovupKkKhEMdoiDbQc6nn8PfH/h7fOvmtZW+q1I5H9iBmxBA34oh4I4joEWjK+myObLs2cvUcMrUMUtUUUpUUGu7KN1qfs8m3CT9x1U/gPVe8B4bKDSuJiJaLodwrI4TAkzNP4lThVNe69+27D//hpv8AWeLOY0SdWJaFVCrVNo9ElmVMTExseJ4SERERERHRKGEoNxENhXq9jkwm0zXI1jRNhEKhgQn0dV0X+Xwe1Wr7wNiFBiVQfFBVq1Vks9mOdXRdRzQaHZjnwLCwbRuWZc3fWpbVc6D8elgY2L0wuLvV1/00F1TeKXjbdQd/Nq8QArlcbn7SmKIo0DRt/ph7bOPx+MAuejtXOocP/K8PIF1Lt60jQcLrt7yeA5+t2GgGcncjAfBhzReu0MrU7TqOnz+O6fx0y4XIcyzZQlktt51UfXXsarxuy+vw2k2vxTXxa+BVBzuAn4iIiIiIiIiINla3gOSpqak16a8vNAr4+ktfx1eOfQWni6dXfb45kpBg2AY87vKD+BpKAzWlBlda377/Lf4tuDx8OXaFd2F3aDd2hXbhstBl8Hv8a/6zGo3GfED3SsY0JEmC3+9f982GU6kUGo3WC0+DwSD8/rV/bIg2mhACmUwG9Xq9Y71wOAzTXN8wf1o/jUYDlUoF+XwelUql7WtdN4qiIBQKIRaLLXo+pKop/J9X/g/+5cS/4Onk06tu76Q5icvDl2PKN7U2r/sCzZChOrpvHjxHBmCAmwgTjYGKVcHLuZdxMn9yaQiJAHRHh9fxQlpGwtiUbwrXbroWt+y8Ba/d+loEPcE1bjV1U6vVkMlk2pZ7vV5EowzAJKLxVWwU8Y8v/SP+7oW/w7nSuRWdQxISVFfFlHcK283tSHgTUKXBnPu55iRcDOmeO9bg0sUVLk4XTuNo5uiyw7nv3XkvPn3zpxE34qtvCK0bhnKPKBcXA7odDH5I99xrmIqLr2E0nlw0N69bfR5n8/mkgcHuNFZS1RSOpI4gVU2t6P43JG7Ae7a/B9dHru/5c7TP5+vLZsZE1NRwGvjWqW/h747+HY6kj6z6fLIkI2EkMOmbxKQ5iaAnuGHrkYUQyNVzmKnM4ELpQse1kr2I6BF8cP8H8f6971+XOSdERKOKodwrJ4TA4eRhHM8d71r3LTvfgv/7tv8bHoWbKhN1UigUUCqV2pabpolwONy/BhEREREREY04hnIT0cCzLAvpdLrronC/349gcDAXzcwtqOzlJVdRFEQiEXg87ExeqNFoIJ1Od3wMVVVFPB7nhJYuhBCwLAuNRmP+GIYg6VYkSZoP6Z475r638HZuUsSlkyPmnk8Lb13Xnb9tdYwK13WRzWZb/p8URYGqqjBNE5s3bx64YO58PY8P/euHcCJ/omO9m6Zuwo7gjj61aohYAHrZK4KB3INJAKV8CSfSJzBbmW1braE0UFG7J69rsob9sf24NnEtro5djb2xvdgR2AFF5sx+IiIiIiIiIiJq6rRpqqZpSCQSqzr/meIZ/M3zf4OvH/86qnZvG932yuN4EBZheBQPNEmDKqvNsQNIzY3vBGALG47rwHItWK6FhtNAXamjptQgpI2dThE34vj/2bvvOMeu+v7/r3uvetf0tjvb+3p3vbvu3V5DsAk2BoxtMHHID0gghQAh8E2+MSEkwNeBUEIgCQbHOLbBGDC2MWsbG9f19vX23sv0UW+3/P7QSiPNSBppRlP3PO37kHR1dXV3RiPpnvM579PuaWeWZxazPLOY6ZlJu6edGe4Zox6Yous6sViMSCSSncSzEoqi4Ha7xywYOBQKEQqFCt5ntVqpra0dk+cVhIkUCASIREoHfU3mugihOE3TiEajxGKxIe+5qVSKWCw2bBh7hslkwm63Y7Vas/3fUS3Kxt6NvHT2JTZ2bkQ3RtevbZbNzPLOYo53Dm6Le1T7ypMJ467k8EykA7lFgJAgXFA0XeNU+BRH+o8MCSCRDAmbZsOqWSver6qotNe3s6Z5DWua1rCqYRVeq7dahy2U0NPTU/KzrqamBptNTCguCMKF5Uz4DD/Z+xOePPhkZaHPBiiGgskwYdJNuBU3rc5Wmp3N2EzivRTID+geZSCpbugcDRxlb89e4lq87Me5LW4+t+Zz3DbvtgkLdhNKE6HcFwiDdDh3JqA7E9o9EXInEZBzLgUhl0E6mDtFdULlRUC3cAExDIPOaCe7e3bTGy8+OVgpLZYWbmm5hevbrqfWPnxfpKIoeDwe7Hb7iJ5PEIThdUY7eXz/4zxx4IkR/21nWGQLza5mWl2tNDgaMMmTa7xkRlyNcyZ8hpOhk3TFuka8H4/Fw71L7uWexfeIcG5BEIQyiFDu0TEMg909u9nXu2/YbVc3ruZb139L9NMKQgmGYdDZ2YmmFW/MrK2txWqtvG5CEARBEARBEARBGEqEcguCMKmpqkpPT0/JxiKYGgNPVVWlr6+PVCo17LaSJOH1esdsEPtUo6oq3d3dJQORJUmivr5+0oUHTxaappFIJLLLdAqXFkYulUoNO2GAzWbLNspbrVbMZvOEDhBIaSk+8cIn2HhuY8ntltctZ2HNwnE6qikkcX4Zjgw4EMXek5UBRCAUC3E0eLRoOHdciRM3lT8QKMOm2Jjjm8M83zzmeOcwy5sO/KlG0I8gCIIgCIIgCIIgCIIw9ZQKiXU6nXi9Ixscsb93Pz/c+UN+e/y3ow4PBXCYHPhtfvw2Px6TB4/uwSE5kKUKGjrNoFt0YnqMaCpKOBUmnAwTSoUIJUKEU+F0mPcEk5BodjZnQ7ozy2zPbJpdzRUNHjUMg0QiQSQSKTsQNpfFYsHr9WI2myt+bCnJZJLu7u6C90mSRFNTkwg0EqaVaDRKf39/yW3sdjt+v398DkgYNV3XicfjZQduq6pKNBotuK0kSVgsFmw2W3aC9754H5vPbWbDuQ3s7dmLZqTrajRJI6kkScrJiieXqLHVMMc7hxnuGdWbwNUgHSKUpPIgIRsguqYE4YIXSAQ4FjzGieAJEtrAe6Ssy9g1O2a98u+huZPwzPPNY1XDKlbUr2BF/QraPe3ie+YYUFWVrq6uojVKJpOJ+vp68bMXBOGCsLdnLz/a/SPWH1uf/R4/HEVPh3CbdTMm3YQsyTQ4Gmh1teKz+sT753ByA7oVRhRKquoqB/oOsL93f9m/N4DLmy/n/ivup8XVUvmTCmNKhHJf4PScxRh0PbNUSiJddy3lXM9dxFu1UCmVdJta5fOqFmbKWcQYAWEaMwyDjmgHe3r2jCjAVzIk3Jqb1bWruX7G9VzceDFmuXT7k9VqxePxVL2/VBAuZDu7dvLw3od5/tjzqMbIPwxNsok2Vxtt7jYaHA2V1Y9MArFUjBOhExwLHiOULDyp+XC8Vi9/vOyPuWvRXdhNYhIBQRCEYkQod3Xs793Pzu6dw243yzOL7934PWZ4ZozDUQnC1BSPx+ntLX5eK/rYBUEQBEEQBEEQqkeEcguCMGnpuk53dzeqWrrTdCoEcmcYhkEgECAajZa1vcPhwOv1XtANYeUGs9fU1GCz2cbpqKYGVVWJxWLE4/GywuCFC1MsFiMcDpfcxuVyYbeniy4URcFms2UHfI/n+5NhGPzd63/HU4efKrndXO9cVjasvKDfO4cwSIdxJ8vYVgHsiGLbyU4HIoABkVSEE8ETnI2cHRJeFDVFSSrl/OKHJ0tyOujHPZOZnpnMcM+g3dPOLM8sWt2twxabCoIgCIIgCIIgCIIgCFNTZ2dn0f46v9+fbT8u19tdb/ODt3/AK6deGdVxWRUrjY5GGp2N1NvrcZjPT3abBCqdq84EWEm3j5ag6RrBZJBAIkB/oj+7qHq1EhFGzyyb0wHd3tnM9c1lrm8uC3wLmOmZOWxYdyqVIhQKEY9XPtmf0+nE7XYjy9VpXDYMg46OjqITzYq+QWE6SaVSdHd3l5xI12KxUFtbK/q/JjnDMPKCuEdSlqdpWjacW5blbN+sLMucDZ9lc8dmNp7byMG+g8NOFKFJGik5RUpOoUlawfAni2xhpmcms72z8VpHNtFG4ScnHcadovIgK5l0f2WVcsEFQZgedEOnI9LBidAJzoTPZMMwTboJu2pHMSp/04grceJKPO/90WPxsLxuOcvqlrGsbhlLa5dS76iv1j/jglZqwicAr9eL0+kcxyMSBEEYP4ZhsOHsBh7c9SAbzm4YdnvJkDDr5nQIt2FCMtIfVk6zk1ZXK83OZsyKqNUasVGEksbUGLu6d3E8eLzsxzjNTj675rPcMf8OcV4/iYhQbmFYhcK5M7elIpeCMBZ00n1f1SkHT5MZ+Cwc4YQVgjDZZcK59/XuoztWeCLg4g8Gq2bFrtlxWVxc0XwFV7ZeyUL/wqLf5yRJwul04nK5qtZfKggXGlVXeeHECzyy5xG2d20f8X4kJBqdjczyzKLZ2Vy9iWgnkGEY9MR7ONJ/hFPhUyOa+L7OXsfHL/o4dyy4Q4z/EgRBKGDUodyZSb8KXRo52xSqn5CKLDJDJwGbAo4GjrKlY8uw2/msPv7t+n9jdePqcTgqQZiaent7S9bzut1u3G73OB6RIAiCIAiCIAjC9CRCuQVBmJQMw6C3t5dEIlFyu6kUyJ0rGo0SCATKGoBpNpvx+/2YTKUHyU9HmqbR09MzbDC7GJAzQARxTz+SJOUtuQzDyFtGariADUmS8Hq9mM35BReZQeB2u31cArp/sOMHfHf7d0tu0+Js4fKWy6t6LL2ne/nePd9DTabfi+ZeOpcPf/PDVdv/mDOAGFBOJowCOKha52zf2T6+dce38tZ95LsfYfbFs4s+5ti2Y/z4kz/O3l57x1pu+cwt1Tmg6SYnmBsgqSU5Gz7L6fBpourABCARU4SUMrafCSbJRJu7jTneOcz1zWW+fz4L/Qtp97RPiwI2QRAEQRAEQRAEQRAuPEeOHGHp0qXZttN3vOMdPPfccxN8VONP13XOnTtX9P6mpqayBxW/3fU239v+PV4/83re+o5fdND1q67sbXOtmYX/urDgPuwmO62uVvb9xz72/XYfAJIs8dH//Chti9rSbaGl53nNJwM20qEDI2QYBuFUmP54P32JPvriffQl+iZVUDekQ1fn+eexuGYxS2qXsLRuKQv8CwoOthxpOLcsy3i93oqD2ovp6+sjFosVvM/pdOL1VjE8VhAmiK7rdHV1lZyk2mQyUVdXJ0IcJinDMEgkEtl++mqU4tlsNqxWK8lUkh1ndrDp7Ca2dGzhdPj0yI8TA1VW04ukUuesY5ZvFi3Olur05RikP4NVRhbEnWEhPVHGFBlMKggXsomspVB1lbORs5wKneJc5ByarmHRLdg1eza0tFyGZBBX4iTkRNH3nnp7PUtql7CoZhGLaxazqHYRLc6WEdemHDt2jNmz8+smXnrpJa677rqij/n973+fd/8nP/lJvvvd0jU0k42u63R2dhadeEeWZRoaGsR3HkEQphXd0HnxxIv8cOcP2d2zu/iGBiiGkg3izp1sQkKi3lFPm7sNv9U/9qHOg0NeCi0UuBzMKHKZG0STWXRGfg4xWgr5oaRl6on1sK1zG/2J/rIfc1XrVfzjFf8oJvyYJEQotyAIk13B8+6vfzgdzl1JX1g5Mp+DmQkrLrC2uZf++yV+/+Dvs7e9TV4+/eSnSz7ml//0S7Y/ux3I6atc0jaWhymMQnesm329+zgXKd7vXYisyzhVZ/b7ea2tlkubL+XS5ktZ4F+ALA1tw5BlGZfLhdPpFBOyCEKZAokATx58kv/d978V/53mcpldzPLOot3Tjt1UnZqFySiuxjkaOMrh/sPEtconXJ/pnslfXPwX3Nx+s3ifEgRByFFRKHemPiKz6OeXsSafX5RBl5PQmfAZNpzdMOxEEibZxP+97P9y+/zbx+nIBKE8k6WGXNM0Ojs7i9ajSZJEXV3dkAyQqeT+++/nS1/6UvZ2e3s7x44dK/mY++67jx//+MdA+jz8zTff5JJLLhnDoxQEQRAEQRAEYboTodyCIExKgUCASCRSchuHw4HP5xufAxoDyWSSvr6+koNrM2RZxufzYbPZxuHIJgdd1+nu7h42kHuqBrNXk67rxONxotEoyWRyQo9FkqSqDDKe7hRFyVtkWc5e5i6VFDYYhoGu69lF07TsoqoqqqoWHdRmGAaBQKBkkLuiKPh8vqID3zIB3Q6HA4vFUvZxl+vZI8/y+Vc/X3Ibv9XPtTOuxSRXdxKDRz77CAffOAikCyY//uOP0zSvghmOgWBXkP6z/QQ6AkT7o6TiKXRNx+q0YnVZqW2rpXF+IxZblX92GukQmnI6dE2AnaoW0Y4klBvgfz/3vxx4/QAAkiLxsR9+jOYFzdU7sOlEIx3MncMwDPoT/ZyLnKMz2klKTxE2h1Hl8Q8Bsik2FtYsZFndMi6qu4iVDStpdjaLwq0qCSQC7O7ZzcG+g5wInqAj2kF/op+ElkBCwm6y47P6aHY1M8c7h6V1S1noX1j190lBEARBEARBEARBmI5uvfVWnnnmGSDd/rlt2zYuuuiiivYRjUbZu3cv+/fvp6enh2AwiMPhwO/309raytq1ayd9X1csFqOvr6/gfWazmfr64QNk9vbs5bvbv8srp14peP9wodyKpNDmbqPd0069vR5Jkgh0BvjOnd9BTaTbvFoWtvAn3/yTysLbrKSDP8egqWpwUHd/op/+eD9JfWL7cQazKlaW1C5hZcNKVjesZlXjKjyWgX63kYZz22w2vF4vijK6kNdSrz+TyURDQ8Oo9i8IE80wDHp6ekr28U6HgTPTkWEYxONx4vE4iUSiaD9sJRRFwW63o5pUNpzbwCunX+H106/TH+/Holuwata8YL6RcJqdNDubaXI2YTPZhg7YHBy+V0huaJ7OwADT0QYBKaQnyhBzvQrClDFZailUXaUj0sGZyBnOhs4ip2RsWuU1doZkEFNiJOVkWecIboubRTWLWOhfyAL/AhbWLGSebx4WZfi6j5GEcgO8+93v5umnnwbSnxubN29m5cqVwx/sJBKJRAgEAkXvF5PvCIIwXaT0FM8eeZYf7vohRwNHC29kkA3hNhvmIRNLmGUzLe4W2lxt1Q0Ty3zvH7xk1k+kTDi3XmAZj9JkGTBTdkC3YRgcDhxmV/eusifo81q9/MPl/8C69nWjOVKhCkQotyAIk13J826ddDj3MJPjJeNJuk92032qm1gwRiKawGw1Y3PZ8NR5aFnQgt1V4HuGwkBIt8K0D+keSSj3kL7KRS38yX9X2FcpjLtAIsCBvgOcCJ7AKPcLpgFW3Ypdzf9b8Vq9rGpYxeqG1SyvW47dnH+/oii43W7sdrsYOyEIRRwLHOORvY/wq8O/IqYWnqx7OBISra5W5vjmZOtJLhSarnEydJL9ffsJJUMVP/6i+ov47JrPsqph1RgcnSAIwtRTMpTbID1BeW4Q92QhMXAOl1kmycdhd6ybN06/UVbN5D2L7+Ezaz6DWRb1UcLkMJlqyMPhMMFgsOj9FouFmpoagskgvYle+uLpmuVgIkgwGSSSihBOhYmmosTUGAktQUJLkNJSpPQUqpHOINFzwihk0lknJsmEWTZjUkxYZStWxYrNZMNmsuEwOXCanTjNTlwWF26zG7fFjcfqwWf14bP6yqqfGEko96lTp1iwYAGxWPo8Ys2aNbz11luiXUYQBEEQBEEQhBETSVyCIEw60Wh02EDuzEDuqcxisVBfX09fXx+JRKLktrqu09vbi8vlwu12T/vOYU3T6OnpGTaQ2263X9CB3Mlkkmg0SiwWG5MgbEmS8oKjTSbTkNDoTMOkJElDXpeZYzIMIxsYPTg4enCIdOb6VA72zgRsF1sqDdsuV+7vqxhd11FVlVQqRSqVIplMoqoqkiTh8Xjo6+srOmBc0zRCoRAej6fg8eu6TjQaJRqNZgeM2+32qgQEbO/czt+//vclt3GYHFzZemXVg2b3vbovW8wKsOKdK4YdRJqIJji65Sgnd57k1O5TdB7uJBYcvjhIUiTalrSx8l0rWbZuGVaHdXQHnyIdyF0OC+lB7pPETX92EwffPIihGxiawbP/+iwf/cFHJ/qwJicFcADRgVWSJOG3+fHb/CysWUhfoo/uaDfHteMEteIdb2MhrsXZ0bWDHV07eIRHAGh0NLK2aS2XNl/KZc2XFZ8xXBhC0zW2dW7j5ZMv8+bZNznQd6DifbjMLi5vuZybZ93MdW3XpUM3BEEQBEEQBEEQBEHI89RTT2WLqQHuvffesoqp4/E4zz//PL/73e946aWX2LlzZ8mQTkmSWLJkCffddx/33XcfNTWTL/CjVB/WcBM0Hg0c5bvbvsv64+tH9Nwei4e5vrnMdM/ErOS3NXsbvFz2gct47eHXADiz/wxbn9vKmnetGX7HJtLtoWNYey1JEm5LusB8BjOAdH9JTI3Rn+gnkAikl2SAcDJc/qDvKktoCbZ1bmNb5zZ+xI+QkFhcu5hLmy/lipYruLjhYmpqakgkEoRCobInh43H4ySTSTweDw6HY8THZ7UWbydXVRVN00Yd/C0IEykYDA77d+Xz+UQg9ySRmSw7E8RdjT5tSZKwWC2cip9iw7kNvH7mdXZ07UA3cr4/SJBUkiSVJCbdhEWzYNHLn+jXqlhpcjbR5GzCbXEP+kedX0qVZmS6hsfqo0oi/bksXuaCMKVMploKk2yi1d1Kq7sVo9GgJ97DufA5+gJ9JGKla/LynseQcKgObJKNuBIfNpw7lAyx6dwmNp3blF2nSAqzvbNZ4F+QDepe4F9QtTCYr33tazz77LPZOqtPfvKTvP7666Pe73hyOBxEIpGidYHRaBSn04nJJMrbBUGYmpJakl8e+iUP7nqQ0+HTQzc4H8Rt0S2Y9cJfgp1mJzM9M2lyNKHIo2z3yEzAk3s5mUvQM+11hf7ZhSYGGv38SEOfI3F+yQR0mynajihJEvN882h1tbKjcwenwqeGfYpAIsBfv/zX3DbvNr5wyRdwmEfediYIgiBMX8Oed8uk29SspD8TU+kllUxxZOsRju44ytEdR+k81omhl2jYk6B+Zj2rbl7FyptX4nCf/1zKhMwlB7bLC3jLTOpxARvSV7nvDFuf2sqa28roqxQmjNfqZW3TWpbVLeNI/xGOBI6Q0IZpP5IgoSRIySkcqgOTnm6zCCQCvHzyZV4++TKKpDDfP5/ldctZUruE+f75APT39xMOh3G5XCKcWxDO0w2dN8+8yU/2/oTXTr824v3YTXZme2cz2zu7uhNZTSGKrDDLO4t2TztnI2fZ27OXvkThSc8Lebvrbe79zb2sa1/Hp1d/mhnuGWN4tIIgCFNQpq0wE8Y9WWUCw3O73mTSNZITPNFSnb2O62dez2unXyOSKp0d88jeR9jfu58Hrn2AWnvtOB2hIBQ22WrInU4n3cFuTgdO0xXtojPWSU+sJ73Ee+hL9HFOPUdELv13NhEcJgd+m58aWw21tlpq7eml3l5Pvb2eBkcD4WS44v22tbXxl3/5l3z1q18FYPPmzfz3f/83H/vYx6r9TxAEQRAEQRAE4QIhGVM59VIQhGknkUjQ29tbcvCixWKhtrZ22hQiGIZBKBQiHC6vschqteL3+6ftLG2qqtLb2ztsILfVaqWmpmbavA7KZRgGsViMSCRCKpWq2n4lScJsNmOxWDCZTJjNZkwm04T9fAsFdWeu564b7wDv3DDyUqHbU4mu6ySTSZLJJOFwmO7u7pI/U4fDgdPpLHv/ZrM5G9A9kmCMk6GTfOjZD9Eb7y3+HLKZ62dcj8da3ZB+XdP593v+nZ4TPUB6oOenHv0UtW2lO/T2v76fRz/36Kie213v5p1/+U6W3rC08gcbpDt7y8tGSRfjjjL/u5i+s318645v5a37yHc/wuyLZw/72J/+3U/Z87s92dsf+MoHWHL9kqof47RRRgi7YRiELWE6E510RbvojnUT1+LjcnilzPPN45q2a7h+xvVcVH8RsjS13kfHw77effzy0C957uhz9MR7qrZfj8XD7fNu50NLPiTC0QVBEARBEARBEAThPE3TWLp0Kfv37wdAURT27dvHvHnzij5mz549fPnLX+bpp58uu79nMJfLxde+9jX+9E//dFL1fXR0dKBphUeV1NTUYLMNnfCrM9rJ97Z/j18e+iWaMfyIlI5fdND1q67sbVu9jfsevY8Ge0PJn0WkN8K/3fFvpBLp/hKn38lf/ugvsdiKBJVO0tBPTdcIp8IEE0GCySChZCi9pEL5oawTwG6yc1nzZVw34zquabsGJ06CwWDR10QhNpsNn8834v6Trq6uon1iPp9vVKHfgjCRotEo/f39JbdxuVwX9CTVk4GqqtkQ7mQyWbW+6ageZWdgJ5u6N/HGuTcqb/s3wKJbsGpWFGNoH6xFsdBgb6DR2YjP6ptU3y2yJNITB1uY3KGAgiAMMZVqKRLJBN393QTCAfoSfcTUcmc2B13SywrnLofP6mOhfyELatJh3a6Qi3UXr8vb5qWXXuK6664bdl8f+MAH+NnPfpa9/cQTT3DHHXeM7gDHWTwep7e3eB2QzWablJNWCYIglJLQEjx58El+uPOHdEQ78u88//3drJuLBnED1NpqmemZSY1thLXRCkPDMqc7g4HQ0MwyFiXFCgMB3SV+NWfCZ9jasbXsmrx2Tztfu+ZrLK0dQZ2oMGrLH1qed/uGmTdQYxPfQQRBmHgjOe/uPNrJKz96hQOvHyAZK3cQQT6L3cJNf3wTa29dW953EYmBcO7Mkgl7m2LtfS/990v8/sHfZ297m7x8+slPD/u4SH+Ef3vvv5GKn++rrHHylz/7Syz28idVFCaWpmucCp/icP/hkmOWcpk1Mw7NgWQUf6GbZBPzfPNY4F/APN885vjm0ORqwu12i3Bu4YIVSUX41aFf8ei+RzkWPDbi/dTaa5nvm0+Lq0WMPRrEMAw6oh3s6dlT9ntahlk286ElH+Jjyz+Gy+IaoyMUBEGY3JY/tBzJkLDoFiyahdX1q6mz1030YVWHQn5I9ziLq3HeOPNGWZ9PDY4GvnHdN1hRv2IcjkwQhprIGvKvfvWrvPtD7+ZI8AiH+w9zNHCUY8FjnAieIBgL4k65S+4jaAmiSxNbczwSg2vIPY0e/u/T/5cWVwstzhZaXC20ulqHTHLa3d1Ne3s70WgUgMbGRg4fPlxRDoogCIIgCIIgCEKGaaIPQBAEIUPTNPr6+koOYlQUZdoFMUuShMfjwWKx0N/fX3K2O0gHl3d1deH3+7FYplehjqqq9PT0DDuQ32KxTLvXwXA0TSMSiRCNRod9jZRDkiSsVisWiwWLxYLZbJ5UP89M8LXJVPqrimEYGIaRDerO3M5czw3tHnwJZP/NuZeyLA+5zIRwT6afUbXIsozNZsNms+HxeHC73fT09GSDugf/PUaj0WyAezlSqRSpVIpgMIjVasVut2Oz2coK3wgmg3zqxU+V7GiTkLi85fKqB3IDbH92e7aYFWDpDUuHHURaLaGuED/7u59x9sNnuelPbyr/gTrpYOZy81DsTLoAmoyr7706L5T7d//1OxZdswhZEUVTBZkZmH27CEmScKfcuN1u5vrmYhgGUTVKX7yP3ngv/Yl+AokACa3ETsbAof5DHOo/xIO7HqTOXseNM2/kHbPewcUNF6PIE9DLP0kktATPHX2Ox/Y9xq6eXWPyHMFkkIf2PMQj+x7hjvl38IkVn5g+xSKCIAiCIAiCIAiCMEI//vGPs8XUkA48K1VMDfDGG2/w2GOPjep5w+Ewn/zkJ1m/fj0/+9nPMJsnvuEulUoV7bORJGlIO3E4GebBXQ/y8J6HRzUZnFWx0uhoHObgwGlycvEfXMxbv3wLgEhfhA2/3MA1H7xm6PZm0oHck7CZX5EVvFYvXqs3b32m/S6UDBFOhgmnwtnLSCqCMSYJQ/liaoyXTr7ESydfQkJiVcMq1rWv48q6K7FptrLCaePxOF1dXfh8PqzWymeItFqtRUO5k8mkCOUWpqRkMkkgECi5jdVqxe0uPZBGqD7DMEgkEiQSCeLxeEWTEJSS0lMc7D/I2/1v81bPW+zp3zP8g0qRIKkkSSpJFF3BrJtx4aLR3kiDswG/1T95gwhEGLcgTHlTqZbCarHS2tBKa20rxCGWiNEf76cv3kcgESCiRoo+VjZkHKoDm2QbdTh3f6Kft869xVvn0ucuya6hAWl7e/ayKrFqyHnBYF/84hfzQrn//u//nttuuw1FmTp96zabDavVSiJRuDYhMyHGSM4fBEEQxltCS/DEgSd4cOeDdMY6B+4wwKybs2HcxUhINDmbmOmZidtS4TlgJkAls1yI368lBsJkMnTS9ZPq+aUaTWiZwO84A+HcBcqbW1wt1NnreLvr7bJC5o4Hj/OhZz/EX138V3x4yYcn73mcIAiCMK5Gct59cudJdr0wulrjZCzJs//+LIe3HuYD/+cDKKZhzjNzJ8cYLBPYnRvcLeUsmdtTnNPn5OI/vJi3fnq+r7I3woafbuCajxToqxQmJUVWaPe00+5ppz/ez9HAUU6ETpDSC/dNAqSUFAE5gF2zY9UKt12ousq+3n3s692XXee2uNPP5W1ndv1s5jfMZ65/7rBtQYIw1R3uP8xj+x7jqcNPEVWjI9qHLMnMcM9gnm8efpu/ykc4fUhSuo2h0dHIucg5dvfspj/RX9ZjU3qKH+36EU8deoq/uPgvuG3ebeIcVRCEC44r5cKkT9P4pcy5W4KBNlUz49aubDPZuLbtWjad28Sp8KmS23ZGO/mj3/wRf73mr/nQ4g9Ny1wHYXKbyBryT33qU3zhh19g5idnIpkGvfZlSCiJouehAA7VQdg8slDwySSmxvjx7h8PWV9rq6XN3Uabu42Z7pnMcM/g9g/dziP/+QgAHR0dfOtb3+KLX/ziOB+xIEx+mRymwXlMg69nlsGPyb09eL/FFMpyylzmLplcp8HrMov4LiAIgiAIwniZpq1CgiBMNYZh0NfXVzJsWJIkampqygpynYpsNht1dXX09fUVHVieoWka3d3duN1uXC7XtDiJTCQSw74GAMxm8wUVyJ1MJolEIsTj8bKCDUoxmUzZQU0Wi2Va/AxzG1WE6nC73aRSKeLxdGCKqqqkUikSiUT2vSkUCuHz+Soe0JcZwC5JEjabDbvdjtVqLfhaTOkpPvvyZzkSOFJynxc3XkyDo6Gi4yiHpmq8/ODLeeuu+tBVI9qXt8nLjOUzqJ9VT117HU6/E6vDiq7pxMNxuo52cXTrUQ6+eRBdzX8PfO3h17A6rVx979XDP1GKdCB3OSTAwYTMalyu5gXNzL1kLoc3Hgag+1g3b69/m5V/sHJiD2wys5IucB46fjdfFHCApEg4zU6cZidt7rbs3Qk1QSAZIJQMZZdM0M9Y64518/j+x3l8/+M02Bt45+x38odz/5CFNQvH/Lkni0AiwGP7HuN/9/1vWbN/V4Oqqzy+/3GePvI0f7riT7ln8T2YZNFcIAiCIAiCIAiCIFx4UqkUX/rSl/LWff7znx/RviRJYsmSJVxzzTVceumlNDU1UV9fTywW4/jx4/zud7/j0UcfJRrNH3T4q1/9irvuuouf/exnE96On2knLsRisWTb5lN6iicOPMF/bP8P+hJ9FT2HLMnU2Grooqu8Bxikw2/Od6VdcccVbHxqI4ae7kN544k3uOTdl2Bz2tIbSKTDuCc+47xikjTQfocz/z7d0ImkItmQ7tx2vJhabkNxZQwMtnZuZWvnViQkLmm4hJsabmJt/VrsJnvJx2qaRk9PDy6XC7fbXdFru9QkocVC/ARhMitnsnKTyYTf75/wz4ELRSqVIplMEo/HSSaTo+6Xh3QNzKnwKXb27GR773a29m0lpIWqPpjRbrLT6mql1dVKnb0OSZfSn5EpqhM8V00K6SBuE9MicEcQLlRTspYC0u9BTrBb7ditdppdzQAk1SSBZID+RD/BRJBgMohm5KeJ5YZzJ5QECTkxJu9jX9/0db4X/h6trlaW1C5hcc1iFtcuZkntEmpsNdntVq5cyc0338z69esB2Lt3L4888gj33ntv9Q9qDHk8Hrq7u4t+7gYCAerr68X3IUEQJq1iYdwm3YRFSwdxSyU+MBRZoc3Vxgz3DGwmW3lPqjAQQD1NgizHRCb4M9MeqDMQ0K1WYf+Zc67Mc5jPXz/PolhY07SGVlcrWzq2DDt5oaqrPLD5ATae28hXrvwKPpuvCgcpCIIgTFXVPO9GgvpZ9bSvbKdtURsunwuH04GaUOnv7Ofo9qPsenkXqUT++LX9b+7nia8+wQf+zwdGfk6WCewu4xjzlsHrKHBZ6no5Cm1f6NQ00xRRbP/n119x1xVs/PlGDO18X+X/vsEl78vpqxSmDJ/NxyrbKi6qv4gz4TMcCx6jI9pReGMJYqYYCTmBQ3OUFd4YSobY1b2LXd27ID1EJh2oZrPS6m2lxdVCi7OFJmcT9Y56GhwN1Npq8dv8uMzTY+yocOFIakleOP4CPz3wU7Z0bBnxfqyKlbm+uczxzin/3FlAkiSaXc00OZs4HT7Nru5dhFPlhTP2xHv4hzf+gcf3P84XLvkCKxtWju3BCoIgTCLTNpB7MIOBNk4YaHM2M6ZtzoqscGnzpbh73Ozt3VtyW9VQ+fqmr7Pp3Cb+8Yp/FG2mwrgZqxrytWvXork1epQe9p3bx65Duzi8+TD9G/oxkvmNEqFtIU5+/yQzPjljyHlgTIlh1s3IRuFclUwfVVIZLmRgauqJ99AT72FH147suuTCZLqP5nw7zpf/5ct4b/CytHUpc7xzaHW1osiTOExDECqQG6KdWQbfHrwuN3R7qsoEc+cGdSuKkl0yt0WAtyAIgiAIo3WBtAwJgjDZBYNBksnSjTt+vx+zeQqOmK+AyWSirq6OQCAwJIShkFAoRCKRwOfzYTJN3bf0aDRKIBAY9kTebDZTW1t7QQQwx+NxwuHwsH8XwzGZTNjtdmw227T/+xGqx+fz0d3djaqqmEym7OtI13WSySSJRIJQKITX6x1Rw5RhGMRiMWKxGLIsY7fbsdvt2WANwzD46ltf5c2zb5bcz0L/QmZ7Z4/o3zicnc/vJNgRzN5uW9ZG0/ymsh5rtppZcNUCFl65kPlXzMdT7ym5/dxL5nLZnZfRf7afX3/91xx+63De/S//8GUWXbOI+ln1hXcwKIRmWApgJ28gyGS1+rbV2VBuSBepilDuYWTqzEp9fBhkg7kLBbNbTVYaTA1DAu8HB/2Ek2FCqRDhZJioOvz3lkp1xjr5nz3/w//s+R8W+hdy27zbuGXOLfht/qo/12TQFe3iod0P8dMDPx2z4KThRFIRHtj8AM8efZavXPkV5vlLz+AsCIIgCIIgCIIgCNPNo48+ysmTJ7O3L7/8clasWFHRPtra2vjIRz7Cfffdx9y5cwtuc+WVV3L33Xfz1a9+lY997GP84he/yLv/5z//OQ8//PCEh6qVCjy2Wq0YhsErp17hgc0PcCx4rKJ9K5LCHN8cFvoX8qbzTfazf/gHaaQnJszJ4/PWe5m/dj4H3joAQDwcZ+tzW7nijivS1RB2pmVAkSzJuC1u3Bb3kPtSeopQMkQwESSQDKQvE4FhA4AqYWDwVudbvNX5Fh7Jw/X113P9jOtZ5F9Ust8g0/fl9/vLnvgzM9FsoX5ETdOyfRmCMBVkJivXtOJpJJIk4ff7L4g+8YmiaVp2MuFkMlny91GJvngfu7p3sbNnJ1t7tnIueY6UnCocGjMKXos3Hc7hasFn9eW/7yrnFxvpz8sU6c/PagTPjUSRkDpBEKauKVVLUUhmUHkKSIDFZKHeVE+9I70PwzCyk94EEoFsf7hu6MiGjF21p8O55QRxJT4m5xqnw6c5HT7N88efz65rcbawtG4py+qWsbxuOR/56EeyodwADzzwwISfP1bKbDbjcDiIRApPDq6qKtFoFKfTWfB+QRCEiZLUkvzi4C/4z53/SWc0HcYt6zIW3YJFtxQNQsiwKBZmuGfQ5mrDrAxT0ysz8NmlMC3buMaFTHqCIAsDAaEqo5/MSAcS5xfz+f3nNHc1u5q52X4zWzu2cip8atjdvXLqFd736/fxwLUPiNAzQRCEC9hozrszPA0eVvzBClbdsoqatpqhG+gwU53JResu4qY/volff+vX7HtjX94me1/by44Xd7DyppUj+WeUz2ByTC44uO5eB8rLLsVrP99XueF8X2UoztafbeWK919ROmg8s8iD7hMmnCIrzPDMYIZnBnE1zqnQKU6GTtIT7xmyrS7rhOUwJt2EXbWjGJUFfVk1K0TgeOw4B+WDJOVkwdeCSTbhtXjxWD24ze7sxM52kx2ryYpNsWFVrFgUCybZlF4kE4qsIEsyMulQIkmS8iYPMs7/Aeb2wxqGQfa/Av2zmX1IpPenSOnnUGQFRVKyz2+WzVgUCxbZglWxYjPZ0otiw2F2YFNsIihpGjrUd4gnDz3Jrw//mv5E/4j347V4me+fzwz3DBGgNwqSJNHmbqPF1cKxwDH29Owpu25kT88ePvybD/PuOe/m06s/nW1DFwRBEIaR+Y6fWTK3M/flnhMM/qplDFr084uRczkWMpMZxhnzgG5JklhatxSv1cumc5uGTJY82EsnX+J9v34f/3L1v7C2aW31D0gQBqlWDfndH7qbNe9eQ5eji22d2/h+1/eJRs6PwXcAF0HrRa00vr+RMz8+Q3BLMG8fwc1B+t/ox3/loLH0EkRNUVwpV9Hnt6t2UnIKQ5oMDS5jz1JrwX2Rm9D2EJCuIf+nf/sn6v6gDkhP9DPbO5u5vrnM881jgX8BC/wLaHQ0inNSYcIVCtTWNG3IutzlQpT5dw9X55sJ7s6EdWdykjLXRU22IAiCIAjDESMTBUGYcLFYrOjgigy3243NdmHMZixJEj6fD4vFUlZQdTKZpLu7G4/Hg8PhGKejrA7DMAgGg8P+/uHCCOQ2DCMbxp1KlZuuO5SiKNmQYxHELYyELMv4/X66u7vz3oNkWcZms2Gz2dB1HVmWsVqtJBKJEc+Op+s6kUiESCSSfe0+eexJfnrgpyUf1+pqZVndshE9ZznefCw/EHzNbWvKfuycNXOYs2ZOxc/pa/Zx9/+7m8c+/xgH3zyYXa+lNDY9uYl3/fW7hj4oRbqzs9wfv5n0IPwp0k+w8KqFuOpchLvTVa2dhzs59NYh5l0qgoJLsjEwY3UxwwRzF1Iq6EfV1XTQTzId8BNIBOhP9JPQigc3VWJ/336+tulrfGPLN7hx5o28b8H7uKTpkmnR6dUR6eCHu37Izw/8nKQ+OWYg3tOzhw8+80E+u+az3LnwzmnxcxYEQRAEQRAEQRCEcnzjG9/Iu/3xj3+87MfOmjWLv/u7v+MjH/lI2QHBdXV1PPnkk/zRH/0RDz30UN59n//857n77rsnLGxY07SSE4eeiJ3gm298kw1nN1S0X1mSmeOdw6KaRdhMFfT9JUm3hRaw5l1rsqHcABt+uYFL77wUxXFhDs40y2ZqbDXU2PKDFuJqnP5EP33xPnrjvfTGe6vSfhc0gjzV8RQvnH6BWY5ZrGtfx7Vt1+IwF+63TCaTdHV14ff7sVqtw+5flmXMZnPR12MikRCh3MKUUc5k5T6fT/TxVlnmMy0Twq2q1Umojqtx9vbuZWf3Tt7uepsjkSOk5NRAaEaVPoZkSabeXk+zs5lmVzNOc5nhpDKQeZvNBM/lLmMx9irz784M1Jy+5R2CcMGaMrUUwzGTfp9SSQdpnh+3JklStk+8xdUCDExeHUqGBpZUCFvSRkJJh3OP9YDWM5EznImcGQjq1sDmtxHvS58k7dy5k+d++xzvfMc7x/Q4qs3tdhOLxYoOHAyFQtjt9mldLygIwtSR0lP8+vCv+f6O73M2chYMsOgWrJq1rOA9m2Kj3dNOi6uldKCYCfF9eixJDPx8beQHdI9mHHvq/KKQDuc2pZ/Loli4tPlSmkPNbOvchqqXPh/tiHZw33P38Ver/4p7l9wrarYEQRAuQKM57/Y1+7jmj65hxR+sQDGV+L6RM2GF0+7kg1/9IL/4yi/Y8dsdeZu98MMXWH798rInWb2QrfmDNdlQboANv9jApe+5tPKfXaGw7sFhfuLrwbiymWzM889jnn8e0VSUM+EznA6fpjvWnQ20BlBllZA5hEW3YNNsw07WM5hJN6WDvbGTUtL9DKqkZn/fqq7SE+8pGAw+VUlIOMwOnGYnLrMr2ybnsXjwWr14rV78Vj9+W3qpsdVQa6vFb/MjS+JkaTIJJAL85uhveOrwU+zs3jmqfTU7m5nvn0+9vV6cD1WRLMnM8c1hpmcmB/oOsL93/7AhqBm/PvJrXjzxIp9Y8Qk+tPhDw08wJgiCcCHJrY/IDeKu5PGVyNR9ZMK6M7Uf1TQ4oDvTp1rlj+U2dxsui4s3z7xJJFU6Y6Qj2sFHf/tR/njZH/PJlZ8Un0XCmBppDXlSS9I8o5lrPnIN0iUS6/vW8+zBZ4d9nMltYuafz+TUf52i//X+vPs6ftqB7zIfkpL/B6jKKgklkZ7kqQAJCafqJGwuc7axaaDmuppsKDdAz/M91N5ci6RIJLQE+3r3sa83f0I6t8XNAv8CFtUsYqF/IYtrFzPXO1e8xwijNlzA9uB1QvUYhoGmaUXDuzOB3ZmwbrPZnA3tFm0QgiAIgiCACOUWBGGCqapKf39/yW3sdjtu99Dwx+nO4XBgNpvp7e0ddsYmXdfp7+8nFovh9XqnxMDzzO9+uEHHABaLhZqammk7wMYwDKLRKJFIZMQDgCVJwmazYbfbsVqt4qRfGDWz2YzX6y36Hi3LMoZhYLPZ8Pl8xGIxYrFYWX/TxWiaxsuHXubBzQ/iltwklSRJOTlk8KLf5mdt09oxe50f336cjoMd2dtmm5klNywZk+caTDEp3PK5W/jW+76FoQ/8u/e9si9/IKlOumOzkrcMG+kC2ilEMSksX7ecNx8dKDDe+MRGEcpdjkwwd6nXyAiCuYsxyaZswWN294ZBTI3RG+9NF4HGeuiL9+UVoFYqpad47thzPHfsOdo97bx/wft5z9z34LP5RvcPmADnIuf44c4f8vODPyelj3wyDrfZTa29Fq/Vi8viwqbYMMtmDAxSeoqYGiOYCNIT76Er2lV28VxCS/CVt77C1s6tfOmKL2E32Ud8jIIgCIIgCIIgCIIwFbz66qvs2DEw2NzhcPD+97+/rMfeeuutfOQjHxlxiOp//Md/8NJLL3HixInsunPnzvHyyy9z0003jWifoxWPF07ADiVD/PTgT3ni7BPoRmXFoLM8s1hSu6RoWHNBBum20BLNJ/PWzMPpcxLpTw/QCHYF2f/WfpZcPz7tulOFzWSjydREk7MJSLffRVKRbLtRV6xr2EEuxRiSQcQc4UD8AKf2nOLRfY9ybdu1vGv2u2h2NQ/ZXtd1enp6cLvdZfUDW63Wov0PyWQSp7PMgFpBmECZ/uBSXC4Xdrtoix0NwzBIpVKkUimSySTJZHLYeotK9n00eJS3u97m7a632de3jxgxUnKKlJxKD0asErvJTpMz/Z7d6GjEJI+yBiQ3eC5j8IBN/fy6zGWpfeUGw2QWBREaKAjT3JSopaiERNFw7lyFJq/O9IWHk2FCqRD9Wj9dWhdhfZwGtirgvNRJ/LmB87a7v3A3dyp3sqphFasaVrG8bnllEyFNAFmWcbvdBAKBgvfruk4oFMLr9Y7zkQmCIAzQdI3fHPsN/7H9PzgROoGiKzg0Bxa9vEI8h8nBLO8smpxNxYPjxjDcRBiGcn6xkj4/SpH+XjDSMfAaEGPge4YFJFmi3dNOnb2OjWc3DhukqBoqD2x+gO2d2/nHK/8x7zuIIAiCML2N5rx7wZULWPmulaXDuAs5325469/eyrHtxwh0DJyfhfvCHNt5jLkr51a2zwtQwb7KN/ez5KoK200MBtpmizVrF2qbHUkAoFAxh9mRDehOakk6o52cjZylI9JBXIuDRHYM1EjDuSUkLJoFi2bBkAyScnp/mqRNu3MFg3RfdSQVoZPOsh+nSAq1tlrqHHU02BtocDTQ6Gyk0dFIk7OJZmczTc4mLMoUGzw1xcTUGK+ceoVnjjzDq6dfHXYColJMsolZnlnM883DZXFV8SiFwUyyiSW1S5jtnc3u7t0cCx4r63FRNco3tnyDJw8+yRcu/QJXtFwxtgcqCIIwWeXWXUxEfUTm+XNlajwyAd0q1ZugPRPQDWPShu2z+rhx5o1sPLuRc9FzJbc1MPjhrh/y2unX+MpVX2FhzcLqHIQg5Kikhjylp9jVvYsNZzaw4ewGtiS2UPMPNew27Ybeyp+75SMtRPZFSPUMFEqrAZXIvgiupUO/I8eUGGbdPOSc06JYsMgWLIqFOlsdskXGolgwy2ZMsil7qUgKsiRnLyVJQkLKy+0wDAMDA8Mw0A0dHR1N19CM9KLqanZJ6SlUXSWpJ0lpKZJakqSeJKElRnWuUC7XRS4Uj4IWTDempHpTBLcG8a4tXmcQSobY0rGFLR1bsuvMspkF/gUsqV3C0tqlLKtbxlzf3NHX7QlTWiboebhw7cxiGNX6IBaqLfM7SqXyB6VIkpQX0m02mzGbzdM230wQBEEQhOLEN39BECaMYRj09fWVPKk0mUz4fL7xO6hJxmw2U19fT19fH4lEYtjtE4kEXV1duN1unE7npA1mjsViBAKBsmbuslqt1NTUTNp/y2hkwrjD4fCIBwIrioLT6cThcIiTeqHqHA4HyWSSaDRadJtAIIDZbMbpdOJ0OlFVNRvQXWnI/NHAUb697dsYGCiGgl21Y8eOKqvZYjK72c6VLVeOaQP29me3592ef/l8LLbxK8jyNfloWdzC6d2ns+uCnUHUpIrJbIIk6cGg5ZKoSujyRFly/ZK8UO5DGw4R7g3jqhHFViVJgJ30IJ9xCuYecgiShMPswGF20OZuA0DVVbpj3XREO+iIdBBMBke8/+PB4zyw+QG+vfXb3DzrZj6w8AOsrF856b8znAmf4Yc7f8gvDv1ixGHc9fZ62txtNDubywqxanW1AumBgmcjZzkSOEJntLzi1d8c/Q3HAsf49g3fzgY2CYIgCIIgCIIgCMJ09OMf/zjv9rve9S4cjvLCo5uaRnfObLfb+dM//VO+8IUv5K1//vnnJ00ot2ZorD+2np8d+Bm9ei+6qfxkmlZXK0vrluKxeCo7iEyIzTBPJSsyi65YxJZnB4qjtz+7XYRyD0OSJFwWFy6Li3ZPOwCRVISOSEe6/S7aUXFBfEpJEZSDqKrK+uPrWX98PasbV/Oeee9hoX/oYJhQKEQqlcLn85Xs57JYirfRl9OHKggTLZlMFg2bzLBarRfkZOWjpWlaNnw7E8ZdzYENPbEe3u5Oh3Dv7N5JMBUkKSdJySlUk1q1wYYSEnX2umwQt8fiGfv+jkIDNgcb/KOc3F0wgiCMsUldS2EZRQ3J4HDuOMMOVs/tC2+gIbs+JaUIEqRf66c/0U8gESCQCJQ9cXIlvGu89Dw3EOrZ93YfL+95mddOvwakA1aW1i5ldeNq1jSuYVXDqkkZauNwOIhEIkVrjKLRKE6nE5NJlLsLgjC+DMPgdyd/x3e3fZfDfYex6BY8mqfsUD2n2ckszywanY2Fw7gznz0iiHvyyAR0w0BAd4qRhdgYpOs8k6R/19b0a+LaGdeyt2cve3v3DruLF068wMH+g3zjum+wwL9gBAchCIIgTDWjOe92146ubdlsNbPm9jW8+P0X89Yf2XmEuVfPHZjcLxP0NtIJLKapgn2Vz2+vPJS7HJng7kK/g2ITKYqhZlVnUSy0udtoc7dhGAahZIjOWCfd0W66Y93EtfiowrkBJEPCqlmxalYMySAlpScIVWUVQ7pwA540Q6Mz1klnrJM97Cm6XZ29jhZXC63OVlrdrbS6WmlztzHDPYMmRxOKPEUHWE2gaCrKa6df4/njz/P7U78npsZGtT+X2cU83zzave2Y5SrOfCsMy26ys6ZpDXN9c9nRtYPuWHdZjzsWPMbHn/84N868kc+t/Vx2nJIgCMK0ltuPqDD52nIl8ts1IX2ukAnU1qhOSPcYBXRbFAtXtl7J3t697Okp/t0uY3/ffj74zAf5+EUf56PLPyq+QwhVNVwN+enwaV4//Tqvn36djec2Ek4NTNgteUf3xyBbZGqur6HjiY689eHd4WwotyIpuMwunGYnTrMTl+zCnXJjNVmxKullSK2ZkwnPltANnYSaIK7FSWgJ4mqcuBYnrsaJqTFiaoxeeQRJ5jkkWcJzsYe+l/uy6/pf6y8Zyl1ISk+xu2c3u3t28zN+BqS/Oy+uWcyK+hXppWEFdfa6UR2vMLEMwygYtF0seFuEbE9/hmFka49zKYqCxWLJhnSLoG5BEARBmP5ElbIgCBMmEAgMOSnJJUnStA1jroQsy9TW1hIOhwmFQsOetBuGQTAYJBaL4fF4sFqt43Skw9M0LXts5bDb7fh8vmn3GjAMg1gsRigUGnEYt9Vqxel0YrUWaCAVhCryer0FG5EyDMOgt7eX+vp6ZFnGZDLhdrtxu92kUimi0SjxeHzY13pvrJevb/w6CW1oeIZJN2HSTbhlN2sa1mDTbemOwTFos0olUux5Kb/zbvF1i6v/RMPwNfnyBpICRHuieFyeyjpBzYCNvM5NXdfpONhB59FOwr1h1ISKxWGhZWEL7Svbh91luDfMqV2nCHWHiAVjWOwWfC0+Wpe0jrqgt5C2pW14GjwEO9Phzbqm8/Zv3+aKu66o+nNNO5lg7ijpDvRiDCDCuHSwmWRTNlCC+nTIz9nwWU6HT9MV6xrRPpN6kqePPM3TR55mvn8+75v/Pm6de2vlIU9j7EjgCD/a9SOePvw0qlH57L52k53Z3tnM8swqK4i7EEVWsgXA/Yl+dnfv5mzk7LCP29u7l3ueuYfv3fQ9MZO4IAiCIAiCIAiCMC3FYjGeeOKJvHV33HHHuB7D1VdfPWTdyZMnq7Z/wzDYtm0bO3bsoLMzPVlXfX09c+fO5fLLL88LPdY0LS/oeE/PHh7c9SAnQ+njSZqTwz5f7HgMS4+FmkQNqqRywHOA+ln1tC5tTU8+OJwk6TC8Mi25cUneQHcxud/IOM1O5vjmMMc3B93Q6Yp2cSZ8htPh08S18n4hhmQQMUewalbsqp0tHVvY0rGFhTULuX3e7UMm1ovH43R3d1NTU1M0aM9isSBJUsF+Ul3XSaVSmM1ikI0wOWmaVtZk5X6/X/T7DiNT/J4bwj3S/vZiUnqKvT172d61nR2dOzgVPoUmaaTkdNiFZqne87nMLhodjTQ6G2lwNIzppMQjJl6SgiCcN6lrKQJRPPWj7xvWDZ2OI+drKTrDqDEVi81Cy/wW2peVUUvRH+bU3lOEekLEwjEsLgstbS2sXbIWySNlQ7r7E/109pU3gXIp9rl2TDUm1N7zfc8a9L/ZT9070wNAVV1lR9cOdnTt4MFdD6JICktql7CmaQ2XNl3KqoZVI+53riZJkvB4PPT2Fh5om6mFrKmpGecjEwThQrbh7Aa+teVb7Ovah1Wz4tXLH7TvNDuZ7ZlNo7Nx6DmeCOKeOjJBNlbSYTMpBkJnKpUJ9zalgzWW1i2l3lHPxrMbh21vOx48zoee/RD/cPk/cMucW0Z4AIIgCMJUMBnOuwuNIwh0BopP7qczENSt5yxFmsINw+DsobN0HO0g0h8BwOF1UNNcQ9vitvL6Dytw9vBZuk90E+oNoakadred+pn1tC4ss6+yQkuuGtRXufkQ4f4wLt849lUaDASn58qEdSuDLsV30qqQJAmP1YPH6mGebx6GYRBJReiN99Ib76Uv1kckFsGsmlGMkQ3WkAwJi2HBoqf79DP9FqqsokrVm0B0OumOpQPS3+56e8h9JtmUDeme6Z6ZXjzpy1Z3qwh3zHE2fJZXT7/K70/9ng1nNpDUh68VGU6zs5m5vrk0OgqcNwtjLzO5A+C3+Lm25VpOh0+zq2sXUTUKpCcTTm96fkMp5zrwu+O/4/VTr/PRiz7Kfcvuw6pMnvHrgiAI1WBIBkk5iWbXoPrDpceeDFjOL5A+P8gN6R6tKgd0S5LEktol1NnreOvsWwUzBvKeXlf59+3/zvPHn+dLV3yJZXXLRvbEgpCjUA35e25/DxvObuCVU6/w2unXOBo4OqbH4Fg4tO/eFXFxTds1uC1ubIpt6PfnGOn+h2LigIP09znD4OyBs3Qc7CDSd75dxu+gprWGtmVj0C5z4Czdx7oJdZ9vl/HYqZ9VT9vSoc8l+SXOcCZ722qysrxuOdFUlIgaIZJKL7pRfJY471pvXih3aFcINahi8ozu3xVTY2zt3MrWzq3Zda2uVlY1rGJVwypWN65mjneOOLeZYIVCtgsFbWduC0I5NE0jFovl5aOZTCbMZjMWiyUb2C0IgiAIwvQxCUfzCIJwIYjFYkSj0ZLb+Hy+ogOvL0Qulwur1UpfXx+qOnxlbyqVoqenB5vNhtvtntCTOcMwiEajhEKhshspPB4PLtf0CkowDIN4PE4oFCrrdziYJEnY7XacTqc4ORfGjSRJ+P1+uru7i/79appGf3//kAFwZrMZr9eLx+MhmUxmG50Ghw7E1Bhf2/w1ehOlZ7JcVrsMt+IeCGJRGOi0q1JA95FNR0hEcjrtJJh7ydzq7LwCanLoe4RNsQ0byP3Ne7+ZLn4979o/vpbr/+R6ACJ9EV5/5HW2P7udaP/Qz+CFVy8sGcp9ZPMRXn3oVY5tP4ahFTgQCdpXtHP1R65m3qXzSh9oBSRJYs7aOWx/Znt23d6X94pQ7nJJpDvNhgvmhnQwt4NxPUt0mp3M889jnn8ecTXO6fBpToZO0h3rHtH+DvYd5F82/gvf2PINbm6/mdvm3caapjXI0sTMPGkYBls6tvA/e/6Hl0++nFeIVq4aWw0L/AtocbVU9d/hs/q4svVKOqIdbOvYljczcyGdsU7ue+4+vnvjd7m48eKqHYcgCIIgCIIgCIIgTAYvvPACwWAwe1uSJNatWzeux1BXVzdkXU9PT1mPnTVrFsePH8/e/od/+Afuv/9+AKLRKN/85jf593//d86eLTw5l8vl4p577uH++++nqamJeDzdCNsX7+PhvQ/z+unXs9sakoEmF25o0lWdnud66H+ln0Rn4cERFoeFi95xEdf80TXFg/MMyg/klgEHtK9ux2Q1oSbSbau6prP/1f2sfs/qMnckDCZLMo3OdFjsyoaV9MR7OBk6yanQqWEHvwAklAQpKYVTdaIYCvt79/PVjV9ltnc2d8y/gzWNa7KF6KqqZoO5cwPiMyRJwmKx5IXF5z1XIiH6zoRJyTAM+vr6SgZHZ/riZHli2rEnM1VVs+HbyWQSVVWHncR8JM5GzrK9czvbu7azt2cvcT1OSkqHcKsWFUOqznOaZTMNjoZsELfT7KzKfgVBEMbDpK6lcNmGfdw33/tNAudGWEtx+cKSodxHth3h1cdf5djbxzD00rUUSy9dCkCf3McOduRt5jK7kJDK7lOWJAnXUhf9r/Zn1wU3B7Oh3INphsbO7p3s7N7Jj3b9CJNs4qK6i7is5TIub76cZXXLJmyCCJvNhtVqLfp9Px6PE4/HsdmG/10LgiCMxs6unXxry7fYfmY7Vs2Kyyi/htlpdjLbO3toqFgVQkmECSSR/v2ZSYeMZgK2RzJePxNWo0CDpYF17evYeG4jHdGOkg+LqTH+9tW/ZVf3Lv56zV+LgEBBEIRpajKcdzu8Q8OfYoFYgS3Pk88vpgLn3fddy/X3XQ8GJKNJNvxsAxt/tZFwT+FaZYvdwvLrl3Pdh67DXTPy1Ds1pfLmz99k62+30ne2r+A2FruFi264iGvuvgZP7egn+cpoX1agr/LN/az+g0nQV1ksrDs3pDuzCKMmSRIuiwuXxcVMz0wg3V8UToUJRoNEo1Hi8TjRVJSoGkXTK09GVAwFRVOyv1NN0lBlNX0pqeiSLs4/SlB1lePB4xwPHud1Xs+7T5EUWlwtzPTMpN3dnr70tNPubqfZ1Tw5J1itomAyyNaOrbx19i3eOPMGRwJHqrJfq2JllmcWc3xzRP/YaGUmwBhuocD1AiQk2qQ2muuas38XmlHe+9Ijbz3C8zuf54+W/RFrm9YiyzKSJOVdDr6eua0oSva6IAjCZJGSUySURHbSE0Opfn3KhMidgFBnoJ1ypJMQ5srdzygnpWxwpNtMN53bNGybKcCBvgPc/czd3LXoLv581Z/jskyvTBJhfA2uIUeCb4a+SXL96CelKcVpduK3+vHZfKDDUfKDv5WYQoOjofgObIAK3/zwoGyJe67l+g9fDxokg0k2/GIDG5/cSLi7SLuMw8Lym5dz3Uevw107ynaZR99k61Nb6TtTpF2mjBpyRVJYWLMwb51hGMS1OJFUhHAyTDgVzl6GkiEcCxxIFgkjef69W4Pg1iA111V/8u/T4dOcDp/m6SNPA+mx+qsbV7OmcQ1rm9Yy3z9/wjINpgPDMDAMY0jAdrFF07QxqSmdbnLP1UpdzyyZxxS6nblejszvJvN7zb2eu+i6PuR67u95slBVFVVVs0HdsixnA7ozId3iPFcQBEEQpq7p3QMkCMKkpGkagUCg5DZOpxO73T5ORzR1mM1m6uvrCQQCw4aaZ2QGpdjtdlwu17gPSM+EUKdSpabZGyBJEj6fb9r9/iv9OeSSZRmn04nT6RSDsYUJYTKZ8Pl89PYWD82Ox+OEw+GCYfqSJGG1WrFarXi9XuLxOLFYjEQigaqrfHvbtzkWOFbyGBb6F1LvqM9fmSkMTJAt6MREuoNwhG1VB988mHe7cW5jwQLTsdZxKL/T0N/sx2IbGkRSrr2/38uv/vlXxEPlJtkMSCVSPP3/nmbHsztKb2jA8e3HOb79OBf/4cXc8tlbRni0Q81ePTsvlPvUnlNEg1EcnvH/3UxJlQRzRwE76U7wcWYz2Zjrm8tc31wiqQgngic4Hjw+bFh0IQktwa+P/JpfH/k1ra5W3jX7Xdwy5xbm+sanQD2cDPPs0Wf56f6fsr9v/4j20ehoZFHNIursdWPaAN/oaGRd+zp29eziYN/BktuGUiE+/vzH+fYN3+bylsvH7JgEQRAEQRAEQRAEYbw9++yzebeXL19ObW3tuB7DuXPnhqwbPBFipbZt28Ydd9zB0aNHS24XDof5wQ9+wGOPPcZTTz3F/IXzs20bMTV/sH1SLlxkHjsW49QPTpE4WzqsORlNsvkXm9n5/E5u//vbWXT1oqEblVsbayE9WEQCk8XEjOUzOLp54N968M2DIpS7SiRJos5eR529jhX1K+iIdHA8eJwzkTPoRvFiV13WCZlDOFQHFj3dxn00cJQHNj/AbO9s7lx4JyvrVyJJErqu09PTg9frxeEY2vZbKqQvmRzbwQ+CMFLBYHDY16fP5xOh8oCu69nw7czlWBXTp/QUu3t2s61jG9u7tnMucg5VVlEllZScQlO0qoRVSEjU2muzQdx+m18MuBEEYcqatLUULX4s9jGupTCR7kNPktffnkqkePo7T7PjhQpqKd59Mbd8rnAtxeqm1cycN5NAIkBfoo/+eD99iT4CiUDRoG7X4vxQ7uiRKGpYxeQavjRc1VW2dm5la+dWvrf9e7jMLi5tvpQrWq7gipYraHO3DbuPavJ4PHR1dRW9PxgMYrVaxeA1QRDGxJHAEb6z6Tu8cfwNLJoFO+XXLztMDmZ7Z9PkbBp4j1IYCCARpwDTh0y6LdJKOmgmyciCazQgBlbZylUNV7EvtI/dPbuHfdhP9v6Efb37eODaB6i1j2/btSAIgjD2JsN5d7h3aL223TPCcV0SYIKz+8/y+P95nP4z/SU3T8aSbHl2C7te2cVd/3wXsy6alR8kCoVDRXOunzl4hif/5Um6T3YP+1ybn9nMzpd3cvvf3M6iyxcNbQ+WGL6NeNCpusliYsbiGRzdntNXueng5AjlLiYzHid3mJsyaBHfZ6tCkiTcFjduixt8pMMQk2AkDVJqipgaI67FiWtxElqChJYgqSZJ6klSeoqUlio5kVs2pDuHJmlokoYu6XnXL+iw7oLz6eX/QHRD51TwFKeCp3iDN84/LP1Ak2yizdXGDM8MZrhnMNMzkxnuGbS522h1tWJVrGP+T6gm3dA5ETzBzu6dvN31Ntu7trO/d3/ZkwaWo8HRwBzvHFpcLaKPbDgG6fcGPee6UeD6GFFkhTm+OTS7mjnUd6isMFSAzmgnX9/4dVY2rOSPlv4Rzc7mip43E9SdCenOXGauZxbRLiwIwniImCMTfQhjTyZdc2kh/bmSCdWuPPpiqMyEhufPBzFT8Vh/m8nGVa1XcbD/ILu6d5WsS4T097T/3fe/rD++ns+s+Qy3zL5FfGYIFTEMg8P9h/nXh/81b72tzUbSVqLmL/O9rIKXm1k2U2urpcZeQ40tvViUgVqHY8ePDXnMsO0yEpTq0jp76CyP/9Pj9J/rL7mbZDTJll9uYdcLu7jra3cxa9Ws0s9bwJn9Z3jy/ifpPj5Mu0w5NeQFSJKE3WTHbrJTZ8+fqNwwDOJqnMDSAKe3nc6uj+6Mjkko92D9iX5ePPEiL554EUiHdK9tWstlzZdxWfNlzHDPuGDfm3KDlSsJ2hYKKzThUe7tYpMiTfWMqkKvHU3T0DRtyPXxDmjXdT2b6Qbp35HFYsFqtYqQbkEQBEGYgkQotyAI48owDPr6+kqeCJvNZjye6s02P91kQqttNhuBQABNK2/m4VgsRiwWw2az4XA4xnSQimEY2YDeSkKoTSYTfr9/Wg06Hk0Yt6IoOJ1OHA7HlG/oEKY+m82Gy+UiHC4ejhsMBjGbzVitxQuJJEnCbrdjt9vRNI1/fu2f2dS5CQWl6GMyRUslnS9KI0m6E0FhIKS7gj+fQ28dyrs9c8XM8h9cDRocePUA/Wf781YvvnJxeY8v8La+c/1Ofv6ln4+o+CaVSPHo3zzKkU1HKnrc1qe2Eg/HWfdn6yp/0gIGd+AYmsGRjUdYdtOyquz/gpAJ5o4x/GCgGOnXy8jHLo+a0+xkce1iFtUsojvWzdHAUU6FTw3bkV7I6fBp/mvnf/FfO/+Leb553DjzRq6bcR1LapdUtbAvoSV488ybPHv0WV468RJxrfIQfIBmZzOLaxdTYxv7zr4MRVZYUb+CRkcjG89uJKkX7zCOa3E+9eKn+M4N3+GK1ivG7RgFQRAEQRAEQRAEYSz99re/zbt99dVXj/sx7NgxNMitrW3kIWhvvvkm69atIxIpf7BIIBDg5nfczA3/cAOhmlDBbQqFcseOxjjxwAlSkfL7QhLhBI9/4XHu/Jc7y35MngITy81aNSsvlPvI5iNoqoZiKt7+LFROlmSaXc00u5pJaAmOB49ztP8ooVTh1wwSRM1RNE3Drg6MADgaOMpXN36VhTULuWvRXSyuWYxhGPT396Oq6pD+YouleINlMpnEMAxRuCpMKtFodNj3YJfLNe0mqy6Xqqokk8lsCPdI+tMr0RPrYWvnVrZ1bmNX9y6iepSUnEqHcVvUqoVQeCweGhwNNDgaqHfUY5anT+2DIAgXtgmvpQAOvF6gluL6MmspCqiolsJ8flGBBKSiKR790qMc2VphLcWvtxIPxFn3icK1FIqspAfh2gf6ijVdoz/RT2+8l754H73x3uzE1o6FgwLadIjsjuC91FvRcQGEU+G8gaKzPLO4svVKrm69mjVNa8Y8VMhsNuN0Oot+f1JVlXA4jNvtHtPjEAThwnI2fJbvb/k+Lxx8AVmXsVRQLGU32bNh3LIkp88pzKTrrUS57/SXqU3VSQfNJKm8PlMHKS6x2LqYuqY6NnRtIKGVnvBwc8dm7nz6Tr51/bdYWrd0RIcuCIIgTE6T4bz73MGhEwh7GkY+tu/kzpP8z1/9D6lYZf2HP/nsT/jof36U5gXlh3qe3nuahz//cOlJtwY/VyTB418631c5+GugBFRy+nn+e8CsNbPyQrmPbD+CZj7fV5kJUy21TAaZoO4MmYGAbjHpTPXIgA0kq4RFtWBJWfCqxdtzDMNA1VVUQ01f6iqacT54yNDQDC0dUGTo6KQDiLL/5YQRZdfJBoY0sCCRvo6RH0pfKKDeyN9X+v/082SeWzd0dCN9bJlLTdeyx5r9N+ja+adJP4lkSAPXkQreLrV9Zl12+0HrqqE/3k9/dz872Zn9eWV+FjX2GhodjTQ6G6l31NPoaqTBmZ64tcHVgNvizoYL54ZjjXUft27odEW70v36gaMc6j/Egb4DHOg7kG1nrCaHyUG7p51Z3lk4zc6q73/KGhy4Pfj6JGE32Vlev5zWeCv7e/cTSZVXc7S9czuf7f4st86+ldvn347NZCvrcZlANVUtPdAtN6A7dzGZTCK0WxAEYaQybdpmwEb1AroNCgd0l5lsJUkSC/wLaHA0sOnsJgLJwLCP6Y5184VXv8BP9/+Uz6/9vGg7vcAZhlEyCFjVVHZ17+KN02+w4ewGzobPsv2l7Xn78M314U2eP0czhk7mUw6ryYrP6sNv8+O1eXGZXenvLJldxck79zq3t0C7TJ1nIA+g2LmaiYJ1byf3nOR/vvg/pOIVtst8eoTtMn9VYbvMaGvIc0iShN1sZ8GaBfmh3Huj3Np+KyEtRDAZJJAIpJdkAFUfyayr5elP9PP88ed5/vjzALS6Wrm85XKubLmSS5svTU/YNQVk/pZy/6YGXw53fbwDkqeaTPtA7uREgycuGhywfSGSJCl7DlhK5vWnaRqqqg65HI/Ad8MwSCQSJBLpPl9ZlrMh3VarFZNJRH0KgiAIwmQmPqkFQRhXkUiEZLJ4yJ8sy/j9/gv2ZLASNpsNi8VCKBSqKFAhM8uSoig4HA5sNlvVQrBVVSUWixGNRssOC89wOBx4vd5p87tPJBKEQqGSr/diTCZTdgD2dPl5CNOD2+0mlUplG4EK6evro76+fthGLYCH9z7MT4/9FCwgGzIWzYJFtyAbA5V6tbZaFtQsqOxAc2fohYGOu2EKAcO9YfrP9Oeta5rXVNlzj0RmYEYKOo928tT/eyrvbpvLxuW3X156HybSHZ+D3jK6jnXx+k9ezxa+2b12lt6wlNmrZ+Ouc6OYFIJdQU7tOkUiOvT3+st/+mXBQO7WJa0sW7eMhjkN2D12In0Rzuw9w871O7OzmO753R5cNa7yfgbD8DZ6sXvsxIKx7LpTu0+JUO5KZWa9LSeYO076dTO242qHJUkS9Y566h31rNRWZgsBg8ngiPZ3qP8Qh/oP8YO3f0CNrYZLmi5hdeNqltcvZ75vft7MwsNJ6SkO9h1kW+c2NpzdwFtn3yKmxoZ/YBEtzhYW1y7Gb/OX3rBY4XWhotcKNTmbuLH9Rt44/UbJgoWknuQvXvoLvnfj97ik+ZLRPakgCIIgCIIgCIIgTLDOzk6OHj2at27FihXjfhwPPfTQkHU33XTTiPZ16tQp/vAP/zDbf9TQ0MCdd97JtddeS0tLCyaTidOnT7N+/Xp+9KMfEY8PFEIn4gl+/++/Z9UXVyEp+Y0NuqSjyfn9P764j0PfODQkkFuxKCy5bgkLrlyAt8mLLMsEOgIc2nCI3b/bTTKaxNANnvzSkyy5ekn5/ziFdBtXgXbepvn57bnJaJKuo11D1gvVY1WsLPAvYL5vPp3RTg71H+Js5GzBbRNKAlVScamuvIHP+3v3c/8b97O6cTV3L7qbNncb4XAYXdfz+g7NZjOyLBcsitV1nVQqVTK4WxDGUzKZJBAoPSjMZrNdMKGSuq5nw7czl2Nd4G4YBkcCR9jSsYUtnVs4HDycDuCWVFJKqmoVc1bFmg5TcKRDFezmCzNkXRCE6W3CailydB7t5KmvDqqlcNu4/M5haimKGHEtxfkAzl/+yy8LBnK3Lmxl2XXLaGhvwO62EwlEOHPgDDtf2kn3yfO1FK/sweUrv5ZCkRVq7bXU2muz65Jakp54D701vRxxHUENDxQARA9HRxTKPdix4DGOBY/xyN5HsJvsXNJ0Cde0XcM1bdfQ5Byb37/b7SYWixX9nhAOh3E4HGXVJAmCIJTSG+3lwa0P8uz+Z0lpKeQKkgWtipXZ3tm0uFrSYdwm0iGOYlTOhUkmXV9nIV2PlyQ/xLIcOtRTz7q6dWzq3URHsqNkDVhHtIN7f3Mv919xP++e++6RHrkgCIIwiUyG826AHb8ZOoHwnDVzRrSvYFeQRz//aDaQ2+l3svTGpcxaNQt3nRtZkQl2BTm88TDbn9mOmhw4r1WTKr/8p1/ysQc/VtbEu8GuYMHgp4r6Kq+roK+ykPOf3U0LCvRVniqzr3JwQHex4Nbxlnn+TFewRH5ItzhFH53cIESdgRDEQd8pJUnCrJgxD561erwMDn8rl1H6um7oaLqWDhvXVFJGKnuZ1JKk9BQpLX09qSfTl1oyHYY9CUjpRO5sQF8gGiAQDXCg+0DB7a2KFb/Nj8/qw2v14rV4cVvcuK1uXFYXbosbp82Jw+zAYXZgs9iwmq1YTBYsJguSJGEYBpqhZX82MS1GNBUllEyHzPUn+umJ9dAd66Yj2sHZyFnOhM8MOwHQaJlkE22uNmZ6ZlJvr79wx8Pmvm8PXibHy7ZsNbYaLm2+lFOhUxwOHM6G6Jei6iq/PPxLXjn1CvcsuYcrW66s2mtB07SS49UzAd2ZkO7cywv29SgIglCJ8QrozoRzl9Ge7rP6uGHmDezp3cP+3v1lPd22zm188JkP8u457+bPV/05za7yQ4WFyc0wDDRNy4b9FrquaVo2kHWwpJbk7a632XhuI9s6t+WNTzeCBonu/O/LrjZXxZP7ZL7v+21+/FY/dtOgnJhhJuTa8VyBdpllcyBa4kmLHGKwI8ij9z+aDeR2+pwsvXYps1afb5cxyQS7gxzeNE3aZc4rVEPef6KfpvlN1Dvqs+sNwyCqRtOTLiXSS1+8j7hWfqh4JU6HT/PEgSd44sATKJLCivoVXN12NVe3Xs0C/4JRf18dHJ5dbAHywrKLXc+9LVQmdxKu4QK2xeRC1Zcb3l1oPEFmQihVVUmlUtnrlWajVULX9WzGG6TPXa1WazYvTpbFDIiCIAiCMJmI8j9BEMZNMpkkFAqV3Mbr9YqZfSogyzJerxebzUYgEBh2RuBcmqYRCoUIhUIoipI9abNYLGUPXMkMnk0mk8Tj8Yqef/C/wW6fHoNTM6/zUqHFxZhMJtxuNzabTTSgCJOSJEn4fD66u7uLNi7puk5fXx+1tbUlX8e/PfZb/nXLvw48TtKJm+LEiaPoChbdQo1Sw7L6ZenBK6OR23EH+YWACumBETKc2XtmyEMb5zWO7rmLHY/GQMekDn3n+ti2fhtv/vxNUomBnkrZJHP7527HXVsklEEm3clZ5KNzz+/2ZK9f/O6LWfepddjd+e+3rbSy+NrFQx/70h52v7g7b53JauLWv7mVlX+wcsj28y+bz9X3Xs3rj7zOiz94EQzY+PONhQ9sBBrnNXJs67Hs7dN7ThffWCiukmDuBOnirwKB7xPBoliY75/PPN88umPdHAkc4XT4NLoxsgrj3ngvzx17jueOPQeASTIxwzODdnc7jc5G/DY/TpMTk2xCMzRiaoz+RD8dkQ5OhE5wNHCUlD7aygJocbWwpGYJPptv4P1hcAF15nq5fViZwlc5Z8m89w3zluo0O7lu5nVsOLOBjmhH0e0SWoJP/e5T/NfN/8WK+vEPKhMEQRAEQRAEQRCEatm0adOQdRdddNG4HsNLL73E1q1b89b5fD6uv/76Ee3vwQcfzBbD/tVf/RVf/vKXcbnyQ9/Wrl3Lbbfdxmc+8xmuvuFqzp4YCFKOnorSs6OHuovr8h6TkAf6PlxmFysaVvDS/S8NKaZuXtjMHfffQV17/uPblrax9IalXPcn1/HLL/+So1uOkowm2b5+e3n/MAvpkJsibVWNc4e2557ec1qEco8DSZJodDbS6GwknAxzsP8gxwLH0Iz8vgRN1giag7hSLhQjvz9yS8cWtnVu44aZN/D+Be8H0n0OmQmdJUnCYrHkhcjnSiaTIpRbmBQ0TaOvr6/koASTyYTP55uWfcKGYaCqal4I90jqCEYipafY1b2LzZ2beavzLTqTnaiSiiZp6c+QKpAlmTp7HY2O9Hue1zJ9Jh4XBEEoZtxqKQroO9PHtme28eajb2YHjML5Wor/ezvuupFNcDHqWorfFail+PNbWXnTyiHbz18zn6vvvJrXn3idF398vpbi1wVqKVKk+4XLKJGxKBaanc00O5vZtGBTXi2FdlzDptiqOmA0psb4/anf8/tTvwdgoX8h1864luvarmNp3dLR1/WcJ8sybre76OQmhmEQCASoqampyvMJgnDhCUQCPPb2Y/xi3y+IpCIVPdaiWJjlmUWrqzUdQpAJKBHjZAXID63JhHNXeCpsk2xcWXMlB/oPsD+6Pz2hVBFJPckXX/si+3r38enVn8Yki7EXgiAIU9lEnndnHN1ylLP78yddtbltzFo9a0T72/b0tmzN82V3Xsb1/9/1WB3WvG0y591X3HUFD//Vw/Sd6cve13Gog/2v7S8rlOnprz89+r7K32wf0b9zsFH1VeaGHpcyuL48t+58PBgMjINJIEK6q0km3ZdhoWRA94SoZBxDBWRJRlbkdNh4mV9pDcPIhnPHtTgJLUFCSxBX48TV9O24Fh/xGJOxlNASnIuc41zk3Igeb0gGOjq6dH7JvS7p6bDyceyyUiSFJmcTM9wzaHY2o8gX0BtAseDtyfeyGxVZkpnpmUmjo7HkBO2D9SZ6+c6277D++HruW3ofs72zx/hIB0K7C43pzg3sNplMmM1mTCaTCD8TBEEoplBAd4qK2zuHMEi3myYHPUeJrxCKrLC8bjmtzlY2d2zOC1Iu5ddHfs1vj/2WuxbdxZ8s/5P0uF1h0soEAGc+zwstxSZ1LiWcCrOtYxsbz21kR9eOohPVhI+Hh6xztjmH3b8iKfhtfmpsNdTYanCanSOuITu64yhnDw1ql3HZmLViVukHGoMuz9v2Yk67zC2Xcf0Hr8dqt6brr8/P9dQ6s5XFFy/mindfwcP/52H6zg5ql3lxP0uuWZI/QdPgCZskePprU69dRpIknGYnTrOTVndrdn1cjdMb76Uv3kdfvI/eRC9JLVn4yXJ+5pmJmgZP3JS9zAl4l5DYdXYXu8/u5vsbv0+9vZ7Lmi/jsubLWNWwCqtizQvRLjdsWxg75YRr594WJi9ZlrOZarl0Xc+GdKdSqez1sfj70jSNaDRKNBrNjovIhHSLrD1BEARBmHji01gQhHGh6zr9/f0lTzrsdvu0CWYeb1arlfr6eiKRCOFwuOKGRU3TiEQiRCLpInNJkrKzAcuynG0AzDRqZmaAGkkDZi673Y7H4yk7BHwyS6VShEKhokEApZhMJlwuF3a7XQzYFSY9RVHw+/309PQUfU/PhNN7PJ6C92/p2MIXXv1C0efQZA3DbLBixgrMknmgaK9a7Va5hYAZEnTsHRo+W9tWO7rnOV/w2Hmok5d+9FLev0FXdRLRBD1negh1D520wl3r5rbP3Mbci+cO3bfEQOdHGW8bl915Ge/8y3eWfeiaqvGbf/tN3jpZkbnzn+9k/uXziz5OVmSuvvdqbC4bzzzwTFUL72pn1uYNJO04XDwsWBhGJcHcmYG/dibNIDJJkqh31FPvqCehJjgWPMbRwFHCqaGdn5VQDZWjgaMcDRyt0pGWNsM9g0W+RXgVb7pYNkL1imZz3n+GyIR15xZAD3ofMctmrmy9ks3nNnMidKLo08TUGH/2wp/x0DsfYp5/XpUOXhAEQRAEQRAEQRDG19tvvz1k3YIFC8bt+ePxOJ/85CeHrP+Lv/iLEQcMZ9puv/rVr/L5z3++6HZd0S6+c+I7OP8/J9xPXttEx+sdQ0K5k0oSRVJYUruE+f75HN5wmAOvH8jbpmFOA/d+617snuJ9ft56L3f/89385LM/4fjO48O3I0qkB3iYS2/ma/ZhsphQkwONXqIdcfy5LC5WNaxiSe0SDvUd4nD/YZL6QGG6IRmEzCEcqgOLPqiw1dB54fgLvH76dd47/738wew/QNd1ampqssWwxfriEonEkPB5QRhvhmHQ19dXdHJbSLdx+/3+aVOAnzuRdyaEezwHe0RSEbb3bOfNzjfZ0LmBsB7GkM4/f5XKEDwWT3riAUcj9fb6CytUQBAEgcLfqWtnjKKWIkfnkU5e+u+X8tZlaylO9hDqKlBLUefmtr+/jblrC9RSVKhqtRRfuZP5q+enB5EX+BiUFZmr77wam9PGM98tUkuRBMKk+2/NFOzHLWRwLUX0ZJRb5txCJBWhO9ZNV6yLrmgXUTVa7j9zWPv79rO/bz//+fZ/Umev49q2a7lh5g1c2nwpVsU6/A5KcDgcRKNRUqnCQaTxeJx4PI7NZhvV8wiCcOHQdZ1QJMQz+57h8b2P0xvvrejxJtlEu7udGZ4ZmKymgfdoQSjGdH7RSQdVFs/WHkKWZBb5F+E1e9ndt5uwEkaVixf4/c+e/+FA3wEeuPYBvFbvKA9cEARBmChjed5djlQixTP/+syQ9Ze+/1JM5hF+8Tl/3nvTn97EVR++quSmNW01fPBrH+QHf/QDdG2g8Hnbr7cNG8p98M2DI+urbPBy9wN385NP/4Tj28voqyzTuPRVZroWCjVT54bCajmXY6lYSHcmoFs0p49MbkB37s+4muOqpihJkrCarFhNVtwUnrDPMAwSWoKYGsuGdcfUGFE1SkyNFQ3jm+wkQ0JBGTL5dS5d0tEkLRvUrUlaNry7GoHdZtlMs7OZFlcLTc6m6T1BUO4ECIOXC+zv0GqysrRuKa2uVvb37SeUHNpuX8j+3v184dUvcMPMG7hz4Z0Tdt5aLLBbluW8kO7MMh3GuguCIFRNbnh25nvpWAR0m3KWAt9Zauw13NR+E/t697Gvd19ZE7Ak9SQP7XmIJw4+wYeXfJgPLf6QaEOdQJnPY1VV8y4zS7X0xnrZ1LGJjec2srdnL5ox/L4jp4ZO4mpvKNym4DK7qLXXUmuvxWf1VWXy6lQyxTP/XqBd5j1VaJe55yauuj2nXSbBwNjy82paavjg//0gP/jUoHaZZ7ex5PJh2mU2HeTAG4PaZWY1cO8/nW+XyXxtlPIvvU4vd3/5bn7ytz/h+NsF2mUM0vkHuY8tJuexPl+Bdpn9Hel9FXqOQWyGjRZaaLG1gDV9bhlTYwQSgexS7nfhcoXDYV44+AIvHHwBq2Jled1y1jStYXXjajyWwjktwuhIklQyVHvwbZH/NP3JsozVasVqHah3MgwjG9Cdu1STYRgkEgkSiQTBYBCTyZQN6LZYLOK1JwiCIAgTYBr3eAiCMJkEg0FUtXgLq8lkwusVDZmjIUkSLpcLh8NBKBQiGo2OeLBr7gniWDCZTHg8nmkxOEZVVUKhELFYbPiNB1EUBbfbLcK4hSnHYrHgdrsJBovPKhsOhzGbzUMmWzjcf5g//92fk9KLv78oksKVrVfisDjSKzIddhoDHXbVLmAxyJvFE8BsNWOX7BBkIMQW8mfyHLSPIct5ka4Ie1/dW9ah1LbVsurmVay9dS1Wx6DBihIDxXVlvm00zG1g3SfXlbfxefte2TdkcOsl77ukZCB3rrXvXcvBDQc58NqB4Tcuk6c+v/MgGU0S6Y/g9A0/46xQgAQ4gDjpzutSMoHRDiZdYazVZGVhzUIW+BfQGevkWOAYp8Ony+pYnwhmw8wc1xzmuObglJ3p97Sx+bpVXOb9NLc/WWagcEEBpPQgr7VNazHJJo4EjhTdXTAZ5BMvfIJH3vUIjc6hMwkLgiAIgiAIgiAIwmR39Gj+BF0OhwO/3z9uz/+Zz3yGvXvz2w7b2tr4m7/5m1Ht953vfGfRQG7DMPjV4V/x9U1fJ5QMYZthw7vaS2BjILtN8FAQwzCy/RcpOUWrp5UVdSuwm9Ptvht/vjFvv5Iicfvf315ykDs6EEu3k7z3b97Ld/+/75KKl2ggUahowjh3nZu+MwNtvbnXhfFlVdIDMxfULOBw/2EO9B0gqZ1vjJQgao6iqzo2bWh/YUyN8cjeR3jxxIt8ZMlHuES/hNra2ryC18EyQcCiz02YSMFgkGSydKO73+/HbB5mloFJLFNHkAniLlUHMhZMJhOBVIANXRt45cwrbOrahJbb4F2lQIFGRyONzkaaHE3Zzz1BEIQL1eDv1GabufR3/gpE+iPsfbnMWoqZtay6ZRVr37sWq3N0wc9Q5VqKK87XUlhI17QkKRh6tfbWtemBqW+VqKXInWQ+M8i9RKV3oVqKaCCKy+fCZXExyzsLSE9k0RntpCvaRWe0k7hWeLKbSnXHuvn5wZ/z84M/x26yc1XrVVw/43quabtmRAPbJUnC6/XS3d1ddJtgMIjVahXf/QVBKCmVShEOh3n12Ks8uvdRToVPVfR4WZKZ4Z7BLN8szHZz+v1YvO0IlZBJt2taGQiYKVOzqxmXxcXbXW8TSoWIKTE0uXB4yIazG7jrmbv4zg3fYa5v9JOWCIIgCONvLM+7y7H+O+vpPpZ/DuZp8HDlPVeOar/zLps3bCB3RuPcRhZft5jdL+7Orjvx9olh+31G1Fd5ntlq5r3/8F6+e/d3ScWqV8w9oX2VMoX7VDPh3LlB3WMVKJsbIA0ipLsacoMQIf07VEn/HkVId0GSJGEz2bCZCo9b1XSNmBrLBnVHU+mw7mgqWrU2s4kiGzKyUbi4IhPUnQnrzoZ3Uzqw22vxpvvMnE3U2euqEjw4qRQK3R7L98mJNigQseB9uXJDDh0+LrFfwqnQKQ73HUbVh+8nNjB48cSLvHnmTd47/728c/Y7McuTo688d/LpXLIsZ4O6cy9Fe7QgCBe8sQzozh1jmxvQnfO1Q5ZkltQuoc3dxraObXTFusrafSQV4fs7vs9P9vyEuxbdxYeWfIgaW80oD1ooZHDodu71kWbeDMcwDM6Ez7CpYxObzm3iUP+hivcR784/B5AtMiZnuoNekRRqbDXUOeqotdUWPccYjfX/uZ7uE4PaZeo8XPn+UbbLrJyXH8idkSDdd5Hz1aZxdiOLr1rM7t/ntMvsLqNd5teD2mVkids/ezt2tz3/+3SBX79ZNvPezxWpIc+8L4yAu9adlxfSd6ZvxPuSkHCYHDhMDpqdzQCoukogEaA/0U9/vJ9gMlhW+Hs5ElqCzR2b2dyxGVmSWVizkEuaLmFt01rq7fVVeY7pqlSodqF1gjAcSZKwWCxYLJbsOsMwsueQmaWan2+Zz85IJIIsy9hsNmw2m6iPEgRBEIRxJEK5BUEYc7FYjGg0WvR+SZLw+Xzi5LVKZFnG6/XidDoJh8PEYrExa6islCzLuFwunE7nlD/pU1WVcDhc8rVdjCzLuN1uHA7HlP85CBcul8tFKpUqGUgfCASyxQcA5yLn+PjzHx92FsjLmi/DbxsUepM7220mUDbTaVelt7hARyDvtrvWPXAj85xjzGw10zK/hdq2Wsy2nCKTEYRxZ1z94atRTJVVL257elv+cdnMXPfR6yrax7o/WzemodwAgXMBEco9WjbSr6nEMNsZpIO57QwUdE4ikiSlAzIcjSS1JCdCJzgeOE5fYoJDlwwwGSY8kofZrtm0OdowK+bsfZOGTv7gLxNgBskksaphFRIShwOHiz68I9rBn734Zzz0zodwWVzjcMCCIAiCIAiCIAiCUD3Hjx/Pu93S0jJuz/3jH/+Y733ve3nrJEniwQcfxOkcXbvX/fffX3D9ucg57n/zfl4//Xre+rpVdXmh3FpcI94Zx95ox2lyMqdtDo3ugQm5Qt0hDm3IL2BfduMymhc2Fz+oFOlJ4s63i3jrvVz6h5fy2k9fK7y9hXRoTQVtop4GT97g9sC5QImthfFgls0sqlnEPN88DvUf4kDvAZJ6uiEqboqjSRpOtfDr/VzkHF/b9DVWn1jNfcvvY8mMJciyjK4PnZQvM+lwbgGsIIynaDRKJBIpuY3b7Z5SE1frup4XwJ1KpQr+/Y2VTGG72WymI97BK+de4cWTL7Kze2fVn8tv9WcDBWpsNdMvUEAQBGEUBn+ndte5i2w5dsw2My2LWqidWZsORq2CMamlyB2MngnnHjQQfd2frCsdyp0rMwA9s99M3UyOcmspnGYns72zme2djWEYhJIhOqIddEQ76Ip2VWWgaEyN8fzx53n++POYJBNrm9Zy48wbuWHmDdQ7yh8karFYcDgcRevyMnV7bvf4vxYFQZjcDMMgHo8TiUTY2bGTR/Y+wv7e/RXtQ0Ki2dXMnNo52Bw2MdpGGD2ZdI1ebjh3GXVjboubS5ouYVf3LnriPaTkFHElXjCc+2ToJPc8ew9fu/prXDvj2uoevyAIgjDmJvK8e9sz29j05Kb8lRK854vvwWIfXX9LpWMPBodyJyIJek72UDezruD2I+qrHMTb6OXS913Kaw8X6ascgUnZV5kJ685tUskN6s4sY6FUSPegkD2hTDLpfvSMzO8y93c6mcYpTEKKrOCyuAqOe8gN7I6kItmw7qgaJaENN+hmchsusDsT1G2z2Khx1FDrrKXB2YDdNA0mrzUoHr491UiDFrnI+szCoOujfnqJGe4ZNDQ2sLt7N0cCR8BIrweQDInsf+evy4ZMwkjw4P4Hee7Uc/zx0j/mksZL0HV90ox7z6XrOolEgkQi/2/eZDJlA7oz42UzY2YFQRAuOIMDujPh3KMN6GbQfmQGzh2U9PN6LB6uabuGk6GTvN31dtmTqoRTYf5r53/x8J6HuX3+7dy75F7a3G1VOOALh2EYaJpWMHRb07Rx+1w3DIOD/QfZdG4Tm89t5kzkzKj2l+jN/8y3+qy0uduos9fht/pR5LGbXWrb+m1serpAu8xfvweLbZTtMh+4rvAdOuk8gUEljIuvzA/lTkQT9Jzuoa6tSLtMT4hDmwe1y1y7jOZ5FbTLDFdDPgKeOk9eKHegs7rtMibZRK29llp7LQC6oadDuuP99CX66E/0oxujr6/UDZ29PXvZ27OXh3Y/xFzvXC5pvoTLmi+jydk06v1PZpIkDQnVHm4RmU3CeJAkCavVitVqBdKfR6qqZuurE4lE1eqrdV0nGo0SjUaRZRmr1ZoN6Ravd0EQBEEYO6K1VxCEMaVpGoFA6YYKl8slBkePAZPJhM/nw+12Z8O5x3OAbC5FUXA6nTgcjikfvq5pWjaMu9LG4ekUSi4IAF6vl1QqhaoW7inTdZ2+vj7q6uoIJoN84vlP0BHtKLnPixsuptk1TIN7bkC3jXQHQIqBoO4RioXyA8atDuvIdzZCqUSKnS/tZOdLO6ltreXdn343s9bOSv9bR/C2YbaZWXzd4ooeo+s6J94+kbdu0TWLsLkqC4mon1VP29I2Tu0+VdHjirG6hv4+YsHiofBCBTLBRuX0P8dI/61VGIY0niyKhXm+eczzzSOUDHEydJJToVMEk8HxOQADzLoZq2GlxdZCi7OFWlvt1PrszylekEwSK/0r0XSNY6FjRR9yoO8An/39Z/nujd/FJIumBkEQBEEQBEEQBGHq6O3tzbvt8QwNNBsLL730Eh//+MeHrP/bv/1b1q1bN6p9t7e3c+mll+atMwyDpw4/xdc2fo1QKn/iRFmX8bX6huwn2Zdk2YJlzPTPRHHnF5Yf33EcQ8/vJ1n5rpWFD8ggXcSdHHrXynUrhxZUS4CDEVUzDG5HFG2Ik4dJNrGoZhFzvXM50HeAg30HUQ2VlJIiLIVxppzZgZqDbenYws7undw2/zbeO/+9RbdLJBKi31mYEMlkcti6CJvNhss1uSc11HU9WxyeCeEeTyaTKRvCbbFYOB4+zi9P/JLnjz/Pwb6D1X0u2USjo5FmZzNNziZspqkTli4IgjDeBn+ntjonoJYinmLn+p3sXL+T2pm1vPvz72bWqlkj3t+41FJk6loyNS2p9PX6GfW0LWrj1L4KaikMBkI8cwe5KyOrpZAkCY/Vg8fqYb5/Prqh0x3rpiOSDunuT/SXf2xFqIbKm2ff5M2zb/KVt77CivoV3NR+Eze130Srq3XYx3s8HuLxeNF6x3A4jMPhQFHGbhC0IAhTh6Zp2UGpJwIneHTvo2zq2DT8AwdpcDYwp34OLqdLhAMK1SeRrrmzUHY4t1kxs7JhJYf6D3E8eByzbiYlp4iZYuhS/mdkJBXhz3/35/zlxX/JHy/746lVqyYIgnCBm6jz7qNbjvL0158esv6qD1/F3Evmjmrf3iYvbUsrCzdrmj80UCjYGSwayl1RX2UJK9+1sqqh3FOmr7JQUPfgkO6xGIJYKKQ7E7AnQrpHJvO7zFUsfHjyZd+Ov1Jfk6Xzgd0mFy5c1Bv5k8xpukZUjaZDu5MxomqUuBrPhngbU/AHbJJNuM1uPFYPXosXr9WL1ZTzPhZj4DUmk/5bLfSamywKve51ps5rP/OzlQpcDg7anmBWxcrFjRcz2zub7Z3b6Yn3AGBIpX/Yh5KH+OK2L7K6cTWfW/s5ltQsQdM0dF0veJlZJkN4dyZ8NB4fGHwnSVI2oDsT1m02m8U5uSAIFxaJdJunhfTnrsrAeP/R0hloS4XseYOkSMx0z6TF1cK+3n0c6DtQdghuXIvz6L5HeXz/49w480buWXwPFzdcLN67z8sEbxcK3Z7Iz+SklmRX9y42d2xmc8dmAonqBS2rkfxgCp/Hx6KaRVXbfzFHdxzl6e8UaJf5wFXMvXiU7TINXtoWlGiXyZyX59RoN80p0C7TFSwayn18d4F2mXUrKz7WgjXkozA4G2Rwdki1yZKM3+bHb/Mzm9loukYgGaA33ktvvJdgojq5BocDhzkcOMyj+x6l3dPO5c2Xc1nLZTQ7yw9BH2+SJOUFbJcbti3ej4WpInM+aDabcTqdAKRSqWz9dSKRqMrnpq7rxGIxYrFYNhjcbreLgG5BEARBGAMiKUsQhDFjGAZ9fX0lg6AtFsukH3g61SmKgtfrxePxEIvFiEQi4zZ41mKx4HA4sNvtU/5kTtd1wuEwkUik4hNfSZKyYdxTPZRcEHLJsozf76e7u7vo30UqleJc9zk+u+mzHA4cLrm/hf6FzPHNGcGBkB6sAANFepmA7goKAZOJ/EQYk6V6X5Vnr5jN/c/dn/988STxSJyu412c2H2CHS/soL+jP3t/z+kefvy5H3PLZ25h7XvXjuh5mxY0Vfzv6DzSSTKa/7OYs2YEvxdgzto5VQvlNlvNQ9YlYwVSfISRsZDuAC+nfylJ+u/LTrojexJzW9wsqV3CktolhJIhzobPci56ju5Yd1Vmm80ywGSYcBgOGi2NNNgbqHPUYZaHvm6nHBUkVWK1czVyQuZ4/DiaXLgq4vUzr/O1jV/j/1z2f8b5IAVBEARBEARBEARh5CKRSN5tu90+5s+5ZcsWbrvtNpLJ/Pat97znPfzTP/3TqPd/+eWX593ujnXzpTe/xMsnXx66sQFO1YnhGdrGO9c2l9m+2el2oEFO7cpv95NNMu2r2oduqJFucyrSFFM3ow5PnYdgd07hcWbw9QgMbkdMxkUb4mRjVswsrVvKXN9c9vbs5UjgCKqsEjaHcakuJKNwn2JSS/LTfT/ljeNvcO+ye1nRuGLoNknx+xbGn6Zp9PX1lexDzkyoPdn6zCcyhFuW5Wz4duZSkiQO9B1g/YH1PH/8eY4Gjlb1OV1mF82uZpqdzdTZ65Al0XcvCIJQjsHfqU3WKtZSXDyb+9+4f8jzxUNxuo51cWLHCXb8Zgf9Z/uz9/ec6OHHn5pCtRSZmhYr6X72JMxZNaeyUO5cgwK6zdLoaylkSabB0UCDo4HlLCemxuiIdHAuco6OaAcpfXTfEQwMtndtZ3vXdh7Y/ABLapewrn0d69rX0e4pcB5J+ruC2+0uOvGJYRgEAgFqampGdWyCIExtiUSCaDRKPB6nN9bLEwef4HcnfldxTZDX4WVewzz8Lv+kCbcSprEKw7klSWK+fz5us5u9vXtBB3PSTEJJEFfieYFnBgb/tvXfONh/kPsvv19MQCUIgjBFjOV5dzFn9p3hsb99DC2VXw+88OqF3PCxG0a9/xnLZlT8GFft0LGF8XC8wJZpZfdVDqOuvQ5Pg4dgZ3VCkqZ0X6VC/tiATKBt7lJtBgOTmcFAP3EmqFs0449MqdDkTECxnnO92DIepJxLqcC6al6OkoKC+/x/wMDPyEi3VcVSMSKpSDqkO5Ve4qk4cTVOUk2iaipS5j9DQkYu2jdebRbFgt1kx2Fy4DA7cJlduCwubMowAU4Ghf/+M2HRExHWXSh0PrNMdvKgRRp0fQry2/xcN+M6ToZO8nbX28S14p/dubZ0bOGDT3+QW+fcyp+v+nNaXC1FtzUMY0hI92QJCDUMg2QyOaRWZXBIt9lsFuPKBUG4MMiMXUA3DPleYlJMLHMtY7ZjNrv6d3EyfLLsXemGzvPHn+f548+zwL+AOxfeyS1zbsFpdlbpYCevzOfq4NDtzOVk0RfvY2vnVrZ0bGFX9y4SWqLsx+qSjoGBIRl5lzp69ntXnaOOJmcTe429hAlnH2uym8h85QdG9z3NGHR5/vqZfWd47B8LtMtceb5dRiL/vCz3sozztRmLZ6TPq9USG8XJywhw+Qu0y0RKtMvsLdAus3wE7TKFashHYaLbZRRZocZWQ40tXceQ0lL0xnvpifXQE++p6HVczPHgcY4Hj/PY/seY7Z3NZc2XcUXLFTQ4Gka971yZEO3ccO3Bl8NdF4QLTeb8D9Lni5mQ7kQiQSqVGvW5q2EYxONx4vE4kiRhs9mw2+1YrVbxNycIgiAIVSBCuQVBGDPhcLjkwOdMmKv4Yj8+JEnC4XDgcDiyM/LG4/GqD043m83ZEzeTaep/zIw2jNvpdOJyuUSnqTBtmc1mvF4v/f39Be9XdZWvv/Z19vXtKxneO9M9k2V1y0Z/QBJgPr9Afkj3MIWAgzsvFPPYpg1bbBYsNgueWg9z18zl2vuuZcMvNvDif72Iljx/LAY888AzuOvcLLqm8plV69vrK35M9/HuIeuaF45spsyRPq6QQr8PNVGqR0iomJl0x3eU4YsodSAC2Eh3kk8Bbosbd42bBTUL0HSNnngPPbEeeuO9BBIBomq04n06ZSd1pjpqlVr8Vj9eq3d8QjykQdcLdc5W/SklVvhWQBd0JbqIK3FScmpIp/Jj+x9jnm8edy66c+wORhAEQRAEQRAEQRCqKJHIL3C1WMa2sWP37t284x3vIBjMLyBet24djz/+eFX6E5qamrLXf3fid9z/xv30JfoKbmvTbCiGgmEr0LCQYmDQ8yDdJ/LbERtmN2Ay52yYCYoro364eV5z1QqqB7cjijbEyctmsrGqcRXz/PPY2bWTM5EzhMwhXCkXslH87+BM9AzffuPbrJ65mg8v/zBuy8AIiGQyiWEYov9ZGDeZicpLDUiSJImamppJ0V+s63o2gHu8Q7gzxeYWiwWLxZKtJTAMg329+1i/Zz3rj63nROhE1Z5TQqLWXkuLq4VmZ3Pe+4UgCIJQvmz9wnmKaZxqKeo9zF07l2v/+Fo2PL6BF38wDWopzp9fNS+rUi2FAYpU/VoKu8nOLO8sZnlnoRs6PbEezkbOcjZ8llAqNKp9A+zp2cOenj18a+u3mO+fz7r2dbyj/R3M8eWHnDscDqLRaNHvLJn6R5tNBI4KwoVE13VisRiRSARVVYmmojx1+CmePfpsxQPZrXYr8xvm0+RpGn5jQai2CsO5m1xNOMyObMiZVbNi0SzElTgJJZFXw/XMkWc4ETzBt67/FvWOyr/3CIIgCONrvM+7O4908vCnHyYRyf/uNGftHN7/5fdXpS27UMD2cKwO65B1pSadGravsgLNC5qrFso9rfoqM0GxuWNzMmNyMmN0qk2EdI+9zM+w3LeaQiFwxbbJGDzWodB906E7N+ffIiHhUBw4bI6im2u6RlxLh3QntAQJLUFSS5JUk6S0FKqmoms6mq5haEZ6wikdJCMd4m0YRjrQWzof6C1LKJKCIimYZBMmyYRZMWOWzVgVKxbFgtVkxabYMMlVHuNbTlh3blB3JX+7uYHxeoHbk13mZ6Ccvz6egeUTRJIkZnpm0uJqYW/vXg72HSx7wrSnjzzN+mPruWfxPXx0+UfxWr0F968oCopS+I3LMIy8oO7BwaITEdidOY5YLJZdpyjKkKDuYv8mQRCEaWFwQHeK6p9HnP8+4sTJpa5LWWRdxP7AfjoSHWiShiZpeZMaFnOg7wBf3vBlHtj8AO+c9U5un387K+tXTtkaxMyEFsVCt3V9cn6pMgyDQ/2H2N61na0dWzkSOJJ3vy7p2bBtXdLR0TEkI2+dgVH0XMMkmWhyNmXryMxK+mRXV/N/HopZqd75SoHzn84jnTz82SLtMl95P7Klgi+OmYDuQQ9x1bvASzoDIHcyJgZdJkgHc0tF2mVKBFp3nxrULtM+inaZsawhT05su4xZMdPobKTR2YhhGIRTYbpj3XTHugkkCk9QXomjgaMcCR7hkf2PMN8/n6tar+Kq1quod9Znw7SLLYUCt3NvC4IwOpIkZeul3W53tn47kUgQj8dH/XlsGAaxWIxYLIYsy9hsNmw2mwjoFgRBEIRRmPppqYIgTErJZJJQqPQADK/XKzqNJojJZMLlcuFyudA0LTvgNplMoqpq2SdvkiRhMpmyJ4IWi2Xa/E51XScSiRCJRCo+mc0EoLtcrmnz8xCEUhwOB8lkkmg0P9BWMzS+ve3b7OjagQNHuhNLHtpj1uBoYE3TmrFp3CkU0q2TH9J9/k98cGP/4JDuqh9XpjDQBMggI3PFPVdQN7uOxz7/GLo28N7z1FefYs7aOVjslQUC2dyVDzyMh4bOXOquG1lIgbu+euEGhTo+TFZxOlN1CuAEYpTXwR0n3RluY0oViSmyQoOjIW/mV1VXCafCRFPRbIGjqqsYpIsXFVnBLJuxSTYcOHDhwiyZSzzLCOUWIuYuUs5SSu5sx/qgZZRva4qssLxuOVs7txJKhjAkg4ScIKEk8goU/mXjvzDbO5tLmi8Z3RMKgiAIgiAIgiAIwjgYHN5V7clMc+3fv58bb7yRnp6evPXXXnstv/rVr7BahxY1j4TH4yGaivL1TV/n5wd/XnQ7s2bGpqX//YXaZw3dSAfDFBAP5rcj5rUh6pTfvjT4saM0uB1RtCFOfm6Lmytar6Az2smOzh0ECeJKuVCMwn1supRuO99yYgs7unZw7/J7uaLlCiQpPRA5mUxW7W9JEIYTCASG/dzw+/0TNpm1YRikUqlsIfdYfsblkmUZi8WSDeE2m815AzUMw2BPzx7WH1vP+uPrORk6WbXnNkkmGp2N2QFUFmWKzCwqCIIwiQ3+Tq2pY1hLUYAsy1xx1xXUtU+jWorGMa6l0E3pAbaZupRMAMwIyJJMvaOeekc9F9VfRDgZ5kzkDGfDZ+mOdacHNo/Cwb6DHOw7yPe2f4853jncPOtmbm6/mXm+eUiShNfrpbt7aCB6RiAQwGKxiEGhgnABSKVSRCIRYrFY+lxHT7H+2HqePPQk4WS47P3oko5iVZjfMJ8ZnhliQKow8QaHc5fIlvdYPaxtWsvO7p30J/qRkLBrdqy6lZgSI6UMTGSxs3snH3zmg3z7hm+ztHbpGP8jBEEQhNEYz/Pu7uPdPPQXDxELxPLWt69q566v34XJUp22bKuz8n6aon2VRZTsq6zQWI55mFZ9lbkB2VYmJqQ7E3QrQrrHz3QK0Z5giqzglJ04zc6R7SA3mDp3rEjuuomW+76QO89eZhyMPOg25IcETs58yMKKhY9fwH8rJtnE8rrlzPbM5u2utzkTOVPW45J6kh/t/hE/P/hzPrr8o9y96G5spvLb8DNj3E0m05BakUxgdyYkO5VKZa+Pd1h3Jjg8Hh/4DiPLcl5Id+bfIdqrBEGYdmTS5xBWxi6gG/CavVxSdwm98V4O9x8mkAhgSEY2oFuTNHRJLxrWHVNj/OLQL/jFoV8wwz2DW+fcyrtmv4tZ3lnVPdBR0nU9+7lSaNF1fUImpRiJ/kQ/O7t2sr1nO9u6ttGX7MuGb+vmdPC2Lukj/o5lVay0OFtocbXQ4GhAkYfWpE7pdpki482tHmv6781Eum6gFBNgT080NJhhMtLZAblB3ueXeHhQu0ztKNplxrKGvEptXQUVmpCq2CRV5ydzclvduF1uZjObpJ6kM9rJueg5OmOd2TyDjMz7lHH+PySy9xcKoN8S2cKWA1v49oFvc3HjxfzBrD9g3ax1+G3+qv2TBUEYOVmWsdvt2O12ID1mKRPQnUqlhnl0abquE41GiUaj2YBuu90uxlMIgiAIQoWmUa+uIAiTha7r9PX1ldzG4XBkTxSEiaUoSt6JGww0xuq6ntfwmjuzWanZhacywzCIRCKEw+ERhXHb7Xbcbve0/NkIQiler5dUKpVt8DEMgx/s+AFvnX0ru41TdRIyh/I6q3xWH5e3XI4sjVM1XGaW+dw/0fMdAGZHfriumlTT24+m72lwuG4ZM9wvuGIBl7zvEjY8viG7LtofZdsz27j0fZdW9PRmW+WBwbFgbMi6kRTGAticlQ9kLUZNDB1IWunAWqFMMuAgHbhdThuuCoRJd66ZmbJFZCbZhM/qw2f1Db1zDDv7s+9JmWW0b4e5HamDv47kFl+q55cK3+PMipkV9SvYdG4TCS2BTbNh02wklHQ4ty7paIbGZ37/GR679TFaXa2j+/cIgiAIgiAIgiAIwhhzOBx5t2Oxoe1j1XD48GFuvPFGOjo68tZfeeWVPP3001XtN+uKdfH+X7+fE6ETRbcxa2acanqwp1WxsrBmIa/xWv5GmcHMBcRC+T+nbBtiknS7UrlMYPONXTuixSbaEKeKBkcDN7bfyNHAUXZ17cKatKbDBAvQJA2TYUKNqXx3y3d5/czrfHTZR6m114pQbmHcRCKRIRPWDuZ2u4dM/jDWVFXNhnAnEolxGWSVG75tsVgKhpAbhsG+3n389thvqx7EbVWstLhaaHEWH0AlCIIgjJzZOqiWokDf/XiY9rUUDsDOQL90mQqFcltsloHwGxiolckEZ42iT9plcbHAsoAF/gUktSRnI2c5Ez7Ducg5NGN0nelHAkf4/o7v8/0d32e2dzbr2tdxc/vNNNgbip6ra5pGKBTC6/WO6rkFQZicDMMgHo8TiUSykwzphs6rp17lpwd+SneseGj/YCk5hWExmF83n7m+ueNXrygI5coN506QbmctwGqycnHjxezv3c/p8GkAZEPGqTrRNI2YKYYqp78fdEY7+chvPsI/XfVPvHPWO8fjXyEIgiCMwHidd/ee6uWhv3iISG9+GtOMi2Zw9/+7e8hxTHZF+ypHwOYSfZUjUiykO1NvPxY5Zpk6+MwYh8wx5NbhT9GxDMIIZULhcq8Pvp27baHLwfeXq9hrrdxgtgoD3LLj04p1g+WOFRm8VKO7sNjPr9DPv9TvJdfgMXeZdszJ8nec+zOXBy1CUS6Liytar6Aj2sHbnW8TSAbKelwwGeSbW77JI3sf4RMrPsFt827DLI/u+0luYPdgmqblhXRnlkrHlI+GruvZfv3cY84EdOcGdougbkEQpo3BAd2ZyTyqeCpcY6vB3+inL9HH0f6j9CX6MBWI1sqEdBuSkQ1/1qV0EPSp4Cn+Y/t/8B87/oNFNYtY176Om9pvYo53TvUO9DzDMLJ5Lplsl8FZL7nXx/OzqlpkWcZkMpEixb7+fWzt3MrGzo3s7d+bHy5chaYJp9mZDeKus9cN+xk6rdtlFNI1CKXK4lXS/RGFmlUU0n0WBcQig9plPFbw5KwY7vwhZzubd1C7jHz+uCs5jzn/a1bVQe0ydkt6XznbFHrciO8fBQsW2hxttNHG/8/encc5ctZ34v/UrdKtbvXd09fc9/i+DT6wcSBhIfG+wgbI5pdlcxM2WXYTsptNyLHJvthNIARYWAIE24Fw2MYYYzP2GB/j257Lnvvs6fvSXarz+f1RU2qpW61Wd6vv73te9ZJUkkrVPS2p6nm+z+dxmIPh3DD6M/3oz/Qjb8+lEL8UA8MbQ2/gjaE38D9f/Z+4sfVGvK/7fbiz4875TxJFCKk5WZYhyzJCoVBhAqd8Pg/DMBZU910c0O3lyfn9/rLnxYQQQggpRd+WhJCaSyaTsO2ZKydEUUQ4HJ7xfrL8eJ4Hz6+v3mHGGHK5HDKZTMW/35l4Ydx0IkrWK47jEIvFMDo6Ctu28dWjX8XPLv+s5DE84+G3/MhKbodBUAri1rZbF1wgsWBXipP80dLwnbyWB0KYuSiowrZKlnm44d/eUDKQFABOHzw954Gky62WQQ/57PQOBDVCE3wsGg5uR5OA6kOU8nA733xYG2eaDG6HYo079QtFv14B8FLWRRVPTOB99BYHdFf5c/pEH/Y17sPrg68XBlYrtgLFVmDyJvJCHgk9gU8e+CT++b5/hirSe5UQQgghhBBCyMoVj8dLbieT1Q0Gm4sLFy7gzjvvRF9fX8n6G2+8EU888QSCwWBNX+8Hp3+Ahp0NM94v2zL8ltse2hxoxtbYVkhCmXbaubbxZDG3wdU+ALJbAFwr+UxpYxa1Ia4uPMdjY3Qj2kPtODZyDCNjI2WDuS3egmiL4MDBb/vx1uBb+M9j/xkf3fFR3LvxXoRCoWXYe7Ke6LqOVCpV8TE+n29J/hYZY4WBuvl8fl593XPB83whfNsL4p6ptsAL4n7q4lN46sJTFSeLmKuAFEBrsBVtwTbU++ppEDIhhCyiabUUmfkP/luoNV1LwcHtw5Uwp0mjy9ZShKacB3l9315/cHFI99SJ7edAFmR0hjvRGe6E7dgYzg2jL9OH/mw/DHuGJNEqnU+ex1eOfAVfOfIVdIW6cEfsDtzYciM6Q53Tvvez2SxUVYUsr+GgM0LWGdu2CxMheeESjDG8NfwWHjrxUNWT/DCOQed1WKKFTXWbZm4DI2Ql4VBoN4WBsuHcPMdje/12BOUgTo2fKrSvCkxA0AzC5E1oggaHd6DbOj71s0/h9MRp/M6+36FAekIIWYGW4rx7YmAC3/y9byI9ki5Z376zHR/53x+B4l8Dk50uoIm4pmMe1nNfZXFIN7A0Id0MbhuKWbRuapsHdR+sHuVCnZ0Z1s8U8rzezBbsDbiBdlN/pzMFdlcKKl+M37f3OTH182Gpw7q9bU8N36bPjwVp8jfh7s67cT55Hm+PvQ3d1md/EtwJpj7z0mfw9WNfx2/v+23c13XfokzKLAgCBGH6dr1wbi+w27tcKowxGIZRmKDOMzWkWxTFsvtPCCGrivedK6H0/MHEgo89OI5Dna8Odc11mMhP4GLq4rSJPgUmQGCVP0sZGPoH+/GNwW/gG698A23hNtzQfAOub7keO+I7Zu1zYIwVluLbxSHctTwnXU7ed6s3IYYgCLA5G8fHj+P1wdfx6uCrODxyGKZTdAJXo+OtqBJFa9AN4o7IkTnVka35dhkJ7nurUve9joVPPDP1Vz7T+UkZjCvzHphnd14+V6ZdZhV0DfIcj+ZAM5oDzbiq8SqM58fRl+lDX6YPWTM7+wZmYDMbL/a9iBf7XoQiKHhX+7vwc90/h9vab4MsUH0FISuFIAgIBAIIBAJwHKcQ0K3r+oKOE2zbRiaTQSaTgSzLUFUVqqquuzw5QgghpFprISqNELKC5HI5aNrMU6VxHIdoNEoH6GTFYIxB0zSk0+l5DVD2BlNL0ipojSNkkYmiiHAkjM8+91k8fenpso+RHAmKpYBTONzWfht8oq/s45ZDpDlScrvQubGAcO35irXEEGmOIDk4GQA0eGZwSV5bDU8v/NSzOkR57qcOeq66gplqpIanh1tEm6M12z6ZgQy3sEyDW3A3GwdADu6ZpoJ5D95dNosVxC0WLSvtMJiH+/8so3QwtlnpSUBIDmFXfBcOjxwuWS85EiRHgsmbODV6Cn/x0l/gr279KwpEIYQQQgghhBCyYnV2dpbcnhqcvVC9vb244447cOlSaRDptddei5/85CeLEtjqsJkbchRbgWqpkAUZ2+q2odHfOPOGKpzOTw1505N69QOpeUxOCAe3/bFWUiOl7YjUhrg6KYKCa5qvwVh4DCf6TkDLl/Y/29zkHxsHDn7Ljxxy+L9H/i9eHngZf/KeP0FLsGWpd5usE7ZtY2JiomKhsyiKiEaji7oPXgj3QouuZyOKYiF8W5ZliKJYsb2XMYaTEyfx5IUnax7EHZJDaAu2oS3YhqgSpXZnQghZIjPWUiyDdVNLwcPtb1cwa0B3arRMLUVTtPL2Zwrp9kKr5hE4I/ACWoItaAm2gDGGUW20MEhUs2auJ63GhfQFPJh4EA+ffhjNgWbc2HIjbmy5EV3hrsLxQDKZRDwep+MDQlYxb8KhXC6HfL50wPzJiZN46PhDODF+oqptWbzlhnHzFrqj3dhev50mlCerD4/JcG4dZWu5NoQ2ICAFcGTkCCxnstjNq9/SBR15IQ/GMXzlyFdwNnEWf33rX8Mv+advjBBCyLJZ7PPu5FAS3/zdbyI5VDoxceu2Vnzk7z4CJbA6A7mn9VVm5t/fSH2Vi2Q5QrpRZtvF7R4U1L08yoVsT72kkO35mU9othd+Wfw87//BRmlw93JZjLDucqHbU38XpOY4jkNPtAcbQhtwYvwETidOV6xpKtab7sUfP//H+OqRr+K39v4W7um6Z0kmmvKCRH2+yTGvjLFCQHdxWLc3mdxS8MLCi/MbvAm1i4O6Z+vPJ4SQFav4/MEH91jE68/0jlHmKeaLIeaLIWNm0JvqxWB2EDar7oSEAweOTX6uDiYH8WjyUTx68lEExAB2xndiT3wPdsV3oTnQvOY/g6eGbhdfchyHpJ7E4ZHDODR8CG8MvYFjo8dgOAubzLkcDhziarwQxB2QAvPe1rpol1Ew+3n4HLv0qV1m8XAch3q1HvVqPXbHdyOpJ3E5cxmX05eRMTPz3q5u63jq4lN46uJTCEkh3N15N+7rvg/XN1+/KJPgEELmh+d5+P1++P3+Qg2FpmnQdX1B56De5E+pVAqKosDv90NRlDV/7EIIIYTMBYVyE0JqxrIsJJPJio8JhUKQZZoxi6wMXhj3fGYqVhQF4XCYwrgJKeIwB//70P/GY5cfgw8zh22HWRj7mvctqJNjMcRaYiW3LcNCNpFFILo8+xmqD5UMJNWSCxukWC1faPr/XXo0jUBs7r+HWnY+Td2W7Jfhj9DgkCUhAAgAyGPWoOYCr8NbwmSw90rlFeyZqP7nq4YI9+cXsXoKdzm4+yzBLV6w4M6APENna4O/ARujG3E2cXbafd7grmdOPoOH6h7Cr+z8lUXbbUIIIYQQQgghZCG6u7tLbufzeYyOjiIejy942319fbjjjjtw4cKFkvVXXXUVnnrqKUQikfJPnCPdrq4g2Wf54LN9aPA3YHtsO2Rx/n12vnBpO2J6vMq2QBlugXdRe0lN2xFHS7cVbYnWbNtk6dX763HTxptwcegiLoxdKAyGcTgHDAzclT+kQjC3mMPhkcO4/+H78Yc3/iH+zaZ/Q8WipKYYYxgfH69Y2MzzPOrq6mo+Ublpmsjn88jn8zDNWjZml/JCuBVFgSzLEITZG/gXM4g7IkfQFmpDe7AdYSVcs+0SQgipHtVSuJatlmKWgO6p50CyKsMfnmMtRXFIt3d6WRxUxWNOff4cx6HB34AGfwP2NuzFRH4CfZk+XM5cRtbMzm3frjAEA7IjYzA7iEfOPIJHzjyCpkATbmx2A7q7I93IZDKLMvEVIWRx2baNXC6HXC4H2y4tULmYuojvnPwO3hh6o6ptGYIBnddh8zbagm3YFd+FkEyfC2SV8yY4lOHW702p46rz1eH65utxeOTwtO9ZxVYg2zLyYh46r+PpS0/jY098DJ+/8/NoDbYu0Q9ACCFkNot53p0aSeEbv/sNJAYSJeubtzTjo3//UfiCM4/5WOmm9VWOzr+/kfoql0i5kG6vjcPC4gUAT52cDJgM4i1u9+Cwemr+V5LiUO1Kgdtk5fLCqoHSxIni/1dv8cbeLJfZwrq9SQcFuGNiikO46f29rCRBwu6G3eiJ9uDt0bfn1J98LnkOn3ruU/i/R/4vfmPPb+A9ne9Z8tBAjuMKwdfFbNsuCev2AruXiuM40HUduj5ZN8ZxHERRLAnqliSp5vULhBCy6Hi4baJeievUCX7mcYwZlILYXr8dm6KbajK5cNbK4tXBV/Hq4KsAgJgSw/b67dga24qtdVvREe6AwK3kgdXTcRw3LWy7+LK4FlO3dZwaP4VjY8dwbPQYjowcwYXUhUXbN5EX0exvRmuwFc2BZshCbTKL1kW7DAe3nyGLmp2fUbvM0uA4DlFfFFFfFDvrdyJpJHE5vfCA7rSZxsNnHsbDZx5GXI3j3q57cV/3fdgT30M114SsIBzHwefzwefzlQR05/N5MDa/D3TGWKEWned5qKoKVVUpC5AQQggBhXITQmqEMYZEIlHxoF1RFAQCKyuAlaxP+Xwe6XR6XoOVZVlGOBymE0pCprAdG3/20p/hkTOPAAIgMhGiM/1QU+AF7Gvch4gTcQtyVlB/ftOmpmnrxnrHlm0gqamXfkbJ6tJ87sQ7p4cNDZwaQPPm5jlva+DUQC12CQAwemm05Ha5/y+yiLxONxHu4J5q22m9oGsRbgf4SjkDLS6utVC7Qk+veE/Eivp8m5figG4Hbji3iWm/q65wFzJmBkPZobKbkRwJX33pq+hWunFD9w1VBbgQQgghhBBCCCFLac+ePdPWnTp1asGh3IODg7jzzjtx9mzpZFZ79uzBT3/6U8RisRmeWT3TMfGFt76AMW1s1seqlgo/82Nr/Va0BFoWVjRrA/HWOM7gTGHV8MVhWKYFUZqhAai4fWmKgdO1aUecGJiAbZSOvqR2xNWP53l0N3ejOdCME8MnCn/vFmdBYpMDHouDuXNGDn968E+x/9J+/NlNf4YGf8Ny7T5ZYxKJxKx9zNFoFKK48MZwxhhM0ywUUE8Np6sVURQLAdzVhnB7+3di/ASeuvhUzYO4o0oU7aF2tAXbKECPEEJWAKqlcK2IWoqpAd0WMNo3pZaiu0bnQOXCbaaGdFcRZsNxHOrUOtSpddgV34WkkURf2h3knjJSc9qlnJhD2JicpGMoO4RHzz6KR88+ikZ/I25ouQHv2fEe7GveRwNFCVnhvIGeuVyuJDTIM5gdxHdPfRcv9r0INktRj8M50AUdOq8DHBBX49gd3416tX6xdp+Q5SEACMCtdcujJLTSL/lxbdO1ODZ2bFpbMQcOqqVC4RTkxBxOTpzEhx//MP7u3X+Hq5uuXsIfgBBCyEwW67w7PZbGN3/vm5jom5j2eh/73MeghtUFbX+5xTviOPNSUV/l+Vn6KiugvsplUlyrDixdSDcwGTA8NTe1uM2jeFmPzQxTQ7VnCtwma5cXVF9uXIyD5Qvr9vbJu+SnrMOV/TFQGtZdHMa/Ht/TK0RACuD6luuxObYZR0aOYEQbqfq5ZxJn8KnnPoWeSA8+vufjeG/XeyHyyztITRAECIIARVEK6xzHKRvUPd/AtLny6gym1jUIgjAtqHtquCohhKxoXj+lx0FpUPcczh8kQUJXpAud4U6M5cfQn+nHSG5k1v6I2UzoEzjYfxAH+w8CABRBQVe4Cz3RHnSFu9AV6UJbsA0SL82ypcXF8/yMwds8z0/7bmCMYTg3jLOJszidOI1TE6dwYvwEziXOwWKLOxlFQAqgJdCClmALGtQG8FztB22vm3YZbwLQXG02R+0yS4/jOESVKKLKlYBuPYnedC96073IWfP/jx3VRvHg8Qfx4PEH0RZsw3u73ov7uu/DltgWOlYkZAWZGtCdz+ehaRp0XZ/3+abjOMhms8hmsxBFEX6/H36/nyZ1IoQQsm6tlEg0Qsgql06nYRjGjPfzPI9oNEon3WRZ6bo+69/qTGRZRigUKukgJYS4TNvEp1/4NH5y4SfuCg7IilmEzTA4Nvm5L3AC9jXsQ0SJuAUuGgA/VkwxS+v21mnrhs4MoWN3x5Lvi2M7SA4lS9YF64NL8tqNPY2Q/TKM3ORn5bnXzuGq9101522de+1czfZr6Exp4G/b9raabZvMgQS381qHG9BcLS/8moMbzi1h6UOrvcJZb6kVHpMFwWu1jZkH4IM7uNuCW6B4pW+S4zhsr9uOrJGdcWZd27Hxtwf/Fn8r/i1a61oRDAapQZ4QQgghhBBCyIpx3XXXTVt35MgR3HzzzfPe5vDwMO68806cOnWqZP2uXbvw9NNPo75+4aFAA5kBfOq5T+HwyOHKD2SA3/KjQWrAzvqd8Ev++b+oN3GXAbRvbS+9y3Jw8ehFbLx64/TnSXDbFsq0BY9eGkVqaG5BbDMZOj190jBqR1wjOEANqdgn7MNQcginEqdgOzYkW5ryMDeY2+Zs5MU8nrv8HD74ww/iv9343/Dervcu086TtSKdTkPTtIqPCYfD8Pl8834NxhgMwygEcTtO7RMnvEHB3jKXtlrGGN4Ze6cQxH05c7lm+xVTYoUg7qC8NH1ihBBCqkO1FK4VV0vBA5CBofNTaim2LuI5ULmg7uIwm1nCqkoGicZ3IqWncDlzGZfTl6sK6HY4B5qgQbWnD1Aezg3jsbOP4ZFzjyAQDeDuzrvxns73YF/jvkUZnE0ImR/LspDL5ZDL5cqe74xpY/jBmR/gwKUDsFnlNC+TN6ELOizeLQIKy2Hsiu9a+GR0hKx0ItxwbhNuDd+VMdaSIGFfwz6cSZzBxdTFaU/jGY+gGYTFW0g4Cfz6U7+OP73xT/HBzR9cwp0nhBBSzmKcd2fGM/jm730TY5dKJ2to7GnExz7/MfgjC+gvXCHad7UD35m87VgOLh66iI3XlemrrID6KleQqSHdXsjeUoR0e7yA4XL7NrXdo1wQ8ErHpiyVQrcJqcR7L0w1Nax7pvdUJeXeX1OvV8sLC5/axEBh3csu5ovh9vbbMZQbwrHRY0joiaqfey55Dn/8/B/jH9/6R/x/u/8/fGDjByALSzN5ZzV4ni9MiO1hjE0L6jZNc8mCugHAtm3Yto18Pl9Yx3FcIaBbFMWSsG5CCFnxvO/w4kl+vO99B1UFdXMch7gaR1yNw7AMDOYGMZAdQNpI12QXdVvHyYmTODlxsrBO4AQ0+ZvQHmpHa6AVzcFmNPmb0OhvRJ2vrmb9mt7EETMFb0/lMAdj2hj6Mu7kyr3pXlxKXcKF1AWcT56fcdxwrfEcj7gaR0ugBc2BZoTk0KK/5rpqlxHh1nLnZ3vg7KhdZnlxHIeoL4qoL4pd8V0Yz4+jN92Ly+nLyNvz/w/uy/Tha8e+hq8d+xq6I914b9d78d6u96In2lPDvSeELBTHcVBVFaqqwnGcQkC3YRjzPs+0LAupVArpdBqKosDv90NRFKq9IIQQsq5QKDchZMF0XUcmU7khLRKJUEcMWTaGYSCVSs0rjFuSJIRCoQUNniZkLcuZOfzhz/4QL/S9ULKecQwZMYOQ6XZ48ByPPY17EPPFJh9kwx0UsELeXoFoALG2WMmso1ODoJfKxcMXkU+XNno39jQuyWvzPI+OvR0lM5SeeO4E8tk8fIHq/7NGL43i8rHahDEkBhPTfh/tu9pneDRZdN6MuCLczre5tM0yuO97/cp2xCvLYhSweSHcxZ3ptVJc9LueDnGLf24vnNsCRF7EnoY9eHXwVVhO+cTz8fw4Pv/m5/HpGz6NXC6HUCgEv99PjfGEEEIIIYQQQpZdPB7Hxo0bcfbs2cK6w4dnCbquYHR0FHfffTeOHz9esn7Hjh14+umnEY/H571tz896f4ZPv/Dp2UPLrgRybwttQ1ekq3LBfqU5SYvCuD2dOzvB8RyYM9k4dHj/4dJQbg6T7UgzOPTjQxV/hLkYPDNYclv2y0vWrkqWAAdwfg7NXDPq1DqcHjuNTGp6HzUHDkEziJSUAuMZknoSn/rZp/DMpWfwJzf8iTtxKCFzpGka0unKA65UVUUwOPdQUMYYdF2HpmnQdb3mQdzeoF8vhFsU51Yq5jAHR0aO4KcXf4r9F/ejP9tfs32LKTG0hdrQHmynIG5CCFnBqJbCtWpqKa5ud4M6vcmqK2faLpwXrDO1m5ivsFwRVsLYoezAjvodSBtpXE67Ad1JI4mZ6IIO2ZEhsPId9QITMJGawAPHH8ADxx9AXI3jro67cHfn3bi26VqIPJXNE7LUHMeBpmmFQaDlJPUkHjnzCH568acwHXPGbTGOQed16IIOxrltUn7Rjx31O9AZ7qQaFLJ+cABkuDVcBtxaPLgDsDfHNiMgBXBi/AQcNr2NQXREhJ0wdEfH/3jxf+DUxCn84bV/SN+RhBCyjGp93p1NZPHPv//PGL0wWrK+obsBH/v8xxCIBua97ZWkc2+ZvsofH55z+BP1Va5gU0P2vGA9r71jKUK6PTMF+xabGhpcbkGFy9lef6brrGhd8XUH00O4CVlss4V1A6V/kzO9bxYbhXWvCBzHoTngBpH2pnvxztg7cwr8vJy5jM+89Bl86dCX8JEdH8H9W+5fkuDO+eA4DpIkQZJKJ56fGtRtWRZse7Eb9ScxxgqvXW5/i4O6vTBXQghZsThMjlX2eJPOlFumHB/LooyOcAc6wh3IGBkM5gYxnB1GzsrVdDdtZqM/21+2/krgBMR8MdT56hBTYogoEYSVMIJSEEE5iIAYgE/0QZVU+CQffJIPsiRDFuVCCDfHc2Acg8EM6JYO3dKRy+eQNbNIG2mkjBQSegIT+QmMaWMY1UYxoo1gKDc047jgxRaUgoVjggZ/w5K3Va+7dhkZkzXhC0DtMisHx3GoV+tRr9Zjb8NejGgjhYDuSv2uszmfPI8vHf4SvnT4S9gc24x7O+/FPV33oDvSXcO9J4QsFM/z8Pv98Pv9VdVmzIYxhnw+j3w+D0EQoKoq/H7/nGvPCSGEkNWIvu0IIQviOA4SiUTFx/j9fqiqujQ7REgR0zSRSqWg6/qcnyuKIkKhEP3triFJPYnzyfPoTfdiKDeEMW0MKSMFzdJgOiY4cJB4CQEpgLAcRlyNoznQjA2hDeiKdCEgrY3iw1oaz4/jd5/+XRwdPVr2fpu3oYkaAnYAexv2ot5XP/1BBtwCFWn6Xcth0w2b8NoPXivcvnjo4pLvA2MMB756YNr6bbdvW7J9uPr9V5cMJDXzJp77+nO453fvqXobP/3Hn9Zsfy6+Vfr/wAkceq6jWTWXnQT3jLJoYM+cTA1zElBavFZcXDcTrxivuEN8toLXhfACqelMerJIwQZgAH74sTO+E4eHZw4tOzp6FN8//X3cv+V+JJNJZLNZhMNhmvyEEEIIIYQQQkjVHMeBbdtwHAeMsWmLF7zjXfI8D57nwXFcyfWp7r33Xnzxi18s3H7++efntX8TExO45557cPRoaZvptm3b8Mwzz6CxcWHFvaZj4h/e/Ad8/e2vz/5gBtSjHvsa9pVOlliOD26hdZltII+yxdeh+hA2X7cZp145VVh39NmjuPmXbkZzT7O7PQUV23aSw0m8+r1XZ/1RqjW1HbHn2h7wQoUgcrL6XAl6lyFjZ+NOTAgTuJC8AMMp/SPlwSNqRJFQEoWQrifOP4E3ht7AX936V7ix5cZl2HmyWhmGMWtNhCRJiEajVW+TMQbDMKBpGvL5fM2DuItDuCVJmnMwneVYeGPoDey/uB/PXHoGw9pwzfaNgrgJIWR1oloK16qppfD63hVMBswUT2i9FIqDdqaaWhPAAyEhhO1127G9fnshoLsv04eEnih9LgfkxBxC5szhKqqtwuRNOLyDUW0U3zn5HXzn5HcQVaJ494Z34+6Ou3Fj641QhEqzUxFCFqJ48qF8Pg/Gyqe/pY00Hjv7GH5y4SfQ7ZmLjyzegs7rMHmz0NYk8zK21W/DxshGCDwFApF1ioP7fS/Brd+7kq3QGmyFKqo4OnoUhl1+wLViK5AdGd899l2cmTiDz777szSZHSGELKNanXdrKQ3f+uS3MHy2tE033hnHr/7DryJYt3baZEPxEDbftBmnXizqq9x/FDf/u5vRvLm5qm1QX+UqMzWkuzhUdykmJpuNtz9k6ZULPi8XhA5MH59SqQttpvtmCjifup6VWV/ueqXHzbTN5VLu91x8fbalmDcWyAvZ9y6XMnB/KgrrXhYcx6Ej3IH2UDsuJC/g+PhxaJZW9fNHtBH83Rt/h68c+Qp+afMv4Ve2/wpagi2LuMe144VeF49ht227ENBdHNa9lLx6hqkhbjzPl4R0ewuFdRNCViwOk/2mU3ljkovHJl85PgkKQWxSNmFjdCMyRgbDuWGMaCPIGNVPHjEXDAyMY7BhY1AfxIA+AIdzwMBKLr3rq/0YRBEUNPobC8tKyO9Yd+0yCspPvD1VhfupXWZl4jiu8N66qvEqDGYH0ZvuRX+mHzabf6PF6YnTOD1xGl849AVsjm3GPZ334D2d78HG6NxC2Akhi4vneQQCAQQCAViWBU3TkMvl5j35k23byGQyyGQykGW5kB9Ik6UTQghZqyhKjBCyIIlEouLBtyiKiESoQJMsLdM0kU6nkc/n5/xcQRAKYdx0Irh6mbaJY2PH8MbQGzg8chjHx45jKDf/WSkBoC3Yhp31O7GnYQ+uaboG2+q2LflsmyvJheQF/PbTv43edG/Fx1mihT2Ne1AvlQnk9miYLEhZZptv2lzScTJyfgSZ8UzVHR1DZ4cgKiLq2yv8vBUwxvDE3z2BS4cvlawP1gex5ZYt89rmfGy9bStCDSGkR9KFdS9/52X0XNeDTTdsmvX5rz/yOk4+f7Jm+3P+zfMlt9t3tMMf9tds+2QBZhjYMy+VBgCX+0peqoJCEZNB3HRoMJ0AQAWgAA1SA3qMHpxLnJvx4d8/9X1sjW3FnoY9sCwL4+PjUBQFkUiEZskkhBBCCCGEEAKgdHCRbduwLAuWZRWCuBdKEATwPA9BEArL3XffXRLK/fbbb2N4eHhOIdrJZBL33nsv3nrrrZL1W7ZswTPPPIOmpqYF7fdQdgifeu5TeGv4rdkfDKDb14094T2QhFlmRFQx86SJMwRye677+etKQrmZw/DwZx/Gv//Cv4fqqzzpqWVYePgzD8PIVXiBObAMC73HSturN9+0uSbbJivMlWBuaEAsEENQCuJS6hJG86MlD5MdGUEziIyUKQRzD+eG8fGnPo6PbP8IPnnNJymEj8zKtm1MTExU/P7heR51dXVV9S17QdyaptU0iJvn+UIIt8/nA8/PfTCJbut4uf9lPH3paRzoPTA9AHMBokoU7aF2CuImhJBVjGopXKuyloLD5ITLwPKFdBerFK7DASE+hO3qdmwPbEfWzKJP60Nftg8TxgQcOLB5G7qgQ7FnPp4PWAGkpXRJH39CT+CRM4/gkTOPwC/6cVv7bbir4y7c1nYbHaMQUiOmaSKXy816zpMxM3j83ON44vwTFUOWdEGHzutw+MltCZyAzbHN2BrbOnu7FyHrBQ+3vUyGW8NnATFfDNc1X4cjI0eQNtJln8YxDqql4p1L7+BXHv0V/P17/h6bYrMf0xBCCKm9hZ53A0A+k8e3/tO3MHhqsGR9fUf9ygp+qqHrfvG6kvAnZjM8/BcP49//47+HGqK+yjWvuM3Dm5jMC/b1luUM9yXz44UfVwp2Lr5/PZst7Lua5wGVQ8rLBZvXSvH/ZzEK6163eI5HT7QHneFOnE+ex4nxE8jb1Y8Pz5pZfPOdb+KB4w/gPZ3vwUd3fBR7GvYs4h4vDq+WrZjjONNCuk3TrEkd3Vw4jlM2rJvjuJKQ7uKFxugTQlYsL7C74kM4hFgIoboQNmIjskYWQ9khDGfdkG7HccDN8sXPrhx8MY5NXr8Swr0WAraroQgK4mocDf4GNKqNCMmhFff9sO7aZbwa3CwqH2ebcGvI5fJ3U7vMysZzPFqDrWgNtsJyLPRn+tGb7sVgdrDweTQfXkD3Px76R3RHunF3x924s+NO7KzfueLe24SsZ6IoIhQKIRQKlUyoPt/ade9cMJlMQlVV+P1+yPIMXxCEEELIKkWJV4SQectmsxVDjzmOQywWoxNnsmQsy0I6nYamVT8TskcQBASDQfj9fvqbXaVGtVEc6D2A5y4/h1cGXpnTjNjV6Mv0oS/Th6cuPgUACEkh3NByA25vvx23td+GuBqv6eutZC8PvIw/fPYPkTJSFR8ncAJuabvF/d1kULmwJwcggOnFNEus+9puKEEFekYvrDv76lnsfe/eqp7ff6IfP/ybH2LXXbuw97696L6mG4JYXdr44JlBPPm5J3H+jfPT7rv39+6F4l+6cBBBFHDfJ+/Dv/7JvxbWObaD7/zxd/D+//L+GX8fju3gpW+/hP1f2u+u4LDg4GTGGM69Vhrwu/1d2xe2UVJ73sAeBQsP5y5naeuEKIh7Pq78DXS3dmPMHEMymyz7MAaGf3jrH/A3t/0N6lV30L2u6xgZGUEgEEAwGJxXYAwhhBBCCCGEkNWJMQbDMGCaZuGy0mSwtWDbdiH427N3716Ew2GkUpNtno8++ih+5Vd+pTBIqNL5aiaTwX333YfXXnutZP2mTZtw4MABtLS0LGifD/YdxB89/0eY0Ceqenyr2oqro1fP3t9RHMg9j/aXzdduxpYbtpQEcw+dG8K3/uBb+NCffQjxjvLt5qmRFB75y0dw4c0L7ooatCNePHwRlm4VbnMChy23Ll04H1li3qAAA5AsCRujG1GXr8OF1AUYjlukLzoiBCYgaAanhfE9cPwBvDzwMv7mtr/B1rqty/IjkJXPcRyMj49X/F7yaiKmDootZhgG8vk8NE2r6XecKIrw+XxQFAWyLM+rjztlpPD85efxzKVn8ELfC8hZuZrtHwVxE0LI2kK1FK41UUsxU0h38bLU/fPFvP25IoAAtvi2YItvC3JmDkO5IQznhpGwE1BsBRzj4HBOYeC6A/e6wAT4bB/yYvn61pyVw5MXnsSTF56EyIu4oeUG3NVxF+7YcMe6qgEjpBZM0ywM4LQsq+JjM2YGT5x/Aj8+9+MZzz9szg3eN3ij5FyeA4eeaA+2122HT/TV8kcgZO0QAPjhTr6RB1RRxTVN1+CdsXcwnBue8Wk845FMJPHxRz6OT7/r03hPz3uWao8JIYRcsdDzbj2n44E/eAD9x/tL1te11+FX/+FXEYqHarq/K8XmGzdjyy1bSgKghs4M4Vu/T32V65IXqlfc5FTc7lEc7kuWXnGYdnGo9tR1pHqVwrRXMwrrXvcEXsCm2CZ0R7pxLnkOJ8dPzimc22Y2fnLhJ/jJhZ9gT3wPPrz9w7i3895VPbkbz/OQZbkk8IwxVqh/Kw7qXuy6u3IYY4X9mEoQhLJh3ZVqLAghZEUpmgwm4Augx9eDnvoeOMzBmDaG4dwwhrVhjGvjCwq5XUvCchh1vjrE1Tjq1XoEpeCKz25Zl+0yHNz+hCwqt4nkrzy2zKEUtcusHiIvoiPcgY5wBwzbwOX0ZfSmezGijSxou+eT5/HVo1/FV49+FU3+Jtyx4Q7c0XEHrmu6blUff5O1jTEG0zGh2zp0W4flWDAdE7Zjw2buMnUCJJ7jIfACRE6EyIuQeAmyIEMWZCiCAp5b2bkYiqJAURQwxpDP55HL5aDr+uxPLIMxhlwuh1wuB1EU4ff7oaoqneMRQghZEyiUmxAyL6ZplgQTlBMOhyFJdKJMFp9t24Uw7rnO7svzPILBIAKBwIpv0CXTJfUknrzwJJ44/wTeGHpjSTss0mYa+y/tx/5L+8GBw9VNV+PerntxT+c9hZDRtYYxhgePP4jPvv5Z2KxyB73ES7i17dbJ34XXMD/jxgFoVx63jG9FSZGw886dePOHbxbWHX/2eNUdJ4A7k+fRp47i6FNHoUZUdOzpQPPmZjT2NEINq/AFfQAHGDkDqZEUhs4M4eyrZ6fNfuq57kPXYfc9uxf8s83Vjjt2YOddO/H2028X1pl5Ew9/5mG8+v1Xsfvu3Wjc2AhfyIdcIof+E/048uQRjF4YLTz++l+8Hq9+79UF7cflty8jNTx5zMEJHHbfu/S/D1Kl4nBu48qyWghwOwYlULHbAnAChz2de3DgwgE4eQeyM32Wy5SRwufe/Bz+9OY/hci5zRKMMWQyGWiahnA4DFWtPBMyIYQQQgghhJDVyRuEo+s6DMOAYRhzbtdfDD6fDz//8z+PBx98sLDuBz/4Ad73vvcVbvM8XzI4SJKkwiChT3/603jppZembbexsRGf+MQn5r1fDQ0N2Pkfd+IrR74yp/bvqBCdvc/DD7c9xII7wdo8J1l7/++9H19854vIpycHwPWf6MeXP/Zl7LhjB7bcsgWRpgg4gUNqKIUzL5/BsaePwci5DUeyX8aOd+/AoR8fmt8OXPHOgXdKbm+6YRNC9SuwkJ3UDgcgBHeSQBuI+WIIykFcSF3AeH4cPHiAAQIEBK0gMmKmpN3vTOIMPvz4h/H7V/8+Prrjoyu+IJYsLcYYEolE2UGjxSKRCBRlehCobdvI5XLQNG3WULq5kGUZPp8PPp8Poji/kq/B7CAO9B7AM5eeweuDr8Nitds/CuImhJC1i2opJq25WoqpId3AZKhN8bIC+CU/uiPd6I50Q7M0DKeHkU6lkTXLFyQFrSDSYhq2YBfCuguXnFO4btkWXux7ES/2vYjPvPQZ7InvwR0dd+CODXegJ9JDNYWElGFZFjRNq/qcJ22k8fi5x/GTCz+BZmnT7mdgMAQDOq/D4aenaXWGO7GjfgcCUqAm+0/ImicCCAAwAFEXsTu+G+eT53Euea7i0yzTwmf2fwYndp/Ab13/WxAFGm5GCCFLZaHn3U9/+WlcPnZ52vpALIAn/u6Jee9XIBbA+z/1/nk/fym8/7+8H1/8CPVVkhmUa/coDvUtDvZd/rKF1WlquHa5oG3qhiW1UCms28H09zSFda96Ai9gc2wzeiI9OJ88j5MTJ8u2K1VyZPQIjjx/BJ997bP4xS2/iPu33I/mQPMi7fHS4jiuUMNWPP7KcZxpQd2WZS1bfZ5t27Bte1rgm1eHVy60m9rkCSGrAc/xaPA3oMHfgJ3YCduxMZ4fx6g2ivH8OMbz49Dt+YVdriZ+0Y+YL+YuSgx1vrpVGcS7bttlvDyA8vPoTvIOwcr811K7zOojCzJ6oj3oifZAMzX0ZnrRm+rFhD6xoO0O5Ybw7ZPfxrdPfhtBKYibW2/G7e23l+bNEFIDhm1gIj+BhJ7AhD6BpJ5EUk8iZaSQMlJIG2mkjTQyZgZZI4uslUXOzEGzNGiWhryVr3kulSIo8Ik+BMQA/JIfQSmIgBxAWAojrIQRlsOIKBHEfDFElSjqfHWo89WhXq2HIkyvQV8sHMdBVVWoqgrbtqFpGnK53Lzr3C3LQiqVQjqdhqIo8Pv9UBSFzukIIYSsWlQlRQiZM28AaqVOGJ/Ph0CAip/J4rJtG5lMBrlcbl5h3IFAAIFAADxP1R2rie3YeGngJfzg9A9woPcALKd2A8Xni4HhjaE38MbQG/jbV/8WN7fejF/Y+Au4o+OOJW0EWUxZM4s/P/jneOLC7I3/PsGH29pvQ0SJTK4U4DbMV6p9sOGGZ/gWtq8Lte/n9pV0nJx5+Qz0nA7FP/f/Sy2p4eTzJ3Hy+ZNz3xEOuPnf3Yx7fueeuT+3Rv7Nf/s30FIazr1WOhij7+0+9L3dV/G5O+7YgZt++aYFDyQ9/uzxktubrqeOkFWBh/te9sK5TSxvUdtMxKKFDgdqRhEUXN92PQ5cOgDd0aHaKkSntPnh5MRJfPvEt/GR7R8pWW/bNiYmJpDL5WiSH0IIIYQQQghZIxhj0HUd+Xwe+XwejrMSGwmA+++/vySU+9lnn0U2my30dzmOUwgSL8ZxHIaHh8tu8+DBgwvap2BjEF03di1oG9M4mGyzWWjzugCEO8P46N99FN/6T98qKaq2DAtHnjyCI08emfHpHM/hQ3/6IQycGljQbjiOgxPPnShZt+/n9i1om2SVKO57sN0JQzdHN2NEG8HF1EWITITFWRAdEQErgKyYLRnQajomPvv6Z/FC3wv4y1v+Ek2BpmX6QchKk06nkc/nKz4mGAzC7/cXbjuOg3w+j1wuN+27Yr44jisJ4hYEYc7bYIzhnfF38LPen+HZ3mdxfPz4rM+Zi5gSQ3uoHW3BNgriJoSQNY5qKSat+VoKLxCmuKu2OKjbu76MYVWqqKIz1gn4gXw+j4n8BMbz48iYmcJjOHAIWkFkkZ012KY4sPvMwBmcGjyFL7/2ZbSGWnFr+624veN2XNN8zaocRE5IrZimWWjfm20CI09CT+Dxc4/jqQtPIW9PP8eyeAs6r8PkzbLv07ZgG3bW70RYCS909wlZfzi47cASwBkceqI9CEgBvDP2DmxWecaNfz36rzg3cg5/fPsfozHauCS7SwghZGHn3XqufNBX79HeBe1TpDky+4OWWbiB+irJHHlB3VM5FZb1iEN1gduUsUOWGwe3bmBqNyqFda8ZAi9gU2wTeqI9uJi6iJPjJ0vagasxlh/DV458Bf/v6P/Du9rfhfu33I+bW2+GwM+9/32l43keiqKUTC7OGCsJ6PYubXv5ZuT06vDKKRfU7QV4E0LISiXwQiGkG3A/e3NWDol8AgndXVJGasbJhlc6iZcQlt1Q0bDiBotGlShkQV7uXauZddsuI2L2/A9cub/M+TS1y6xuqqRiS2wLtsS2IGNkcDlzGb2pXiSN5IK2mzEzeOriU3jq4lPgwGFn/U7c2n4rbmm9BbviuyDyFHdIStmOjbH8GEa0EYzkRjCijWBUG8WYNla49Ca9mOv54FLQbR26rSOpz/29E5JCiPvjaFAb0OhvRKO/EU3+JjQHmtESaEFLoAURJVLzoGtBEBAMBhEMBmEYBnK5HDRNm9eEToyxQi2JIAhQVRV+vx+iSO91Qgghqwt9cxFC5iyVSlUsqBYEAdFodOl2iKw7juMgk8kgm83O+YSO4zgEg0EK416FJvIT+P7p7+N7p76HvkzlAWzLyWY2nu97Hs/3PY+wHMbPb/x5fGjzh7AltmW5d23e3h57G//1uf+Ki6mLsz42KAVxW/ttCEhlJmaQ4BaOVMohMOAWjyxjP0zHng40b2nG4KlBAG7D/9vPvI2r33/1rM/lhdp8rjRubMT7P/V+dOzpqMn25ktSJHz4f30YP/pfP8LhJw5X/byrf/5qvO9T70NqJLWg17ctG0efOlqy7vpfun5B2yRLzBvco8B9/5tXluUanMtjMoSbitQWVZ2vDnsb9uLQyCFk+AwkW4Jqq+DZ5OfkY2cfw9bYVlzXfN205+u6jtHRUQQCAYRCIZoVkxBCCCGEEEJWGS+YNJ/PQ9f1eRVnLbXrrrsOu3btwrFjxwC4oV6PPfYYfvmXf7ni8xhjixY0rtvlC7WL8RwPWZBhokzfXblBhjJQ7qFzIsBt77lSbdC2ow0f+/zH8P3/8X2MXRqrahNKQMEH//sHse32bQsuqD7z8hlkxycHK4QaQth2+7YFbZOsIhLcv3UNhQG0DWoDwnIYpzKnMGQNuQ9zpLLB3ADw8sDL+MXHfhF/ftOf467Ou5Zy78kKlM1mkclULtr2+XwIhUKFiSc0TUM+n6/J9x3HcVAUpRDEPZ8+bc3S8OrAq3j28rN4rvc5DGvlJ4+YrzpfHdqCbWgPtZfvEySEELImUS3FpHVZS1EuqNsLkZkabLOUTQAy4LN9aBHcgWi6rWMiP4GJ/ARSZgo8eCiOAl2ofH7NMQ4ChGn7PjExgccmHsNjRx9DQApgX+M+XNt8La5puQZRNQqe5yEIQsmlt1AfN1ntGGMwDKPQxjeXkJ5RbRSPnX0Mz/Q+A8MuLRZkHIPO6zB4Aw5fvk2t2d+MnfGdiPliC/oZCCFwv799ACSgSWiCX/Tj8MjhskH5xV4ffB2f+PEn8Nfv+2v0RHqWZFcJIWS9W8h593pHfZWkJry2j6kYpve7s6JLb1ktpgZqzxS6Tc0aZLWjsO41h+d4dEe60RXuwuXMZZwcP4mEnpjTNhzm4EDvARzoPYCWQAs+uPmD+OCmD6I50Lw4O71CcBwHSZIgSaWTTjqOUxLS7V1frDq4atm2Ddu2oeul7focx80Y1k1ZAYSQlYbjOASkAAJSAG2htsJ6y7GQNtJIG2lkzAwyRgZZK4ucmYNmzZaKvLh8gg9+yQ+/5EdACiAoBRGUggjJISiCsub7Ptd1u0y181LnAPgxLamO2mXWhqAcxLa6bdhWtw0pPYXedC8uZy4jbaQXtF0GhmNjx3Bs7Bi+fPjLCEkh3NByA25suRE3tNyAznDnmv98We9M28RQbgiD2UEM5YbcJTt5OZwbxmh+FA5bnzPjpc000sk0zifPz/gYVVTRFmxDa7AVrYFWtIfasSG0Ae2hdrQH2+GX/AvaB1mWIcsyIpEINE1DLpebcRKl2di2jUwmg0wmA1mW4ff7oaoqvc8JIYSsChTKTQiZE03TkM1WnoEvGo1SBwZZFI7jIJvNIpvNlu3YY4whoScwmBvESG4EY9oYJvITSBpJZMwM0kgjzaVhOAZsZoMDVwir8It+BOQAInIEdb46NPgb0BxoRmugFRtCG9Dob6STvGVycvwkHjj+AH587scwnPmduC+XlJHCg8cfxIPHH8Tehr24f8v9uLfrXvhE33LvWlUsx8I/HfsnfOnQl2Axa9bH1/vqcXPbzVCECrN+esG8lcbo5DEZnLtMbv7wzfjBn/+gcPuNR96oquNk73v3onVbK868cgYX3ryAvuN9yIxWN9teKB7Cphs3Ye99e9G5b+U0IEuKhA/+9w9i73178dw3nsPFQxfBnDJVg5zb6XT7v78dm27YVJPXPvXiKaRHJxvrG7obsOnG2mybLAOvoM0Ht2DNxOTnwWIVogpFi4h1W3y2XDZGN2JUG8XlzGWYggmTN6E4Cny2Dxxz/zO+dPhL6Ax3otHfOO35jDFkMhlomoZIJAKfb3V8fxJCCCGEEELmJmfmcDlzGYNZt113Qp9AUk8ia2ah2zpMx00PFjgBsiBDFVUEpSAiituW2+hvRHOgGc2BZkh8tVWhZDEsRjDpUvuN3/gN/N7v/V7h9gMPPDBrKPdi4xgHNqXxhLvSyBEUg7ih5QZc5KdMqGjBLX6e2pXiw8LaXaeEcRdr3dqK3/rn38LBfzmINx97E4n+RNlNyH4Zu+/ZjXf92rsQbggvYGcmvfHoGyW3b7j/Bgji1JGVZM0S4bY1+uD2L1z5u1cEBbuiuxC1ozidOA2HOZAcCX7bj5yYm7aZpJ7EJ5/9JD60+UP4r9f91wUXqJLVSdM0JJPJio+RJAnBYBDpdBqaps0pmG4mXhC3qqpQFGVe9Rb9mX48d/k5PN/3PF4ZeKWqiR3mIq7G0RZsQ1uwjd4fhBCyjlEtxSSqpYDb/17uHHMpg204uJNPXckVVQSl0E5k2iYmjAmMa+MYsodg8guboSprZvFi34t4se9FCJyAzbHNuLrxalzddDXag+0lf5scxxXCucuFdguCULi9Uv6mCQFQCLvxlrkG8PSl+/Do2UfxQt8LsFnpuZLBGzAEAxZnzVi/06A2YGd8J+JqfL4/AiFkJgKAABCSQ7heuh5Hho/MGl7Wl+nDv3v83+Hlf/fykuwiIYSQ+Z93E+qrJIuoOKx6JsXB3ShzvTi4m01ZX2mbxedO5a4XX3Iz3J66ELLerZWwbi+o2/tZ1tF7nOM4N3ws2I7h3DBOTZzCUG5oztsZyA7gi4e+iC8f/jJuar0JH9z0Qdyx4Q7IgrwIe70y8TxfCF8rZtt2SUi3d325w7oZYzBNE6Y5va1fEIRpQd3eJbXBE0JWEpEXEfPFyk5K6jAHeSvvLnYeuq1Dt3UYtgHTNmE67mIzG7Zjw2EOHDhgjBVqjDlwbj8lrvRJcgJEXoTIi5B4CRIvQRZkKIICRVDgE3zwie7Cc5QPtK7bZRYYzE3tMmtLWAljp7ITO+p3IGWkcDl9uSYB3YAbQLz/0n7sv7QfANDob8R1zdfhuqbrcE3TNRTSvcowxpDUkxjIDqA/24/B7CAGMpPXB7ODGNVGp42FIXOjWRrOJM7gTOJM2fsb1AZsCG1AR7gDneHOwtIR6phTthXHcfD7/fD7/bAsC7lcbkF18oZhwDAMJJNJqKoKv98/7fyTEEIIWUk4thpHQxNCloVlWRgZGakYohAMBhEO16bxgxAPYwzZbBaZTKbQcWczG5dSl3A2eRbnk+dxMXURl9OXy84CqQs68kIejJv/V54qquiOdGNzdDO21m3F9rrt2FG/gwYcLxLGGF7oewHffOebeGXglZpsk+d4hOQQwnIYASkAVVQhCzIkXgLP8WBgcBwHpmNCt3VoloasmUXKSCFjZGrW0BOSQ/jAxg/g/q33oyfSU5NtLoZ3xt7Bnx38MxwfP17V4ztCHbim6RoIfBWN1A6ALCoXkHEAAqhcuLaIbMvG5//t55EcnAxe+I9f/49o3do6522lR9MYvzyOxGACWkqDmTfBGIMSUKAEFITqQ2ja1IRgXbCWP8KiyYxn0Hu0F+nRNPLpPCRVQrQ5ivZd7QjVh2r6Wt/6T9/C2VfOFm5/4NMfwFXvv6qmr0FWiOKiNS+k20F1Yd08JgtdvcUrMCPLzrRNPH3paWTMokH1DFBtFYrtTuLQE+nBZ275zKzheaqqIhwOQxCoQ5QQQgghhJDVyGEOLqQu4NjoMRwfO47TE6dxLnkOI9pITbYvcAJaAi3ojnRjU3QTNsc2Y1vdNnRHuiHyNE/xYjIMA5qmQdO0RR18Uy7Qyiu65Di3kNzrw/IuHceB47hF547jwLbtiv1clmXhlltuweXLlwvrfvKTn2D37t2L9nN5EnoC//jWP+LI6JGqHt/kb8K2um2QBMltR9FQuS1FvrLMh3jluXN4K/Wf7MfoxVFkRjOwTAv+iB/xzjjad7ZDlGv3nkwOJ/H3v/j3YLb7wysBBZ/8wSehhtSavQZZ4RwAmaLrRcHcAAA/kLEyeHvs7UJBuC7o0MTpfYqejlAH/ua2v8HuhsV/75OVQ9d1jI+Pz/g94TgOLMuCz+er2fedF8Tt8/nmHMRtOiYODR/C85efx3OXn8PZ5NnZnzQHHDg0+hvRFmxDa7B11Uw6TAghZHFRLcXMqJaiSlPDbabeni8d7mQ9MzCZiVGMYiQ/gjFtbFpY8ELF1TiuarwKVzVehZ31O+d07FQc2l0c1l18fT6TthBSDcYYDMMohHCXC7SpxsmJk/jhmR/i9aHXS9ZbvOWGcfNGxWCouBrHjvodZSd0J4QsAgY4hoOTAyfRl+6r+NCEksDRXz26RDu2tHZ/s7Tt786OO1Hnq1umvSGEEFctz7vXO+qrJIQQUhMrMax7Jt44Kn7K9XUgqSdxeuI0LqUvwWHz/88Jy2Hc130fPrDxA9gV30VhgFMUh3UXL7WYyHyxcBxXEtJdvFC7O5lqalvRrW23ojnQvEx7QwhZDtQuA7f+1qjicSoqBnlTu8zalNJTuJy5jL5MH5J6cvYnzEOdr65Qe7G3YS+21W2j2tVlZDs2RrQRN3Q701+47M/2YzAziP5sf9mML7IycODQEmhBV6QLXeEudEe6C0uD2lDVOS9jDLquI5fLQdf1imOxqiGKYiH4m87JCCGErDQUyk0IqQpjDKOjoxULriVJQjwep44mUjOMMeRyOaTTaRiWgdOJ03h77G0cHz2O04nT0G294vMN3kBezMPhFqeXm+d4bIxuxL6Gfbi66Wpc23QtdbAskGmb+PH5H+Mbb39jxlm6qiXyIhrVRjT4GxBX44gokXnP0mk5FhJ6AmPaGEa0EYzkRmoyOOuapmvwS1t+CXd33L1iGgMn8hP4x0P/iO+e+m7VRQi747uxJbZlbp//Ntxg7kp4uMHcy/S18taP3sKjf/1o4fbOu3bi/r+4f3l2Zh0aPDOIL3/sy4Xb9R31+J0Hfwe8QI1r6w7D9EApDsv22UDmLqEn8MylZ6Z9r/AOD7/th+iIuKfzHvz67l+fdVs8zyMUCiEQCCzW7hJCCCGEEEJqhDGGUxOn8PLAy3ht8DW8NfwWUkZqyfdDFVVsr9uOvY17cVWDW6AX9UWXfD/WGsuyoGkacrlczQfWcBwHSZIgSdK0gTC16IPywrm9Zeogoe985zv4gz/4g8Ljf+EXfgFf+tKXFvy6lRwdPYovvPUFJPTErI/lwGFLbAvaQ+2Tv488AKvCkwS4BdBzwcEtmJaxogfp/eRzP8HL33m5cPuOj9+Bd/3au5Zxj8iyyGBy0OvUkHofANGdHOJs4iwupi4CAPJCHnkxP+MmBU7Ab+/7bfz6rl+vbkJSsqqZpomxsbGyYduGYSCfz8M0TUQiEYjiwgaFyLJcCOKe6wSEg9lBvND3Al7oewEvD7yMrDlbZ9vcCJyA5kAz2oJtaA40QxbmO5sDIYSQtYxqKZbXmq+lKA7qnumyHAb3PKBSqZMAwAdYzMJ4fhwjuRGMaqMwnfmFEM9E5EVsr9uOfY37sLdhL9qD7QtuzyiepGym4G6a3JpUgzEG0zSh6zoMw4BhGPMeNGkzG68NvoYfnfsRTk+cnlzP2TAEN4ibcZW3Xa/Wu2HcaiPVnhOyHBzg4shFnBk+AzbDjI8Uyk0IIUuPzrtXF+qrJISQdWq1hHVzKA3p9i7XaDNM3srjXPIcziXOIW/PXA9Sja5wF97f8378XM/PYUNoQ432cG3yJjgvF9a9kmNseJ6fFtTtBXhTW+X6RKHchBCA2mUAVB/M7YNbY76MqF1m+WSMDPoz/ejL9GEsP7ZoryNyIjbHNmNXfBd21O/Ajvod2BTdRLWtNaJZGgayAxjMDGIgO1Cy9Gf6MZQdgsUqDVIhq1VQCpaEdPdEetAT6UF7qB0iX75O3rbtwhgyy1rY3wXHcVAUBX6/H4qi0DkYIYSQFYFCuQkhVUkkEsjlcjPez3EcGhoaFjwAlRDALfzXNA0Xhi/gtQE3rOXY6LFZQ7g9Jm9CEzQ4/NL3Ym8IbcD1zVM9CS4AAQAASURBVNfjptabcGPLjYgokSXfh9UoY2TwvVPfw7eOfwvDueF5b8cn+NAWakNbsA1xNT7vEO7Z2I6NUW200FC40E76sBzG+3rehw9s+gB21O1YlgaDrJnFQ8cfwtePfR1pM13Vc2RexvUt18+/Y9GEOyCuEhFuWMwytKE4toMvfuSLGL04CgDgeA6/+y+/i/oN9Uu/M+vQd//7d/H2028Xbt//l/dj5507l3GPCCELcT55Hm8MvVH2PsmW4Lf9+MS+T+DWtlur2p4sy4hEIpCkCtMpE0IIIYQQQpZczszhpf6X8OzlZ/FC3wsY1UaXe5fK2hLbguubr8eNLTfi2uZrEZBo4p9qOI4DTdOgaRoMo5pq2+rwPA9FUSDLMmRZhiiKy1ZUxRiDruvYt28fTp48Wdi/5557Dt3d3TV/PYtZ+N7J7+GRM4/MGHpSzCf4sLthd2nfgwW3AHomHNw21mqby0W4YdwiVvwgvFwyh7/70N/B1NwAtUAsgE989xNQ/Moy7xlZchrcPgdPcTC3CLf4/4qJ/ESh31ETNehC5f7Hqxqvwl/f+tdoD7XXfLfJymDbNkZHR0smmbBtG7quI5/PF9ZHIhHI8vwK+SVJgqqqUFV1TmGJeSuPN4fexIv9L+LFvhdxNnl2Xq9fiSzIaA20ojXYiiZ/E4XQE0IImRXVUiwvqqVA+bBuBvf8OFt0u9xptoySgcEOc5DQExjJjWAkN7LgGrBy6n312Nu4F3sa9mB3fDeCUrDmrwG4NbyVgru92zSQbX1xHAemaRYCuBcSwu3RTA0HLh/AE+efKNR62pwNgzdgCiYcbva63bgax476HWhQG+hvkpAVYDQziqO9R2Fb0yfgpFBuQghZenTevXpQXyUhhJBpVktYN4/yYd1rhMMc9KX7cCZxpiYBgXsb9uK+7vtwb9e9iKvxGuzh+sAYg23bZQO7y02YvlJwHAdBEMoGdvP8GnqjkGkolJsQAlC7TEG1wdzKlWUZULvMypG38m6Qc2YAQ7kh2Gx6f1MtiZyIrkgXttZtxeboZmyKbkJPpAetwVaqfy2i2zqGs8MYzA1iKDeEoewQBrODGMwNupfZQST0xHLv5pITOMFdeAE8x7sL3EuvdoED546jYSiM8WGMgYHBYQ4c5sBmtnvp2GsquFziJXSGO9ET6cHG6EY3rDvag65wV0kYvmEYyOVy0DRtwTUogiBAVVX4/X7KLSSEELKsKJSbEDKrXC6HRCJR8TGxWAyqqi7NDpE1izGGU8On8MzpZ/BK3ytzHlS8nGHc5fAcj13xXbi17Vbc3nY7ttdvX7SQ6NVqODeMB44/gO+e/C4yZmZe2xA4Ae2hdnSGO5dlkAZjDKPaKHrTvehN98J0zNmfVMHGyEa8f+P7cW/XvUsyi/aoNorvnPwO/uXEvyCpJ6t+Xr2vHje03AC/5F/YDlTTKC+jJDBjKZ14/gS+/V+/Xbi99+f24oP/7YPLszPryMiFEXzxo18Es91Tlfad7fj1r/w6DcIiZBVjjOH1oddxMXVxhgcAMS6Gz9742aoDjjiOQyAQQCgUos8HQgghhBBCllHWzOLZ3mfx1IWn8GL/i1VPrrhSiLyIqxqvwi2tt+C29tuwObqZzjGKeCHVmqYhn88vuGAKcM/nZFmGz+eDLMsrcsKlH/7wh/jABz5QuP2rv/qr+NrXvjZtgJBpmvMeJDScG8Y/vPUPODVxqqrHx9U4dtbvhCQU/b4Y3ODhSrvggxtKXIlYtKyiboz9X96PF/75hcLt933qfbjug9ct4x6RZVNuElAbbh8EB2BKV4ZpmzgxcQJD2SHkxBwMoXJHRUAK4NM3fBo/3/Pz9B2xxjiOg9HRUViWBcYYDMNAPp+fNvlEMBiccz2EVySsqmrV33WMMZxNnMXB/oM42H8Qrw+9vijHVkEpiNagG8Rd76unv2tCCCFzRrUUy4NqKapgYHLiKjbDosANmSkO9IZ7LJYxMhjRRjCqjSJlpGq+exw4bIxuxO74buxp2IMtsS0Q+aUdUFZNcDcFi6xOjDFYlgXDMApB3JZVu8Gf/Zl+PHnhSTzb+yzydh4O58DgDRi8UXXNbqO/EdvrtqPB31Cz/SKE1IZmanjt8mvIZ/Pg2eT3AIVyE0LI8qDz7tWB+ioJIYRUbbWEdXvh3PyU66tYQk/gXOIcLqUuLTgojed4XNd8He7tuhd3d9yNmC9Wo71cf2zbLhvYbdt2TeoTF4sgCJAkCZIkQRRFSJIEQRCon2aNoFBuQoiH2mWuqDaYe5lyQKhdZmWyHRvD2nAh9DlrZpfstWVeRke4A53hTmwIbcCG0Aa0BdvQFmxDS7AFirD6A9sZY9AsDWPaGEbzoxjV3GUkN4IR7cqSG8FwbnjNBm6LvAhFUCDzsnspyJMLL0MSJEj85CLyYmERuMU5dmfMDeu2mAXTNmE5FkzHhOmYMGwDhm1Ad3T30tKh2zrydh66pa+aQG+e47EhtAHdkW5sjGxET7QHGyMb0RXuAmdxyOVy02rv50OWZfj9fqiqSudZhBBClhyFchNCKjJNE6OjoxU7MQKBACKRyBLuFVlrBjID+NGpH+G5M8/hQuLCnJ9v8RY0QYPNL+6MaQsVV+O4vf12vLv93bix9Uao4voNsj85fhL//M4/48fnfwzLmV8jQVSJoifSgw3hDZD4lRHaYjs2BrIDOJ88j6Hc0IK3t71uO+7ouAO3t9+O7XW1C3U3bRMH+w/ih2d/iGd6n5nz/8H2uu21DZnPAZhtF3xwG+WXwYP/+UGcPngagDuj6W984zfQvIk6UxfTQ//lIZx6wQ0j4ngOH//ax9G6tXWZ94oQslCWY+GZS89UHLzcHezGF27+AkRW/cBjURQRiUSgKKu/Q4wQQgghhJDVwrRNvND3Ah4//zie7X121QVxV9ISaMHt7bfjjg134Prm60sDkNcR0zShaRo0TYNtL7ztneM4+Hw++Hw+KIqyKsKd3v/+9+Pxxx8H4AZWvfXWW9izZ8+0xzmOM22AkLfM5Pm+5/FPR/8JOSs3635w4LApugkd4Y7phWUmgEpvPxHlC535K/cJVy5XYb1aaiSFz//bz8PS3d9zy7YWfPz/fXxV/G2RReAAKDf3qxfMrWLaQFHGGAazgzgxcQJpPj1rMDcA3NN5D/70pj9FRKG+8bXAcRyMjY0VJp7Qdb3sRAt+vx+BQKCqbXrfd36/H7IsV1UQPJ4fx8v9L+OlgZdwsP8ghnPDc/5ZqhFX42gJtKA12IqQHFqU1yCEELK+UC3F0qNaiirNVofEAQig9ByhOKD7SkCOZmgYzg1jJDuCCW0CcNxz9FpSBAXb67djd3w3dsd3oyNU5tx/GXAcV1Vw90rY1/XKcRyYplmYNM67XuthITaz8ebQm/jpxZ/i8MhhWLwFkzdhcmbVQdyA2+a6rW4b6tX6mu4fIaS2bMfGwb6DSKQTUG23xp1CuQkhZPnQeffKRn2VhBBCamI1hHVzmB7SLWDV1RqZjoneVC/OJ89jQp9Y8PYETsD1zdfjnq576HyyhhhjZcO6LcsqW8+xEnAcVwjoLr4UBGG5d43MEYVyE0KKUbvMFdUGc4tw63SX6BiR2mVWB8YYMmYGg9lBDOWGMJIbgc2WLxOqzleHRn8jGtQGNPgbEFfjqPPVIabEEPVFEVEiCMthhOUwAlJgUSdYd5iDnJlDxswgY2TcSzODlJ5CykghqSeR0BNI6klM6BOYyF9Z9AlolrZo+7VcOHDwiT6oogqfcOVS9MEn+qAIyuSl4IPAr63jbMuxoFka8lYemqUVlpyZQ87KIWfmYDgLD7teTC2BFvREetAV7EKH2oEWuQWt/oXVqvM8X1KPTwghhCwFCuUmhMzIcRyMjIxUDFuQJAnxeJyK68mc5cwc9l/ajx+e+CGO9x8Hz+bewGXxFvJCHha/OmZ+KqYICm5quQnv3vBuvGvDuxBX48u9S4vOdmw83/c8HnjnAbwy+Mq8tsGBQ1uoDZuim1Dvq1/Rnz1ZM4sLyQs4nzyPvJ1f8PZiSgzXNl+LfQ37sCu+C1tiWxCUg1U913IsnE2cxeGRw3h54GW81P8SMma5dIrKQlII1zZfW/vBMQxAFrMXjPjhNsovsfG+cXzxV74Iy3A/azZevxEf/fuPLv2OrBMXD13E13/764Xb133oOrzvP79vGfeIEFJLaSONpy8+XXHmzrs77sZfXv+XSKfTcyqa8vv9CIfD1HFKCCGEEELIImGM4fj4cTx65lE8cf6JmgxOWekCUgC3td2GOzvuxG1tt1XdHrda2baNfD6PXC4H0zQXvD0vmFRVVSiKsqLbc8s5d+4cdu7ciXzebd+955578OSTT1b9fMbYtMFBE7kJfOWtr+DF/her2oZP8GFXfBeivmiZF4AbNFapt98PdyBc8eINkFvlHvmrR3Do8UPuDQ74D1/5D2jf2b6s+0SWWQbl+xksuO+TGeZY0CwNx0aPod/uhynM/tnXqDbiL275C9zcdvMCdpYsN9u20d/fj3Q6XfE7z+fzIRSavShYURSoqgqfzzdr+6Ru63hr+C281P8SXup/CcfHj895/6sh8RKaA81oCbSgOdAMWaCiZEIIIbVFtRRLi2op5oDBPT+odL4swD1nrrKpwnIsDOeGMZAZwGBmEIZlgGc8OHDgGQ8ePDjGlaybj4gSwY76HdhZvxO74rvQ7G9ese0pHMcVwrqLL8tdX6k/w0rnBdCUC6GpxSR6lYxr43jm8jN4+uLTGDKG3CBu3gTjqh92woFDe6gdW+u2IqpEF29nCSE1lTWzeOL8E+AYB9VWkRNzFMpNCCHLhM67VzbqqySEELKoVktYt1eHVFyPtAqaAifyE7iQuoBLqUswnYXXyPEcj2ubrsVdHXfhro670BRoqsFekqkcxynbTroYkxXWgiAIkCSpZKGg7pWNQrkJIcWoXaZItcHcPNw++CWoUad2mdXJYQ7GtDEM54YxnBvGeH4crGJhx/LyCT74JT9UUYUiKFAEBZIgQeIliLwIgRPAc3xhcncG5vbxMxs2s2E5FkzbhOEYMGwDeTuPvHVlqUH+0GrBczxUUYUqqvCL7u9TlYquX/n9Ul3JzCzHQtbMTlsyZgZZMwuHraTGAgAMEJmIRqERHf4OtAXb0BZsQ2uwFe2hdsR9c8spFEURqqpCVVWI4jIEThFCCFk3KJSbEFIWYwzj4+PQdX3Gx/A8j3g8TgespGqMMRwZPYIfnP4B9p/ZD6YzCGzunUg2Z0MTtbJh3BIvISSHEJSC8Et++EU/fKIPsiBD5mWIvAie48Fzbmuewxw4zIHpmDAdE4ZtlMwclTWzSBvpRZ05igOHPQ17cMeGO/DuDe9GT6RnTTUYTOQn8OiZR/Gdk9/B5czleW1D4iV0R7qxKboJfslf4z1cXA5zMJAdwLnEOQzlhmq67Qa1Ae2hdjSoDYgqUSiiAoETYDomMkYG4/lx9Gf6cSm9sAIBDhy21m3F9rrtizdznAM3mHu2I9MA3EINQgghq1ZvuhevDFSeoOP3r/59/NqOX0MqlYKmVT9rK8/zCIfD8PtX1/ECIYQQQgghK1kin8Dj5x/Hw6cfxsmJkzXfPs/xZdtzJV4qFKoBbpGawxxYjgXDNqDbOvJWHpqlIWtmkbNyNd+3YhIv4caWG3F3591494Z3r5lgBsdxkM/noWlaxT6hanEcB1mW4ff74fP51lRb90Id7DuI/37wv2M4NwwwFEK7eHYluOtKgJf3r1FtxO763ZD4GZKEDbhhw4A7oG3qogLwYVUMdiOkJqot/C/DYQ4upC7gaPYoTL66/pRf3vrL+INr/wCqqM7vRcmyMAwD2WwWQ0NDs37vybKMcDg843eZV+Tr9/srDpx0mIPTE6cLE+e+MfTGohXUh+VwIYi7Xq0vHEcRQgghhKw7FtyJrCqR4J47zxFjDAk9gcHsIAayAxjPj5d9HMe4wvl+8bl/SZj3LOHd9b567KzfiR3xHdhRvwONauOqbGvhOG7G0O5yy2r8GeeDMQbHcQrB28WLFyizlMM8LGbhraG3cKDvAF4eeRk6p8PirDm3LQmcgK5wF7bUbUFACizOzhJCFo0Xyl2MQrkJIYQQQgghZIXwwrrtKZcrKSnEC+eeGti9AtmOjf5sPy4kL9R03O/u+G7c2XEn7uy4Ez2Rnpptl5TnTW5YHNK9VJMbzhXP89OCuimjY+WgUG5CCKlAv7LMxqtfp683UgXLsTCmjWFEG8GoNorx/PjKCxcms5J5uTAWrNwlBW4vLsYYNEtDxswgY2SQNtPupZFG1swue/A9xzjIjgzFVgo1UrIgoyXQUgjrbgu2oSXYgpZACxRBqbg9WZYLAd08v0IbGwghhKxaFMpNCCkrmUwim81WfExdXR18Pt8S7RFZzdJGGo+dfQzfO/09nB87D9VS5x3GnRfyMAV3MLzMy6hX6xHzxRBTYogqUfjExQn50C0dSSOJhJ7ARH4C4/lxZM3K75H52hDagHe1vwvv2vAuXNN4DSRhhsCNFcxhDl4dfNUNYL+4f96B0H7Rj82xzeiKdM0cPLKKZIwMzifP40LqAnR74QE3S6E50Iy9DXsRkkOL/2I23GDuSji4wdzUPkIIIavaoeFDOJM4M+P9PMfjS3d9CTe33Qxd15FMJmFZ0ydkmYmiKIhEIlScRAghhBBCyDw5zMErA6/g+6e/j2cuPbOgCd+KCZyAOl8d6tQ6xJQYIkoEQSlYkzZd27GRMTNIGSkk9SQm8hOY0Cdg2LWfcJHneFzTdA3u7rgbd3fejUZ/Y81fYzExxkqCuGvRXewVN/l8vorBpOtRxsjgf7/xv/G9U9+r6vE8x2N3fDc2RTfN/N5wAGQqbIQDEAQFcpP1ZbbQPQWzDgpI6Am8mnoVKTtV1Ut2hjvxV7f+FfY27K12L8kyGh4ehmmaSKfTswZyi6KIaDQ67XOY4zj4fD74/X4oysyFv4PZwUII98sDL88Y1LhQIiei0d+I5kAzmgPNq25yY0IIIYSQRVXNwGAfAHmBL2PrGMoOYTA7iKHc0JxrwjjGTQvqnjqRF3flBL9erceOOjege3v9djT7m9fk4EWO4wrh3OUCu4vvr3R9qTDGCovjOCXXZ1ps24bjrIwBxZczl/GzwZ/hqd6nMGQOgXHzaytUBAUboxuxMbpx1oGShJCVi0K5KZSbEEIIIYQQsgo5KB/WvZJ44dxTlxXSvKlZGi6lLuFS6hKSRrJm2+0Kd+GODXfgjo47sCe+BwJPdXVLyWuLLQ7q9paVErHjBXXLsly4pHC55UGh3IQQMgsDQL7Kx9agH56sPw5zMJGfwFh+DOPaOMbz48hZs83GThabKqrTgrYDUqBwXeQpx2GlcpiDrJlF2kgXlpSRQtpI12yM4FwIjgDFUSDbM39BNKgNaA22ojXYirZAW+F6VCmt6ec4DoqiFOr512LtFCGEkKVHodyEkGlyuRwSiUTFxwSDQYTD4aXZIbJqnRg/gW+f+DZ+fP7HMA0TPtsH0Zn7CbXDOdAEDUxkaPQ3otHfiAZ/A8JyeFlPjHRLx1jenXltJDeChJ6o+Wv4RT9ubLkRt7TdglvabkFbsK3mr1ErjDGcmjiFJ84/gcfPP47B7OC8txVRItga24r2UDt4bu11IDrMQX+mH+eT52s6i3YtxZQYdsV3oSnQtLQvbALQZnkMBXMTQsiq5zAHz/Y+WzGMJiyH8e33fxsbQhvAGEM6nUY2m6268InjOIRCIQQCAWpMJ4QQQgghpEqD2UE8cuYRPHLmEfRl+ha8PQ4c6tV6NPmb0OhvRMwXW9L2PsYYsmYWY/kxjGqjGM2NIm2ma/46exv24j2d78HdnXev2DZcx3GQz+eRz+drFsQtCAJUVYXf76dJkWbwQt8L+POX/rzq9vKgFMQNLTcg5otVfmAebnHzTFQAq3+eS0LmhgGo9BHvhzvwdJZ8PNMxcTR3FOcy56p6WZ7j8Ws7fw2/ve+3IQs0imAl6+vrm3cgtyRJ8Pv9UFW17ODHrJnFa4OvFUK4zyWr+/uZj7AcLoRwx9X4muxLJYQQQgipmRzcCXwq8QOoUbMGYwwT+oQb0J0dwnh+HAw1KNNnmBbUzYNHvVKP3XW7sT22Hdti27AhtIGOD4sUh3NXuj4bL2Tbu168rvi+1YLneWScDJ4feB4/ufwTvJN4Z0HbC8thbI5tRkeog0KFCFkDKJSbQrkJIYQQQgghawRD+bDuldaUVRzQXRzcvYxDgBJ6Ar2pXvSme2saAhhTYrit/Ta8q/1duLn1ZgTlYM22vdZMbZOdSztspXbg4vbgmcK6bduu5Y8yL6IolgR1S5JE4+KWAIVyE0JIFarJAfGIcOvZ6SuMLEDeymMiP4EJfQKJfAIJPUFB3TUkcMJk2LbohyqpJcHbqqhSDcoaxBiDbuslId3epWZV+yG/kB0AZEeG7MhVZ9CpoorWQCtagi1oC06GdbcEWqCIClRVhaqqkGUay0EIIWT+KJSbEFLCMAyMjY1V7JxQFAV1dXXUgE/KMm0T+y/tx0PHH8KhkUMQHXFBYdy8j0dDuAEtwZYVP6jYsA0M54YxlBvCUHZoURpzOkIduKHlBlzffD2ubb4WcTVe89eYC9uxcXT0KA70HsAzl57BhdSFBW2v0d+ILbEtaPI3rZvPmJyZw6X0JVxMXUTaqH0g0FzV++qxrW4bmgPNy/d/oGPWUAzwcIO518efCSGErEmaqWH/pf3Q7Zk/9DdFN+GBn3sAASkAADBNE8lkEoZRKXmslCRJiEQi1JBOCCGEEELIDAzbwIHeA3j4zMN4qf8lOMxZ0PZEXkRLoAWtwVY0+5shCSsrGVizNAznhjGcG8ZgdrDiOcl87Kjfgbs77sbdnXejO9Jd023PlWma0HUd+Xx+TudRlXAcVyhYUhSlJttci8a0Mfyv1/4Xfnz+x1U/pyvchX2N+yDys/SnOAAyFe4X4LadErIeVQrckwH4UF0fBIB+px+vj74Ow6nu83NTdBP+8ta/xM76ndXtK1lyJ06cqCqQOxKJgOd58DxfmHxCkkqPZ2zHxrGxYzjYfxAv97+MIyNHYLHZ0h7nR+ZlNPob0RRoQrO/GaqkLsrrEEIIIYSsSQxAFu659Ew4uOfRi1CWaNgGRnIjGMoNYTA7uLiDQxkQkkLYG9+LPXV7sKt+F7ZEtkDmZdi2DcdxYNv2qguQJgsniiJkWUbOyeHFoRfx5KUn8drQawtuB24JtGBTdBMa/Y3rptaTkPWAQrkplJsQQgghhBCyxjFMhnQXB3avtGZDDqWB3fyUdUuAMYax/Bh6U724nLlc0zpDkRdxTdM1uL3tdtzWfhu6wl1rpo2NMQbHceA4zozXy923FJMgekHdPM9XvO44btup166+3O3rHMcVwrllWYYsyxAEmiCx1iiUmxBCqmTBDeau5muRgxvMXaNJsgkB3DqMlJFCUk8ipacKYcJ5O7/cu7aiCJwAVVQnlyuB26o4Gbwt8TT5Cyll2ua0oO6UkULWzC7K6/EOD9mRoTgKODb3v0UOHBr8biZdW6ANbeE2dNV3YXPjZjSHljEzixBCyKpEodyEkALLsjA6OlroLChHFEXE43Hw/MoNRibLY1QbxXdPfRf/evJfMaqNQnREqJYKgc29Y8cn+VAfqUdLrAURJbIqT3IYY0gbaQxkBzCQHcCYNga2CD3TG0IbcFXjVdgd341d8V3YHNsMRVi8IBTbsXEmcQZvDr+J1wZfwysDryBlpBa0TQ4c2kPt2BLbgpgvVqM9XX0YY0jqSfRmetGX7kPGrJSqUlsCJ6A91I6N0Y0rp6BcgztbZiUCAD8omJsQQlaxkdwInrv8XMXjpDs23IG/v+PvC5OzMMaQy+WQSqXmVNAUCAQQCoXoXIYQQgghhBC4x9XvjL2DR88+ih+f/zGSenJB2xM4Aa3BVmwIbUCTvwkCvzoK/hljSOgJDGYHMZAdwHh+vKbb3xjZiLs678LdHXdjW922RW/rtm0buq5D13UYhgHbtmu2bUVRoKoqfD4fnVdVYDs2vn/6+/jcm5+ruu1c5mVc3XQ12kPt1b3IbG2nflDxMlm/DAAz1VTzAIJXruevPLYSDtBkDa+PvI6h3FBVLy9wAn5t16/hN/f+5qL215H5OXz4cMX7BUFAJBKB3++H3++Hz+cr+e6+nL6Mg/0H8VL/S3hl8JVFm2yXA4c6Xx2aAk1o8jehzkcTphNCCCGELIgNN5i7Eh5uMPciHnYxxpA1sxjKDWEoN4SR3AhMZ7biqIURORFb6rZgX8M+7G3Yi72Ne9GsNk8LEvGW4vVkdRIEoRDMIkkSUlYKz/U9h59e/CleGXhlwZMJSbyErnAXNkY3IigHZ38CIWTVoVDuFVJDTQghhBBCCCFLbWpIt3e5Us0U1u1drzGHORjJjeBy5jL6Mn0w7OomeK9We7Adt7XfhlvbbsV1zddBFZd3sm4vHLuaQO1yAdtr2dTfDQDwPA9BEJa0tkMQhEJAt9ceTBaGQrkJIWQOHAA5VH+8qACQQZkgZFGZjomMkUHGzCBrZgtLzswhZ+UWPGn1SiHzMhRRgSIo8Ik++AQffKIPqqiWXKfAbVJLtmMXArq9sG5vqUmWGgNEJkKxFUhObc5tVElFS7QF7XXt6K7rRnfEXTaENkDi6fyJEELIdBTKTQgB4M7WOTo6CsuaueCa4zjE43FqmCcl3hl7Bw8efxBPnH8CpmNCsiX4bN+cw7hlQUZjoBFNsSZEg1Fw/No6uTdsoxDsMpgdXLQBNQInFAY9dEe60RnuRGuwFc2BZjSoDZAFuartaJaGoewQ+jP9uJC6gHPJczg1cQonxk9As7Sa7KvES+iOdGNTdBP8kr8m21wrikPdh3JDGNVGa97IJ3ACmvxNaAu1oTXYuvIaDRjccJnZxgGJcGfIXFsfGTXxztg7ZYsYOI7Djvody7BHZMkwTBZDFS+saCmHK1r4Mgu9z8giOT1xGodHKgfifHz3x/GJqz9Rss62bSSTSeTz1c9e6wXr+Hy+ee0rIYQQQgghq93l9GU8cf4JPHbuMZxPnl/w9hr9jegMd6It2AaRX/0pwHkrj8HsIPoz/RjKDcFmtQu1bg204s6OO3Fnx524uvHqmgSXW5YFwzAKS6U+nvkQRRGqqsLv90MQVkfQ+nI6NHwI//PV/4l3xt6p+jlN/iZc23xt9QOpHACV5nMU4IaIlUHthWRdmO09EsTk4Msqg7mZynAucw5HRo5U/b3QHenGZ27+DPY17qvq8WRpVArlFkURra2tCIfDhe+8rJnFqwOv4mD/QRzsP4hL6UuLtm9BKYgmfxOaAk1oUBsgCSus344QQghZQ+jcaJ0y4dYhVSLAnehqiWojHOYgkU9gWBvGUHYIY/mxJRkEWu+rx56GPdjTsAe747uxs37ntHBlxti0oO5yt8ny4TgOoihCkqSSS0EQcDF1Ec/2PotnLj2DQyOHavJ3FVNi6In2YENow5poByaEzIxCuSmUmxBCFgudjxNCCCGrkDcGbWpQ92rIsisO6ObKXF9AeLfDHIxoI+hL96Ev0wfd1muyyx5FUHBt87W4pfUW3NJ2C7rD3fMO05spNLuagG1SHcdxYFlWyYSXjuNAEIRCWLd3fTHxPD8tpJtCGOeGQrkJIWvZorTLVJsF4uEB+OBmgxCyxBhjMBwDmqUhb+WRt/LQbR15Ow/DNgqL6ZiFxXJqOzanmMiJEHl3kXjJvRQkyLxcuJQFd1EEpXCpCAp4bnGPKwmZC4c5yJgZpPV0SVh3ykjNezwexzjIjgzZluecXTfjfnIODN6AwRvgBR7tofZCSHdPpKdwPSSHavJ6hBBCVicK5SaEgDGGsbExGEblUcexWAyquryzq5KVwXIsHOg9gAfeeQBvDr8JMEBy5h7GzYFD3B9HS7AF8UgcvMKvi8BPb1bkgewA+jP9yFm5JXvtoBREWA7DL/nhE3yFARKWYyFv55E1s0jqyUXdp4gcwcboRnSEO2iARpVsx8aEPoFxbRwT+gSSehIZMzOnATOqqCKiRFDnq0NcjaPeV1+T4J9FxeDOkjlbWwsFc5f1g9M/KPs3wnM8PrT5Q8uwR2TReIVNFiYLnBYDD3cQqgD3fUdt9qRGGGN4bfC1WUNt/urWv8IvbPyFaes1TUMqlZrToF9VVUsCdgghhBBCCFnLBrODeOrCU3jywpM4MnpkwdsLSkF0RbrQEepY05PtWY6FoZw7cWF/pr+mEy3GlBhub78dd3TcgZtabqrq92hZFizLgmmaME0ThmEsyuAXnuehqipUVYUsVzfJ43rXn+nH5978HH58/sdVP0fgBOxp2IOeSM/cBr9ocIPEZuLHjMXK1F5I1o0MZm4jVa4sntneU4Db9+AH0nYarw2+hvH8eFW7wYHDh7d9GJ+4+hMISDOk5ZMlNTWUm+M4yLIMv9+P5uZmiJKIE+MncLD/IF7oewGHhw/DYotTVC/xEpr8TWj0N6Ip0ER/I4QQQsgSonOjdayaiXlEuOfWy8B2bIzlxzCcG8ZwbhgT+QmwGWddrx0OHHoiPdgZ34md9TuxK74LW2Jb4BMrT3TtBbN4QSOVrlOAy/wJglAI2xZFsbAIglBoU9JtHW8MvoHn+57H833P42LqYk1eW+RFdIQ60B3pRswXq8k2CSErH4VyUyg3IYQsFjofJ4QQQtYQhtKQbu9yNSaWcFUsKHP9yiVjDGP6WCGge8Hjspm3aQ4c48CBQ4u/BTe23ogbmm7A1U1XIygFS8KzywVuU7D28vImvvRqPi3LguM4hZDu4jbfxQrr5jgOkiRBUZRCUDeFdFdGodyEkLVsUdtl9CtLtWS4dbz0tURWOMYYbGa7i+NeOswpLF49R3F0I8e5x/Acx4HnePDgwXM8BE6AwAsQOAE8x9NxGVnzGGPQLG1aUHfaSM9pYive4aE4CmRHBsdq877xArpN3oTNl2aTNKgN6In0oCvShZ5ID3qiPegOd6PR30jvW0IIWQcoCZMQgkQiMWsgdygUokBugqSexMOnH8ZDJx7CQHYAYIDsyPDZPvCs+o4fv+hHa7AVLcEWKH7FbThbR+cePMejKdCEpkAT9jbsRdJIoj/Tj4HMACb0iUV97YyZQcbMLOprlMNzPNqD7eiJ9qDeV08nm3Mk8ALiahxxNV5Y5zVCaJYG3dZhOiZsxwYDA8/xEDl3NjxVVBGQAqszAP1K0AWyqBwybMENzqBgbrJeMLh/996yVEVLzpXFC6nxQrrFKwu9/8g8cRyHa5quQdpIVzwW+h8H/wdaAi24rvm6kvWqqkJRFKTTaWSz2apeU9M06LqOUCiEQIACbwghhBBCyNrCGMPZxFkc6D2AZy49g2Njxxa8TYET0B5qR1e4C3E17rbveYNrAPc6K7o+dV3JDs7wIuXOK2ca1FJmgEutibyItmAb2oJthYkW+zJ96M/0I2/nF7TtCX0Cj559FI+efRSKoODGlhvx7g3vxu1tt6NOqSsMxLBtG6ZpFgZlLBae5+Hz+QpB3NR+W52J/AS+dvRreOjEQ3MKbW9QG3BN0zUIysG5vWBxu0w5XhsNIeudiJmD9iyUhnL7MNneOhMGIAuEAiG8e8O7cXL8JN4Ze2fWcDwGhodOPIRnep/Bn9zwJ3j3hndX/zOQRSWKInw+HxRFQc7O4VD+EF5+5WW82P9i1aHrc8VzPOp99YUQ7pgSo+9bQgghhJCl5oN7bl3p+N+CG95dOY96UQi8gEZ/Ixr9je6uOBZGtVGM5EYwoo0sWkg3A8PZ5FmcTZ7FD8/+0N0XTsCm6CbsqN+BbXXbsKN+B7bEtpRMLMdxHARBqGoi7EoB3sWBMestxJvjuEIIS7lFFMWy5w22Y+P4+HG8MvAKXh54GW8MvTGnQYuziatxdEe60RZsW501h4QQQgghhBBCCCFkcXFwx5NNbRp0ipbiwO6VbKYaxypx4BBHHHFfHHt8e5A2026bbm4EaTNdtk2X84odWdH1CjLJDPYn92P/8f0QOAEboxuxp2EP9jTswcboRogcteGtNBzHFSZY9EwN6tZ1HbZtgzFWdmLGhdaUMMZgGEYhP4RCugkhhCwaBW7eQB7VHVcZcOvhfQCkRdwvQhaI4ziInAgR4vRzH0JIRRzHwS/54Zf80ya6MWyjENRdHNadNbPTzqEd3oHGa9CYBpGJkG0ZsiMvaN94xsNn++CzfWAcg8mbMHgDFmdhRHNrtF4ZfKXkOUEpiO5IN7oj3W5Y95XAbqqrIYSQtYVjxdOtEELWnVQqhUymckCvz+dDXV3dEu0RWYnOJc7hoRMP4YdnfwjN0sAxDoqtQHGUqmcS4sChwd+AtmAb6tQ6cDJHM9iVoVkaBrODGMgMYDg3DItVGgW08tX56tAZ7sSG0AbIwsJObMk65sAN5p7tqFUEBXMXWdRZS8nS84JhTFQeILqcRLgdYBTQTeYpZ+bwzKVnKobbheQQvnXft7AxurHs/YZhIJFIwLKqf6PIsoxoNFpS7EQIIYQQQshqkzJSeG3wNRzsO4gX+l5Af7Z/7hu5MsiDYxx48OAYh6gcRWeoE22BNkicNDkIZYGDURbF1NDuSoHe1Tx2BowxjOXHcDl9GX2ZPmiWNvu+Ff1uOXDgGQ8ePHjGF37fPHOXjZGNuLrpalzddDW6w92LNvCB53koilKY6IgGWFQvqSfxrXe+hQeOP4CsWd3kUIAb9L47vhs9kZ75/b41VA7lDqBiwSe1F5J1wwKQq3B/EO4AAA+D+/6qpjnpyvssoSfw+uDrSOiJqnfrro678EfX/9G0wlKydE6dOgVJlnApewmHRg65S+YQLH5xGt0jcgSNgUY0+ZsQV+NUdEsIIYSsEHRutM4xuOcLswXBKCid0GcF8EK6R7XRQkh3ub/lxcKBQ2e4E1tiW7C1biu2xrZic2wzWgIti9Ku5IVzTw3tZowV1lW6DqCwbrHxPA+O4woh21OvC4IAnufLLtUwHRMnxk7gzeE38frQ63hz6E2kjFRNf4aAFEBnuBMdoY65TyRHCFlTsmYWT5x/omTd0V89ukx7s7h2f3N3ye07O+5EnY/GzBBCyGKh83FCCCFkHXNQPrB7pdUf1phmaRjNuW264/nxRZl0URVV7Kjfgd3x3dgT34PWYCvVAa4ixUHdpmkWArsBTAvpFkWx6jblalBI93RT24pubbuV6rwIIWvGkrTLOHBrcecyKYsAt1+eSisJIWTdc5iDrJkthHQXL6ZTNJCKAZIjQXEUiE5tv0AKAd28BcbNfg4v8RI6w53Twrq7wl3wib6a7htZvYrr2RzHKaybusz0XACFWjRvKV7n1ad5l4SQ+aPTEkLWsXQ6PWsgtyzLiMViS7RHZCWxHRvP9z2Ph44/hJcGXgIAcIyDaqtQ7OpHnMiCjLZgG9qCbe4Jgwx3qV3fz5qiimphdiSHORjVRjGUHcJQbmhOA/uXU1SJoi3Yhg2hDTRAg9QGD8APd1BcpXYLC+4smj5QIDBZGxjczicTlQOXVgoLk8E1ItzvezrjJHPgl/y4qfUm/Ozyz2YcPJw20vjN/b+JB+57AE2Bpmn3y7KMhoYGZDIZZDKZqgbYGoaBkZERBINBBINBamwkhBBCCCGrwkR+AoeGD+Gt4bfw2uBreGf8nenH0VeCoHnGl1wWh24Xr+OuNKiIvIjmQDPaAm0IKSF3W3MpjlwubMrlQpUL7Ib7O40jjngwjr2BvUgaSQxmBzGcHUbOyhUmsuS8f1VObOk5mzyLs8mz+O6p7yKqRHFV41W4qvEq7I7vhl/yL+hHEgQBPp8PPp+PBlHMw6g2igfeeQDfPvntOYVxA0BrsBVXNVwFVVLn9+JeG9FMRFQM5CZkXfEmDZzp+8CC23bp4eBO+FlNMHcWgN/tC7uz404cHzuOE+Mnqho4+fSlp3Gw/yB+a+9v4SPbPwJJkGZ9Dqmtfzr9TzgycgRpIw3GMaTFNBy+diGGPsGHpkATmvxNaPQ3UjEtIYQQQshK5B3/Z1G5DUm/crmCgrm9NjsvAMJ2bIznxwtB3WP5MVjO4s3yzsBwIXUBF1IX8NTFpwrrg1IQm6KbsCm2CZuim9AT6cHG6EY0qA0LanuaS2j1bIoHL802mKn4OZ5yA5qmXq8VxhgGs4M4NnYMR0eO4vDIYbwz9k7Fyc3nSxEUtAfb0RHuQJ2vjtoKCSGEEEIIIYQQQsji4FF+PDnD9KBub1kDVFHFhvAGbAhvgOmYGNfGC225hm3U5DU0S8MbQ2/gjaE3AAB1Sh12xndiV3wXdsd3o16tr8nrLJWpba/zbYst1yY89fpKwHFcIXDb53NrTBhjJSHd+Xwetu0W8PI8D1EUIUlS4XK+7bqMMRiGAcMwCvtCId2EEEIWxMsDMTDZ3z4bG25+iAi3b55q4QkhZN3iOR4hOYSQHCpZzxiDbutIG2lkzAxSRgoZI4O0kUbSSEJ2ZMi2DIEt/EtEciRIjjvGw+ZsmLwJkzdhc3bZDCvTMXEmcQZnEmdK1nPg0BpsLeTHeaHd3ZFuxJQYnWutcl64tm3bZS+LA7i960tlaki3IAgll9517zYhpBRFpBGyTmWzWaTT6YqPEUURdXVUaL3eTOQn8PCZh/GvJ/8VfZk+AIDgCFBsBbIjz/LsSRElgvZgO5oCTe5BmBfGTX9OVeM5Ho3+RjT6G7Ebu6HbOkZyIxjVRjGijSCpJ5d7FwEAAicgrsbREmhBS7AFASmw3LtE1iIBbkP8bFk3JtyCDBX0eUNWLwdup5P397waeQHdHAAJNCEHqVq9Wo9rm67Fq4OvzviYwewgfnP/b+Ib7/0GIkpk2v0cxyEUCkFVVSQSiUKBUCWMMaTTaWiahkgkAkVZQaOrCSGEEELIupfSUzgxdgLHx47j+NhxvD3yNvrSfYVgbQ4cfMxXcptncz8Jq/fVoyXYgga1AQJPFY1gmPW8nAOHqBBFNBzF1tBWZMwMhnPDGM4Nzzm0uZyEnsCB3gM40HsAAidgc2wzrmq8Cnsb9qIr3DVr/w3HcZBlGYqiQFEUSBIFwM7HyfGTePD4g/jRuR/BdOY2c1pACmBvw160BlsXthOzFSfTaSwhpSS4bazlmCgN5QbmFsydA+AHeJHHzvhOtIXa8Prg61VNrqtZGv7PG/8Hj5x5BH98wx/jxpYbZ30OqZ0X+14EgJoFcgucgAa1AU0BN4Q7LIeptoIQQgghZDXwBgPP1nSjwz1XqL5kckkJvIAGfwMa/A0A3D7vpJ7EaH4UY9oYxrQx5Kzcou9Hxszg0MghHBo5VLI+KAXRHelGV7gLneFOdEXcyw2hDUteX7gY4dm1YNomzqfO4/TEaZycOImT4ydxfOw4JvSJRXtNmZfRFmxDe6gdDf4G8BwV8xBCCCGEEEIIIYSQZcLBHTMqwK3zKOZUWFYhiZfcSb4DTWCMIaWnCm25KSNVs9cZ18fxfN/zeL7veQBAc6AZu+K7sLN+J3bU70BUidbstWbihTx5AVDV3J4awr2YvGDu4pCs4rCsSstS8MKxi2s9HceBaZqFJZfLFUK9BEEohHR7y3x+j+VCur1wbq/2dCW2sxNCCFlhOEyGa+dR/bGbl0kgwu2fpzQ8QgghV3AcB5/og0/0oQENJfc5zCkEdKd1Nyckn89DN3RYzmyDQioTmADBFuCzfWAcg8VZhZBuxlUebMjA0JfpQ1+mDy/0vVByX1SJoivcha5IlxvYHe5GV6QL7aF2SDyN+VspbNuGbduwLKtw6V1fqvaB+WCMFSb2mk1xQLcgCBBFseQ6tQGQ9YhOQwhZh3K5HJLJymG+PM+jrq6OZrRYJxhjODRyCN89+V08eeFJGI4BMEB2ZCi2UvVsQBw4NAYa0RHsQMQXmRyUQmHcNaEICtpD7WgPtQNwZ0waz49jIj+BifwEEnqiJkEvs5F5GTFfDPVqPRrUBtT56iikhywNAUAAsw+Ks1AIxaDPHrKqWHCDYhbWvreyMLg/kwHqDCNV6wh3IG2kcXz8+IyPOZM4g99++rfx1fd8FX7JX/YxoigiHo8jl8shlUpV1cBpWRbGxsagqirC4TAEgY5xCCGEEEKIyyvEX6zrjuNgIj+BvnQfLqcv43LqMi6nL6M31YtRbXTa/gRQm+CagBRAS6AFzYFm+ERfTba5XnEch5AcQkgOYWN0I7JmFsO5YYxqozWZYNFmNk6Mn8CJ8RP4lxP/gogSwZ74Huxt2IvdDbsLA2YkSSoJ4qYiiPnRbR0/vfhTfPfkd/Hm8Jtzfr7ACdhatxVbY1sX3n5uo3J7kQi37ZQQMknEzKHcNtxC/6nd4HMN5lYBSG5h5J0dd+LUxCm8M/YOHDZ7G9S55Dl8/KmP466Ou/CH1/4hNoQ2zPocUhsLDeSOKlE0+d2BsvW+euojJYQQQghZrQS4dUWzZVbnr1yu0GDuYhzHIeqLIuqLYlN0EwB3YqAxbQxj+TGMa+OY0CeqOmephYyZwdHRozg6enTafXW+OmwIbUB7qB1twTa0BdvQEmhZk+2UjDGM5cfQm+7FxdRFXExdxPnkeZxLnkNvqhcWW/wiIZ/gQ2uwFW3BNgriJoQQQgghhBBCCCGrA4/pdR0eL5ybTbn0rlfOxlp2HMch4osg4otgY3QjdEvHeH4co/lRjGvjMB2zZq81mB3EYHYQ+y/uBwC0Bduwo34HdtbvxPb67TOGdM81UHtquPZK5u3jXPMzvGCr4sVxnGm3FwPP84V6UG9fTNOEZVkwTRO6riOfdzs0OI4rCeqWJGleY+MYY9B1HbquI51Og+M4KIqCurq6mv5shBBC1igRbiaIAXcy7Gp54dwCJvMIVvahxerFplx612f6fXMV7iOEkGXCczzCShhhJQyEJtczm0HP68hpOWT1LHJmDjkzh6yZRd7Oz7zBGXCMg8QkSI4bmu1wDkzehMVZsHhr1pDuYgk9gUMjh3Bo5FDJeoET0B5qR1e4C53hTnRFutAZ6kRHuANN/qYVf6692niTc5UL3bYsqzDedy3zJiAzzfLtUMWTfxUvlEdK1jKKQyNkncnlckgkEhUfw3Ec6urqIIr0EbHWJfIJ/Ojcj/D909/HmcQZAO6JgM/2QXEUcKy6A3JJkNAWbEN7sN0dEEFh3EtC4iV30Le/qbDOdEyk9TTSZhppI+2eGFo5aJaGvJWHzaqbzUbkRaiCClVSEZACCEpBhOUwIkoEqqjSyRpZPtUOirNBwdxkdSgOra5luwwH9/3CFy3clGXqfhQvTtFi12jfvM4wHoAEOk4gFe2o34GsmcWl9KUZH3Nk5Ag+8cwn8IW7vlBxUK7f74eiKEilUtA0rarX92bCDAaDCAaDdOxDCCGEELLGFBfAe4t32+tUL74sDs9eiJyZw4g2glFtFCO5EYxoIxjJjWAwN4jh3DA0q7rj1YXyCT40BZrQHGhGUKLj3cUSkALurPWRbuStvPt/nxvFeH4crAYn2kk9ief7nseBgQOwOAsdsQ5c13odbmq7CddEroFPXjvhRUuFMYbDI4fxo3M/wo/P/xhpIz2v7XSEOrArvmvGSaTmbLZiZPqvJmQ6AW7b40wftxbKh+rNJZhbu7J92S3o3Fa3DW3BNrwx9EbZCTXKefrS03ju8nP4yPaP4D/s+Q8Iy+GqnkfmZz6B3N5xk9cnq4jKIu4hIYQQQghZUiImj/8rycM99l+Fh4KqqKI91I72UDsAwGEOEnoC49o4xvPjmMhPIG3Or/1jIcbz7usfHjlc9v6YEkOjvxGN/kY0+BsQV+OIq3HU++oR88VQ56tDVIkiokQg8stX56zbOibyExjLj2FMG8NIbgTDuWEM5YYwmB1Ef7YfA5mBeQ3oW6ioEnWDzoMtiCkxagMmhBBCCCGEEEIIIWtHpcBuoHRsXPHl1OsrJF9JERW0BN22PMYYknrSnWgxP46knlzQthnHwMDgcA4YGM7nzuOcdg6PXX4MjGNoD7VjX+M+7Gvah2uar0FrqLUQrk1KcRxXCKCaiRfoVRzk5V3WMrCb4zjIsgxZdoufvMBw0zQLS/H4OZ7nIUlSIahbFMU5/x8zxgrB34QQQkhVOLh97CLcPvfqYm5cNtx+fA6TeQSU/zizqZPUeIcdTtH9tTwGLs6p8K5PzbEozrYghJAlxgkcfAEffAEf6uw6d1yICcABLMeCZmrImllkrWwhrDtn5eCw6s7beMZDsRUoV4rJHM6BxVswOXPOId0em9m4mLqIi6mL0+5TRRUbQhvQGe5ER6gDG0Ib0BF2Lxv9jeC55f+S9MbhFo/RLTc+17vuPaf4+d55avFl8cRaUycM43kegiDM2I7hnSuXO0e3bXtdBG8vhNe+MZXXxuBNBOZdp7YkshZQ4i4h60g2m0UyWbkDhuM4xGKxQkM8WXssx8LB/oN49MyjONB7wJ01lwGSI0F25MKsPNUISkFsCG9As78ZAi9MNoxJoMaRZSLxEurUOtSp5WebtR0bpmPCciw4zIFzpSWNBw+BFyByIiRBWhEnXITMSET1wdzZK4+lP2my0jiYDOOuBR7ue0PAZBj3XMw2Q6kXzm3DbXRcSB2IAzdMSsfkJB70HiVTcByHa5uvRd7OYzg3POPjXhl8BZ888El87s7PQRFmHgUtCAJisRhUVUUymYRtz96DyxhDOp2GpmkIh8Pw+SjljBBCCCFkNfE6yU3TLOkwX6xOc8YYEnoCI5obvDKqjWJMG8NofhSj2ihGc6PIWbM1ZiweVVTdEBu1ARElQh3dS8wn+rAhtAEbQhtg2qYb0pMfwZg2BsuZLfXVxcBg8zZsrnTxzudPpU/h1MlTePDkgxA4ATvjO3F98/W4ruk67GvcV7uA6DXGdmwcHT2K/Rf346cXf4r+bP+8t9Xob8Tu+G7EfLEa7iAqBwNLoHYVQsrh4LaXmjPcb6J8KLf33GqDuaeE84XkEN7V/i5cSF3AkZEjbj/sLEzHxNff/joePvMwfnPvb+Lfbvm3kITq+2tJ9dJSGg5XuXGbA4e4GkdzoBlNgSZEZDpuIoQQQghZ0yS4x/Sz5Uh4E2atwmDuYjzHo85XhzrfZG2hYRuY0CcwkZ9clrMdE4C7P/oETk6cnPWxQSmIsBxGUA4iKAURkALwS36oogpVVOETfJAFGbIgQ+IlCJwAgRfAgSsc6zvMgcMc2I4Ni1kwbAOGbUC3dWiWhpyVQ87MIW2kkTEzSBkpJPXkkk2yWA2Jl9zJhK5MxqiK6nLvEiGEEEIIIYQQQgghy6M4CLCSqeGErIr1mGFdjXA8h6gaRdQfxUZshOEYGMuPYVQbxUh+BHk7D+b949j060XrqhnjfzZ3FmcvnMX3L3wfANASaMHVTVfj6sarcVXjVdgY3UhjzeeA5/mSsOxiXh2zt5imWbOw7uLAcFVVC69nmiYMw4BpmtB1HbquFx5fHJwlSRLVxhBCCFk8AoAA3DwFHXM7fmKYzGIQ4NYFr7fa+XITzky9vlz7VC0epSHdPNz/z9myLQghpBa8/B0FgAOIpoiQFELIDpU8jDEGzboS1m1eCeu23OuzjfvjGQ/ZliFfGaDicA5szobFW7A4q2Ts33xoloZTE6dwauLUtPsUQUFbsA3toXZsCG1wrwfb0RZyL2s1ltALuC4Xbu0FcS8Hb7+mBnx7KoV2k/lxHKekjQFw2xkEQSi0NXgLz6+ngzayFlAoNyHrRDWB3AAQjUYpbG4NYozhyOgRPHH+CTxx/gmM58cBuAf1PtsHxVHAseoPHhvUBmwIb0BMibkHnQLcgesiqNFjhRN4d1AJIatetcHcDiaDuelPn6wEtQzj9jqP5hPCPVdeZ4eXBeOFdJuYPZimEu934c1US+9TUoTneNzUchN+dvlnSOiJGR/3Yv+L+P1nfh9/f8ffwydWPpfx+XxQFAXpdBqZTKaq/bAsC+Pj4/D5fAiHwxBFakohhBBCCFlpvAJ1wzAK12tRqD6VzWyM5EYwkB3AQHYAQ9khDOYGMZwdxog2UlXw5lKKKBHEfXHE/XEEpeDiFBBwKC2G46asL15X7rmeqUV5bMr1JRzYstgkQUJzsBnNwWY4zHHD3HMjGNVGoVkaHM4pFOB4lzZng3HV/6A2s3Fk5AiOjBzB/zv6/yByInbU78DVTe5gmX2N+0rCl9ab8fw4Xu5/GS/2v4gX+l4o9JnMV71aj531O9Hob6zRHhbRZ7l/lYeBEbKoJMwcyu1NQDhTe+Rcgrm9QQKK+zyO49Ad6UZLoAVHRo7gUvpSVbub0BP4m1f/Bg+88wB+96rfxX3d99HAxhqbKZA7IAXQ5HeD6xr9jRB5av8jhBBCCFlXvGyMaoK5HQA+rKkaSVmQ3TBnf1NhnW7rSOQTSOiTS9pIL+NezixjZpAxM2592DoicALiahwN/gY0+hsna3kJIYQQQgghhBBCCCHVqXUIYKXyPu++qa83y+vLkNESbkELWsAYQ9pMYzg7jKHcEEZyI7DYQgb1TTeQHcDj5x7H4+ceB+BOTr+3Ya9bc9iwD7viu2oW6LXeCIIAQRCgKKUFf8Vh3aZpFsK6i0PEFvJ6XmbI1JBuwzBgGJMDXL1wbgrOIoQQsmhkuHW9Xjj3XHl1vzrcvAPxyuKFO69WM4VtF1+udpXCw738CmHKJSGELAYe7piPKwHdsCYXjuPgl/zwS340oKHwFMYYDNsoBHTnzFwhuFu3y3+h8YwHz3hIjlRYZ3M2bN4uhHQvNKjbo9s6ziXP4VzyXNn7Y0oMrcFWdwm0oiXYUrhsCbQgLIdL6o28kGvv3NS7nBp8vZQcxykEf09dqh0/7J0ji6JYcp1qrWqDMVZo29A0rbBeEATIskztDWTVoJFkhKwD6XQa6fTsBfGRSKQwA+ZqYdom+rP9GMwOYjg3jPH8OJJ6EhkzA83SoNs6HOaAMQaBEyAJEnyCD37Jj5AcQlSJIuaLucXpagOa/E2QBGn2F14FbMfGkdEj2H9xP/Zf3I/+bL97BwNkR4bsyBCd6r8GRF5Ea7AVG0IboIpX/k5ETIZxE0LIUhPhzoyZw+xFC1m4ARpr4yOerEZeR89Ca128IO7lngijOKSbwf25FhLQbV5Z6NiCTCEJEm5tuxUHeg8ga848ivbF/hfxO0//Dv7hzn+YtcCL4ziEw2GoqopEIgHTrC44MZ/PQ9d1+P1+hEIhavAjhBBCCFkmjLGSgnDDMGoewM0Yw2BuEBdTF9Gb7kVvuhd96T4M5AZgO8szc3c1fIIPdb461Kl1qPPVQRbk2Z80FQf3fI+rsBTfv5IwTA/rnnq92vtqbWpY+ZXfI8/xqPPXoa6uDlu4LUibaQzkBjCYHcSoNgpWo52xmIUjo0dwZPQIvvH2NwAAneFO7InvwZ6GPdgd340tsS1rpn+kGGMMg9lBHB45jDeH38TrQ6/j9MTpmmy70d+IbXXb0KA2LE4hjlfgNRMJVHRKSCVeof1MH6UmKk8S6AVz5zFzuLfHgFuYqaLwme8Tfbi+5Xp0RbpwaPgQUkaqqt2+nLmMP3r+j/C1Y1/D7+z7Hdy54U4q9lskm6KbsDG6cfEmLyGEEEIIIatHtcHcJtxzjKJj/7VIERQ0BZrQFJgM6rYcC0k9WVgSRgIpPbXiJipcqxRBQZ2vDnE1jrgaR8wXo4mcCCGEEEIIIYQQQghZSSq1GdegPZnjOITlMMJyGJtim+AwB+P5cQxlhzCsDWNcG69ZvaEnbaTxQt8LeKHvBQBuveOW2BbsbdhbqDvsDHdSW+UClAvr9sKsvJBub1lIANpsId1Tw7MEQYAkSYWwblGkwZ6EEEJqgIMbhCrBzVyYb1ezA7du15tfQpiyrJRDE2+MytTA7eLr6533+5g6ZqL4/9K7XMM1GoSQZcDDrReTMZmX4y1Fp14cx0ERFSiiW7dTzLRNN6S7KLA7Z+aQs3LTzs8FJkCwBciYHOvpcE4hoNtbHM6p6efdhD6BCX0Cb4+9Pf1OBgSFIFp8LWhSmhBX4qiX61Gn1qHeV4+YL4Y6Xx1CUmhRx1owxgqh21PDt71A7oXytlc8ORWAQji3d967GEHdPM+D4zjwPF+47r2Gd714XTmMscIy9bbjOIXLWo+xXijbtqFpWklQ99RJwSiom6wk1PpFyBr2/7N353Fu1fX+x9/nZE9m78x0pTuUQqFQQECo7CIgCKhcBS47Aur1uqIiihd+V6+KXLcroBQFURFQ2VG2QtktW0uBtlA60L2dfc1+fn+kJ5NMMjNJZslM5vXs4zwy803OOd+ZTpJzPvme99eyLLW1tam7u3vQx1ZUVCgQCIxCrwoTt+J6v/19vdX0ltY1r9P61vVqaGvQtq5tilvDdzBgyFCdr07TyqZpRvkMzSyfqZkVMzW7YrZmV85WwDV2f0eS1Bps1QvbXkh+yNUcbE7cYUmuuEvuuDttFp1clLnKNKN8hqYEpshp7n7bcClxUjHQheoAMBockvwaPJhbknqUKMp6BnkcMJyiSnyoM5QwbocS770ujc0PDAz19s9Sb8B2ITl1dqHSod5ZbzHheZ1efWTGR/TUpqfUE+3p93H/2v4vXfLoJfq/4/5P1d7qQbfrcrlUW1urrq4udXZ25lRktCxLXV1d6unpUVlZmQKBAIE9AAAAoyAajSoUCikUCg17CLdlWdrSuUUbWjfovbb3tLFtoxraG/qdrXwsCbgCqvJUJRZvVe9kiqn6C9jur308G86fob8A71z60Pc2xz4ZMlThqFCFt0ILahYoEotoR/cObe/aru1d2xWMDZYKlZ/329/X++3v64H3HpCUmJhzr+q9tLBmofau2Vt7Vu+pPav3VIW7Ylj3O5Ji8Zi2dG7ROy3vaF3LOr3d9LbWNK1RY0/jsO3DkKHp5dO1V/VeGQOqht1gL0PUOYGB2XXLcD/3RyR5c9iGHbbX33ZsUSUmCPUrbUB/vb9ex886Xu+2vqu3mt5SNJ5bsfidlnf05eVf1sKahbpi8RU6eo+jqUMNsz2r9xzzn/8DAABgFOUazB1VYpyST2PnYt5R4DSdmuSbpEm+Sck2y7LUE+1Re7g9sYTa1RHuUHuYsO6hcBgOVXmqkhe31XhrFHAxNgEAAAAAAABAL9Mwk5P47at9FY1Htatnl3Z179LO7p1qDbUO+z7jVlxrm9dqbfNa/WXdXyRJ5e5yLZq0SItqe5d6f/2w73siMQwjGUxlsywrGSBmh2lHo9GCg7r7hnTbodz29u3AMptpmnK5XJo2bdrQfjgAAKTE5+w+JcbCp4ZrFyqm9EwD+3qZ1KXvNTSFsgZY4n1uh3e+lIml7/+pNHbD1wGMf6l5OVLi9cfOvRkgM8flcKnSUalKVaa1x614MqC7K9KlrmhX8uuYlXKeZZkyLVOuPqE6cSOeDOyOG3HFFU+2WUaBby7W7mBwyyFn3Jn8WlKilqBd/a7qMB2q9lQnFm918jpW+5rWSnelKr2VqvJUyWVmBgTZYdGpIdt9vx/KJFRDYZ/7hkK9F/DZQd2pYd2pY7YMw5DD4ZBpmllv+wvgHi327zM15Dxb6HmxArz7Tgom9QZ1u93uZD2EcXIoBkK5gRJlWZZaWloUDA4e0lBRUaGysrJR6FXuovGo1jSu0cs7XtYrO17Rql2r1BHuGPH9WrK0s2endvbs1Ou7Xs+4v95XrzmVczS7crbmVs7V7MrZmlMxR5MDk4syk+uOrh1a3bhar+54VS/veFnrmtf1zpRjSU7LmQjijrlk5FEZMg1Tdf46zSiboSpPVeIgxVBvOCbFCQBjiUNSQImwi8HOs0NKFD3sEA1gpESU+BCokGBq23icBMM+XnAr8bOHVdhMtTElgvRDSnyo5hTP2Qku4Aokg7kHCkd8o/ENnffIebrx+Bs1o3zGoNs1DENlZWXy+Xzq6OjIaUIjKVGMbG9vV1dXlyoqKuTzZQk/BAAAQMEsy1I4HFYwGFQwGEwb2D1U3ZFurW9dn5j8sWW9NrRuGHDyl7HC7/ar3F2uMneZKryJ4GaXw5U9WLtUQraLaQz8/lwOl2aUz9CM8hmJSVjDbdretV07unaosaex97OAYRKNR/VW01t6q+mttPZ6X73mVM3RnIo5mlUxS3uU76EZ5TM0NTBVfpd/WPuQi3AsrB3dO7Stc5u2dG7Rpo5N+qDjAzW0NYxooL7H4dGcyjmaWzl3dH7uwSY8c4vPaoBcDBTKbSkxUDKXUTNeJd4XBnuJiSvxWYUvfbumYWqv6r00s3ym1jSuUUN7Qw47TXi7+W19afmXtHfN3rpkv0t0/Mzj5TDHU9EYAAAAGEdyDeaOKeux/0RjGIb8Lr/8Lr+mBKYk2y3LUigWUke4Qx3hDnVGOtUZ7kzcRjoVt4pzUc9Y5Hf6VeGpUKU7cXFapadS5e5yLiwCAAAAAAAAkBen6dTUwFRNDUyVlBhnt6t7l3b1JEK628PtI7LfjnCHXtj2gl7Y9kKyrc5Xp30m7ZNc9q7ZW5P9k6l7DoFhGMlQMJtlWcmAbvu20PHW9rZTQ7ojkUhyicfjaSFlAAAMC1OJ8blu9eYRDMclEpayhzqnyvWaGyvllpDt4soWvt43qJvDTQDDwX5N8aj3PcUO6c5hyJNpmCpzl6nMnZ7naI+n6o4mArpTg7uDsd7BanZYt7OfQWl2QHdciZBuS1YisDvl1pKVDOB2Wa5kAHchYvGYGnsa1djdmMwvNGTIsAwl/+3+2u/wq9xVrnJXuQLOgMpd5fI7/Qq4Aipzlcnv9ie+dwbkd/rlcXoK7tdIicVisixL8Xhc0Wg0OaGVz+eT1+uV1+tNOzcfa0wzcbGhwzHw/3lqQHc0Gs34ejSD0vsGdds1kNSg7r7h6MBIGLvPbAAFi0ajamlpUSQyeAJiZWWlAoHAKPRqcDu6dmjFlhV6dvOz+tf2f6kz0lnsLmWwA7tf2v5SWrvP6dMe5XtoVsWsREhG2QxNL5uuqYGpqvfXZxwk58OyLDUFm7S5Y7Peb39f77W9p3da3tHbzW+rsaexz4MlV9yVXPIJ4pYSA/ynlU3TtMA0uZ27rzBxKFHEIgwTwFhmKhHM3a3BixhR9V4YR24FhpOl3jDuQq8fNNU7CcZ4f991KPE886p3ptp86y5xJcK5U8O+x/vvBQUrd5frIzM+ohWbVwwYstbQ3qBzHj5Hvzj2F1pctzinbTscDlVVVcnv96utrS2ncykpUVRtaWlRZ2enysvLk4OPAAAAkD97wHYwGFQoFBq22ZY7wh1a27RWbza/qbeb39b7be8XFGZsyZIMJddN3qbMMJ52X7bH7m7PaNu9HZfpkt/lV5m7TAFPQBXuCpV7ylXuLi/KpJAYOwzDSM4gv3fN3orGo9rVvUs7undoR/eOEZ3UNPm5yLaXMu6r9FSq3l+vOl+dJnknqcZboypvlSrcFSpzlSngCsjr9Mrj8MjlcMlpOJN/y5YsxeIxReNRhWIhhWPh5MCizkin2sPtag+1qyXUopZgS2IAT0+jmoPNI/azZjMlMEWzK2ZrWtm00XseWho8+Nc9yP0AEuwBkf0NrA8r91EzHiXexwcL57OU+KzCs3tJ4XV6dfCUgzWvap5W71qtXT27cty5tLZ5rb7+9Nc1u2K2Llx0oT4+9+NyO3gxAAAAAIadPS5hsHn87GN/+yJhJBmGIa/TK6/Tqzp/Xdp9lmUpGAuqK9KVXLoj3eqOJi4064n2KGYN3ySNY4HH4VHAFUheYFbuLk8uTpNLOQAAAAAAAAAMP7fDrenl0zW9fLokKRQLaVf3LjX2NGpX9y61hdtGbN+7enbp6c1P6+nNTyfbarw1WlC9QHvX7K0FNQu0oHqBZlXOkst0jVg/Sp1hGHK73XK7ez+kiMViyYBuO6y7kCAtO6Tb5/PJsqxkSDcAACPCDuf2KJEDEtbAgdrDYaKHbBv9fJ2N1ed2rLDUG5Jrs8eNO0VIN4DhYSjxmmIP74mrd5KAHEO6k5tKGU9V461Juy8WjyXHTiXHUUUT46jCsXDaY+3Q7rRtW4YcliMZxG0q/X47qLtvW/JnTDRkzSq0g7dzEY1H1RJpUYtacnq8KVN+VyKo2+fyJW6dvozF6/Ambnf//rwOrzxOT97Zimn7Nk05HI6MxTTNZLB1Kvs8u729XaZpJs/H7dDo8RYYbf+cLldmXcayrGRAt10PsL8ejbBuexKySCSi7u5uSYnnj8vlSi5ut3tMh6NjfOIvCigxoVBILS0tOQWWjIVA7o1tG/X4+4/riQ+e0JtNbxa1L0PRE+3R+pb1Wt+yPuv9PqdPk7yTVOWpUqWnMnkw6Ha4k4PqY/GYIvGIeqI9icCJULuagk3a1b1L4Xg463alxEFxahB3vhyGQ/X+ek0rm6YqT1XvAZ5LiYtFCKydODYoeyHMkDRvlPsCFMIO5u5RevE0m7h6g7mH+rk9zx1YKjx02mZPglGK40gM9QbQ2KHl+X4YZodRhdV7jEIe3YRU6anMKZi7Odisi/5xkb57+Hd1+vzTc96+2+1WbW2turu71dHRkXMQZCQSUXNzs1wuF+HcAAAAebAsS8FgUD09PQqFQsPyoWwwGtTbzW9rTeMarWlakzWE2zIsxRVPzgyeNhP47hnCk7cpQdpD5TJd8jv9ifrw7hqxHcYScAUI1kTOnKZTU8umamrZVElST6QnEZ7dnVh6ooOlRg2PtlCb2kJteqflnVHZ32ip8dZoj/I9tEf5HvI6i3B+F9HAA7P61kWoT45N/L+MHS71X4+0B0LmWmvMNZxPStQzY0p8DtHnWKLaW62PzPiItnVt0xuNb+Q1uUJDe4Ouef4a/eq1X+nshWfr03t9WpWeypzXBwBgVHFMhNHA3xlGgj2RencOjw0qcezvFRd05sAwjOQFU7W+2oz7LctKjqPtifYoGA0qGAsqGA0qFAslJ3ezb4sZ4O0yXXI73PI4PPI6vMkLv+yfz64FE7wNAACACaW/8/SRuEafmgAAAEDOPA6PZpTP0IzyGZKkcCycCOju2aXG7ka1hlozxtoOp+Zgs17Y9oJe2PZCss1lujS3cq72qt5L86rmac/qPTW/ar6mBKbINLhwsBB2iJh9XZsdZGWHh4XD4ZyvlbOlBl8BmOA4D8dIM5T4rN4e9xvZvYy1MOhsNqv/58eMUdi/uXsxUm77W4YiNaA7dYln+T6/Q47hYQfl2hFdDqUHdTOmA8BQ2a+39ulRaki3vRTAYTpU7i5Xubs8475ILJIM7LbHU/VEehQJRxSNRbOGcPc1YLD2ML3Pxne/8NvX60pKuz7XMnp3lFp/aLfapYhkRHr7l9pXwzKS/TesRLtpmTJkyGt65XMkQrvtoG6PwyO3wy2vwyu30y2vyyuP2yOvyyuf2yefy5f42ulLjvlyO9xyOpxymI6cwrXj8biCwaCCwWCij7snzfJ4POM2pDuVYRjJybr6Sg3qTg3rHmmWZSXrGjY7VDx1IagbQ8FfD1BCOjs71dHRMWhwiWEYqqqqks/nG6WepdvUsUmPbHxE/2z4Z78h1qWmJ9qjzZ2btblz89A3ZklOy5kM4e47c02uqj3Vmlo2VXX+ut6ZZB3qLVKN3+M6FOo9UQjH+GcoEXARVKLIPhg7wHsoF8bx3Jm44uoN4y6UU4kgl4lyZmYfZ9gz1eZbW0kNQHeLcO4JqtJTqaP3OForNq8YMGguHA/ru899V2/sekNXfuhKeRyenLZvGIYCgYB8Pp/a29uTs+flwg7ndrvdKi8vl8eT2z4BAAAmEsuyFAqFkmHcQw3ijltxbWzbqNW7VmtV4yqtb1mvWDwxgiFmxBRzxBK3RiwZxD1StU+vwyu/KxG0nQzfTrl1ORiQjpHhc/k0yzVLsypmybIsdUW6tKtnl3Z179Kunl2jFtI9XhkyVOev07TANE0rmya/y1+8ztgTk/XHnvwsFfXJsYn/l7HDpcRnBv0JK/EZQT7bM5UI5xvsMCaq3glC+0yEbBiGppVN05TAFL3f/r7eanorr9frXT279PNXf66bV92s0+adprMXnq15VfxxAQDGGI6JMBr4O8NIcUryKzG2aLBj/4gSx/9+ZRz7Iz+GYcjtcMvtcOc0AVHciiscCysSjygajyZvY1ZMsXgscWvFFLfiiltxWdbuC71S69JGoj5kGokLthyGQ6ZhymE65DAccprO5GIHcbtMF6EwAAAAQDb9nafP0vCP1aAmAAAAUDC3w61pZYnxcpIUjUfVFGxSU0+TGnsa1dzTrKg1sqFKkXhE61rWaV3LurR2n9OnuZVzE0vVXM2pmKPZlbM1s3wm43DzZAeDud3uZFs0Gk0L6R6N8CwAJYLzcIwmO0jZq/ER0L1FIxfKbQdtm/18PZrZUEaf28GkBnT3XUbj/7JvQK6pxFgQ+++Lj/w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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bikebench.benchmarking import score_report\n", + "import importlib\n", + "importlib.reload(score_report)\n", + "all_evaluation_scores_dict = {f\"CTGAN\": CTGAN_all_evaluation_scores, \n", + " f\"TVAE\": TVAE_all_evaluation_scores,\n", + " f\"Train Set Sampling\": dataset_all_evaluation_scores\n", + " }\n", + "main_scores_dict = {f\"CTGAN\": CTGAN_main_scores,\n", + " f\"TVAE\": TVAE_main_scores,\n", + " f\"Train Set Sampling\": dataset_main_scores\n", + " }\n", + "\n", + "dashboard = score_report.ScoreReportDashboard(\n", + " requirement_scores=all_evaluation_scores_dict,\n", + " overall_scores=main_scores_dict,\n", + ")\n", + "\n", + "for m in all_evaluation_scores_dict.keys():\n", + " dashboard.show_model(m)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ef8389c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bike-bench-cuda", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/bike_bench_internal/env.yml b/bike_bench_internal/env.yml new file mode 100644 index 0000000000000000000000000000000000000000..97c6f4cc0998970842b514db03d134f832a6b9f9 --- /dev/null +++ b/bike_bench_internal/env.yml @@ -0,0 +1,28 @@ +name: bike-bench +channels: + - conda-forge +dependencies: + - python=3.10 + - pygmo + - pandas + - beautifulsoup4 + - dill + - pillow + - lxml + - networkx=3.2 + - scikit-learn + - attrs + - pip + - seaborn + - pymoo #Only required for the NSGA-II Notebook + - cvxopt #Only required for the Grad-Opt Notebook (Used by libmoon) + - cvxpy #Only required for the Grad-Opt Notebook (Used by libmoon) + - diffusers #Only required for OAGM Notebook (Used in OA-DDPM) + - pip: + - torch==2.7.1+cpu + - transformers==4.51.3 + - --extra-index-url https://download.pytorch.org/whl/cpu + - openai #Only required for the llm notebook + - sdv #Only required for the Synthetic Data Generation and Tabular GM Training + - cairosvg #Only required for svg to png conversion + - imageio[ffmpeg] # Only required for animations in introductory notebooks diff --git a/bike_bench_internal/env_cuda.yml b/bike_bench_internal/env_cuda.yml new file mode 100644 index 0000000000000000000000000000000000000000..e66333d80d1297079fa1d50ef662fc267c487795 --- /dev/null +++ b/bike_bench_internal/env_cuda.yml @@ -0,0 +1,28 @@ +name: bike-bench-cuda +channels: + - conda-forge +dependencies: + - python=3.10 + - pygmo + - pandas + - beautifulsoup4 + - dill + - pillow + - lxml + - networkx=3.2 + - scikit-learn + - attrs + - pip + - seaborn + - pymoo #Only required for the NSGA-II Notebook + - cvxopt #Only required for the Grad-Opt Notebook (Used by libmoon) + - cvxpy #Only required for the Grad-Opt Notebook (Used by libmoon) + - diffusers #Only required for OAGM Notebook (Used in OA-DDPM) + - pip: + - torch==2.7.1+cu118 + - transformers==4.51.3 + - --extra-index-url https://download.pytorch.org/whl/cu118 + - openai #Only required for the llm notebook + - sdv #Only required for the Synthetic Data Generation and Tabular GM Training + - cairosvg #Only required for svg to png conversion + - imageio[ffmpeg] # Only required for animations in introductory notebooks diff --git a/bike_bench_internal/pyproject.toml b/bike_bench_internal/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..3b1a5837f34edce36169d28d33d3b9a752ccded6 --- /dev/null +++ b/bike_bench_internal/pyproject.toml @@ -0,0 +1,14 @@ +[build-system] +requires = ["setuptools>=62", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "bikebench" # display/distribution name (hyphens OK) +version = "0.0.0" +requires-python = ">=3.9" + +[tool.setuptools.packages.find] +where = ["src"] # tell setuptools packages are under src/ + +[tool.setuptools.package-dir] +"" = "src" # root package dir is src/ diff --git a/bike_bench_internal/src/bikebench.egg-info/PKG-INFO b/bike_bench_internal/src/bikebench.egg-info/PKG-INFO new file mode 100644 index 0000000000000000000000000000000000000000..af8ad0c1e7881efbd835b4eb272a2b82ad1720bc --- /dev/null +++ b/bike_bench_internal/src/bikebench.egg-info/PKG-INFO @@ -0,0 +1,4 @@ +Metadata-Version: 2.4 +Name: bikebench +Version: 0.0.0 +Requires-Python: >=3.9 diff --git a/bike_bench_internal/src/bikebench.egg-info/SOURCES.txt b/bike_bench_internal/src/bikebench.egg-info/SOURCES.txt new file mode 100644 index 0000000000000000000000000000000000000000..f4a98fc6ae515d0808c0b9cf04a8927c4e25eeee --- /dev/null +++ b/bike_bench_internal/src/bikebench.egg-info/SOURCES.txt @@ -0,0 +1,131 @@ +README.md +pyproject.toml +src/bikebench/__init__.py +src/bikebench/exceptions.py +src/bikebench/resource_utils.py +src/bikebench.egg-info/PKG-INFO +src/bikebench.egg-info/SOURCES.txt +src/bikebench.egg-info/dependency_links.txt +src/bikebench.egg-info/top_level.txt +src/bikebench/benchmark_models/__init__.py +src/bikebench/benchmark_models/benchmarking_utils.py +src/bikebench/benchmark_models/generative_modeling_utils.py +src/bikebench/benchmark_models/libmoon/__init__.py +src/bikebench/benchmark_models/libmoon/example.py +src/bikebench/benchmark_models/libmoon/problem/__init__.py +src/bikebench/benchmark_models/libmoon/problem/mop.py +src/bikebench/benchmark_models/libmoon/problem/mtl/__init__.py +src/bikebench/benchmark_models/libmoon/problem/mtl/fair_classify.py +src/bikebench/benchmark_models/libmoon/problem/mtl/mnist.py +src/bikebench/benchmark_models/libmoon/problem/mtl/objectives.py +src/bikebench/benchmark_models/libmoon/problem/mtl/loaders/__init__.py +src/bikebench/benchmark_models/libmoon/problem/mtl/loaders/adult_loader.py +src/bikebench/benchmark_models/libmoon/problem/mtl/loaders/compas_loader.py +src/bikebench/benchmark_models/libmoon/problem/mtl/loaders/credit_loader.py +src/bikebench/benchmark_models/libmoon/problem/mtl/loaders/multimnist_loader.py +src/bikebench/benchmark_models/libmoon/problem/mtl/model/__init__.py +src/bikebench/benchmark_models/libmoon/problem/mtl/model/simple.py +src/bikebench/benchmark_models/libmoon/problem/synthetic/__init__.py +src/bikebench/benchmark_models/libmoon/problem/synthetic/dtlz.py +src/bikebench/benchmark_models/libmoon/problem/synthetic/maf.py +src/bikebench/benchmark_models/libmoon/problem/synthetic/re.py +src/bikebench/benchmark_models/libmoon/problem/synthetic/re_original.py +src/bikebench/benchmark_models/libmoon/problem/synthetic/vlmop.py +src/bikebench/benchmark_models/libmoon/problem/synthetic/wfg.py +src/bikebench/benchmark_models/libmoon/problem/synthetic/zdt.py +src/bikebench/benchmark_models/libmoon/solver/__init__.py +src/bikebench/benchmark_models/libmoon/solver/gradient/__init__.py +src/bikebench/benchmark_models/libmoon/solver/gradient/base_solver.py +src/bikebench/benchmark_models/libmoon/solver/gradient/core_solver.py +src/bikebench/benchmark_models/libmoon/solver/gradient/epo_solver.py +src/bikebench/benchmark_models/libmoon/solver/gradient/functions_evaluation.py +src/bikebench/benchmark_models/libmoon/solver/gradient/functions_hv_grad_3d.py +src/bikebench/benchmark_models/libmoon/solver/gradient/functions_hv_python3.py +src/bikebench/benchmark_models/libmoon/solver/gradient/gradhv.py +src/bikebench/benchmark_models/libmoon/solver/gradient/mgda_core.py +src/bikebench/benchmark_models/libmoon/solver/gradient/mgda_solver.py +src/bikebench/benchmark_models/libmoon/solver/gradient/min_norm_solvers_numpy.py +src/bikebench/benchmark_models/libmoon/solver/gradient/moosvgd.py +src/bikebench/benchmark_models/libmoon/solver/gradient/pmgda.py +src/bikebench/benchmark_models/libmoon/solver/gradient/pmtl.py +src/bikebench/benchmark_models/libmoon/solver/gradient/run/__init__.py +src/bikebench/benchmark_models/libmoon/solver/gradient/run/run_grad.py +src/bikebench/benchmark_models/libmoon/solver/gradient/utils/__init__.py +src/bikebench/benchmark_models/libmoon/solver/gradient/utils/util.py +src/bikebench/benchmark_models/libmoon/solver/mobo/__init__.py +src/bikebench/benchmark_models/libmoon/solver/mobo/dirhvego.py +src/bikebench/benchmark_models/libmoon/solver/mobo/mobod.py +src/bikebench/benchmark_models/libmoon/solver/mobo/utils/__init__.py +src/bikebench/benchmark_models/libmoon/solver/mobo/utils/termination.py +src/bikebench/benchmark_models/libmoon/solver/moea/__init__.py +src/bikebench/benchmark_models/libmoon/solver/moea/moead.py +src/bikebench/benchmark_models/libmoon/solver/moea/moead_pfl.py +src/bikebench/benchmark_models/libmoon/solver/moea/utils/__init__.py +src/bikebench/benchmark_models/libmoon/solver/moea/utils/decomposition.py +src/bikebench/benchmark_models/libmoon/solver/moea/utils/genetic_operator.py +src/bikebench/benchmark_models/libmoon/solver/moea/utils/population.py +src/bikebench/benchmark_models/libmoon/solver/moea/utils/termination.py +src/bikebench/benchmark_models/libmoon/solver/moea/utils/utils_ea.py +src/bikebench/benchmark_models/libmoon/solver/moea/utils/weight_vector.py +src/bikebench/benchmark_models/libmoon/solver/pfl/__init__.py +src/bikebench/benchmark_models/libmoon/solver/pfl/run.py +src/bikebench/benchmark_models/libmoon/solver/pfl/model/__init__.py +src/bikebench/benchmark_models/libmoon/solver/pfl/model/simple.py +src/bikebench/benchmark_models/libmoon/solver/psl/__init__.py +src/bikebench/benchmark_models/libmoon/solver/psl/run_mtl_condition.py +src/bikebench/benchmark_models/libmoon/solver/psl/run_mtl_psl.py +src/bikebench/benchmark_models/libmoon/solver/psl/run_simple_psl.py 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+src/bikebench/data_loading/__init__.py +src/bikebench/data_loading/data_loading.py +src/bikebench/data_loading/dataverse_utils.py +src/bikebench/design_evaluation/__init__.py +src/bikebench/design_evaluation/design_evaluation.py +src/bikebench/design_evaluation/score_report.py +src/bikebench/design_evaluation/scoring.py +src/bikebench/embedding/__init__.py +src/bikebench/embedding/clip_embedding_calculator.py +src/bikebench/embedding/dataset_rendering_tools.py +src/bikebench/embedding/embedding_predictor.py +src/bikebench/embedding/execute_emb.py +src/bikebench/ergonomics/__init__.py +src/bikebench/ergonomics/joint_angles.py +src/bikebench/prediction/__init__.py +src/bikebench/prediction/aero_predictor.py +src/bikebench/prediction/clip_predictor.py +src/bikebench/prediction/evaluators.py +src/bikebench/prediction/prediction_utils.py +src/bikebench/rendering/BikeCAD_server_client.py +src/bikebench/rendering/BikeCAD_server_manager.py +src/bikebench/rendering/__init__.py +src/bikebench/rendering/animation.py +src/bikebench/rendering/rendering.py +src/bikebench/rendering/visualize_grid.py +src/bikebench/transformation/__init__.py +src/bikebench/transformation/framed.py +src/bikebench/transformation/interface_points.py +src/bikebench/transformation/one_hot_encoding.py +src/bikebench/transformation/ordered_columns.py +src/bikebench/validation/__init__.py +src/bikebench/validation/base_validation_function.py +src/bikebench/validation/bike_bench_validation_functions.py +src/bikebench/xml_handling/__init__.py +src/bikebench/xml_handling/algebraic_parser.py +src/bikebench/xml_handling/bcad_to_bikebench.py +src/bikebench/xml_handling/bike_xml_handler.py +src/bikebench/xml_handling/bikebench_to_bcad.py +src/bikebench/xml_handling/cad_builder.py \ No newline at end of file diff --git a/bike_bench_internal/src/bikebench.egg-info/dependency_links.txt b/bike_bench_internal/src/bikebench.egg-info/dependency_links.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/bike_bench_internal/src/bikebench.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/bike_bench_internal/src/bikebench.egg-info/top_level.txt b/bike_bench_internal/src/bikebench.egg-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..2df55d3854ab7fa019f6c4c7b283e4cd68c18a72 --- /dev/null +++ b/bike_bench_internal/src/bikebench.egg-info/top_level.txt @@ -0,0 +1,2 @@ +bikebench +resources diff --git a/bike_bench_internal/src/bikebench/__init__.py b/bike_bench_internal/src/bikebench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/src/bikebench/benchmarking/__init__ .py b/bike_bench_internal/src/bikebench/benchmarking/__init__ .py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/src/bikebench/benchmarking/benchmarking_utils.py b/bike_bench_internal/src/bikebench/benchmarking/benchmarking_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5a64fba30802ca3d134de58e0cf0f233e1791179 --- /dev/null +++ b/bike_bench_internal/src/bikebench/benchmarking/benchmarking_utils.py @@ -0,0 +1,262 @@ +import os +import torch +import pandas as pd +from bikebench.benchmarking.scoring import construct_scorer, MainScores, DetailedScores +from bikebench.design_evaluation.design_evaluation import get_standard_evaluations, construct_tensor_evaluator + +from bikebench.conditioning import conditioning +from tqdm import trange, tqdm +from bikebench.transformation import ordered_columns + +def get_test_conditions(): + rider_conditions = conditioning.sample_riders(100, split="test") + use_case_conditions = conditioning.sample_use_case(100, split="test") + image_embeddings = conditioning.sample_embedding(100, split="test") + + rider_conditions_repeated = rider_conditions.repeat_interleave(100, dim=0) + use_case_conditions_repeated = use_case_conditions.repeat_interleave(100, dim=0) + image_embeddings_repeated = image_embeddings.repeat_interleave(100, dim=0) + + conditions = {"Rider": rider_conditions_repeated, "Use Case": use_case_conditions_repeated, "Embedding": image_embeddings_repeated} + + return conditions + +def get_single_test_condition(idx=0, device="cpu"): + rider_condition = conditioning.sample_riders(100, split="test") + use_case_condition = conditioning.sample_use_case(100, split="test") + image_embedding = conditioning.sample_embedding(100, split="test") + + rider_condition = rider_condition[idx].to(device) + use_case_condition = use_case_condition[idx].to(device) + image_embedding = image_embedding[idx].to(device) + + condition = {"Rider": rider_condition, "Use Case": use_case_condition, "Embedding": image_embedding} + return condition + +def evaluate(result_tens, device, evaluate_as_aggregate = False): + data_columns = ordered_columns.bike_bench_columns + evaluations = get_standard_evaluations(device) + evaluator, requirement_names, requirement_types = construct_tensor_evaluator(evaluations, data_columns, device=device) + + if evaluate_as_aggregate: + condition = get_test_conditions() + main_scorer = construct_scorer(MainScores, evaluations, data_columns, device) + detailed_scorer = construct_scorer(DetailedScores, evaluations, data_columns, device) + + main_scores = main_scorer(result_tens, condition) + detailed_scores = detailed_scorer(result_tens, condition) + else: + all_main_scores = [] + all_detailed_scores = [] + all_evaluation_scores = [] + + main_scorer = construct_scorer(MainScores, evaluations, data_columns, device) + detailed_scorer = construct_scorer(DetailedScores, evaluations, data_columns, device) + for i in trange(100): + result_slice = result_tens[i*100:(i+1)*100] + condition = get_single_test_condition(i, device) + + evaluation_scores = evaluator(result_slice, condition) + + main_scores = main_scorer(result_slice.detach(), condition, preevaluated_scores = evaluation_scores) #main scores is a series + detailed_scores = detailed_scorer(result_slice.detach(), condition, preevaluated_scores = evaluation_scores) #detailed scores is a series + + all_main_scores.append(main_scores) + all_detailed_scores.append(detailed_scores) + all_evaluation_scores.append(evaluation_scores) + main_scores = pd.concat(all_main_scores, axis=1).T + detailed_scores = pd.concat(all_detailed_scores, axis=1).T + all_evaluation_scores = torch.stack(all_evaluation_scores) + main_scores = main_scores.mean() + detailed_scores = detailed_scores.mean() + + return main_scores, detailed_scores, all_evaluation_scores + +# def get_condition_by_idx(idx=0): +# rider_condition = conditioning.sample_riders(10, split="test") +# use_case_condition = conditioning.sample_use_case(10, split="test") +# image_embeddings = conditioning.sample_image_embedding(10, split="test") +# condition = {"Rider": rider_condition[idx], "Use Case": use_case_condition[idx], "Embedding": image_embeddings[idx]} +# return condition + +# def get_conditions_10k(): +# rider_condition = conditioning.sample_riders(10000, split="test") +# use_case_condition = conditioning.sample_use_case(10000, split="test") +# image_embeddings = conditioning.sample_image_embedding(10000, split="test") +# conditions = {"Rider": rider_condition, "Use Case": use_case_condition, "Embedding": image_embeddings} +# return conditions + +# def evaluate_uncond(result_tens, name, cond_idx, data_columns, device, save=True): + +# condition = get_condition_by_idx(cond_idx) + +# main_scorer = construct_scorer(MainScores, get_standard_evaluations(device), data_columns) +# detailed_scorer = construct_scorer(DetailedScores, get_standard_evaluations(device), data_columns) + +# main_scores = main_scorer(result_tens, condition) + +# detailed_scores = detailed_scorer(result_tens, condition) + +# if save: +# result_tens = result_tens.cpu() +# torch.save(result_tens, os.path.join(result_dir, "result_tens.pt")) +# main_scores.to_csv(os.path.join(result_dir, "main_scores.csv"), index_label=False, header=False) +# detailed_scores.to_csv(os.path.join(result_dir, "detailed_scores.csv"), index_label=False, header=False) +# return main_scores, detailed_scores + +# def evaluate_cond(result_tens, name, data_columns, device, save=True): +# condition = get_conditions_10k() + +# condition = {"Rider": condition["Rider"], "Use Case": condition["Use Case"], "Embedding": condition["Embedding"]} + +# result_dir = os.path.join("results", "conditional", name) +# os.makedirs(result_dir, exist_ok=True) + +# main_scorer = construct_scorer(MainScores, get_standard_evaluations(device), data_columns, device) +# detailed_scorer = construct_scorer(DetailedScores, get_standard_evaluations(device), data_columns, device) + +# main_scores = main_scorer(result_tens, condition) +# detailed_scores = detailed_scorer(result_tens, condition) + +# if save: +# result_tens = result_tens.cpu() +# torch.save(result_tens, os.path.join(result_dir, "result_tens.pt")) +# main_scores.to_csv(os.path.join(result_dir, "main_scores.csv"), index_label=False, header=False) +# detailed_scores.to_csv(os.path.join(result_dir, "detailed_scores.csv"), index_label=False, header=False) + +# return main_scores, detailed_scores + + +# def create_score_report_conditional(): +# """ +# Looks through the results folder and creates a score report for each conditional result. +# """ +# all_scores = [] +# result_dir = os.path.join("results", "conditional") +# for name in os.listdir(result_dir): +# if os.path.isdir(os.path.join(result_dir, name)): +# main_scores = pd.read_csv(os.path.join(result_dir, name, "main_scores.csv"), header=None) +# main_scores.columns = ["Metric", "Score"] +# main_scores["Model"] = name +# all_scores.append(main_scores) +# all_scores = pd.concat(all_scores, axis=0) +# #make metric names the three columns, make models the rows +# all_scores = all_scores.pivot(index="Model", columns="Metric", values="Score") +# #drop the index name and the column name +# all_scores.columns.name = None +# all_scores.index.name = None + +# return all_scores + +# def create_score_report_unconditional(): +# """ +# Looks through the results folder and creates a score report for each unconditional result. +# """ +# all_scores = [] +# result_dir = os.path.join("results", "unconditional") +# for i in range(10): +# c_dir = os.path.join(result_dir, f"cond_{i}") +# for name in os.listdir(c_dir): +# dirname = os.path.join(c_dir, name) +# if os.path.isdir(dirname): +# main_scores = pd.read_csv(os.path.join(dirname, "main_scores.csv"), header=None) +# main_scores.columns = ["Metric", "Score"] +# main_scores["Model"] = name +# main_scores["Condition"] = i +# all_scores.append(main_scores) +# all_scores = pd.concat(all_scores, axis=0) +# #average over condition +# all_scores = all_scores.groupby(["Model", "Metric"]).mean().reset_index() +# #make metric names the three columns, make models the rows +# all_scores = all_scores.pivot(index="Model", columns="Metric", values="Score") +# #drop the index name and the column name +# all_scores.columns.name = None +# all_scores.index.name = None +# return all_scores + + +# def rescore_unconditional(data_columns, device, cond_idxs = None, model_names = None, results_root="results/unconditional"): +# """ +# Recompute main and detailed scores for all unconditional results. +# Overwrites only the CSV score files, leaves result_tens.pt untouched. +# """ + +# evals = get_standard_evaluations(device) +# main_scorer = construct_scorer(MainScores, evals, data_columns, device) +# detailed_scorer = construct_scorer(DetailedScores, evals, data_columns, device) +# device = torch.device(device) +# if cond_idxs is None: +# cond_idxs = range(10) +# for cond_idx in tqdm(cond_idxs): +# cond_dir = os.path.join(results_root, f"cond_{cond_idx}") +# if not os.path.isdir(cond_dir): +# continue +# # fetch the one shared condition for this index +# condition = get_condition_by_idx(cond_idx) + +# if model_names is not None: +# models = model_names +# else: +# models = os.listdir(cond_dir) + +# for model_name in models: +# model_dir = os.path.join(cond_dir, model_name) +# tensor_path = os.path.join(model_dir, "result_tens.pt") +# if not os.path.isdir(model_dir) or not os.path.isfile(tensor_path): +# continue + +# # load results +# result_tens = torch.load(tensor_path, map_location=device) + +# # rescore +# main_scores = main_scorer(result_tens, condition) +# detailed_scores = detailed_scorer(result_tens, condition) + +# # overwrite only the CSVs +# main_scores.to_csv( +# os.path.join(model_dir, "main_scores.csv"), header=False +# ) +# detailed_scores.to_csv( +# os.path.join(model_dir, "detailed_scores.csv"), header=False +# ) + + +# def rescore_conditional(data_columns, device, model_names, results_root="results/conditional"): +# """ +# Recompute main and detailed scores for all conditional results. +# Overwrites only the CSV score files, leaves result_tens.pt untouched. +# """ +# device = torch.device(device) +# # fetch the full 10k‐point condition set once +# condition = get_conditions_10k() + +# # build scorers +# evals = get_standard_evaluations(device) +# main_scorer = construct_scorer(MainScores, evals, data_columns, device) +# detailed_scorer = construct_scorer(DetailedScores, evals, data_columns, device) + + +# if model_names is not None: +# models = model_names +# else: +# models = os.listdir(results_root) +# for model_name in models: +# model_dir = os.path.join(results_root, model_name) +# tensor_path = os.path.join(model_dir, "result_tens.pt") +# if not os.path.isdir(model_dir) or not os.path.isfile(tensor_path): +# continue + +# # load results +# result_tens = torch.load(tensor_path, map_location=device) + +# # rescore +# main_scores = main_scorer(result_tens, condition) +# detailed_scores = detailed_scorer(result_tens, condition) + +# # overwrite only the CSVs +# main_scores.to_csv( +# os.path.join(model_dir, "main_scores.csv"), header=False +# ) +# detailed_scores.to_csv( +# os.path.join(model_dir, "detailed_scores.csv"), header=False +# ) \ No newline at end of file diff --git a/bike_bench_internal/src/bikebench/benchmarking/score_report.py b/bike_bench_internal/src/bikebench/benchmarking/score_report.py new file mode 100644 index 0000000000000000000000000000000000000000..8bcd616f885ba350779853540ef55b37972c6acb --- /dev/null +++ b/bike_bench_internal/src/bikebench/benchmarking/score_report.py @@ -0,0 +1,500 @@ +import warnings +import torch +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +from matplotlib.colors import to_rgb +import re +from typing import List, Optional, Dict +from itertools import cycle +from bikebench.transformation import ordered_columns +from textwrap import fill + +from bikebench.design_evaluation.design_evaluation import ( + construct_tensor_evaluator, + get_standard_evaluations +) + +# ---------------- helpers ---------------- + +def _ordinal(n: int) -> str: + if 11 <= (n % 100) <= 13: + suffix = 'th' + else: + suffix = {1:'st',2:'nd',3:'rd'}.get(n%10,'th') + return f"{n}{suffix}" + +def _format_num(x: float) -> str: + if x == 0: + return "0" + if abs(x) < 1e-2 or abs(x) >= 1e3: + s = f"{x:.2e}" + return re.sub(r"e([+-])0*(\d+)", r"e\1\2", s) + s = f"{x:.3g}" + if "e" in s: + return s + digits = len(s.replace(".", "")) + if "." in s: + zeros_needed = 3 - digits + if zeros_needed > 0: + s = s + "0" * zeros_needed + else: + zeros_needed = 3 - digits + s = s + "." + "0" * zeros_needed + return s + +# -------------- NEW: renderer-only dashboard ---------------- + +class ScoreReportDashboard: + """ + Renderer-only scorecard: + - No internal evaluation. + - Consumes: + * overall_scores: dict[model_name] -> pd.Series with + ["Hypervolume", "Constraint Satisfaction Rate", "Maximum Mean Discrepancy"] + * requirement_scores: dict[model_name] -> torch.Tensor shaped (N, R) or (A, B, R) + where R = #requirements (e.g., 50). If 3D, flattens first two dims. + - Obtains requirement names/types from either: + * requirement_names & requirement_types (direct), or + * construct_tensor_evaluator(evaluations, data_columns, device), using either passed + 'evaluations' or default get_standard_evaluations(device). + """ + def __init__( + self, + requirement_scores: Dict[str, torch.Tensor], + overall_scores: Dict[str, pd.Series], + *, + model_colors: Optional[Dict[str, str]] = None, + # one of these paths must provide names/types: + requirement_names: Optional[List[str]] = None, + requirement_types: Optional[List[int]] = None, # 1=objective, 0=constraint + evaluations: Optional[List[callable]] = None, + data_columns: Optional[List[str]] = None, + device: str = "cpu", + summary_order: Optional[List[str]] = None, + ): + self.device = device + + # --- normalize incoming tensors & collect model names + if not requirement_scores: + raise ValueError("requirement_scores cannot be empty") + self.model_names = list(requirement_scores.keys()) + + # shape-normalize scores to (N, R) + self.requirement_scores = {} + for name, t in requirement_scores.items(): + if not isinstance(t, torch.Tensor): + raise TypeError(f"Scores for model {name!r} must be a torch.Tensor") + t = t.to(device) + if t.ndim == 2: + pass + elif t.ndim == 3: + # support 100x100x50 -> 10000x50 + t = t.reshape(-1, t.shape[-1]) + else: + raise ValueError(f"Scores for {name!r} must be 2D or 3D, got shape {tuple(t.shape)}") + self.requirement_scores[name] = t + + # --- overall scores ingestion + default_summary = [ + "Design Quality ↑ (HV)", + # "Binary Validity ↑", + "Mean Violations ↓", + "Sim. to Data ↓ (MMD)", + # "Average Novelty ↑", + "Diversity ↓ (DPP)" + ] + self._summary_order = list(summary_order) if summary_order is not None else default_summary + + # validate overall_scores against the chosen list + required_keys = set(self._summary_order) + for name in self.model_names: + s = overall_scores.get(name, None) + if s is None: + raise ValueError(f"overall_scores missing entry for model {name!r}") + missing = required_keys - set(s.index) + if missing: + raise ValueError( + f"overall_scores for {name!r} missing keys from summary_order: {sorted(missing)}" + ) + self.overall_scores = overall_scores + + # --- requirement names/types + if (requirement_names is not None) and (requirement_types is not None): + self.eval_names = list(requirement_names) + self.eval_types = list(requirement_types) + else: + # Need to derive from evaluator definition + if evaluations is None: + evaluations = get_standard_evaluations(device) + if data_columns is None: + data_columns = ordered_columns.bike_bench_columns + # We only need names/types; the evaluator function is unused here. + _, req_names, req_types = construct_tensor_evaluator(evaluations, data_columns, device=device) + self.eval_names = req_names + self.eval_types = req_types + + R = self.requirement_scores[self.model_names[0]].shape[1] + if len(self.eval_names) != R or len(self.eval_types) != R: + raise ValueError(f"Requirement count mismatch: tensors have R={R}, " + f"but names={len(self.eval_names)}, types={len(self.eval_types)}") + + # --- color handling + if model_colors is None: + base = plt.rcParams['axes.prop_cycle'].by_key()['color'] + model_colors = {n: c for n, c in zip(self.model_names, cycle(base))} + warnings.warn("No model_colors provided; using Matplotlib cycle.") + self.model_colors = model_colors + + # --- indices + self.objective_indices = [i for i,t in enumerate(self.eval_types) if t == 1] + self.constraint_indices = [i for i,t in enumerate(self.eval_types) if t == 0] + self.objective_names = [self.eval_names[i] for i in self.objective_indices] + self.constraint_names = [self.eval_names[i] for i in self.constraint_indices] + + # --- precompute per-requirement violation rates from provided scores + # constraint is satisfied if score <= 0; violation if > 0 + self.model_const_violation = {} # dict[model] -> np.array shape (num_constraints,) + for name, T in self.requirement_scores.items(): + if len(self.constraint_indices) == 0: + self.model_const_violation[name] = np.zeros((0,), dtype=float) + else: + C = T[:, self.constraint_indices] # (N, C) + self.model_const_violation[name] = (C.detach().cpu().numpy() > 0).mean(axis=0) + + # -------------- plotting -------------- + + def show_model( + self, + model_name: Optional[str] = None, + objectives_per_row: int = 5, + constraints_per_row: int = 40, + total_width: float = 12.0, + summary_cell_height: float = 0.4, + objective_cell_height: float = 1.0, + truncate_tails_magnitude: float = 0.01, + filter_invalid: bool = True, + min_kde_samples: int = 3, + constraint_height_scale: float = 1.3, + summary_title_width_in: float = 1.6, + title_wrap_chars: int = 14 + ): + """ + Render one model’s scorecard: + * Summary row uses provided overall_scores + * Objective KDEs use requirement_scores[:, objective_indices] + - Optional filtering to only valid samples (all constraints <= 0) + * Constraint tiles show per-requirement violation rates computed from provided scores + """ + if model_name is None: + model_name = self.model_names[0] + warnings.warn(f"No model_name given; defaulting to {model_name!r}") + if model_name not in self.model_names: + raise ValueError(f"Unknown model: {model_name!r}") + color = self.model_colors[model_name] + + # validity mask if requested + valid_mask_by_model = {} + if filter_invalid and len(self.constraint_indices) > 0: + for name, T in self.requirement_scores.items(): + cons = T[:, self.constraint_indices] + valid_mask_by_model[name] = (cons <= 0).all(dim=1).detach().cpu().numpy() + else: + for name, T in self.requirement_scores.items(): + valid_mask_by_model[name] = np.ones((T.shape[0],), dtype=bool) + + # gather raw objective arrays (filtered if requested) + all_raw = {} + for name, T in self.requirement_scores.items(): + arr = T[:, self.objective_indices].detach().cpu().numpy() + mask = valid_mask_by_model[name] + all_raw[name] = arr[mask] + + # ---------------- layout sizes ---------------- + obj_count = len(self.objective_indices) + con_count = len(self.constraint_indices) + obj_rows = int(np.ceil(max(obj_count, 1) / objectives_per_row)) + con_rows = int(np.ceil(max(con_count, 1) / constraints_per_row)) + cons_cell = total_width/constraints_per_row * constraint_height_scale + fig_h = summary_cell_height \ + + obj_rows*objective_cell_height \ + + con_rows*cons_cell + + fig = plt.figure(figsize=(total_width, fig_h), dpi=500) + fig.subplots_adjust(left=0.02, right=0.98, top=0.95, bottom=0.05, hspace=0.6) + outer = fig.add_gridspec( + 3, 1, + height_ratios=[summary_cell_height, obj_rows*objective_cell_height, con_rows*cons_cell] + ) + + # ---------------- summary row ---------------- + md = pd.DataFrame(self.overall_scores).T # index=model, columns=metrics + + # Use exactly the order configured in __init__ + summary_cols = [c for c in self._summary_order if c in md.columns] + + def _low_best_from_arrow(label: str) -> bool: + # True => lower is better (ascending rank), False => higher is better (descending rank) + if "↓" in label: + return True + if "↑" in label: + return False + raise ValueError(f"Cannot infer direction from label (missing ↑/↓): {label!r}") + + summary_defs = [(col, _low_best_from_arrow(col)) for col in summary_cols] + single_model = (len(self.model_names) == 1) + + # dynamic grid sized by number of summary metrics + nsum = len(summary_defs) + 1 + + if summary_title_width_in is not None: + # Convert desired inch-width to a fraction of the figure width + title_frac = max(0.05, min(summary_title_width_in / total_width, 0.8)) + # Remaining width split evenly across metric panels + if len(summary_defs) == 0: + ratios = [1.0] # only title + else: + metric_frac = (1.0 - title_frac) / len(summary_defs) + ratios = [title_frac] + [metric_frac] * len(summary_defs) + else: + # fallback to equal-ish ratios (old behavior) + ratios = [0.16] + [0.28] * len(summary_defs) + + gs_sum = outer[0].subgridspec(1, len(ratios), width_ratios=ratios, wspace=0.1) + + ax0 = fig.add_subplot(gs_sum[0, 0]) + + # Wrap long model names (drop "Scorecard") + title_text = str(model_name) + if title_wrap_chars is not None and title_wrap_chars > 0: + title_text = fill(title_text, width=title_wrap_chars) + + ax0.text( + 0, 0.5, title_text, + ha='left', va='center', + fontsize=12, fontweight='bold', + wrap=True # let Matplotlib break long words if needed + ) + ax0.axis('off') + + for i, (col, low_best) in enumerate(summary_defs): + ax = fig.add_subplot(gs_sum[0, i+1]) + sr = md[col] + lo, hi = sr.min(), sr.max() + x0, x1 = lo, hi + val = sr.loc[model_name] + rk = int(sr.rank(method='min', ascending=low_best).loc[model_name]) + # Special case: only one model — keep the metric title but hide axis chrome + if single_model: + # Remove ticks and spines, but DO NOT turn the axis fully off (we want our own title text) + ax.set_xticks([]); ax.set_yticks([]) + for loc in ['left', 'right', 'top', 'bottom']: + ax.spines[loc].set_visible(False) + ax.grid(False) + + # Metric label (kept, like your multi-model layout) + ax.text( + 0.5, 0.75, col, + transform=ax.transAxes, + ha='center', va='bottom', + fontsize=9, fontweight='bold' + ) + + # Centered value + rank (no baseline/ticks) + ax.text( + 0.5, 0.35, f"{_format_num(val)} ({_ordinal(rk)})", + transform=ax.transAxes, + ha='center', va='center', + fontsize=10, color=color + ) + + continue # skip baseline, ticks, end labels, etc. + + + ax.hlines(0, x0, x1, color='black', linewidth=1) + ax.plot([x0, x1], [0, 0], '|k', markersize=4) + for other_val in sr.values: + ax.plot(other_val, 0, '|', color='gray', markersize=6) + ax.plot(val, 0, '|', color=color, markersize=10) + + ax.text(x0, 0.02, _format_num(x0), ha='center', va='bottom', fontsize=7) + ax.text(x1, 0.02, _format_num(x1), ha='center', va='bottom', fontsize=7) + if x1 > x0: + frac = (val - x0) / (x1 - x0) + else: + frac = 0.5 # degenerate case; center it + + edge_margin = 0.15 # keep 15% of axes width clear at left/right + frac = max(edge_margin, min(1.0 - edge_margin, frac)) + + ax.text( + frac, -0.12, f"{_format_num(val)} ({_ordinal(rk)})", + transform=ax.transAxes, # position in axes coords so clamping works + ha='center', va='top', + fontsize=8, color=color, + clip_on=False # allow slight draw below axis + ) + ax.text(0.5, 0.45, col, ha='center', va='bottom', + transform=ax.transAxes, fontsize=9, fontweight='bold') + ax.set_ylim(-0.01, 0.05) + ax.axis('off') + + + # ---------------- objective KDEs ---------------- + gs_obj = outer[1].subgridspec(max(obj_rows, 1), objectives_per_row, wspace=0.05, hspace=0.8) + + if obj_count == 0: + # No objectives—show a placeholder + ax = fig.add_subplot(gs_obj[0, 0]) + ax.axis('off') + ax.text(0.5, 0.5, "No objective metrics found.", ha='center', va='center') + else: + for idx in range(obj_count): + r, c = divmod(idx, objectives_per_row) + ax = fig.add_subplot(gs_obj[r, c]) + + # Gather arrays for this objective across models + valid_raws = [] + for m in self.model_names: + raw_m = all_raw[m][:, idx] + if raw_m.size >= min_kde_samples: + valid_raws.append(raw_m) + if not valid_raws: + ax.axis('off') + continue + + # global percentile bounds (keep lower bound at 0.0 to match your spec) + pooled = np.concatenate(valid_raws, axis=0) + pmin = np.min(pooled) + high = np.percentile(pooled, 100 * (1 - truncate_tails_magnitude)) + + # If the effective range is less than half the (trimmed) max, lift the floor to the min + if (high - pmin) < 0.5 * high: + low = pmin + else: + low = 0.0 + + # prepare trimmed per-model data + data_for_kde = {} + for m in self.model_names: + raw = all_raw[m][:, idx] + trimmed = raw[(raw >= low) & (raw <= high)] + if trimmed.size >= min_kde_samples: + data_for_kde[m] = trimmed + + if not data_for_kde: + ax.axis('off'); continue + + # plot KDEs and collect means + means = {} + for m, trimmed in data_for_kde.items(): + is_focal = (m == model_name) + sns.kdeplot( + data=trimmed, + ax=ax, + clip=(low, high), + bw_adjust=0.5, + color=(self.model_colors[m] if is_focal else 'gray'), + alpha=(0.6 if is_focal else 0.2), + linewidth=1, + fill=is_focal, + gridsize=1000, + warn_singular=False + ) + means[m] = trimmed.mean() + + # baseline ticks + for m, mv in means.items(): + ax.plot( + mv, 0, '|', + color=(self.model_colors[m] if m == model_name else 'gray'), + markersize=(10 if m == model_name else 6) + ) + + # adjust y-axis + ys = [] + for l in ax.get_lines(): + yd = l.get_ydata() + if len(yd): + ys.append(np.nanmax(yd)) + for coln in ax.collections: + get_paths = getattr(coln, "get_paths", None) + if callable(get_paths): + for p in get_paths(): + verts = p.vertices + if verts.size: + ys.append(np.nanmax(verts[:, 1])) + vmax = max(ys, default=0.0) + ax.set_ylim(0, vmax * 1.05 if vmax > 0 else 1.0) + + # focal rank by mean (ascending). If you want direction-aware ranks, add a map here. + if model_name in means: + mean_val = means[model_name] + sorted_models = sorted(means.keys(), key=lambda m: means[m]) + rk = _ordinal(sorted_models.index(model_name) + 1) + y0 = 0.16 * (ax.get_ylim()[1] - ax.get_ylim()[0]) + ax.text(mean_val, y0, f"({rk})", ha='center', va='bottom', fontsize=7) + else: + midx = 0.5 * (low + high) + midy = 0.5 * (ax.get_ylim()[1] - ax.get_ylim()[0]) + ax.text(midx, midy, "Not enough valid samples!", + ha='center', va='center', fontsize=7, color='gray') + + ax.set_xlim(low, high) + ax.set_xticks([low, high]) + ax.set_xticklabels([_format_num(low), _format_num(high)], fontsize=7) + labels = ax.get_xticklabels() + if labels: + labels[0].set_ha('left') + if len(labels) > 1: + labels[1].set_ha('right') + ax.set_yticks([]) + ax.set_ylabel("") + ax.set_title(self.objective_names[idx], fontsize=9, pad=2) + for loc in ['top', 'right', 'left']: + ax.spines[loc].set_visible(False) + ax.spines['bottom'].set_visible(True) + + # blank any unused axes + for j in range(obj_count, max(obj_rows, 1) * objectives_per_row): + r, c = divmod(j, objectives_per_row) + fig.add_subplot(gs_obj[r, c]).axis('off') + + # ---------------- constraints tiles ---------------- + gs_con = outer[2].subgridspec(max(con_rows, 1), constraints_per_row, wspace=0.02) + white = np.array([1.0, 1.0, 1.0]) + + if con_count == 0: + ax = fig.add_subplot(gs_con[0, 0]) + ax.axis('off') + ax.text(0.5, 0.5, "No constraints found.", ha='center', va='center') + else: + # build matrix for ranking (models x constraints) + arr = np.stack([self.model_const_violation[m] for m in self.model_names], axis=0) + + for idx in range(con_count): + r, c = divmod(idx, constraints_per_row) + ax = fig.add_subplot(gs_con[r, c]) + + rate = self.model_const_violation[model_name][idx] # violation rate + adj = np.sqrt(rate) + face = white*(1-adj) + np.array(to_rgb(color))*adj + ax.patch.set_facecolor(tuple(face)) + + # rank lower violation as better (ascending=True) + rank = int(pd.Series(arr[:, idx]).rank(method='min', ascending=True) + .iloc[self.model_names.index(model_name)]) + + ax.text(0.5, 0.65, f"{rate*100:.0f}%", ha='center', va='center', fontsize=8) + ax.text(0.5, 0.22, f"({_ordinal(rank)})", ha='center', va='center', fontsize=7) + ax.set_title(f"C{idx+1}", fontsize=9, pad=2) + ax.set_xticks([]); ax.set_yticks([]) + for loc in ax.spines: + ax.spines[loc].set_visible(False) + + for j in range(con_count, max(con_rows, 1)*constraints_per_row): + r, c = divmod(j, constraints_per_row) + fig.add_subplot(gs_con[r, c]).axis('off') + + plt.show() diff --git a/bike_bench_internal/src/bikebench/benchmarking/scoring.py b/bike_bench_internal/src/bikebench/benchmarking/scoring.py new file mode 100644 index 0000000000000000000000000000000000000000..43ff745fa0ecaab8ca592230649818a05df46dcf --- /dev/null +++ b/bike_bench_internal/src/bikebench/benchmarking/scoring.py @@ -0,0 +1,347 @@ +from abc import abstractmethod, ABC +from typing import List +import torch +import pandas as pd +import numpy as np +import pygmo as pg +from sklearn.preprocessing import StandardScaler +import os +from bikebench.conditioning import conditioning +from bikebench.resource_utils import resource_path +from bikebench.data_loading import data_loading +from bikebench.design_evaluation.design_evaluation import construct_tensor_evaluator, EvaluationFunction +from bikebench.transformation import one_hot_encoding + + +class ScoringFunction(ABC): + def __init__(self, device="cpu", dtype=torch.float32): + self.device = device + self.dtype = dtype + + @abstractmethod + def return_names(self) -> List[str]: + pass + + @abstractmethod + def evaluate(self, designs: torch.Tensor, conditioning: dict = {}) -> torch.Tensor: + pass + + + +def get_ref_point(evaluator, objective_names, eval_names, reduction = "max", device = "cpu"): + if reduction=="max": + path = resource_path("misc/ref_point.csv") + elif reduction=="meanabs": + path = resource_path("misc/default_weights.csv") + else: + raise ValueError("Invalid reduction method. Use 'max' or 'meanabs'.") + if not os.path.exists(path): + #throw error if file does not exist + raise FileNotFoundError(f"Reference point file not found at {path}") + + # ref_point_df = recompute_ref_point(evaluator, eval_names, path, reduction, device) + else: + ref_point_df = pd.read_csv(path, index_col=0, header=None) + ref_point_columns = ref_point_df.index.values + if not np.all(np.isin(objective_names, ref_point_columns)): + raise ValueError("Reference point does not include all objective names. Please provide a valid reference point file.") + + # print("Reference point does not include all objective names. Recomputing...") + # ref_point_df = recompute_ref_point(evaluator, eval_names, path, reduction, device) + ref_point_df = ref_point_df.loc[objective_names] + ref_point = ref_point_df.values.flatten() + return ref_point + +class Hypervolume(ScoringFunction): + def __init__(self): + super().__init__() + + def return_names(self) -> List[str]: + return ["Design Quality ↑ (HV)"] + + def evaluate(self, designs, objective_scores, constraint_scores, objective_names, constraint_names, obj_ref_point): + # 1) keep only feasible points + feas_mask = np.all(constraint_scores <= 0, axis=1) + objs = objective_scores[feas_mask] + if objs.size == 0: + return 0.0 + + # 2) drop invalid (non-finite) rows to save time + valid_rows = np.all(np.isfinite(objs), axis=1) + objs = objs[valid_rows] + if objs.size == 0: + return 0.0 + + # 3) clip each objective to its component-wise upper bound (reference) + ref = np.asarray(obj_ref_point, dtype=float) + objs = np.minimum(objs, ref) + + # 4) normalize to [0,1] for MINIMIZATION (0 = ideal, 1 = at ref) + norm = objs / ref + + # 5) compute HV in unit cube with ref point = 1...1 (minimization) + hv = pg.hypervolume(norm) + hv_value = float(hv.compute(ref_point=np.ones_like(ref))) + return hv_value + + +class BinaryValidity(ScoringFunction): + def __init__(self): + super().__init__() + + def return_names(self) -> List[str]: + return ["Binary Validity ↑"] + + def evaluate(self, designs, objective_scores, constraint_scores, objective_names, constraint_names, obj_ref_point): + return np.mean(np.all(constraint_scores <=0, axis=1)) + + +class MMD(ScoringFunction): + + def __init__(self, batch_size = 1024, gamma=None): + super().__init__() + raw_ref = data_loading.load_bike_bench_test().values.astype(np.float32) + + self.scaler = StandardScaler() + self.scaler.fit(raw_ref) + self.reference_designs = self.scaler.transform(raw_ref) + + self.batch_size = batch_size + + if gamma is None: + gamma = self.compute_gamma(self.reference_designs) + self.gamma = gamma + + + def return_names(self) -> List[str]: + return ["Sim. to Data ↓ (MMD)"] + + def compute_gamma(self, ref: np.ndarray) -> float: + dists = np.sum((ref[:, None, :] - ref[None, :, :])**2, axis=2) + med = np.median(dists) + return 1.0 / (2 * med) if med > 0 else 1.0 + + def rbf_kernel_sum(self, A: np.ndarray, B: np.ndarray, gamma: float) -> float: + """ + Compute sum_{i,j} exp(-gamma * ||A[i] - B[j]||^2) + by blocking through rows of A and B in chunks of size batch_size. + """ + total = 0.0 + for i in range(0, A.shape[0], self.batch_size): + Ai = A[i : i + self.batch_size] + for j in range(0, B.shape[0], self.batch_size): + Bj = B[j : j + self.batch_size] + # compute squared‐distances of shape (len(Ai), len(Bj)) + D2 = np.sum((Ai[:, None, :] - Bj[None, :, :])**2, axis=2) + total += np.exp(-gamma * D2).sum() + return total + + def mmd(self, gen: np.ndarray, ref: np.ndarray) -> float: + K_GG = self.rbf_kernel_sum(gen, gen, self.gamma) + K_RR = self.rbf_kernel_sum(ref, ref, self.gamma) + K_GR = self.rbf_kernel_sum(gen, ref, self.gamma) + + n, m = gen.shape[0], ref.shape[0] + return (K_GG / (n * n)) + (K_RR / (m * m)) - (2 * K_GR / (n * m)) + + def evaluate(self, designs, objective_scores, constraint_scores, objective_names, constraint_names, obj_ref_point): + scaled_designs = self.scaler.transform(designs) + return self.mmd(scaled_designs, self.reference_designs) + +class AverageConstraintViolation(ScoringFunction): + def __init__(self): + super().__init__() + + def return_names(self) -> List[str]: + return self.names + + def evaluate(self, designs, objective_scores, constraint_scores, objective_names, constraint_names, obj_ref_point): + self.names = [f"Mean Violations ↓"] #counts average number of violated constraints per design + validity_boolean = constraint_scores > 0 + return np.mean(np.sum(validity_boolean, axis=1)) + +class AverageNovelty(ScoringFunction): + def __init__(self): + super().__init__() + raw_ref = data_loading.load_bike_bench_test().values.astype(np.float32) + + self.scaler = StandardScaler() + self.scaler.fit(raw_ref) + self.reference_designs = self.scaler.transform(raw_ref) + + def return_names(self) -> List[str]: + return ["Mean Novelty ↑"] + + def evaluate(self, designs, objective_scores, constraint_scores, objective_names, constraint_names, obj_ref_point): + scaled_designs = self.scaler.transform(designs) + dists = np.sqrt(np.sum((scaled_designs[:, None, :] - self.reference_designs[None, :, :])**2, axis=2)) + min_dists = np.min(dists, axis=1) + return np.mean(min_dists) + + + +class DPPDiversity(ScoringFunction): + def __init__(self): + super().__init__() + # Fit scaler on the same reference data as AverageNovelty + raw_ref = data_loading.load_bike_bench_test().values.astype(np.float32) + self.scaler = StandardScaler() + self.scaler.fit(raw_ref) + # Not strictly needed later, but keeping for parity / potential reuse + self.reference_designs = self.scaler.transform(raw_ref) + + def return_names(self) -> List[str]: + # Lower is better (more diverse), matching your wrapper's behavior + return ["Diversity ↓ (DPP)"] + + def evaluate(self, designs, objective_scores, constraint_scores, objective_names, constraint_names, obj_ref_point): + + # Convert to numpy and scale like AverageNovelty + X = np.asarray(designs, dtype=np.float64) + n = X.shape[0] + if n <= 1: + return 0.0 + + Xs = self.scaler.transform(X).astype(np.float64) # float64 for eig stability + + # Pairwise squared distances via r - 2XX^T + r^T + r = np.sum(Xs * Xs, axis=1, keepdims=True) # (n,1) + D = r - 2.0 * (Xs @ Xs.T) + r.T # (n,n), squared Euclidean distances + + D = D / X.shape[1] / X.shape[1] # normalize by dimension + + # Quartic RBF (as in your wrapper): exp(-0.5 * D^2) + # Add tiny jitter to help PD-ness when points are near-duplicates + S = np.exp(-0.5 * np.square(D)) + np.fill_diagonal(S, S.diagonal() + 1e-12) + + # Eigenvalues and negative mean log-eigenvalue + try: + eig_val, _ = np.linalg.eigh(S) + except Exception: + # Match wrapper fallback semantics + eig_val = np.ones(n, dtype=np.float64) + + eig_val = np.maximum(eig_val, 1e-7) + loss = -float(np.mean(np.log(eig_val))) + return loss + + + +class MinimumObjective(ScoringFunction): + def __init__(self): + super().__init__() + + def return_names(self) -> List[str]: + return self.names + + def evaluate(self, designs, objective_scores, constraint_scores, objective_names, constraint_names, obj_ref_point): + self.names = [f"Individual Min Objective Score ↓: {name}" for name in objective_names] + validity_mask = np.all(constraint_scores <= 0, axis=1) + valid_objective_scores = objective_scores[validity_mask] + if valid_objective_scores.size == 0: + return np.ones_like(objective_scores[0]) * obj_ref_point + minscores = np.min(valid_objective_scores, axis=0) + return minscores + +class MeanObjective(ScoringFunction): + def __init__(self): + super().__init__() + + def return_names(self) -> List[str]: + return self.names + + def evaluate(self, designs, objective_scores, constraint_scores, objective_names, constraint_names, obj_ref_point): + self.names = [f"Individual Mean Objective Score ↓: {name}" for name in objective_names] + validity_mask = np.all(constraint_scores <= 0, axis=1) + valid_objective_scores = objective_scores[validity_mask] + if valid_objective_scores.size == 0: + return np.ones_like(objective_scores[0]) * obj_ref_point + meanscores = np.mean(valid_objective_scores, axis=0) + return meanscores + +class ConstraintViolationRate(ScoringFunction): + def __init__(self): + super().__init__() + + def return_names(self) -> List[str]: + return self.names + + def evaluate(self, designs, objective_scores, constraint_scores, objective_names, constraint_names, obj_ref_point): + self.names = [f"Individual Constraint Violation Rate ↓: {name}" for name in constraint_names] + validity_boolean = constraint_scores > 0 + return np.mean(validity_boolean, axis=0) + +class MeanConstraintViolationMagnitude(ScoringFunction): + def __init__(self): + super().__init__() + + def return_names(self) -> List[str]: + return self.names + + def evaluate(self, designs, objective_scores, constraint_scores, objective_names, constraint_names, obj_ref_point): + self.names = [f"Individual Mean Constraint Violation Magnitude ↓: {name}" for name in constraint_names] + constraint_scores = np.clip(constraint_scores, a_min=0, a_max=None) + meanscores = np.mean(constraint_scores, axis=0) + return meanscores + +def construct_scorer(scoring_functions: List[ScoringFunction], evaluation_functions: List[EvaluationFunction], column_names: List[str], device: str = "cpu") -> callable: + evaluator, requirement_names, requirement_types = construct_tensor_evaluator(evaluation_functions, column_names, device=device) + requirement_names = np.array(requirement_names) + isobjective = torch.tensor(requirement_types) == 1 + objective_names = requirement_names[isobjective] + constraint_names = requirement_names[~isobjective] + + obj_ref_point = get_ref_point(evaluator, objective_names, requirement_names, "max", device) #1D numpy array + def scorer(designs: torch.Tensor, condition: dict = {}, preevaluated_scores = None) -> pd.Series: + device = designs.device + score_names = [] + scores = [] + if preevaluated_scores is None: + designs = designs.detach().cpu().numpy() + designs_df = pd.DataFrame(designs, columns=column_names) + designs_reverse_oh = one_hot_encoding.decode_to_mixed(designs_df) + designs_continuous_mapped = one_hot_encoding.encode_to_continuous(designs_reverse_oh) + designs_mapped_tens = torch.tensor(designs_continuous_mapped.values, dtype=torch.float32).to(device) + evaluation_scores = evaluator(designs_mapped_tens, condition) + else: + evaluation_scores = preevaluated_scores + objective_scores = evaluation_scores[:, isobjective].detach().cpu().numpy() + ref_point_exp = np.expand_dims(obj_ref_point, axis=0) + ref_point_exp = np.repeat(ref_point_exp, objective_scores.shape[0], axis=0) + objective_scores[np.isnan(objective_scores)] = ref_point_exp[np.isnan(objective_scores)] + constraint_scores = evaluation_scores[:, ~isobjective].detach().cpu().numpy() + + for scoring_function in scoring_functions: + raw = scoring_function.evaluate(designs, objective_scores, constraint_scores, objective_names, constraint_names, obj_ref_point) + + arr = np.atleast_1d(raw) + + names = scoring_function.return_names() + + for n, val in zip(names, arr): + score_names.append(n) + scores.append(val) + scores = np.array(scores) + scores = pd.Series(scores, index=score_names) + return scores + return scorer + +MainScores: List[ScoringFunction] = [ + Hypervolume(), + AverageConstraintViolation(), + MMD(), + AverageNovelty(), + BinaryValidity(), + DPPDiversity(), +] + +DetailedScores: List[ScoringFunction] = [ + MinimumObjective(), + MeanObjective(), + ConstraintViolationRate(), + MeanConstraintViolationMagnitude(), +] + + + diff --git a/bike_bench_internal/src/bikebench/conditioning/__init__.py b/bike_bench_internal/src/bikebench/conditioning/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/src/bikebench/conditioning/conditioning.py b/bike_bench_internal/src/bikebench/conditioning/conditioning.py new file mode 100644 index 0000000000000000000000000000000000000000..a2a4f1848d1b68b38e6dd157c02b23e99b90e557 --- /dev/null +++ b/bike_bench_internal/src/bikebench/conditioning/conditioning.py @@ -0,0 +1,119 @@ +import numpy as np +import pandas as pd +import torch + +from bikebench.data_loading import data_loading +from bikebench.resource_utils import datasets_path + +# ---------------------------- +# Lazy CPU caches (per split) +# ---------------------------- +_RIDER_CPU = {"train": None, "test": None} # torch.FloatTensor on CPU +_EMBED_CPU = {"train": None, "test": None} # torch.FloatTensor on CPU +_TEXT_CACHE = {"train": None, "test": None} # list[str] + +RIDER_COLS = [ + "upper_leg","lower_leg","arm_length", + "torso_length","neck_and_head_length","torso_width" +] + +# ---------------------------------- +# Per-device caches (by split) +# cache["rider"]["train"][device] -> tensor on that device +# ---------------------------------- +_DEVICE_CACHE = { + "rider": {"train": {}, "test": {}}, + "embed": {"train": {}, "test": {}}, +} + +# ---------- internal helpers ---------- + +def _ensure_rider_cpu(split: str): + if _RIDER_CPU[split] is None: + if split == "train": + df, _ = data_loading.load_aero_train() + elif split == "test": + df, _ = data_loading.load_aero_test() + else: + raise ValueError("split must be 'train' or 'test'") + _RIDER_CPU[split] = torch.tensor(df[RIDER_COLS].values, dtype=torch.float32) + return _RIDER_CPU[split] + +def _ensure_embed_cpu(split: str): + if _EMBED_CPU[split] is None: + if split == "train": + emb = np.load(datasets_path("Conditioning/emb_train.npy")) + cpu = torch.tensor(emb, dtype=torch.float32) + elif split == "test": + emb = np.load(datasets_path("Conditioning/emb_test.npy")) + cpu = torch.tensor(emb, dtype=torch.float32) + else: + raise ValueError("split must be 'train' or 'test'") + _EMBED_CPU[split] = cpu + return _EMBED_CPU[split] + +def _ensure_text(split: str): + if _TEXT_CACHE[split] is None: + if split == "test": + p = datasets_path("Conditioning/text_test.txt") + elif split == "train": + p = datasets_path("Conditioning/text_train.txt") + else: + raise ValueError("split must be 'train' or 'test'") + with open(p, "r") as f: + _TEXT_CACHE[split] = [line.strip() for line in f.readlines()] + return _TEXT_CACHE[split] + +def _to_device_cached(cpu_tensor: torch.Tensor, cache_dict: dict, device: torch.device = None): + if device is None: + return cpu_tensor + if device not in cache_dict: + cache_dict[device] = cpu_tensor.to(device, non_blocking=True) + return cache_dict[device] + +def _get_rider_tensor(split: str, device: torch.device = None): + cpu = _ensure_rider_cpu(split) + return _to_device_cached(cpu, _DEVICE_CACHE["rider"][split], device) + +def _get_embed_tensor(split: str, device: torch.device = None): + cpu = _ensure_embed_cpu(split) + return _to_device_cached(cpu, _DEVICE_CACHE["embed"][split], device) + +def sample_riders(num_samples: int, split="test", + randomize=False, device: torch.device = None): + data = _get_rider_tensor(split, device) + N = data.size(0) + if randomize: + idx = torch.randint(0, N, (num_samples,), device=data.device) + else: + reps = num_samples // N + 1 + idx = torch.arange(N, device=data.device).repeat(reps)[:num_samples] + return data[idx] + +def sample_embedding(num_samples: int, split="test", device: torch.device = None): + data = _get_embed_tensor(split, device) + N = data.size(0) + if split=="train": + idx = torch.randint(0, N, (num_samples,), device=data.device) + else: + reps = num_samples // N + 1 + idx = torch.arange(N, device=data.device).repeat(reps)[:num_samples] + return data[idx] + +def sample_use_case(num_samples: int, split=None, device: torch.device = None): + onehot = torch.eye(3, dtype=torch.float32) + if split=="train": + idx = torch.randint(0, 3, (num_samples,), device=onehot.device) + else: + reps = num_samples // 3 + 1 + idx = torch.arange(3, device=onehot.device).repeat(reps)[:num_samples] + return onehot[idx] + +def sample_text(num_samples, split="test", randomize=False): + text_data = _ensure_text(split) + if not text_data: + return [] + if randomize: + return np.random.choice(text_data, size=num_samples, replace=True).tolist() + reps = num_samples // len(text_data) + 1 + return (text_data * reps)[:num_samples] diff --git a/bike_bench_internal/src/bikebench/data_loading/__init__.py b/bike_bench_internal/src/bikebench/data_loading/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/src/bikebench/data_loading/data_loading.py b/bike_bench_internal/src/bikebench/data_loading/data_loading.py new file mode 100644 index 0000000000000000000000000000000000000000..a8a6a87f7cff42213070b2c6969104b0d4555ca4 --- /dev/null +++ b/bike_bench_internal/src/bikebench/data_loading/data_loading.py @@ -0,0 +1,286 @@ +# data_loading.py +import os +import tarfile +import shutil +from pathlib import Path +from typing import Dict, Optional, Iterable + +import numpy as np +import pandas as pd +import requests +from tqdm import tqdm + +from bikebench.resource_utils import datasets_path + +# ------------------------------------------------------------------------------------ +# Config +# ------------------------------------------------------------------------------------ +DV_API = os.environ.get("DATAVERSE_API_URL", "https://dataverse.harvard.edu/api") +ACCESS_API = os.environ.get("DATAVERSE_DATA_URL", "https://dataverse.harvard.edu/api/access/datafile") +DATAVERSE_DOI = os.environ.get("DATAVERSE_DOI", "10.7910/DVN/BSJSM6") # override via env if needed + +DATASETS_ROOT = Path(datasets_path(".")) + +# In-memory cache of remote file index: {doi: { "dir/label": {"id": int, "size": int} }} +_REMOTE_INDEX: Dict[str, Dict[str, Dict]] = {} + +# ------------------------------------------------------------------------------------ +# Remote index and download-by-name +# ------------------------------------------------------------------------------------ +def _headers() -> Optional[Dict[str, str]]: + tok = os.environ.get("DATAVERSE_API_TOKEN") + return {"X-Dataverse-key": tok} if tok else None + +def _with_pid(doi: str) -> str: + return doi if doi.startswith(("doi:", "hdl:")) else f"doi:{doi}" + +def _list_remote_files(doi: str = DATAVERSE_DOI, *, refresh: bool = False) -> Dict[str, Dict]: + """ + Return mapping 'dir/label' -> {'id': int, 'size': int} for latestVersion. + Cached per-DOI; set refresh=True to refetch. + """ + if (not refresh) and (doi in _REMOTE_INDEX): + return _REMOTE_INDEX[doi] + + url = f"{DV_API}/datasets/:persistentId" + # Try with token (can see draft), fall back to anonymous (published) + r = requests.get(url, params={"persistentId": _with_pid(doi)}, headers=_headers(), timeout=60) + if r.status_code == 401: + r = requests.get(url, params={"persistentId": _with_pid(doi)}, timeout=60) + r.raise_for_status() + + out: Dict[str, Dict] = {} + for f in r.json()["data"]["latestVersion"].get("files", []): + df = f["dataFile"] + dlab = f.get("directoryLabel") or "" + lab = f.get("label") or "" + key = f"{dlab}/{lab}" if dlab else lab + out[key] = {"id": df["id"], "size": int(df.get("filesize", 0))} + _REMOTE_INDEX[doi] = out + return out + +def _resolve_key_to_id(key: str, doi: str = DATAVERSE_DOI) -> int: + idx = _list_remote_files(doi) + if key in idx: + return idx[key]["id"] + # one refresh try (handles recent changes) + idx = _list_remote_files(doi, refresh=True) + if key in idx: + return idx[key]["id"] + # helpful error with suggestions by basename + base = Path(key).name + close = sorted(k for k in idx if Path(k).name == base) + hint = f" Known with same filename: {close[:6]}" if close else "" + raise FileNotFoundError(f"Dataverse key not found: {key}.{hint}") + +def _download_by_key(key: str, dest_path: Path, doi: str = DATAVERSE_DOI) -> None: + fid = _resolve_key_to_id(key, doi) + dest_path.parent.mkdir(parents=True, exist_ok=True) + with requests.get(f"{ACCESS_API}/{fid}", stream=True, timeout=180) as r: + r.raise_for_status() + tmp = dest_path.with_suffix(dest_path.suffix + ".part") + total = int(r.headers.get("content-length", 0)) + with open(tmp, "wb") as out, tqdm( + total=total, unit="B", unit_scale=True, desc=f"Downloading {dest_path.name}" + ) as pbar: + for chunk in r.iter_content(1024 * 1024): + if chunk: + out.write(chunk) + pbar.update(len(chunk)) + tmp.rename(dest_path) + +# ------------------------------------------------------------------------------------ +# Local fetch: non-destructive, download only if missing (or repair=True) +# ------------------------------------------------------------------------------------ +def download_if_missing(remote_path: str, *, repair: bool = False, doi: str = DATAVERSE_DOI) -> str: + """ + If the local file exists -> return it. + If missing (or repair=True) -> fetch from Dataverse by *path name*. + """ + local_path = Path(datasets_path(remote_path)) + if local_path.exists() and not repair: + return str(local_path) + _download_by_key(remote_path, local_path, doi=doi) + return str(local_path) + +# ------------------------------------------------------------------------------------ +# Archive extraction (no checksum; extract once & reuse) +# ------------------------------------------------------------------------------------ +def _key_without_tar_gz(key: str) -> str: + return key[:-7] if key.endswith(".tar.gz") else key + +def _dir_has_files(d: Path) -> bool: + try: + next(p for p in d.rglob("*") if p.is_file()) + return True + except StopIteration: + return False + +def _extract_tar_if_needed(archive_key: str, *, repair: bool = False, keep_archive: bool = True, doi: str = DATAVERSE_DOI) -> Path: + """ + - If extracted dir exists (non-empty) and repair=False -> reuse it. + - Else ensure archive exists locally (download by name if needed), then (re)extract. + """ + if not archive_key.endswith(".tar.gz"): + raise ValueError(f"Expected .tar.gz key, got: {archive_key}") + + target_rel = _key_without_tar_gz(archive_key) + target_dir = Path(datasets_path(target_rel)) + + if target_dir.exists() and _dir_has_files(target_dir) and not repair: + return target_dir + + archive_path = Path(download_if_missing(archive_key, repair=repair, doi=doi)) + + tmp_dir = target_dir.parent / f".__extract_tmp_{target_dir.name}" + if tmp_dir.exists(): + shutil.rmtree(tmp_dir) + tmp_dir.mkdir(parents=True, exist_ok=True) + + with tarfile.open(archive_path, "r:gz") as tar: + # basic path traversal guard + def is_within_directory(directory, target): + abs_directory = os.path.abspath(directory) + abs_target = os.path.abspath(target) + return os.path.commonprefix([abs_directory, abs_target]) == abs_directory + for m in tar.getmembers(): + dest = os.path.join(tmp_dir, m.name) + if not is_within_directory(tmp_dir, dest): + raise Exception("Attempted Path Traversal in Tar File") + tar.extractall(path=tmp_dir) + + if target_dir.exists(): + shutil.rmtree(target_dir) + tmp_dir.rename(target_dir) + + if not keep_archive: + try: archive_path.unlink() + except Exception: pass + + return target_dir + +# ------------------------------------------------------------------------------------ +# Generic file loaders +# ------------------------------------------------------------------------------------ +def load_any_file(filepath: str): + ext = os.path.splitext(filepath)[1].lower() + if ext == ".tab": + return pd.read_csv(filepath, index_col=0, sep="\t") + elif ext == ".csv": + return pd.read_csv(filepath, index_col=0) + elif ext == ".npy": + return np.load(filepath, allow_pickle=True) + else: + raise ValueError(f"Unsupported file type: {filepath}") + +# ------------------------------------------------------------------------------------ +# Dataset loaders (non-destructive; pass repair=True to refetch) +# ------------------------------------------------------------------------------------ +def _pair_paths(name_prefix: str, folder: str, y_ext: Optional[str], y_train_is_npy: bool): + x_train = f"{folder}/{name_prefix}_X_train.csv" + y_train = f"{folder}/{name_prefix}_Y_train.npy" if y_train_is_npy else f"{folder}/{name_prefix}_Y_train.{y_ext or 'csv'}" + x_test = f"{folder}/{name_prefix}_X_test.csv" + y_test = f"{folder}/{name_prefix}_Y_test.{y_ext or 'csv'}" + return x_train, y_train, x_test, y_test + +def load_dataset_pair(name_prefix: str, folder: str, y_ext: str = None, y_train_is_npy: bool = False, *, repair: bool = False, doi: str = DATAVERSE_DOI): + x_train, y_train, _, _ = _pair_paths(name_prefix, folder, y_ext, y_train_is_npy) + X_train = load_any_file(download_if_missing(x_train, repair=repair, doi=doi)) + Y_train = load_any_file(download_if_missing(y_train, repair=repair, doi=doi)) + return X_train, Y_train + +def load_dataset_pair_test(name_prefix: str, folder: str, y_ext: str = None, *, repair: bool = False, doi: str = DATAVERSE_DOI): + _, _, x_test, y_test = _pair_paths(name_prefix, folder, y_ext, False) + X_test = load_any_file(download_if_missing(x_test, repair=repair, doi=doi)) + Y_test = load_any_file(download_if_missing(y_test, repair=repair, doi=doi)) + return X_test, Y_test + +# ---- Predictive modeling dataset functions ---- +def load_aero_train(*, repair: bool = False, doi: str = DATAVERSE_DOI): return load_dataset_pair("aero", "Predictive_Modeling_Datasets", repair=repair, doi=doi) +def load_aero_test(*, repair: bool = False, doi: str = DATAVERSE_DOI): return load_dataset_pair_test("aero", "Predictive_Modeling_Datasets", repair=repair, doi=doi) +def load_structure_train(*, repair: bool = False, doi: str = DATAVERSE_DOI): return load_dataset_pair("structure", "Predictive_Modeling_Datasets", repair=repair, doi=doi) +def load_structure_test(*, repair: bool = False, doi: str = DATAVERSE_DOI): return load_dataset_pair_test("structure", "Predictive_Modeling_Datasets", repair=repair, doi=doi) +def load_validity_train(*, repair: bool = False, doi: str = DATAVERSE_DOI): return load_dataset_pair("validity", "Predictive_Modeling_Datasets", repair=repair, doi=doi) +def load_validity_test(*, repair: bool = False, doi: str = DATAVERSE_DOI): return load_dataset_pair_test("validity", "Predictive_Modeling_Datasets", repair=repair, doi=doi) +def load_usability_cont_train(*, repair: bool = False, doi: str = DATAVERSE_DOI): + return load_dataset_pair("usability_cont", "Predictive_Modeling_Datasets", y_ext="tab", repair=repair, doi=doi) +def load_usability_cont_test(*, repair: bool = False, doi: str = DATAVERSE_DOI): + return load_dataset_pair_test("usability_cont", "Predictive_Modeling_Datasets", y_ext="tab", repair=repair, doi=doi) +def load_aesthetics_train(*, repair: bool = False, doi: str = DATAVERSE_DOI, y_train_is_npy=True): + return load_dataset_pair("aesthetics", "Predictive_Modeling_Datasets", y_train_is_npy=y_train_is_npy, repair=repair, doi=doi) +def load_aesthetics_test(*, repair: bool = False, doi: str = DATAVERSE_DOI): + return load_dataset_pair_test("aesthetics", "Predictive_Modeling_Datasets", repair=repair, doi=doi) + +def one_hot_encode_material(data: pd.DataFrame): + data = data.copy() + data["Material"] = pd.Categorical(data["Material"], categories=["Steel", "Aluminum", "Titanium"]) + mats_oh = pd.get_dummies(data["Material"], prefix="Material=", prefix_sep="") + data.drop(["Material"], axis=1, inplace=True) + return pd.concat([mats_oh, data], axis=1) + +def load_structure_train_oh(*, repair: bool = False, doi: str = DATAVERSE_DOI): + X, Y = load_structure_train(repair=repair, doi=doi); return one_hot_encode_material(X), Y +def load_structure_test_oh(*, repair: bool = False, doi: str = DATAVERSE_DOI): + X, Y = load_structure_test(repair=repair, doi=doi); return one_hot_encode_material(X), Y +def load_validity_train_oh(*, repair: bool = False, doi: str = DATAVERSE_DOI): + X, Y = load_validity_train(repair=repair, doi=doi); return one_hot_encode_material(X), Y +def load_validity_test_oh(*, repair: bool = False, doi: str = DATAVERSE_DOI): + X, Y = load_validity_test(repair=repair, doi=doi); return one_hot_encode_material(X), Y + +# ---- Generative modeling dataset functions ---- +def load_bike_bench_train(*, repair: bool = False, doi: str = DATAVERSE_DOI): + path = download_if_missing("Generative_Modeling_Datasets/bike_bench.csv", repair=repair, doi=doi) + return pd.read_csv(path, index_col=0) + +def load_bike_bench_test(*, repair: bool = False, doi: str = DATAVERSE_DOI): + path = download_if_missing("Generative_Modeling_Datasets/bike_bench_test.csv", repair=repair, doi=doi) + return pd.read_csv(path, index_col=0) + +def load_bike_bench_mixed_modality_train(*, repair: bool = False, doi: str = DATAVERSE_DOI): + path = download_if_missing("Generative_Modeling_Datasets/bike_bench_mixed_modality.csv", repair=repair, doi=doi) + return pd.read_csv(path, index_col=0) + +def load_bike_bench_mixed_modality_test(*, repair: bool = False, doi: str = DATAVERSE_DOI): + path = download_if_missing("Generative_Modeling_Datasets/bike_bench_mixed_modality_test.csv", repair=repair, doi=doi) + return pd.read_csv(path, index_col=0) + +# ---- Original_BIKED_Data (numbered helpers) ---- +def _find_numbered_file(base_dir: Path, n: int, exts: set[str], paren_style: bool = False) -> str: + if not isinstance(n, int) or n <= 0: + raise ValueError("n must be a positive int") + names = [f"({n}){ext}" if paren_style else f"{n}{ext}" for ext in exts] + # direct child + for name in names: + p = base_dir / name + if p.is_file(): + return str(p) + # recursive or stem fallback + for name in names: + hits = sorted(base_dir.rglob(name)) + if hits: return str(hits[0]) + for p in sorted(base_dir.rglob("*")): + if p.is_file() and p.suffix.lower() in exts: + stem = p.stem[1:-1] if (paren_style and p.stem.startswith("(") and p.stem.endswith(")")) else p.stem + if stem == str(n): return str(p) + raise FileNotFoundError(f"No file numbered {n} under {base_dir}") + +def load_biked_original_png(n: int, *, repair: bool = False, doi: str = DATAVERSE_DOI) -> str: + base = _extract_tar_if_needed("Original_BIKED_Data/Images.tar.gz", repair=repair, doi=doi) + return _find_numbered_file(base, n, {".png"}, paren_style=True) + +def load_biked_original_bcad(n: int, *, repair: bool = False, doi: str = DATAVERSE_DOI) -> str: + base = _extract_tar_if_needed("Original_BIKED_Data/BCAD.tar.gz", repair=repair, doi=doi) + return _find_numbered_file(base, n, {".bcad"}, paren_style=True) + +# ---- Real_Extended_Data (numbered helpers) ---- +def load_png(n: int, *, repair: bool = False, doi: str = DATAVERSE_DOI) -> str: + base = _extract_tar_if_needed("Real_Extended_Data/pngs.tar.gz", repair=repair, doi=doi) + return _find_numbered_file(base, n, {".png"}, paren_style=False) + +def load_svg(n: int, *, repair: bool = False, doi: str = DATAVERSE_DOI) -> str: + base = _extract_tar_if_needed("Real_Extended_Data/svgs.tar.gz", repair=repair, doi=doi) + return _find_numbered_file(base, n, {".svg"}, paren_style=False) + +def load_xml(n: int, *, repair: bool = False, doi: str = DATAVERSE_DOI) -> str: + base = _extract_tar_if_needed("Real_Extended_Data/xmls.tar.gz", repair=repair, doi=doi) + return _find_numbered_file(base, n, {".xml"}, paren_style=False) diff --git a/bike_bench_internal/src/bikebench/data_loading/dataverse_utils.py b/bike_bench_internal/src/bikebench/data_loading/dataverse_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e6dfdf9b9e11098bec30990c73947d273899db5e --- /dev/null +++ b/bike_bench_internal/src/bikebench/data_loading/dataverse_utils.py @@ -0,0 +1,263 @@ +import os, json, mimetypes, requests +from pathlib import Path, PurePosixPath +from typing import Dict, Tuple, List, Set, Optional +import time +from tqdm import tqdm + +DV_API = os.environ.get("DATAVERSE_API_URL", "https://dataverse.harvard.edu/api") +DATA_API = os.environ.get("DATAVERSE_DATA_URL", "https://dataverse.harvard.edu/api/access/datafile") + +# ---------------- basics ---------------- + +def _headers() -> Dict[str, str]: + tok = os.environ.get("DATAVERSE_API_TOKEN") + if not tok: + raise RuntimeError("Set DATAVERSE_API_TOKEN") + return {"X-Dataverse-key": tok} + +def _with_pid(doi: str) -> str: + return doi if doi.startswith(("doi:", "hdl:")) else f"doi:{doi}" + +def _list_remote_files(doi: str) -> Dict[str, Dict]: + """ + Return mapping: 'dir/label' -> {'id': int, 'size': int} + Uses latestVersion of the dataset. + """ + url = f"{DV_API}/datasets/:persistentId" + r = requests.get(url, params={"persistentId": _with_pid(doi)}, headers=_headers(), timeout=60) + # If you don't have perms for drafts, retry anonymously for published view: + if r.status_code == 401: + r = requests.get(url, params={"persistentId": _with_pid(doi)}, timeout=60) + r.raise_for_status() + out = {} + for f in r.json()["data"]["latestVersion"].get("files", []): + df = f["dataFile"] + dirlabel = f.get("directoryLabel") or "" + label = f.get("label") or "" + key = f"{dirlabel}/{label}" if dirlabel else label + out[key] = {"id": df["id"], "size": int(df.get("filesize", 0))} + return out + +def _walk_local_files(local_root: Path) -> List[Tuple[str, Path]]: + """ + Return list of (posix_key, absolute_path) for all files under local_root. + Keys are relative posix paths like 'sub/dir/file.ext'. + """ + files: List[Tuple[str, Path]] = [] + for p in sorted(local_root.rglob("*")): + if p.is_file(): + rel = p.relative_to(local_root) + files.append((PurePosixPath(rel).as_posix(), p)) + return files + +# ---------------- pretty printing ---------------- + +def _tree_from_keys(keys: List[str]) -> dict: + root = {} + for k in keys: + parts = [x for x in k.split("/") if x] + cur = root + for i, part in enumerate(parts): + leaf = (i == len(parts) - 1) + cur = cur.setdefault(part, {} if not leaf else None) # None marks a file + return root + +def _print_tree(node: dict, prefix: str = ""): + items = list(node.items()) + for i, (name, child) in enumerate(items): + last = (i == len(items) - 1) + branch = "└── " if last else "├── " + print(prefix + branch + name) + if isinstance(child, dict): + _print_tree(child, prefix + (" " if last else "│ ")) + +def print_remote_tree(doi: str) -> None: + idx = _list_remote_files(doi) + tree = _tree_from_keys(sorted(idx.keys())) + print(f"[remote tree for {doi}]") + _print_tree(tree) + +def print_local_tree(local_root: str | Path) -> None: + root = Path(local_root) + keys = [k for k, _ in _walk_local_files(root)] + tree = _tree_from_keys(keys) + print(f"[local tree under {root}]") + _print_tree(tree) + +# ---------------- diff ---------------- + +def diff_local_vs_remote(local_root: str | Path, doi: str, dv_prefix: str = "") -> Dict[str, Set[str]]: + """ + Compare local tree (under local_root) to remote tree (under optional dv_prefix). + Returns dict with 'local_only' and 'remote_only' sets of keys (posix). + """ + remote = _list_remote_files(doi) + # limit remote to requested prefix (if any) + if dv_prefix: + dv_prefix = dv_prefix.rstrip("/") + "/" + remote_keys = {k[len(dv_prefix):] for k in remote if k.startswith(dv_prefix)} + else: + remote_keys = set(remote.keys()) + + local_keys = {k for k, _ in _walk_local_files(Path(local_root))} + return { + "local_only": local_keys - remote_keys, + "remote_only": remote_keys - local_keys, + # note: no "changed" category (checksums omitted by design) + } + +# ---------------- delete ---------------- + +def delete_remote_prefix(doi: str, prefix: str, dry_run: bool = False, sleep_between: float = 0.0) -> list[str]: + """ + Delete all remote files whose key is exactly 'prefix' or starts with 'prefix/'. + Works against the dataset's DRAFT (publish afterwards if you want the change live). + Returns the list of deleted keys. + """ + # Normalize matching + pre = prefix.rstrip("/") + idx = _list_remote_files(doi) + targets = [(k, v["id"]) for k, v in idx.items() if (k == pre or k.startswith(pre + "/"))] + + if not targets: + print(f"[delete] nothing to delete under '{pre}'") + return [] + + print(f"[delete] {len(targets)} files under '{pre}'") + for key, fid in targets: + print(f"DELETE {key} (id={fid})") + if dry_run: + continue + r = requests.delete(f"{DV_API}/files/{fid}", headers=_headers(), timeout=60) + r.raise_for_status() + if sleep_between > 0: + time.sleep(sleep_between) + + if dry_run: + print("[dry-run] no deletions performed") + return [k for k, _ in targets] + + +# ---------------- upload / replace ---------------- + +def _upload_new(doi: str, path: Path, directory_label: str) -> int: + url = f"{DV_API}/datasets/:persistentId/add" + params = {"persistentId": _with_pid(doi)} + mime = mimetypes.guess_type(str(path))[0] or "application/octet-stream" + data = {"jsonData": json.dumps({"directoryLabel": directory_label, "restrict": False})} + with path.open("rb") as fh: + files = {"file": (path.name, fh, mime)} + r = requests.post(url, params=params, data=data, files=files, headers=_headers(), timeout=600) + r.raise_for_status() + return r.json()["data"]["files"][0]["dataFile"]["id"] + +def _replace_file(file_id: int, path: Path) -> int: + url = f"{DV_API}/files/{file_id}/replace" + mime = mimetypes.guess_type(str(path))[0] or "application/octet-stream" + data = {"jsonData": json.dumps({"forceReplace": True})} + with path.open("rb") as fh: + files = {"file": (path.name, fh, mime)} + r = requests.post(url, data=data, files=files, headers=_headers(), timeout=600) + r.raise_for_status() + return r.json()["data"]["files"][0]["dataFile"]["id"] + +def upload_directory( + doi: str, + local_dir: str | Path, + dv_prefix: str = "", + replace_existing: bool = True, +) -> None: + """ + Recursively upload local_dir into the dataset under dv_prefix. + - If a remote file with the same directory/filename exists and replace_existing=True → REPLACE + else ADD as new. + """ + local_dir = Path(local_dir).resolve() + if not local_dir.is_dir(): + raise NotADirectoryError(local_dir) + + remote = _list_remote_files(doi) + + for rel_key, abs_path in _walk_local_files(local_dir): + # Compute Dataverse path pieces: + # remote key = f"{dv_prefix}/{rel_key}" (posix) + full_key = f"{dv_prefix.rstrip('/')}/{rel_key}" if dv_prefix else rel_key + directory = str(PurePosixPath(full_key).parent) + directory = "" if directory == "." else directory + + # Decide ADD vs REPLACE + if full_key in remote and replace_existing: + fid = remote[full_key]["id"] + print(f"REPLACE {full_key} id={fid}") + _replace_file(fid, abs_path) + elif full_key in remote and not replace_existing: + print(f"SKIP {full_key} (exists; replace_existing=False)") + else: + print(f"ADD {full_key}") + _upload_new(doi, abs_path, directory) + +def _access_params(): + tok = os.environ.get("DATAVERSE_API_TOKEN") + return {"key": tok} if tok else {} + +def download_by_key(doi: str, key: str, dest_path: str | Path) -> Path: + """ + Resolve a dataset file by folder+name (key) and download it by id. + Works for draft/restricted files when DATAVERSE_API_TOKEN is set. + """ + idx = _list_remote_files(doi) + if key not in idx: + # try once more in case you just uploaded and the cache is stale + idx = _list_remote_files(doi, refresh=True) + if key not in idx: + raise FileNotFoundError(f"Remote key not found: {key}") + fid = idx[key]["id"] + + dest_path = Path(dest_path) + dest_path.parent.mkdir(parents=True, exist_ok=True) + + with requests.get( + f"{DATA_API}/{fid}", + params=_access_params(), # <-- pass token as query param + stream=True, + timeout=180, + allow_redirects=True, + ) as r: + r.raise_for_status() + tmp = dest_path.with_suffix(dest_path.suffix + ".part") + with open(tmp, "wb") as out: + for chunk in r.iter_content(1024 * 1024): + if chunk: + out.write(chunk) + tmp.rename(dest_path) + return dest_path + +def download_folder(doi: str, dv_prefix: str, dest_root: str | Path | None = None, overwrite: bool = False): + """ + Download every Dataverse file whose key starts with dv_prefix + '/', + preserving folder structure locally under dest_root (default: datasets_path). + """ + + dv_prefix = dv_prefix.rstrip("/") + dest_root = Path(dest_root) + + remote_files = _list_remote_files(doi) + keys = [k for k in remote_files if k == dv_prefix or k.startswith(dv_prefix + "/")] + if dv_prefix in keys: + keys.remove(dv_prefix) + + if not keys: + print(f"[download_folder] no remote files under '{dv_prefix}'") + return [] + + print(f"[download_folder] downloading {len(keys)} files from '{dv_prefix}'") + local_paths = [] + for key in tqdm(sorted(keys), unit="file"): + dest_path = dest_root / key + if dest_path.exists() and not overwrite: + local_paths.append(str(dest_path)) + continue + dest_path.parent.mkdir(parents=True, exist_ok=True) + download_by_key(doi, key, dest_path) + local_paths.append(str(dest_path)) + return local_paths diff --git a/bike_bench_internal/src/bikebench/data_loading/execute_upload_missing.py b/bike_bench_internal/src/bikebench/data_loading/execute_upload_missing.py new file mode 100644 index 0000000000000000000000000000000000000000..22b81d2f2b19953c70717e600652502f9878713b --- /dev/null +++ b/bike_bench_internal/src/bikebench/data_loading/execute_upload_missing.py @@ -0,0 +1,22 @@ +# upload_missing.py +from pathlib import Path +from bikebench.resource_utils import datasets_path +from bikebench.data_loading import dataverse_utils + +import dataverse_utils as dv +import os + +DOI = "10.7910/DVN/BSJSM6" + +DV_API = os.environ.get("DATAVERSE_API_URL", "https://dataverse.harvard.edu/api") +os.environ["DATAVERSE_API_TOKEN"] = "3c34d30b-da7e-46ba-af84-67c529f0679a" + +# dataverse_utils.upload_directory("10.7910/DVN/BSJSM6", +# datasets_path("Synthetic_Extended_Data/CTGAN/embeddings"), +# dv_prefix="Synthetic_Extended_Data/CTGAN/embeddings", +# replace_existing=False) + +dataverse_utils.upload_directory("10.7910/DVN/BSJSM6", + datasets_path("Generative_Modeling_Datasets"), + dv_prefix="Generative_Modeling_Datasets", + replace_existing=False) \ No newline at end of file diff --git a/bike_bench_internal/src/bikebench/design_evaluation/__init__.py b/bike_bench_internal/src/bikebench/design_evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/src/bikebench/design_evaluation/design_evaluation.py b/bike_bench_internal/src/bikebench/design_evaluation/design_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..b77268ec1560f9b1826cb3c60b73bb2cc8e8bc05 --- /dev/null +++ b/bike_bench_internal/src/bikebench/design_evaluation/design_evaluation.py @@ -0,0 +1,550 @@ +from abc import abstractmethod, ABC +from typing import List +import numpy as np +import pandas as pd +import torch +import torch.nn.functional as F +import dill +from PIL import Image + +from bikebench.embedding import clip_embedding_calculator +from bikebench.transformation import interface_points, framed, ordered_columns +from bikebench.ergonomics import joint_angles +from bikebench.prediction import aero_predictor, aesthetics_predictor +from bikebench.prediction.prediction_utils import Preprocessor +from bikebench.resource_utils import models_and_scalers_path +from bikebench.data_loading import data_loading +from bikebench.validation.base_validation_function import construct_tensor_validator +from bikebench.validation.bike_bench_validation_functions import bike_bench_validation_functions, difficult_validation_functions +from bikebench.prediction.aero_predictor import get_aero_model +from bikebench.prediction.aesthetics_predictor import get_aesthetics_model +from bikebench.prediction.validity_predictor import get_validity_model +from bikebench.prediction.structural_predictor import get_structural_model +from bikebench.prediction.usability_predictor import get_usability_model + + + + +class EvaluationFunction(ABC): + def __init__(self, device="cpu", dtype=torch.float32): + self.device = device + self.dtype = dtype + + @abstractmethod + def variable_names(self) -> List[str]: + pass + + @abstractmethod + def return_names(self) -> List[str]: + pass + + @abstractmethod # 1 = objective, 0 = constraint + def return_types(self) -> List[str]: + pass + + @abstractmethod + def evaluate(self, designs: torch.Tensor, conditioning: dict = {}) -> torch.Tensor: + pass + + +class AeroEvaluator(EvaluationFunction): + def __init__(self, device="cpu", dtype=torch.float32): + super().__init__(device, dtype) + state_path = models_and_scalers_path("aero_model_weights.pt") + scaler_path = models_and_scalers_path("aero_scaler.pt") + state = torch.load(state_path, weights_only=True, map_location=torch.device(device)) + self.model = get_aero_model(dropout_on=False).to(device) + self.model.load_state_dict(state) + self.preprocessor = Preprocessor(scaler_path=scaler_path, preprocess_fn=aero_predictor.calculate_features, device=device) + + def variable_names(self) -> List[str]: + return [ + "Stack", + "Handlebar style OHCLASS: 0", "Handlebar style OHCLASS: 1", "Handlebar style OHCLASS: 2", + "Seat angle", "Saddle height", "Head tube length textfield", "Head tube lower extension2", + "Head angle", "DT Length" + ] + + def return_names(self) -> List[str]: + return ['Drag Force (N)'] + + def return_types(self) -> List[str]: + return [1] + + def evaluate(self, designs: torch.Tensor, conditioning: dict = {}) -> torch.Tensor: + int_pts = interface_points.calculate_interface_points(designs) + assert "Rider" in conditioning, "Rider dimensions must be provided in conditioning to calculate aerodynamics." + rider_dims = conditioning["Rider"] + #if rider_dims is a 1D tensor, expand it to match the batch size of designs + if rider_dims.dim() == 1: + rider_dims = rider_dims.unsqueeze(0).expand(designs.shape[0], -1) + elif rider_dims.shape[0] == 1: + rider_dims = rider_dims.expand(designs.shape[0], -1) + rider_dims = rider_dims.to(self.device, dtype=self.dtype) + combinations = torch.cat((int_pts, rider_dims), dim=1) + combinations = self.preprocessor(combinations) + predictions = self.model(combinations) + predictions = torch.clip(predictions, min=0) + return predictions + +class FrameValidityEvaluator(EvaluationFunction): + def __init__(self, device="cpu", dtype=torch.float32): + super().__init__(device, dtype) + state_path = models_and_scalers_path("validity_model_weights.pt") + scaler_path = models_and_scalers_path("validity_scaler.pt") + state = torch.load(state_path, weights_only=True, map_location=torch.device(device)) + self.model = get_validity_model(dropout_on=False).to(device) + self.model.load_state_dict(state) + self.preprocessor = Preprocessor(scaler_path=scaler_path, preprocess_fn=None, device=device) + + self.converter = framed.clip_to_framed_tensor_builder(ordered_columns.bike_bench_columns, framed.FRAMED_ORDERED_COLUMNS) + + def variable_names(self) -> List[str]: + return ordered_columns.bike_bench_columns + + def return_names(self) -> List[str]: + return ['Predicted Frame Validity'] + + def return_types(self) -> List[str]: + return [0] + + def evaluate(self, designs: torch.Tensor, conditioning: dict = {}) -> torch.Tensor: + + framed_tensor = self.converter(designs) + framed_tensor = framed_tensor.to(self.device, dtype=self.dtype) + framed_tensor = self.preprocessor(framed_tensor) + predictions = self.model(framed_tensor) + validity = predictions-0.5 + return validity + +class StructuralEvaluator(EvaluationFunction): + def __init__(self, device="cpu", dtype=torch.float32): + super().__init__(device, dtype) + state_path = models_and_scalers_path("structural_model_weights.pt") + scaler_path = models_and_scalers_path("structural_scaler.pt") + state = torch.load(state_path, weights_only=True, map_location=torch.device(device)) + self.model = get_structural_model(dropout_on = False).to(device) + self.model.load_state_dict(state) + self.preprocessor = Preprocessor(scaler_path=scaler_path, preprocess_fn=None, device=device) + self.converter = framed.clip_to_framed_tensor_builder(ordered_columns.bike_bench_columns, framed.FRAMED_ORDERED_COLUMNS) + + def variable_names(self) -> List[str]: + return ordered_columns.bike_bench_columns + + def return_names(self) -> List[str]: + return ['Mass (kg)', 'Planar Compliance Score', 'Transverse Compliance Score', 'Eccentric Compliance Score', 'Planar Safety Factor', 'Eccentric Safety Factor'] + + def return_types(self) -> List[str]: + return [1,1,1,1,0,0] + + def evaluate(self, designs: torch.Tensor, conditioning: dict = {}) -> torch.Tensor: + framed_tensor = self.converter(designs) + framed_tensor = framed_tensor.to(self.device, dtype=self.dtype) + framed_tensor = self.preprocessor(framed_tensor) + predictions = self.model(framed_tensor) + predictions = torch.clip(predictions, min=0) + predictions[:, 4:6] = 1.0 - predictions[:, 4:6] + return predictions + +class AestheticsEvaluator(EvaluationFunction): + def __init__(self, + mode: str = "Text", + device: str = "cpu", + dtype: torch.dtype = torch.float32, + batch_size: int = None): + super().__init__(device, dtype) + state_path = models_and_scalers_path("aesthetics_model_weights.pt") + scaler_path = models_and_scalers_path("aesthetics_scaler.pt") + self.preprocessor = Preprocessor( + scaler_path=scaler_path, + preprocess_fn=aesthetics_predictor.remove_wall_thickness, + device=device + ) + state = torch.load(state_path, weights_only=True, map_location=torch.device(device)) + self.model = get_aesthetics_model(dropout_on = False).to(device) + self.model.load_state_dict(state) + self.model.eval() + + self.mode = mode # "Image", "Image Path", or "Text" + self.embedding_model = clip_embedding_calculator.ClipEmbeddingCalculator( + device=self.device, + batch_size=batch_size + ) + + def variable_names(self) -> List[str]: + return ordered_columns.bike_bench_columns + + def return_names(self) -> List[str]: + # if self.mode in ["Image", "Image Path"]: + # return ["Cosine Distance to Image"] + if self.mode == "Text": + return ["Cosine Distance to Text"] + elif self.mode == "Embedding": + return ["Cosine Distance to Embedding"] + + def return_types(self) -> List[str]: + return [1] + + def evaluate(self, + designs: torch.Tensor, + conditioning: dict = {}) -> torch.Tensor: + cond = conditioning.get(self.mode) + if cond is None: + raise ValueError(f"No conditioning provided for mode '{self.mode}'") + + # Prepare a list of items for embedding + # if self.mode == "Image": + # if isinstance(cond, torch.Tensor): + # items = [cond] + # elif isinstance(cond, (list, tuple)): + # items = list(cond) + # else: + # raise TypeError("For Image mode, conditioning must be a Tensor or list of Tensors") + # embed = self.embedding_model.embed_images(items) + + # elif self.mode == "Image Path": + # if isinstance(cond, str): + # paths = [cond] + # elif isinstance(cond, (list, tuple)): + # paths = list(cond) + # else: + # raise TypeError("For Image Path mode, conditioning must be a path or list of paths") + # imgs = [Image.open(p) for p in paths] + # embed = self.embedding_model.embed_images(imgs) + + if self.mode == "Text": + if isinstance(cond, str): + texts = [cond] + elif isinstance(cond, (list, tuple)): + texts = list(cond) + else: + raise TypeError("For Text mode, conditioning must be text or list of texts") + embed = self.embedding_model.embed_texts(texts) + elif self.mode == "Embedding": + if isinstance(cond, torch.Tensor): + embed = cond + else: + raise TypeError("For Embedding mode, conditioning must be a Tensor ") + + if embed.dim() == 1: + embed = embed.unsqueeze(0).expand(designs.shape[0], -1) + elif embed.shape[0] == 1: + embed = embed.expand(designs.shape[0], -1) + else: + raise ValueError(f"Unsupported mode: {self.mode}") + designs = self.preprocessor(designs) + preds = self.model(designs) + N = preds.size(0) + B_cond = embed.size(0) + if B_cond == 1 and N > 1: + embed = embed.expand(N, -1) + elif B_cond != N: + raise ValueError( + f"Number of condition embeddings ({B_cond}) " + f"does not match number of designs ({N})" + ) + + embed = embed.to(self.device, dtype=self.dtype) + + cos_sim = F.cosine_similarity(preds, embed, dim=1) + return (1 - cos_sim) / 2 + +class ValidationEvaluator(EvaluationFunction): + def __init__(self, device="cpu", dtype=torch.float32, only_difficult=False): + super().__init__(device, dtype) + self.clip_parameters = data_loading.load_bike_bench_train().columns.tolist() #TODO maybe include a list somewhere to avoid loading a dataset? + if only_difficult: + validator, validation_names = construct_tensor_validator(difficult_validation_functions, self.clip_parameters) + else: + validator, validation_names = construct_tensor_validator(bike_bench_validation_functions, self.clip_parameters) + self.validator = validator + self.validation_names = validation_names + + def variable_names(self) -> List[str]: + return self.clip_parameters + + def return_names(self) -> List[str]: + return self.validation_names + + def return_types(self) -> List[str]: + return [0] * len(self.validation_names) + + def evaluate(self, designs: torch.Tensor, conditioning: dict = {}) -> torch.Tensor: + # designs = designs.to(self.device, dtype=self.dtype) + predictions = self.validator(designs) + return predictions + +class ErgonomicsEvaluator(EvaluationFunction): + def __init__(self, penalize_constraints = False, constraints_only = False, device="cpu", dtype=torch.float32): + super().__init__(device, dtype) + self.penalize_constraints = penalize_constraints + self.constraints_only = constraints_only + + def variable_names(self) -> List[str]: + return [ + "Stack", + "Handlebar style OHCLASS: 0", "Handlebar style OHCLASS: 1", "Handlebar style OHCLASS: 2", + "Seat angle", "Saddle height", "Head tube length textfield", "Head tube lower extension2", + "Head angle", "DT Length" + ] + + def return_names(self) -> List[str]: + if self.penalize_constraints: + return ['Knee Angle Error (deg.)', 'Hip Angle Error (deg.)', "Arm Angle Error (deg.)"] + elif self.constraints_only: + return ["Arm Too Long for Bike", "Saddle Too Far From Handle", "Torso Too Long for Bike", + "Saddle Too Far From Crank", "Upper Leg Too Long for Bike", "Lower Leg Too Long for Bike"] + else: + return ['Knee Angle Error (deg.)', 'Hip Angle Error (deg.)', "Arm Angle Error (deg.)", + "Arm Too Long for Bike", "Saddle Too Far From Handle", "Torso Too Long for Bike", + "Saddle Too Far From Crank", "Upper Leg Too Long for Bike", "Lower Leg Too Long for Bike"] + + def return_types(self) -> List[str]: + if self.penalize_constraints: + return [1, 1, 1] + elif self.constraints_only: + return [0, 0, 0, 0, 0, 0] + else: + return [1, 1, 1, 0, 0, 0, 0, 0, 0] + + def evaluate(self, designs: torch.Tensor, conditioning: dict = {}) -> torch.Tensor: + assert "Rider" in conditioning, "Rider dimensions must be provided in conditioning to calculate ergonomics." + rider_dims = conditioning["Rider"] + if rider_dims.dim() == 1: + rider_dims = rider_dims.unsqueeze(0).expand(designs.shape[0], -1) + + rider_dims = rider_dims.to(self.device, dtype=self.dtype) + + if self.constraints_only: + predictions = joint_angles.constraints(interface_points.calculate_interface_points(designs), rider_dims) + return predictions + + assert "Use Case" in conditioning, "Use Case must be provided in conditioning to calculate ergonomics." + use_case = conditioning["Use Case"] + if not isinstance(use_case, torch.Tensor): + raise TypeError(f"Use Case must be a torch.Tensor, got {type(use_case)}") + + if use_case.dim() == 1: + # single one-hot of shape (3,) + if use_case.shape != (3,): + raise ValueError(f"If 1D, Use Case tensor must have shape (3,), got {tuple(use_case.shape)}") + # must be exactly 0s and 1s + if not torch.logical_or(use_case == 0, use_case == 1).all(): + raise ValueError("Use Case 1D tensor must contain only 0s and 1s") + # must sum to 1 + if use_case.sum().item() != 1: + raise ValueError("Use Case 1D tensor must be a valid one-hot vector (sum == 1)") + # broadcast to (n,3) + n = designs.shape[0] + use_case = use_case.unsqueeze(0).repeat(n, 1) + + elif use_case.dim() == 2: + # batch of one-hots, shape (n,3) + n, k = use_case.shape + if k != 3: + raise ValueError(f"If 2D, Use Case tensor must have shape (n,3), got {tuple(use_case.shape)}") + if n == 1: + use_case = use_case.expand(designs.shape[0], -1) + # check binary values + if not torch.logical_or(use_case == 0, use_case == 1).all(): + raise ValueError("Use Case 2D tensor must contain only 0s and 1s") + # each row sums to exactly 1 + row_sums = use_case.sum(dim=1) + bad_rows = (row_sums != 1).nonzero(as_tuple=False).flatten() + if bad_rows.numel() > 0: + raise ValueError(f"Rows at indices {bad_rows.tolist()} are not valid one-hot vectors") + + else: + raise ValueError(f"Use Case tensor must be 1D or 2D, got {use_case.dim()}D") + + index_to_label = ["road", "mtb", "commute"] + use_case_list = [index_to_label[idx] for idx in use_case.argmax(axis=1)] + + int_pts = interface_points.calculate_interface_points(designs) + predictions = joint_angles.dist_to_1SD(int_pts, rider_dims, use_case_list, self.penalize_constraints) + if not self.penalize_constraints: + constraint_violation = joint_angles.constraints(int_pts, rider_dims) + predictions = torch.cat((predictions, constraint_violation), dim=1) + return predictions + + +class UsabilityEvaluator(EvaluationFunction): + def __init__(self, device="cpu", dtype=torch.float32): + super().__init__(device, dtype) + scaler_path = models_and_scalers_path("usability_scaler.pt") + state_path = models_and_scalers_path("usability_model_weights.pt") + state = torch.load(state_path, weights_only=True, map_location=torch.device(device)) + self.model = get_usability_model(dropout_on=False).to(device) + self.model.load_state_dict(state) + self.preprocessor = Preprocessor(scaler_path=scaler_path, preprocess_fn=None, device=device) + + def variable_names(self) -> List[str]: + return ordered_columns.USABILITY_COLUMNS + + def return_names(self) -> List[str]: + return ['Usability Score'] + + + def return_types(self) -> List[str]: + return [1] + + def evaluate(self, designs: torch.Tensor, conditioning: dict = {}) -> torch.Tensor: + designs = self.preprocessor(designs) + predictions = 1 - self.model(designs) + return torch.clip(predictions, min=0, max=1) + + +class PreprocessingFunction(ABC): + def __init__(self): + pass + + @abstractmethod + def variable_names(self) -> List[str]: + pass + + @abstractmethod + def process(self, designs: torch.Tensor) -> torch.Tensor: + pass + + +class clip_bools_to_0_1(PreprocessingFunction): + def __init__(self, device="cpu"): + self.device = device + + def variable_names(self) -> List[str]: + return ordered_columns.oh_bool_columns + + def process(self, designs: torch.Tensor) -> torch.Tensor: + # Clip the values to be between 0 and 1 + designs = torch.clip(designs, min=0, max=1) + return designs + +class normalize_onehot(PreprocessingFunction): + def __init__(self, device="cpu"): + self.device = device + + # Define groups of one-hot column names + self.groups = ordered_columns.oh_columns + + # Flatten all variable names for indexing + self._variable_names = [col for group in self.groups for col in group] + + def variable_names(self) -> List[str]: + return self._variable_names + + def process(self, designs: torch.Tensor) -> torch.Tensor: + # List to hold processed group slices + normalized_groups = [] + + current_col = 0 + for group in self.groups: + num_cols = len(group) + group_slice = designs[:, current_col:current_col + num_cols] + group_sum = group_slice.sum(dim=1, keepdim=True).clamp(min=1e-8) + normalized_group = group_slice / group_sum + normalized_groups.append(normalized_group) + current_col += num_cols + + # Concatenate normalized groups along feature dimension + return torch.cat(normalized_groups, dim=1) + +def get_standard_preprocessing(device): + return [clip_bools_to_0_1(device=device), + normalize_onehot(device=device)] + + + +def construct_tensor_evaluator(evaluation_functions: List[EvaluationFunction], column_names: List[str], preprocessing_fn_set = get_standard_preprocessing, device="cpu"): + preprocessing_fns = preprocessing_fn_set(device) + + column_names = list(column_names) + + # Flatten all return names across evaluators + all_return_names = [] + all_return_types = [] + for evaluation_function in evaluation_functions: + all_return_names.extend(evaluation_function.return_names()) + all_return_types.extend(evaluation_function.return_types()) + + def evaluate_tensor(designs: torch.Tensor, conditioning={}) -> torch.Tensor: + n = designs.shape[0] + total_outputs = sum(len(evaluation_function.return_names()) for evaluation_function in evaluation_functions) + results_tensor = torch.zeros((n, total_outputs), dtype=torch.float32, device=designs.device) + + current_col = 0 + for preprocessing_fn in preprocessing_fns: + var_indices = [column_names.index(var) for var in preprocessing_fn.variable_names()] + sliced_designs = designs[:, var_indices] + processed = preprocessing_fn.process(sliced_designs) + + updated = designs.clone() + updated[:, var_indices] = processed + designs = updated + + for evaluation_function in evaluation_functions: + var_indices = [column_names.index(var) for var in evaluation_function.variable_names()] + sliced_designs = designs[:, var_indices] + + res = evaluation_function.evaluate(sliced_designs, conditioning) # Expect shape (n,) or (n, k) + + if res.dim() == 1: + res = res.unsqueeze(1) + + num_outputs = res.shape[1] + results_tensor[:, current_col:current_col + num_outputs] = res + current_col += num_outputs + + return results_tensor + + return evaluate_tensor, all_return_names, all_return_types + +def construct_dataframe_evaluator(evaluation_functions: List[EvaluationFunction]): + + def evaluate_dataframe(designs: pd.DataFrame, conditioning={}) -> pd.DataFrame: + designs_tensor = torch.tensor(designs.values, dtype=torch.float32) + tensor_evaluator, return_names, return_types = construct_tensor_evaluator(evaluation_functions, list(designs.columns)) + results_tensor = tensor_evaluator(designs_tensor, conditioning) + + results_df = pd.DataFrame( + results_tensor.detach().cpu().numpy(), + columns=return_names, + index=designs.index + ) + + return results_df, return_types + + return evaluate_dataframe + + + + + +def get_standard_evaluations(device, aesthetics_mode = "Embedding") -> List[EvaluationFunction]: + + StandardEvaluations = [ + UsabilityEvaluator(device=device), + AeroEvaluator(device=device), + ErgonomicsEvaluator(device=device, penalize_constraints=False, constraints_only=False), + AestheticsEvaluator(mode=aesthetics_mode, batch_size=64, device=device), + StructuralEvaluator(device=device), + FrameValidityEvaluator(device=device), + ValidationEvaluator(device=device), + + ] + + return StandardEvaluations + +# A special set of evaluations that only includes constraints that at least 5% of the dataset designs violate. +# Intended for use in models to test ability to infer constraints based on data. +def get_standard_evaluations_with_constraint_threshold(device, aesthetics_mode = "Embedding") -> List[EvaluationFunction]: + + StandardEvaluations = [ + UsabilityEvaluator(device=device), + AeroEvaluator(device=device), + ErgonomicsEvaluator(device=device), + AestheticsEvaluator(mode=aesthetics_mode, batch_size=64, device=device), + StructuralEvaluator(device=device), + ValidationEvaluator(device=device, only_difficult=True), + ] + + return StandardEvaluations \ No newline at end of file diff --git a/bike_bench_internal/src/bikebench/embedding/META-INF/MANIFEST.MF b/bike_bench_internal/src/bikebench/embedding/META-INF/MANIFEST.MF new file mode 100644 index 0000000000000000000000000000000000000000..14666aa2e67feb4ee3af82b62b973cac87d8fe35 --- /dev/null +++ b/bike_bench_internal/src/bikebench/embedding/META-INF/MANIFEST.MF @@ -0,0 +1,3 @@ +Manifest-Version: 1.0 +Created-By: 1.8.0_202 (Oracle Corporation) + diff --git a/bike_bench_internal/src/bikebench/embedding/__init__.py b/bike_bench_internal/src/bikebench/embedding/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/src/bikebench/embedding/clip_embedding_calculator.py b/bike_bench_internal/src/bikebench/embedding/clip_embedding_calculator.py new file mode 100644 index 0000000000000000000000000000000000000000..9fbfe8fcab4b32dcde31be015a17cbacdfb9f4af --- /dev/null +++ b/bike_bench_internal/src/bikebench/embedding/clip_embedding_calculator.py @@ -0,0 +1,130 @@ +import abc +import time +from typing import List, Optional +import numpy as np +import torch +from PIL import Image +# from torchvision import transforms +from transformers import CLIPTokenizer, CLIPImageProcessor, CLIPModel +from tqdm import trange + + +# _DEVICE = "cuda" +# _MODEL_ID = "openai/clip-vit-base-patch32" +# _CLIP_PROCESSOR = CLIPProcessor.from_pretrained(_MODEL_ID) +# _TOKENIZER = CLIPTokenizerFast.from_pretrained(_MODEL_ID) +# _MODEL = CLIPModel.from_pretrained(_MODEL_ID) # .to(device) + + +# class ClipEmbeddingCalculator(metaclass=abc.ABCMeta): +# @abc.abstractmethod +# def from_text(self, text: str) -> np.ndarray: +# pass + +# @abc.abstractmethod +# def from_image_path(self, image_path: str) -> np.ndarray: +# pass + + +# class ClipEmbeddingCalculatorImpl(ClipEmbeddingCalculator): +# def from_text(self, text: str) -> np.ndarray: +# embedding_tensor = _MODEL.get_text_features(**_TOKENIZER(text, return_tensors="pt")) +# return embedding_tensor + +# def from_image_path(self, image_path: str) -> np.ndarray: +# return self.from_image(Image.open(image_path)) + +# def from_image(self, img: Image.Image): +# start = time.time() + +# img = img.convert("RGB") +# width, height = img.size +# img = img.resize((width // 2, height // 2)) +# result = Image.new(img.mode, (1300, 1300), (255, 255, 255)) +# result.paste(img, img.getbbox()) +# image = np.asarray(result) +# img_processed = _CLIP_PROCESSOR(text=None, images=image, return_tensors='pt')['pixel_values'] # .to(device) +# embedding_tensor = _MODEL.get_image_features(img_processed) + +# print(f"Obtaining embeddings took {time.time() - start}") +# return embedding_tensor + +# def from_image_tensor(self, image_tensor: torch.Tensor) -> np.ndarray: +# img = image_tensor.cpu().clone() +# img = transforms.Resize((1300, 1300))(img) +# result = Image.new(img.mode, (1300, 1300), (255, 255, 255)) +# result.paste(img, img.getbbox()) +# image = np.asarray(result) +# img_processed = _CLIP_PROCESSOR(text=None, images=image, return_tensors='pt')['pixel_values'] +# embedding_tensor = _MODEL.get_image_features(img_processed) +# return embedding_tensor + + + + +class ClipEmbeddingCalculator: + """ + A batch-friendly CLIP embedding class for text and images. + """ + def __init__(self, device: str = "cuda", batch_size: Optional[int] = None): + model_id = "openai/clip-vit-base-patch32" + self.device = device + self.batch_size = batch_size + + # text tokenizer (fast Rust version) + self.tokenizer = CLIPTokenizer.from_pretrained(model_id, use_fast=True) + # new image processor (replaces the deprecated FeatureExtractor) + self.image_processor = CLIPImageProcessor.from_pretrained(model_id) + + self.model = CLIPModel.from_pretrained(model_id).to(self.device) + self.model.eval() + + def embed_texts(self, texts: List[str]) -> torch.Tensor: + all_feats = [] + bs = self.batch_size or len(texts) + for i in range(0, len(texts), bs): + chunk = texts[i : i + bs] + enc = self.tokenizer( + chunk, + padding=True, + truncation=True, + return_tensors="pt" + ) + inputs = {k: v.to(self.device) for k,v in enc.items()} + with torch.no_grad(): + feats = self.model.get_text_features(**inputs) + all_feats.append(feats) + return torch.cat(all_feats, dim=0) + + def embed_images(self, images: torch.Tensor) -> torch.Tensor: + all_feats = [] + n = images.shape[0] + bs = self.batch_size or n + for i in range(0, n, bs): + chunk = images[i : i + bs] + enc = self.image_processor( + images=chunk, + return_tensors="pt" + ) + inputs = {k: v.to(self.device) for k,v in enc.items()} + with torch.no_grad(): + feats = self.model.get_image_features(**inputs) + all_feats.append(feats) + return torch.cat(all_feats, dim=0) + + + + +# def get_augmented_views_gpu(images_tensor): +# transform = transforms.RandomApply([ +# transforms.RandomHorizontalFlip(), +# transforms.RandomAdjustSharpness(0.2), +# transforms.RandomAdjustSharpness(2), +# transforms.RandomPerspective(fill=(0, 0, 0)), +# transforms.RandomRotation(degrees = 45, fill= (0, 0, 0)), +# # transforms.ColorJitter(brightness=0.1, contrast = 0.1, saturation=0.1, hue=0.0), +# ],p=1) +# res = transform(images_tensor.cuda()).cpu() +# return res + + diff --git a/bike_bench_internal/src/bikebench/embedding/dataset_rendering_tools.py b/bike_bench_internal/src/bikebench/embedding/dataset_rendering_tools.py new file mode 100644 index 0000000000000000000000000000000000000000..26f990044a8710e54bee2442aa88f342887ad924 --- /dev/null +++ b/bike_bench_internal/src/bikebench/embedding/dataset_rendering_tools.py @@ -0,0 +1,324 @@ +import logging +import os.path +import time +from concurrent.futures.process import ProcessPoolExecutor +from concurrent.futures.thread import ThreadPoolExecutor +from sdv.single_table import CTGANSynthesizer + +from typing import Dict, Tuple, List +import sys +import pandas as pd +import cairosvg +import torch.nn.functional as F + +import os +from PIL import Image +import numpy as np +import torch +import shutil +import uuid + + + +from bikebench.rendering.rendering import RenderingEngine, FILE_BUILDER +from bikebench.data_loading import data_loading +from bikebench.resource_utils import resource_path, STANDARD_BIKE_RESOURCE, models_and_scalers_path +from bikebench.embedding.clip_embedding_calculator import ClipEmbeddingCalculator +from bikebench.transformation.one_hot_encoding import encode_to_continuous +from bikebench.validation.base_validation_function import construct_tensor_validator +from bikebench.validation.bike_bench_validation_functions import bike_bench_validation_functions + + + +def read_standard_xml(): + with open(STANDARD_BIKE_RESOURCE, "r") as file: + return file.read() + +standard_bike_xml = read_standard_xml() + +def get_bike_bench_records_with_id(num) -> Dict[str, dict]: + """ + Return records in a dictionary of the form { + (record_id: str) : (record: dict) + } + """ + data_train = data_loading.load_bike_bench_train() + data_test = data_loading.load_bike_bench_test() + data = pd.concat([data_train, data_test], axis=0) + if num is None: + num = len(data) + else: + data = data.iloc[:num, :] + + return {str(record_id): record for record_id, record in zip(data.index.tolist(), data.to_dict(orient="records"))} + +from bikebench.validation.base_validation_function import construct_tensor_validator + + +def sample_CTGAN(n=4096) -> pd.DataFrame: + save_path = models_and_scalers_path("CTGAN.pkl") + synthesizer = CTGANSynthesizer.load(filepath=save_path) + synthetic_collapsed = synthesizer.sample(num_rows=n) + return synthetic_collapsed + +def sample_uniform(n: int, p: float = 1.0, seed: int | None = None) -> pd.DataFrame: + df = data_loading.load_bike_bench_mixed_modality_train() + rng = np.random.default_rng(seed) + out = {} + + for c in df.columns: + s = df[c] + + # Booleans: uniform across classes if both present + if s.dtype == bool: + vals = s.dropna().unique() + if len(vals) == 0: + out[c] = pd.Series([pd.NA] * n, dtype="boolean") + elif len(vals) == 1: + out[c] = pd.Series([bool(vals[0])] * n, dtype="boolean") + else: + out[c] = pd.Series(rng.integers(0, 2, size=n).astype(bool), dtype="boolean") + continue + + # Floats: uniform within percentile band + if pd.api.types.is_float_dtype(s): + clean = pd.to_numeric(s, errors="coerce").dropna() + if len(clean) == 0: + out[c] = pd.Series([np.nan] * n, dtype=float) + else: + lo, hi = np.percentile(clean, [p, 100 - p]) + draw = np.full(n, lo) if not np.isfinite(hi) or hi <= lo else rng.uniform(lo, hi, size=n) + out[c] = pd.Series(draw, dtype=float) + continue + + # Ints / objects / categoricals: sample uniformly over observed values + vals = pd.unique(s.dropna()) + if len(vals) == 0: + out[c] = pd.Series([np.nan] * n, dtype=(s.dtype if pd.api.types.is_integer_dtype(s) else object)) + else: + draw = rng.choice(vals, size=n, replace=True) + if pd.api.types.is_integer_dtype(s): + out[c] = pd.Series(draw).astype(s.dtype) + elif pd.api.types.is_categorical_dtype(s): + out[c] = pd.Categorical(draw, categories=s.cat.categories) + else: + out[c] = pd.Series(draw, dtype=object) + + return pd.DataFrame(out, columns=df.columns) + +def sample_n(n, method) -> pd.DataFrame: + if method == "CTGAN": + sampler_fn = sample_CTGAN + elif method == "uniform": + sampler_fn = sample_uniform + else: + raise ValueError("method must be 'CTGAN' or 'uniform'") + sample_datapoint = sampler_fn(1) + sample_datapoint_oh = encode_to_continuous(sample_datapoint) + COLUMN_NAMES = list(sample_datapoint_oh.columns) + tensor_validator, validation_names = construct_tensor_validator(bike_bench_validation_functions, COLUMN_NAMES) + + all_valid_samples = None + while True: + synthetic_collapsed = sampler_fn(10000) + samples_oh = encode_to_continuous(synthetic_collapsed) + samples_oh_tens = torch.tensor(samples_oh.values, dtype=torch.float32) + + validity = tensor_validator(samples_oh_tens) + + valid = torch.all(validity<=0, dim=1) + valid_subset = samples_oh_tens[valid, :] + if all_valid_samples is None: + all_valid_samples = valid_subset + else: + all_valid_samples = torch.cat((all_valid_samples, valid_subset), dim=0) + if all_valid_samples.shape[0] >= n: + break + all_valid_samples = all_valid_samples[:n, :] + samples_df = pd.DataFrame(all_valid_samples.numpy(), columns=COLUMN_NAMES) + return samples_df + +def sample_save_n_records(save_path, n=4096, method = "CTGAN") -> Dict[str, dict]: + data = sample_n(n, method) + #make the data indices random keys using uuid + random_keys = [str(uuid.uuid4()) for _ in range(len(data))] + data.index = random_keys + #save csv to save_path + data.to_csv(save_path) + return {str(record_id): record for record_id, record in zip(data.index.tolist(), data.to_dict(orient="records"))} + +def bike_to_xml(save_path: str, record_id: str, record: dict): + try: + file_path = os.path.join(save_path, f"{record_id}.bcad") + with open(file_path, "w") as file: + xml_data = FILE_BUILDER.build_cad_from_clip(record, standard_bike_xml) + file.write(xml_data) + except Exception as e: + print(f"XML Conversion Failed with exception {e}") + + +def bikes_to_xmls(records_with_id: Dict[str, dict], + process_pool_workers: int, + save_dir: str + ): + executor = ProcessPoolExecutor(max_workers=process_pool_workers) + os.makedirs(save_dir, exist_ok=True) + for record_id, record in records_with_id.items(): + executor.submit(bike_to_xml, save_dir, record_id, record) + executor.shutdown() # waits for all submitted tasks to finish + +def xmls_to_svgs( + thread_pool_workers: int, + records_with_id: Dict[str, dict], + xml_dir: str, + svg_dir: str, + rendering_engine: RenderingEngine +): + executor = ThreadPoolExecutor(max_workers=thread_pool_workers) + + os.makedirs(svg_dir, exist_ok=True) + def xml_to_svg(xml: str): + try: + xml_path = os.path.join(xml_dir, f"{xml}.bcad") + with open(xml_path, "r") as xml_file: + # print("Sending request to server...") + read_file = xml_file.read() + # print("Read file...") + rendering_result = rendering_engine.render_xml(read_file) + # print("Rendering result received from server...") + image_path = os.path.join(svg_dir, f"{xml}.svg") + with open(image_path, "wb") as image_file: + image_file.write(rendering_result.image_bytes) + return True + except Exception as e: + print(f"Rendering failed: {e}") + return False, e + + for record_id, _ in records_with_id.items(): + executor.submit(xml_to_svg, record_id) + + executor.shutdown() + + +def svg_to_png(record_id: str, svg_dir: str, png_dir: str): + try: + svg_file = os.path.join(svg_dir, f"{record_id}.svg") + png_file = os.path.join(png_dir, f"{record_id}.png") + cairosvg.svg2png(url=svg_file, write_to=png_file) + except Exception as e: + print(f"Failed to convert {record_id} with exception {e}") + +def svgs_to_pngs(process_pool_workers: int, + records_with_id: Dict[str, dict], + svg_dir: str, + png_dir: str): + executor = ProcessPoolExecutor(max_workers=process_pool_workers) + os.makedirs(png_dir, exist_ok=True) + for record_id, _ in records_with_id.items(): + executor.submit(svg_to_png, record_id, svg_dir, png_dir) + executor.shutdown() # waits for all submitted tasks to finish + +#load all pngs and stack up +def load_pngs(png_dir: str, + records_with_id: Dict[str, dict]) -> Tuple[torch.Tensor, List[str]]: + """ + Scans png_dir for files named .png (in the order of records_with_id.keys()), + loads each as an RGB image, converts to a float tensor in [0,1], + pads on TOP and RIGHT to the max H/W across the set, + and stacks them into a tensor of shape (N, 3, H_max, W_max). + Returns (tensor, names). + """ + PAD_VALUE = 1.0 # use 0.0 for black padding + + imgs = [] + names = [] + heights = [] + widths = [] + + for record_id in records_with_id: + path = os.path.join(png_dir, f"{record_id}.png") + if not os.path.exists(path): + print(f"Warning: {path} not found; skipping.") + continue + + with Image.open(path) as img: + img = img.convert("RGB") # ensure 3 channels + arr = np.array(img) # H x W x 3, uint8 + t = torch.from_numpy(arr).permute(2, 0, 1).float() # 3 x H x W + imgs.append(t) + names.append(record_id) + heights.append(t.shape[1]) + widths.append(t.shape[2]) + + if not imgs: + return torch.empty((0, 3, 0, 0)), [] + + H_max = max(heights) + W_max = max(widths) + + padded = [] + for t in imgs: + _, H, W = t.shape + pad_top = H_max - H + pad_right = W_max - W + # Pad order for 3D (C,H,W): (left, right, top, bottom) + t_pad = F.pad(t, (0, pad_right, pad_top, 0), mode="constant", value=PAD_VALUE) + padded.append(t_pad) + + return torch.stack(padded, dim=0), names + + +def embed_pngs( + png_dir: str, + records_with_id: Dict[str, dict], + batch_size: int = 32, + emb_file: str = None, +): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + clip_embedder = ClipEmbeddingCalculator(batch_size=batch_size, device=device) + + all_embs = [] + all_names = [] + ids = list(records_with_id) + + for i in range(0, len(ids), batch_size): + batch_ids = ids[i : i + batch_size] + subset = {rid: records_with_id[rid] for rid in batch_ids} + imgs, names = load_pngs(png_dir, subset) + if not names: + continue + + with torch.no_grad(): + emb = clip_embedder.embed_images(imgs).cpu() + all_embs.append(emb) + all_names.extend(names) + + embs = torch.cat(all_embs, dim=0).numpy() + df = pd.DataFrame(embs, index=all_names) + df.to_csv(emb_file or "embeddings.csv") + + +def process_rendering_stack(records, xml_dir: str, svg_dir: str, png_dir: str, emb_file: str, rendering_engine: RenderingEngine, process_pool_workers: int, thread_pool_workers: int): + bikes_to_xmls(records, process_pool_workers, xml_dir) + print("XMLs created") + + xmls_to_svgs( + thread_pool_workers=thread_pool_workers, + records_with_id=records, + xml_dir=xml_dir, + svg_dir=svg_dir, + rendering_engine=rendering_engine, + ) + print("SVGs created") + svgs_to_pngs(process_pool_workers=process_pool_workers, records_with_id=records, svg_dir=svg_dir, png_dir=png_dir) + print("PNGs created") + embed_pngs(png_dir=png_dir, records_with_id=records, batch_size=32, emb_file=emb_file) + print("Embeddings created") + shutil.make_archive(xml_dir, 'gztar', xml_dir) + shutil.make_archive(svg_dir, 'gztar', svg_dir) + shutil.make_archive(png_dir, 'gztar', png_dir) + print("Zipped all directories") + shutil.rmtree(xml_dir) + shutil.rmtree(svg_dir) + shutil.rmtree(png_dir) + print("Removed all directories") \ No newline at end of file diff --git a/bike_bench_internal/src/bikebench/embedding/embedding_predictor.py b/bike_bench_internal/src/bikebench/embedding/embedding_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..cb190b0324eca3a944eede09a6f51d7cec20f05c --- /dev/null +++ b/bike_bench_internal/src/bikebench/embedding/embedding_predictor.py @@ -0,0 +1,92 @@ +# noinspection PyPackageRequirements +import __main__ + +import dill +import numpy as np +import pandas as pd +import torch +import torch.nn as nn +from sklearn.preprocessing import StandardScaler + +from bikebench.embedding.ordered_columns import ORDERED_COLUMNS + +from bikebench.resource_utils import resource_path + + +def _get_pickled_scaler() -> StandardScaler: + with open(resource_path("scaler.pk"), "rb") as file: + return dill.load(file) + + +def _load_scaled(): + model = _ResidualNetwork(96, 512, 256, 2, 3) + model.load_state_dict(torch.load(_SCALED_FUNCTION_PATH, map_location=_DEVICE)) + model.eval() + return model + + +class _ResidualBlock(nn.Module): + def __init__(self, input_size, layer_size, num_layers): + super(_ResidualBlock, self).__init__() + self.layers = self._make_layers(input_size, layer_size, num_layers) + + def _make_layers(self, input_size, layer_size, num_layers): + layers = [nn.Linear(input_size, layer_size), nn.ReLU()] + for _ in range(num_layers - 1): + layers.append(nn.Linear(layer_size, layer_size)) + layers.append(nn.ReLU()) + layers.append(nn.BatchNorm1d(layer_size)) + return nn.Sequential(*layers) + + def forward(self, x): + residual = x + out = self.layers(x) + total = out + residual + return total + + +class _ResidualNetwork(nn.Module): + def __init__(self, input_size, output_size, layer_size, layers_per_block, num_blocks): + super(_ResidualNetwork, self).__init__() + self.initial_layer = nn.Linear(input_size, layer_size) + self.blocks = self._make_blocks(layer_size, layers_per_block, num_blocks) + self.final_layer = nn.Linear(layer_size, output_size) + + def _make_blocks(self, layer_size, layers_per_block, num_blocks): + blocks = [] + for _ in range(num_blocks): + blocks.append(_ResidualBlock(layer_size, layer_size, layers_per_block)) + return nn.Sequential(*blocks) + + def forward(self, x): + out = self.initial_layer(x) + out = self.blocks(out) + out = self.final_layer(out) + return out + + +__main__.ResidualNetwork = _ResidualNetwork +__main__.ResidualBlock = _ResidualBlock + +_DEVICE = torch.device('cpu') +_SCALED_FUNCTION_PATH = resource_path("model_small.pt") +_SCALED_FUNCTION = _load_scaled() +_SCALER = _get_pickled_scaler() + + +class EmbeddingPredictor: + + def predict(self, x: pd.DataFrame) -> np.ndarray: + return self._predict(self._get_scaled(x), _SCALED_FUNCTION) + + def _predict(self, x_data: pd.DataFrame, prediction_function: callable) -> np.ndarray: + tensor = torch.tensor(x_data.values, dtype=torch.float32) + result_tensor = prediction_function(tensor).cpu() + return result_tensor.detach().numpy() + + def _get_ordered(self, x: pd.DataFrame): + return pd.DataFrame(x, columns=ORDERED_COLUMNS) + + def _get_scaled(self, x): + ordered = self._get_ordered(x) + return pd.DataFrame(_SCALER.transform(ordered), columns=ORDERED_COLUMNS) diff --git a/bike_bench_internal/src/bikebench/ergonomics/__init__.py b/bike_bench_internal/src/bikebench/ergonomics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/src/bikebench/ergonomics/joint_angles.py b/bike_bench_internal/src/bikebench/ergonomics/joint_angles.py new file mode 100644 index 0000000000000000000000000000000000000000..13645494dde29a788b732beb0d3e87736e773310 --- /dev/null +++ b/bike_bench_internal/src/bikebench/ergonomics/joint_angles.py @@ -0,0 +1,353 @@ +import numpy as np +import torch +from scipy.stats import norm + +USE_DICT = { + "road": { + "opt_knee_angle": (37.5, 10), + "opt_back_angle": (45, 5), + "opt_awrist_angle": (90, 5), + "opt_ankle_angle": (100.0, 5.0), + }, + "mtb": { + "opt_knee_angle": (37.5, 10), + "opt_back_angle": (50, 5), + "opt_awrist_angle": (90, 5), + "opt_ankle_angle": (100.0, 5.0), + }, + "commute": { + "opt_knee_angle": (37.5, 10), + "opt_back_angle": (52, 5), + "opt_awrist_angle": (85, 5), + "opt_ankle_angle": (100.0, 5.0), + }, +} + + +# ##### +# Bike Ergonomic Angle Fit Calculator +# Calculates body angles given bike and body measurements +# Calculates ergonomic score/probability of fit given use case +###### + + +############################################# +# Masking Functions for Triangle Inequality # +############################################# + +def deg_to_r(deg: torch.Tensor) -> torch.Tensor: + """ + Converts degrees to radians (works with tensors or scalars). + """ + return deg.to(dtype=torch.float32) * (torch.pi / 180) + + +def law_of_cosines(a, b, c): + return (a**2 + b**2 - c**2) / (2 * a * b) + +def rad_to_d(rad: torch.Tensor) -> torch.Tensor: + """ + Converts radians to degrees (works with tensors or scalars). + """ + return rad.to(dtype=torch.float32) * (180 / torch.pi) + +################################### +# FUNCTIONS FOR CALCUATING ANGLES # +################################### +def min_knee_angle(bike_vectors, body_vectors, apply_penalty, eps=1e-6): + """ + Input: + bike vector, body vector + np array bike vector: + [SX, SY, HX, HY, CL]^T + (seat_x, seat_y, hbar_x, hbar_y, crank len) + Origin is bottom bracket + np array body vector: + [LL, UL, TL, AL, FL, AA] + (lowleg, upleg, torso len, arm len, foot len, ankle angle)""" + + UL = body_vectors[:, 0:1] + LL = body_vectors[:, 1:2] + AL = body_vectors[:, 2:3] + TL = body_vectors[:, 3:4] + FL = body_vectors[:, 4:5] + AA = deg_to_r(body_vectors[:, 6:7]) + EA = deg_to_r(180 - body_vectors[:, 7:8]) + + HX = bike_vectors[:, 0:1] # Hand x + HY = bike_vectors[:, 1:2] # Hand y + SX = bike_vectors[:, 2:3] * -1 # Hip x (flip because convention here is positive x is forward on the bike) + SY = bike_vectors[:, 3:4] # Hip y + CL = bike_vectors[:, 4:5] # Crank length + + CA = torch.atan2(SY, SX) # Crank angle in radians + + + # distance to pedal in furthest position + LX = SX + CL * torch.cos(CA) + LY = SY + CL * torch.sin(CA) + + # Law of cosines for ankle + k2t = torch.sqrt(LL ** 2 + FL ** 2 - 2 * LL * FL * torch.cos(AA)) #knee to toe distance + + + h2t = torch.sqrt(LX ** 2 + LY ** 2) #hip to toe distance + alpha_hkt_cos = law_of_cosines(k2t, UL, h2t) #hip knee toe angle cosine + alpha_hkt_cos_clamped = (torch.clamp(alpha_hkt_cos, -1.0 + eps, 1.0 - eps)) + alpha_hkt = torch.arccos(alpha_hkt_cos_clamped) + + alpha_akt_cos = law_of_cosines(k2t, LL, FL) # knee ankle toe angle cosine + alpha_akt = torch.arccos(alpha_akt_cos) + + ke = torch.pi - alpha_hkt + alpha_akt # knee extension angle in radians + + if apply_penalty: + #NOTE These corrections are only valid if akt is smaller than the target knee angle, which should almost always be the case + case1 = h2t > UL + k2t # results in 180 degrees for hip knee toe angle, results in too small knee extension angle + case1_correction = - (h2t - UL - k2t) + + case2 = UL > h2t + k2t # results in 0 degrees for hip knee toe angle, results in too large knee extension angle + case2_correction = UL - h2t - k2t + + case3 = k2t > h2t + UL # results in 0 degrees for hip knee toe angle, results in too large knee extension angle + case3_correction = k2t - h2t - UL + + ke = ke + case1 * case1_correction + case2 * case2_correction + case3 * case3_correction + + return rad_to_d(ke) + +def back_armpit_angles(bike_vectors, body_vectors, apply_penalty, eps=1e-6): + """ + Input: bike_vector, body_vector + Output: back angle, armpit to elbow angle, armpit to wrist angle in degrees + + np array bike vector: + [SX, SY, HX, HY, CL]^T + np array body vector: + [LL, UL, TL, AL, FL, AA] + """ + UL = body_vectors[:, 0:1] + LL = body_vectors[:, 1:2] + AL = body_vectors[:, 2:3] + TL = body_vectors[:, 3:4] + FL = body_vectors[:, 4:5] + AA = deg_to_r(body_vectors[:, 6:7]) + EA = deg_to_r(180 - body_vectors[:, 7:8]) + + HX = bike_vectors[:, 0:1] # Hand x + HY = bike_vectors[:, 1:2] # Hand y + SX = bike_vectors[:, 2:3] * -1 # Hip x (flip because convention here is positive x is forward on the bike) + SY = bike_vectors[:, 3:4] # Hip y + CL = bike_vectors[:, 4:5] # Crank length + SY = SY + 0.06 # in BikeCAD, "Hip" reference point for upper torso is offset from from lower torso by 60 mm + + #saddle to handle measurements + sth_dx = HX - SX + sth_dy = HY - SY + sth_dist = torch.sqrt(sth_dx**2 + sth_dy**2) + sth_ang = torch.atan2(sth_dy, sth_dx) + + # Law of cosines to simulate elbow bend + shoulder_to_hand = torch.sqrt((AL / 2) ** 2 + (AL / 2) ** 2 - 2 * (AL / 2) * (AL / 2) * torch.cos(EA)) + + + tors_angle_cos = law_of_cosines(sth_dist, TL, shoulder_to_hand) + + tors_angle__cos_clamped = torch.clamp(tors_angle_cos, -1.0 + eps, 1.0 - eps) + tors_ang = torch.arccos(tors_angle__cos_clamped) + + shoulder_angle_cos = law_of_cosines(shoulder_to_hand, TL, sth_dist) + shoulder_angle_cos_clamped = torch.clamp(shoulder_angle_cos, -1.0 + eps, 1.0 - eps) + shoulder_ang = torch.arccos(shoulder_angle_cos_clamped) + + if apply_penalty: + case1 = shoulder_to_hand > sth_dist + TL #results in 180 degrees for back angle and 0 degrees for shoulder angle + case1_correction_back = shoulder_to_hand - sth_dist - TL + case1_correction_shoulder = -(shoulder_to_hand - sth_dist - TL) + + case2 = sth_dist> TL + shoulder_to_hand #results in 0 degrees for back angle and 180 degrees for shoulder angle + case2_correction_back = - (sth_dist - TL - shoulder_to_hand) + case2_correction_shoulder = sth_dist - TL - shoulder_to_hand + + case3 = TL > shoulder_to_hand + sth_dist #results in 0 degrees for back angle and 0 degrees for shoulder angle + case3_correction_back = - (TL - shoulder_to_hand - sth_dist) + case3_correction_shoulder = - (TL - shoulder_to_hand - sth_dist) + + tors_ang = tors_ang + case1*case1_correction_back + case2*case2_correction_back + case3*case3_correction_back + shoulder_ang = shoulder_ang + case1*case1_correction_shoulder + case2*case2_correction_shoulder + case3*case3_correction_shoulder + + back_angle = tors_ang + sth_ang + + + return rad_to_d(back_angle), rad_to_d(shoulder_ang) + + +def constraints(bike_vectors, body_vectors, eps=1e-6): + """ + Input: bike_vector, body_vector + Output: back angle, armpit to elbow angle, armpit to wrist angle in degrees + + np array bike vector: + [SX, SY, HX, HY, CL]^T + np array body vector: + [LL, UL, TL, AL, FL, AA] + """ + + N = body_vectors.shape[0] + device = body_vectors.device + + # Append default ankle (90 deg) and elbow (20 deg) + body_vectors = torch.cat(( + body_vectors, + torch.full((N, 1), 90.0, device=device), + torch.full((N, 1), 20.0, device=device) + ), dim=1) + + UL = body_vectors[:, 0:1] + LL = body_vectors[:, 1:2] + AL = body_vectors[:, 2:3] + TL = body_vectors[:, 3:4] + FL = body_vectors[:, 4:5] + AA = deg_to_r(body_vectors[:, 6:7]) + EA = deg_to_r(180 - body_vectors[:, 7:8]) + + HX = bike_vectors[:, 0:1] # Hand x + HY = bike_vectors[:, 1:2] # Hand y + SX = bike_vectors[:, 2:3] * -1 # Hip x (flip because convention here is positive x is forward on the bike) + SY = bike_vectors[:, 3:4] # Hip y + CL = bike_vectors[:, 4:5] # Crank length + + #saddle to handle measurements + sth_dx = HX - SX + sth_dy = HY - SY + sth_dist = torch.sqrt(sth_dx**2 + sth_dy**2) + + # Law of cosines to simulate elbow bend + shoulder_to_hand = torch.sqrt((AL / 2) ** 2 + (AL / 2) ** 2 - 2 * (AL / 2) * (AL / 2) * torch.cos(EA)) + + arm_too_long = shoulder_to_hand - sth_dist - TL + saddle_too_far_from_handle = sth_dist - TL - shoulder_to_hand + torso_too_long = TL - shoulder_to_hand - sth_dist + + CA = torch.atan2(SY, SX) # Crank angle in radians + + # distance to pedal in furthest position + LX = SX + CL * torch.cos(CA) + LY = SY + CL * torch.sin(CA) + + # Law of cosines for ankle + k2t = torch.sqrt(LL ** 2 + FL ** 2 - 2 * LL * FL * torch.cos(AA)) #knee to toe distance + h2t = torch.sqrt(LX ** 2 + LY ** 2) #hip to toe distance + + saddle_to_toe_too_long = h2t - UL - k2t + upper_leg_too_long = UL - h2t - k2t + knee_too_toe_too_long = k2t - h2t - UL + + all_constraints = torch.concat([arm_too_long, saddle_too_far_from_handle, torso_too_long, saddle_to_toe_too_long, upper_leg_too_long, knee_too_toe_too_long], dim=1) + return all_constraints + + +def all_angles(bike_vectors, body_vectors, apply_penalty): + """ + Input: bike, body, arm angle (at elbow) in degrees + Output: tuple (min_ke angle, back angle, awrist angle) in degrees + """ + N = body_vectors.shape[0] + device = body_vectors.device + + # Append default ankle (90 deg) and elbow (20 deg) + body_vectors = torch.cat(( + body_vectors, + torch.full((N, 1), 90.0, device=device), + torch.full((N, 1), 20.0, device=device) + ), dim=1) + + ke_ang = min_knee_angle(bike_vectors, body_vectors, apply_penalty) + + b_angs, aw_angs = back_armpit_angles(bike_vectors, body_vectors, apply_penalty) + + return torch.cat((ke_ang, b_angs, aw_angs), dim=1) + + +############################ +# FUNCTIONS FOR SCORING # +############################ + +def adjusted_nll(bike_vectors: torch.Tensor, body_vectors: torch.Tensor, use_vec: list[str], apply_penalty = False) -> torch.Tensor: + """ + Adjusted NLL = log(pdf at optimal) - log(pdf at observed). + This ensures that the minimum is zero at the optimal angle. + Output: (N, 3) torch tensor: [knee_nll, back_nll, awrist_nll] + """ + angles = all_angles(bike_vectors, body_vectors, apply_penalty) # (N, 3) + + means = torch.tensor([ + [USE_DICT[u]["opt_knee_angle"][0], USE_DICT[u]["opt_back_angle"][0], USE_DICT[u]["opt_awrist_angle"][0]] + for u in use_vec + ], dtype=angles.dtype, device=angles.device) + + stds = torch.tensor([ + [USE_DICT[u]["opt_knee_angle"][1], USE_DICT[u]["opt_back_angle"][1], USE_DICT[u]["opt_awrist_angle"][1]] + for u in use_vec + ], dtype=angles.dtype, device=angles.device) + + # Log-PDF at observed values + log_pdf_actual = -((angles - means) ** 2) / (2 * stds ** 2) - torch.log(stds * torch.sqrt(torch.tensor(2 * torch.pi, device=angles.device))) + + # Log-PDF at the optimal (i.e., mean), which is the peak of the Gaussian + log_pdf_optimal = - torch.log(stds * torch.sqrt(torch.tensor(2 * torch.pi, device=angles.device))) + + # Difference gives adjusted NLL + adjusted_nll = log_pdf_optimal - log_pdf_actual # = 0 at the peak + return adjusted_nll + +def dist_to_1SD(bike_vectors: torch.Tensor, body_vectors: torch.Tensor, use_vec: list[str], apply_penalty = False) -> torch.Tensor: + """ + Distance to 1 standard deviation from the mean for each angle. + Output: (N, 3) torch tensor: [knee_dist, back_dist, awrist_dist] + """ + + angles = all_angles(bike_vectors, body_vectors, apply_penalty) # (N, 3) + means = torch.tensor([ + [USE_DICT[u]["opt_knee_angle"][0], USE_DICT[u]["opt_back_angle"][0], USE_DICT[u]["opt_awrist_angle"][0]] + for u in use_vec + ], dtype=angles.dtype, device=angles.device) + + stds = torch.tensor([ + [USE_DICT[u]["opt_knee_angle"][1], USE_DICT[u]["opt_back_angle"][1], USE_DICT[u]["opt_awrist_angle"][1]] + for u in use_vec + ], dtype=angles.dtype, device=angles.device) + + # Distance to 1 standard deviation from the mean + dist_to_1SD = torch.abs(angles - means) - stds + dist_to_1SD = torch.clamp(dist_to_1SD, min=0) # Ensure non-negative distances + return dist_to_1SD + +def smooth_piecewise_loss(bike_vectors: torch.Tensor, body_vectors: torch.Tensor, use_vec: list[str], power=10.0) -> torch.Tensor: + """ + Smooth piecewise loss function for ergonomic angles. + Function is z^2/2 for angles with z score under 1 SD and z/2 for angles with z score greater than 1 SD. + Output: (N, 3) torch tensor: [knee_loss, back_loss, awrist_loss] + """ + angles = all_angles(bike_vectors, body_vectors) # (N, 3) + + means = torch.tensor([ + [USE_DICT[u]["opt_knee_angle"][0], USE_DICT[u]["opt_back_angle"][0], USE_DICT[u]["opt_awrist_angle"][0]] + for u in use_vec + ], dtype=angles.dtype, device=angles.device) + + stds = torch.tensor([ + [USE_DICT[u]["opt_knee_angle"][1], USE_DICT[u]["opt_back_angle"][1], USE_DICT[u]["opt_awrist_angle"][1]] + for u in use_vec + ], dtype=angles.dtype, device=angles.device) + + # Smooth piecewise loss + zscore = torch.abs(angles - means) / stds + power_mask = zscore < 1.0 + linear_mask = ~power_mask + power_loss = zscore**power/power + linear_loss = zscore - 1/power + loss = power_loss* power_mask + linear_loss * linear_mask + return loss + + diff --git a/bike_bench_internal/src/bikebench/exceptions.py b/bike_bench_internal/src/bikebench/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..6c9f6f0593a6c1adb08622766b1e7a6471bbf814 --- /dev/null +++ b/bike_bench_internal/src/bikebench/exceptions.py @@ -0,0 +1,11 @@ +class UserInputException(Exception): + pass + + +class InternalError(Exception): + pass + + +def check_internal_precondition(precondition: bool, exception_message): + if not precondition: + raise InternalError(exception_message) diff --git a/bike_bench_internal/src/bikebench/prediction/__init__.py b/bike_bench_internal/src/bikebench/prediction/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/src/bikebench/prediction/aero_predictor.py b/bike_bench_internal/src/bikebench/prediction/aero_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..77f629c690990e64e4ced0106defc2fba1d71544 --- /dev/null +++ b/bike_bench_internal/src/bikebench/prediction/aero_predictor.py @@ -0,0 +1,105 @@ +import torch +import math + +from bikebench.prediction.prediction_utils import DNN + +def calculate_features(X, device="cpu"): + def law_of_cos(a, b, c): + return (a ** 2 + b ** 2 - c ** 2) / (2 * a * b) + + hand_x = X[:, 0] + hand_y = X[:, 1] + hip_x = X[:, 2] + hip_y = X[:, 3] + crank_length = X[:, 4] + upper_leg = X[:, 5] + lower_leg = X[:, 6] + arm_length = X[:, 7] + torso_length = X[:, 8] + neck_and_head_length = X[:, 9] + torso_width = X[:, 10] + + #some parameters as defined in the CAD model + head_diameter = torch.tensor(0.25, device=device) + lower_leg_width = torch.tensor(0.12, device=device) + arm_width = torch.tensor(0.1, device=device) + upper_leg_width = (torso_width/2 - 0.16)/2 + 0.14 + neck_width = torch.tensor(0.12, device=device) + + head_surface_area = head_diameter * head_diameter * math.pi/4 *torch.ones_like(neck_and_head_length) # surface area of the head + + hand_hip_horizontal_angle = torch.atan2(hip_y - hand_y, hip_x + hand_x) # angle from the hand to the hip to horizontal + + hand_hip_distance = torch.sqrt((hand_x + hip_x) ** 2 + (hand_y - hip_y) ** 2) # distance from the hand to the hip + + shoulder_hip_hand_angle = law_of_cos(hand_hip_distance, torso_length, arm_length) # angle from the shoulder to the hip to the hand + + shoulder_hip_horizontal_angle = hand_hip_horizontal_angle + shoulder_hip_hand_angle # angle from the shoulder to the hip to horizontal + + height_hip_shoulders = shoulder_hip_horizontal_angle * torso_length # vertical height from the shoulders to the handlebar + + torso_surface_area = torso_width * height_hip_shoulders # surface area of the torso + + height_handle_shoulders = height_hip_shoulders - hand_y + hip_y # vertical height from the handlebar to the shoulders + + arm_surface_area = arm_width * height_handle_shoulders # surface area of the arm + + neck_surface_area = neck_width * (neck_and_head_length - head_diameter) # surface area of the neck + + # height from the hip to the foot + high_leg_hip_foot_dist = torch.sqrt((hip_x) ** 2 + (hip_y - crank_length) ** 2) # leg on the higher pedal + low_leg_hip_foot_dist = torch.sqrt((hip_x) ** 2 + (hip_y + crank_length) ** 2) # leg on the lower pedal + + #angle from knee to hip to foot + high_leg_knee_hip_foot_angle = law_of_cos(high_leg_hip_foot_dist, upper_leg, lower_leg) # leg on the higher pedal + low_leg_knee_hip_foot_angle = law_of_cos(low_leg_hip_foot_dist, upper_leg, lower_leg) # leg on the lower pedal + + #angle from foot to hip to horizontal + high_leg_foot_hip_horizontal_angle = torch.atan2(hip_y - crank_length, hip_x) # leg on the higher pedal + low_leg_foot_hip_horizontal_angle = torch.atan2(hip_y + crank_length, hip_x) # leg on the lower pedal + + #angle from knee to hip to horizontal + high_leg_knee_hip_horizontal_angle = high_leg_foot_hip_horizontal_angle - high_leg_knee_hip_foot_angle # leg on the higher pedal + low_leg_knee_hip_horizontal_angle = low_leg_foot_hip_horizontal_angle - low_leg_knee_hip_foot_angle # leg on the lower pedal + + # mask for whether knee is below hip. + high_leg_knee_below_hip_mask = (high_leg_knee_hip_horizontal_angle > 0).float() + low_leg_knee_below_hip_mask = (low_leg_knee_hip_horizontal_angle > 0).float() + + # vertical height from the knee to the hip + high_leg_height_knee_hip = torch.sin(high_leg_knee_hip_horizontal_angle) #leg on the higher pedal + low_leg_height_knee_hip = torch.sin(low_leg_knee_hip_horizontal_angle) #leg on the lower pedal + + # vertical height from the knee to the foot + high_leg_height_knee_foot = hip_y - crank_length - high_leg_height_knee_hip # leg on the higher pedal + low_leg_height_knee_foot = hip_y + crank_length - low_leg_height_knee_hip # leg on the lower pedal + + # surface area of the lower portion of the leg + high_leg_lower_leg_surface_area = high_leg_height_knee_foot * lower_leg_width # leg on the higher pedal + low_leg_lower_leg_surface_area = low_leg_height_knee_foot * lower_leg_width # leg on the lower pedal + + # surface area of the upper portion of the leg on the higher pedal. If knee is above hip, we need to use the upper leg width minus the lower leg width to calculate the added area of the upper leg. + high_leg_upper_leg_surface_area = high_leg_height_knee_hip * (upper_leg_width - high_leg_knee_below_hip_mask * lower_leg_width) # leg on the higher pedal + low_leg_upper_leg_surface_area = low_leg_height_knee_hip * (upper_leg_width - low_leg_knee_below_hip_mask * lower_leg_width) # leg on the lower pedal + + # surface area of the leg + high_leg_surface_area = high_leg_upper_leg_surface_area + high_leg_lower_leg_surface_area # leg on the higher pedal + low_leg_surface_area = low_leg_upper_leg_surface_area + low_leg_lower_leg_surface_area #leg on the lower pedal + + leg_surface_area = high_leg_surface_area + low_leg_surface_area # total surface area of the legs + + frontal_surface_area = torso_surface_area + leg_surface_area + head_surface_area + arm_surface_area + neck_surface_area + + features = torch.stack([frontal_surface_area, torso_surface_area, leg_surface_area, arm_surface_area, neck_surface_area]) + + X = torch.cat((X, features.T), dim=1) + return X + + +def get_aero_model(dropout_on = False): + if dropout_on: + model = DNN(16, layer_sizes=(128, 256), dropout_rate = 0.2, classification=False) + else: + model = DNN(16, layer_sizes=(128, 256), dropout_rate = 0.0, classification=False) + return model + diff --git a/bike_bench_internal/src/bikebench/prediction/aesthetics_predictor.py b/bike_bench_internal/src/bikebench/prediction/aesthetics_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..f8fab94e3d3b093623f2a5c7dd0652df38d84c7e --- /dev/null +++ b/bike_bench_internal/src/bikebench/prediction/aesthetics_predictor.py @@ -0,0 +1,61 @@ +import torch +import torch.nn as nn +import dill + +from bikebench.resource_utils import models_and_scalers_path +from bikebench.prediction.prediction_utils import TorchStandardScaler + + +def remove_wall_thickness(x, device): + # indices_to_drop = [27, 28, 29, 30, 31, 32, 33] + first_chunk = x[:, :27] + second_chunk = x[:, 34:] + x = torch.cat((first_chunk, second_chunk), dim=1) + return x + +class ResidualBlock(nn.Module): + def __init__(self, input_size, layer_size, num_layers): + super(ResidualBlock, self).__init__() + self.layers = self._make_layers(input_size, layer_size, num_layers) + + def _make_layers(self, input_size, layer_size, num_layers): + layers = [nn.Linear(input_size, layer_size), nn.ReLU()] + for _ in range(num_layers - 1): + layers.append(nn.Linear(layer_size, layer_size)) + layers.append(nn.ReLU()) + layers.append(nn.BatchNorm1d(layer_size)) + return nn.Sequential(*layers) + + def forward(self, x): + residual = x + out = self.layers(x) + total = out + residual + return total + + +class ResidualNetwork(nn.Module): + def __init__(self, input_size, output_size, layer_size, layers_per_block, num_blocks): + super(ResidualNetwork, self).__init__() + self.initial_layer = nn.Linear(input_size, layer_size) + self.blocks = self._make_blocks(layer_size, layers_per_block, num_blocks) + self.final_layer = nn.Linear(layer_size, output_size) + + + def _make_blocks(self, layer_size, layers_per_block, num_blocks): + blocks = [] + for _ in range(num_blocks): + blocks.append(ResidualBlock(layer_size, layer_size, layers_per_block)) + return nn.Sequential(*blocks) + + def forward(self, x): + out = self.initial_layer(x) + out = self.blocks(out) + out = self.final_layer(out) + return out + +def get_aesthetics_model(dropout_on = False): + if dropout_on: + model = ResidualNetwork(80, 512, layer_size=256, layers_per_block=2, num_blocks=3) + else: + model = ResidualNetwork(80, 512, layer_size=256, layers_per_block=2, num_blocks=3) + return model \ No newline at end of file diff --git a/bike_bench_internal/src/bikebench/prediction/evaluators.py b/bike_bench_internal/src/bikebench/prediction/evaluators.py new file mode 100644 index 0000000000000000000000000000000000000000..82623a972fe06b33f654a6b0fdbdf10ec0459434 --- /dev/null +++ b/bike_bench_internal/src/bikebench/prediction/evaluators.py @@ -0,0 +1,65 @@ +import pandas as pd +import numpy as np +import torch +from sklearn.metrics import f1_score, r2_score, mean_squared_error +from sklearn.metrics.pairwise import cosine_similarity +from bikebench.data_loading.data_loading import ( + load_validity_test_oh, + load_structure_test_oh, + load_aero_test, + load_usability_cont_test, + load_aesthetics_test, +) + +def evaluate_validity(model, preprocessing_fn, device="cpu"): + X_test, Y_test = load_validity_test_oh() + X_test_tensor = torch.tensor(X_test.values.astype(float), dtype=torch.float32).to(device) + X_test_tensor = preprocessing_fn(X_test_tensor) + predictions = model(X_test_tensor).detach().cpu().numpy() + predictions = predictions >= 0.5 + return f1_score(Y_test, predictions) + +def evaluate_structure(model, preprocessing_fn, device="cpu"): + X_test, Y_test = load_structure_test_oh() + X_test_tensor = torch.tensor(X_test.values.astype(float), dtype=torch.float32).to(device) + X_test_tensor = preprocessing_fn(X_test_tensor) + predictions = model(X_test_tensor).detach().cpu().numpy() + return r2_score(Y_test, predictions) + +def evaluate_aero(model, preprocessing_fn, device="cpu"): + X_test, Y_test = load_aero_test() + X_test_tensor = torch.tensor(X_test.values.astype(float), dtype=torch.float32).to(device) + X_test_tensor = preprocessing_fn(X_test_tensor) + predictions = model(X_test_tensor).detach().cpu().numpy() + return r2_score(Y_test, predictions) + +def evaluate_usability(model, preprocessing_fn, device="cpu", target_type='cont'): + if target_type == 'cont': + X_test, Y_test = load_usability_cont_test() + X_test_tensor = torch.tensor(X_test.values.astype(float), dtype=torch.float32).to(device) + X_test_tensor = preprocessing_fn(X_test_tensor) + predictions = model(X_test_tensor).detach().cpu().numpy() + return r2_score(Y_test, predictions) + elif target_type == 'binary': + from bikebench.resource_utils import datasets_path + X_test = pd.read_csv(datasets_path('Predictive_Modeling_Datasets/usability_binary_X_test.tab'), index_col=0, sep='\t') + Y_test = pd.read_csv(datasets_path('Predictive_Modeling_Datasets/usability_binary_Y_test.tab'), index_col=0, sep='\t') + X_test_tensor = torch.tensor(X_test.values.astype(float), dtype=torch.float32).to(device) + X_test_tensor = preprocessing_fn(X_test_tensor) + predictions = model(X_test_tensor).detach().cpu().numpy() + predictions = predictions >= 0.5 + return f1_score(Y_test, predictions) + +def evaluate_aesthetics(model, preprocessing_fn, device="cpu"): + X_test, Y_test = load_aesthetics_test() + X_test_tensor = torch.tensor(X_test.values.astype(float), dtype=torch.float32).to(device) + X_test_tensor = preprocessing_fn(X_test_tensor) + predictions = model(X_test_tensor).detach().cpu().numpy() + + cosine_sim = cosine_similarity(predictions, Y_test) + diag = np.diag(cosine_sim) + worse_than_diag = cosine_sim <= diag[:, np.newaxis] + matchperc = np.mean(worse_than_diag) + print(f"Predicted embedding more similar to GT than : {100 * matchperc:.2f}% of test set designs, on average.") + + return mean_squared_error(Y_test, predictions) diff --git a/bike_bench_internal/src/bikebench/prediction/model_definitions.py b/bike_bench_internal/src/bikebench/prediction/model_definitions.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/src/bikebench/prediction/prediction_utils.py b/bike_bench_internal/src/bikebench/prediction/prediction_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..71102e98308a95e4e71ba451155e1063d3fdc864 --- /dev/null +++ b/bike_bench_internal/src/bikebench/prediction/prediction_utils.py @@ -0,0 +1,75 @@ +import torch +import dill +from torch import nn +from bikebench.resource_utils import models_and_scalers_path + + +class TorchStandardScaler(nn.Module): + def __init__(self): + super().__init__() + # these will be set in .fit() + self.register_buffer('mean', torch.tensor([])) + self.register_buffer('std', torch.tensor([])) + self.fitted = False + + def fit(self, x: torch.Tensor): + """ + Compute per‑feature mean and std from a [N, F]-shaped tensor. + """ + # flatten any extra dims into the batch + N = x.shape[0] + feats = x.view(N, -1) if x.dim() > 2 else x + self.mean = feats.mean(dim=0) + self.std = feats.std(dim=0, unbiased=False).clamp(min=1e-6) + self.fitted = True + return self + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if not self.fitted: + raise RuntimeError("Scaler has not been fitted yet") + # preserve any extra dimensions beyond the feature-dim + original_shape = x.shape + N = x.shape[0] + feats = x.view(N, -1) if x.dim() > 2 else x + scaled = (feats - self.mean) / self.std + return scaled.view(original_shape) + + # alias + transform = forward + +class Preprocessor(nn.Module): + def __init__(self, scaler_path, preprocess_fn, device: torch.device = None): + super().__init__() + self.device = device or torch.device('cpu') + self.scaler: TorchStandardScaler = torch.load(scaler_path, map_location=self.device, weights_only=False) + self.scaler.to(self.device) + self.preprocess_fn = preprocess_fn + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if self.preprocess_fn: + x = self.preprocess_fn(x, self.device) + return self.scaler(x) + + __call__ = forward + +class DNN(nn.Module): + def __init__(self, input_dim, layer_sizes=[256, 128], output_dim=1, dropout_rate=0.2, classification=False): + super(DNN, self).__init__() + layers = [] + prev_dim = input_dim + for size in layer_sizes: + layers.append(nn.Linear(prev_dim, size)) + layers.append(nn.ReLU()) + layers.append(nn.Dropout(dropout_rate)) + prev_dim = size + + layers.append(nn.Linear(prev_dim, output_dim)) + + self.network = nn.Sequential(*layers) + self.classification = classification + + def forward(self, x): + x = self.network(x) + if self.classification: + x = torch.sigmoid(x) + return x \ No newline at end of file diff --git a/bike_bench_internal/src/bikebench/prediction/structural_predictor.py b/bike_bench_internal/src/bikebench/prediction/structural_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..768cb2e8fd2835c3cae7f5e5873dc0532fa92365 --- /dev/null +++ b/bike_bench_internal/src/bikebench/prediction/structural_predictor.py @@ -0,0 +1,7 @@ +from bikebench.prediction.prediction_utils import DNN +def get_structural_model(dropout_on = False): + if dropout_on: + model = DNN(39, layer_sizes=(128, 256), dropout_rate = 0.2, output_dim = 6, classification=False) + else: + model = DNN(39, layer_sizes=(128, 256), dropout_rate = 0.0, output_dim = 6, classification=False) + return model diff --git a/bike_bench_internal/src/bikebench/prediction/usability_predictor.py b/bike_bench_internal/src/bikebench/prediction/usability_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..daee705e6c044b310cfeb9c4ee88c2235a878b8c --- /dev/null +++ b/bike_bench_internal/src/bikebench/prediction/usability_predictor.py @@ -0,0 +1,7 @@ +from bikebench.prediction.prediction_utils import DNN +def get_usability_model(dropout_on = False): + if dropout_on: + model = DNN(3, layer_sizes = [160, 512], dropout_rate = 0.1404) + else: + model = DNN(3, layer_sizes = [160, 512], dropout_rate = 0.0) + return model \ No newline at end of file diff --git a/bike_bench_internal/src/bikebench/prediction/validity_predictor.py b/bike_bench_internal/src/bikebench/prediction/validity_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..8836206274811fbe5024cc5b068a21380a450f57 --- /dev/null +++ b/bike_bench_internal/src/bikebench/prediction/validity_predictor.py @@ -0,0 +1,7 @@ +from bikebench.prediction.prediction_utils import DNN +def get_validity_model(dropout_on = False): + if dropout_on: + model = DNN(39, layer_sizes=(128, 128), dropout_rate = 0.6, classification=True) + else: + model = DNN(39, layer_sizes=(128, 128), dropout_rate = 0.0, classification=True) + return model diff --git a/bike_bench_internal/src/bikebench/rendering/BikeCAD_server_client.py b/bike_bench_internal/src/bikebench/rendering/BikeCAD_server_client.py new file mode 100644 index 0000000000000000000000000000000000000000..74eae787a0c305a300cb5a4e9d656bf8b55d85b8 --- /dev/null +++ b/bike_bench_internal/src/bikebench/rendering/BikeCAD_server_client.py @@ -0,0 +1,18 @@ +import requests + +from bikebench.exceptions import InternalError +from bikebench.rendering.BikeCAD_server_manager import ServerManager + + +class RenderingClient: + + def __init__(self, + server_manager: ServerManager): + self._server_manager = server_manager + + def render(self, bike_xml: str): + endpoint = self._server_manager.endpoint("/api/v1/render") + result = requests.post(endpoint, data=bike_xml) + if result.status_code == 200: + return result.content + raise InternalError(f"Rendering request failed {result}") diff --git a/bike_bench_internal/src/bikebench/rendering/BikeCAD_server_manager.py b/bike_bench_internal/src/bikebench/rendering/BikeCAD_server_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..c8cbd328e3dee19271c64154faefae670249ca7c --- /dev/null +++ b/bike_bench_internal/src/bikebench/rendering/BikeCAD_server_manager.py @@ -0,0 +1,132 @@ +import atexit +import os +import subprocess +import time +from abc import ABCMeta, abstractmethod +from concurrent.futures import Future +from concurrent.futures.thread import ThreadPoolExecutor +from typing import List + +import psutil +import requests + +from bikebench.exceptions import InternalError, check_internal_precondition +from bikebench.resource_utils import resource_path + + +def get_java_binary(): + b = os.getenv("JAVA_HOME", "java") + if b.endswith("java"): + res = b + else: + res = os.path.join(b, "bin", "java") + print(f"Using {res} as the Java binary") + return res + + +JAVA_BINARY = get_java_binary() + + +class ServerManager(metaclass=ABCMeta): + def __init__(self): + self._server_pids: List[int] = [] + atexit.register(self._kill_live_servers) + + @abstractmethod + def endpoint(self, suffix: str) -> str: + pass + + def _start_server(self, port: int, timeout_seconds: int) -> None: + if not self._check_server_health(port): + print(f"Starting BikeCAD server on port {port}...") + process = subprocess.Popen( + [JAVA_BINARY, "-jar", resource_path("BikeCAD-server.jar"), f"--server.port={port}"]) + self._server_pids.append(process.pid) + self._await_start_or_throw(port, timeout_seconds) + print(f"BikeCAD server started on port {port}.") + + def _await_start_or_throw(self, + port: int, + timeout_seconds: int + ) -> None: + seconds_waited = 0 + while not self._check_server_health(port): + time.sleep(1) + seconds_waited += 1 + if seconds_waited > timeout_seconds: + raise InternalError(f"Could not start server on port {port}...") + + def _kill_live_servers(self) -> None: + for server_pid in self._server_pids: + if psutil.pid_exists(server_pid): + print(f"BikeCAD Server with pid {server_pid} exists. Killing...") + psutil.Process(pid=server_pid).kill() + print(f"BikeCAD server with pid {server_pid} killed successfully.") + else: + print(f"WARNING: pid {server_pid} does not exist") + + def _check_server_health(self, port: int) -> bool: + try: + health_response = requests.get(self._endpoint(port, "/actuator/serverInformation"), timeout=1) + except Exception as ignored: + return False + if health_response.status_code != 200: + return False + return health_response.json()["serverName"] == "BikeCAD-server" + + def _endpoint(self, port: int, suffix: str): + url = f"http://localhost:{port}{suffix}" + check_internal_precondition(suffix.startswith("/"), f"Invalid url {url}") + return url + + +class SingleThreadedBikeCadServerManager(ServerManager): + SERVER_PORT = 8080 + + def __init__(self, timeout_seconds: int): + super().__init__() + self._start_server(self.SERVER_PORT, timeout_seconds) + + def endpoint(self, suffix: str) -> str: + return self._endpoint(self.SERVER_PORT, suffix) + + +class MultiThreadedBikeCadServerManager(ServerManager): + STARTING_PORT = 8080 + + def __init__(self, number_servers: int, timeout_seconds: int): + super().__init__() + self._port_range = [self.STARTING_PORT + i for i in range(number_servers)] + self._request_count = 0 # used for round-robin load-balancing :D + futures = self._start_servers(number_servers, timeout_seconds) + self._await_servers(futures, timeout_seconds) + + def _start_servers(self, number_servers: int, timeout_seconds: int) -> List[Future]: + executor = ThreadPoolExecutor(max_workers=number_servers) + futures = [] + for port in self._port_range: + futures.append(executor.submit(self._start_server, port, timeout_seconds)) + return futures + + def endpoint(self, suffix: str) -> str: + n_servers = len(self._port_range) + selected_port = self._port_range[self._request_count % n_servers] # modulo for safety + + self._update_request_count(n_servers) + return self._endpoint( + selected_port, + suffix + ) + + def _update_request_count(self, n_servers: int): + self._request_count += 1 + if self._request_count >= n_servers: + self._request_count = 0 # restart counter + + def _await_servers(self, futures: List[Future], timeout_seconds: int): + seconds_waited = 0 + while not all([f.done() for f in futures]): + time.sleep(1) + seconds_waited += 1 + if seconds_waited >= timeout_seconds: + raise InternalError("Failed to start servers in time") diff --git a/bike_bench_internal/src/bikebench/rendering/__init__.py b/bike_bench_internal/src/bikebench/rendering/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/src/bikebench/rendering/animation.py b/bike_bench_internal/src/bikebench/rendering/animation.py new file mode 100644 index 0000000000000000000000000000000000000000..89bb05dad205e65ee614285d60fe7de18a9efda7 --- /dev/null +++ b/bike_bench_internal/src/bikebench/rendering/animation.py @@ -0,0 +1,67 @@ +import os +import io +from tqdm import tqdm +import imageio +import cairosvg +from bikebench.rendering.rendering import RenderingEngine # Adjust if needed + + +def render_to_animation( + data, + mp4_filename, + renderer=None, + fps=25, + max_frames=None, + rider_dims=None, +): + """ + Renders each row of a DataFrame using the given renderer and creates an MP4 animation. + + Args: + data (pd.DataFrame): The dataframe containing rows to render. + mp4_filename (str): Path where the MP4 will be saved. + renderer (RenderingEngine or None): If None, a new RenderingEngine will be created. + fps (int): Frames per second for the MP4. + max_frames (int or None): If set, limits to the first N frames. + rider_dims (array-like or None): Optional dimensions for the rider, if applicable. + + Returns: + BytesIO: In-memory MP4 object (read from saved file). + """ + if renderer is None: + renderer = RenderingEngine(number_rendering_servers=1, server_init_timeout_seconds=120) + + output_dir = os.path.dirname(mp4_filename) + if output_dir: + os.makedirs(output_dir, exist_ok=True) + + frame_dir = os.path.join(output_dir if output_dir else ".", "frames") + os.makedirs(frame_dir, exist_ok=True) + + png_files = [] + data_iter = data.iterrows() + if max_frames is not None: + data_iter = list(data_iter)[:max_frames] + + for i, (_, row) in enumerate(tqdm(data_iter, desc="Rendering frames", total=max_frames or len(data))): + try: + res = renderer.render_clip(row, rider_dims=rider_dims) + svg_bytes = res.image_bytes + + png_path = os.path.join(frame_dir, f"frame_{i:03d}.png") + cairosvg.svg2png(bytestring=svg_bytes, write_to=png_path) + png_files.append(png_path) + + except Exception as e: + print(f"[Warning] Rendering failed for row {i}: {e}") + + with imageio.get_writer(mp4_filename, format="ffmpeg", mode="I", fps=fps, codec="libx264") as writer: + for png_file in png_files: + writer.append_data(imageio.imread(png_file)) + + # Load and return video as BytesIO + with open(mp4_filename, "rb") as f: + mp4_buffer = io.BytesIO(f.read()) + + mp4_buffer.seek(0) + return mp4_buffer diff --git a/bike_bench_internal/src/bikebench/rendering/rendering.py b/bike_bench_internal/src/bikebench/rendering/rendering.py new file mode 100644 index 0000000000000000000000000000000000000000..0527f71d0c5060165efbf0b6f9c05dde09993eb9 --- /dev/null +++ b/bike_bench_internal/src/bikebench/rendering/rendering.py @@ -0,0 +1,50 @@ +import attrs + +from bikebench.rendering.BikeCAD_server_client import RenderingClient +from bikebench.rendering.BikeCAD_server_manager import SingleThreadedBikeCadServerManager, \ + MultiThreadedBikeCadServerManager +from bikebench.resource_utils import STANDARD_BIKE_RESOURCE +from bikebench.xml_handling.cad_builder import BikeCadFileBuilder + +FILE_BUILDER = BikeCadFileBuilder() + + +@attrs.define(frozen=True) +class RenderingResult: + image_bytes: bytes + xml_file: str + + +class RenderingEngine: + def __init__(self, + number_rendering_servers: int, + server_init_timeout_seconds: int + ): + with open(STANDARD_BIKE_RESOURCE, "r") as file: + self.standard_bike_xml = file.read() + self._rendering_client = self._init_rendering_client(number_rendering_servers, server_init_timeout_seconds) + + def render_xml(self, bike_xml: str) -> RenderingResult: + return RenderingResult(image_bytes=(self._render(bike_xml)), xml_file=bike_xml) + + # def render_biked(self, biked: dict, rider_dims) -> RenderingResult: + # xml = FILE_BUILDER.build_cad_from_biked(biked, self.standard_bike_xml, rider_dims) + # return RenderingResult(image_bytes=(self._render(xml)), xml_file=xml) + + def render_clip(self, clip: dict, rider_dims=None) -> RenderingResult: + xml = FILE_BUILDER.build_cad_from_clip(clip, self.standard_bike_xml, rider_dims) + return RenderingResult(image_bytes=(self._render(xml)), xml_file=xml) + + def _render(self, xml: str) -> bytes: + return self._rendering_client.render(xml) + + def _init_rendering_client(self, + number_rendering_servers: int, + timeout_seconds: int + ): + if number_rendering_servers > 1: + manager = MultiThreadedBikeCadServerManager(number_servers=number_rendering_servers, + timeout_seconds=timeout_seconds) + return RenderingClient(server_manager=manager) + else: + return RenderingClient(server_manager=SingleThreadedBikeCadServerManager(timeout_seconds)) diff --git a/bike_bench_internal/src/bikebench/rendering/visualize_grid.py b/bike_bench_internal/src/bikebench/rendering/visualize_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..fb9abc9ee03006e670852f9aeecb2efaf3840004 --- /dev/null +++ b/bike_bench_internal/src/bikebench/rendering/visualize_grid.py @@ -0,0 +1,326 @@ +import io +from PIL import Image +import matplotlib.pyplot as plt +from cairosvg import svg2png +import numpy as np +from matplotlib import gridspec +import itertools as it +from abc import ABCMeta, abstractmethod +from tqdm import tqdm + + +def plot_bike_grid(renderer, df, cell_width=3, dpi=100): + n = len(df) + strat = SquareStrategy() # your class, unmodified + + # 1) compute the nearly-square arrangement + arr = strat.get_grid_arrangement(n) # e.g. (3,3,2) + nrows, ncols = len(arr), max(arr) + + # 2) render first image to get pixel aspect ratio + first_svg = renderer.render_clip(df.iloc[0]).image_bytes + first_png = svg2png(bytestring=first_svg) + w, h = Image.open(io.BytesIO(first_png)).size + aspect = h / w + cell_height = cell_width * aspect + + # 3) let get_grid() spin up its own fig+GridSpec + specs = strat.get_grid(n) # internally does plt.figure(constrained_layout=True) + fig = plt.gcf() # grab that exact figure + + # 4) now resize the figure so each "cell" is cell_width x cell_height inches + fig.set_size_inches(ncols * cell_width, nrows * cell_height) + fig.set_dpi(dpi) + + # 5) populate each SubplotSpec in order + for spec, (_, row) in zip(specs, df.iterrows()): + ax = fig.add_subplot(spec) + ax.axis("off") + + # render + convert + image + png_b = svg2png(bytestring=renderer.render_clip(row).image_bytes) + img = Image.open(io.BytesIO(png_b)) + ax.imshow(img) + + return fig + +# re-use your SquareStrategy from grid_strategy.py +#The following taken from https://github.com/matplotlib/grid-strategy +class GridStrategy(metaclass=ABCMeta): + """ + Static class used to compute grid arrangements given the number of subplots + you want to show. By default, it goes for a symmetrical arrangement that is + nearly square (nearly equal in both dimensions). + """ + + def __init__(self, alignment="center"): + self.alignment = alignment + + def get_grid(self, n): + """ + Return a list of axes designed according to the strategy. + Grid arrangements are tuples with the same length as the number of rows, + and each element specifies the number of colums in the row. + Ex (2, 3, 2) leads to the shape + x x + x x x + x x + where each x would be a subplot. + """ + + grid_arrangement = self.get_grid_arrangement(n) + return self.get_gridspec(grid_arrangement) + + @classmethod + @abstractmethod + def get_grid_arrangement(cls, n): # pragma: nocover + pass + + def get_gridspec(self, grid_arrangement): + nrows = len(grid_arrangement) + ncols = max(grid_arrangement) + + # If it has justified alignment, will not be the same as the other alignments + if self.alignment == "justified": + return self._justified(nrows, grid_arrangement) + else: + return self._ragged(nrows, ncols, grid_arrangement) + + def _justified(self, nrows, grid_arrangement): + ax_specs = [] + num_small_cols = np.lcm.reduce(grid_arrangement) + gs = gridspec.GridSpec( + nrows, num_small_cols, figure=plt.figure(constrained_layout=True) + ) + for r, row_cols in enumerate(grid_arrangement): + skip = num_small_cols // row_cols + for col in range(row_cols): + s = col * skip + e = s + skip + + ax_specs.append(gs[r, s:e]) + return ax_specs + + def _ragged(self, nrows, ncols, grid_arrangement): + if len(set(grid_arrangement)) > 1: + col_width = 2 + else: + col_width = 1 + + gs = gridspec.GridSpec( + nrows, ncols * col_width, figure=plt.figure(constrained_layout=True) + ) + + ax_specs = [] + for r, row_cols in enumerate(grid_arrangement): + # This is the number of missing columns in this row. If some rows + # are a different width than others, the column width is 2 so every + # column skipped at the beginning is also a missing slot at the end. + if self.alignment == "left": + # This is left-justified (or possibly full justification) + # so no need to skip anything + skip = 0 + elif self.alignment == "right": + # Skip two slots for every missing plot - right justified. + skip = (ncols - row_cols) * 2 + else: + # Defaults to centered, as that is the default value for the class. + # Skip one for each missing column - centered + skip = ncols - row_cols + + for col in range(row_cols): + s = skip + col * col_width + e = s + col_width + + ax_specs.append(gs[r, s:e]) + + return ax_specs + + +class SquareStrategy(GridStrategy): + SPECIAL_CASES = {3: (2, 1), 5: (2, 3)} + + @classmethod + def get_grid_arrangement(cls, n): + """ + Return an arrangement of rows containing ``n`` axes that is as close to + square as looks good. + :param n: + The number of plots in the subplot + :return: + Returns a :class:`tuple` of length ``nrows``, where each element + represents the number of plots in that row, so for example a 3 x 2 + grid would be represented as ``(3, 3)``, because there are 2 rows + of length 3. + **Example:** + .. code:: + >>> GridStrategy.get_grid(7) + (2, 3, 2) + >>> GridStrategy.get_grid(6) + (3, 3) + """ + if n in cls.SPECIAL_CASES: + return cls.SPECIAL_CASES[n] + + # May not work for very large n + n_sqrtf = np.sqrt(n) + n_sqrt = int(np.ceil(n_sqrtf)) + + if n_sqrtf == n_sqrt: + # Perfect square, we're done + x, y = n_sqrt, n_sqrt + elif n <= n_sqrt * (n_sqrt - 1): + # An n_sqrt x n_sqrt - 1 grid is close enough to look pretty + # square, so if n is less than that value, will use that rather + # than jumping all the way to a square grid. + x, y = n_sqrt, n_sqrt - 1 + elif not (n_sqrt % 2) and n % 2: + # If the square root is even and the number of axes is odd, in + # order to keep the arrangement horizontally symmetrical, using a + # grid of size (n_sqrt + 1 x n_sqrt - 1) looks best and guarantees + # symmetry. + x, y = (n_sqrt + 1, n_sqrt - 1) + else: + # It's not a perfect square, but a square grid is best + x, y = n_sqrt, n_sqrt + + if n == x * y: + # There are no deficient rows, so we can just return from here + return tuple(x for i in range(y)) + + # If exactly one of these is odd, make it the rows + if (x % 2) != (y % 2) and (x % 2): + x, y = y, x + + return cls.arrange_rows(n, x, y) + + @classmethod + def arrange_rows(cls, n, x, y): + """ + Given a grid of size (``x`` x ``y``) to be filled with ``n`` plots, + this arranges them as desired. + :param n: + The number of plots in the subplot. + :param x: + The number of columns in the grid. + :param y: + The number of rows in the grid. + :return: + Returns a :class:`tuple` containing a grid arrangement, see + :func:`get_grid` for details. + """ + part_rows = (x * y) - n + full_rows = y - part_rows + + f = (full_rows, x) + p = (part_rows, x - 1) + + # Determine which is the more and less frequent value + if full_rows >= part_rows: + size_order = f, p + else: + size_order = p, f + + # ((n_more, more_val), (n_less, less_val)) = size_order + args = it.chain.from_iterable(size_order) + + if y % 2: + return cls.stripe_odd(*args) + else: + return cls.stripe_even(*args) + + @classmethod + def stripe_odd(cls, n_more, more_val, n_less, less_val): + """ + Prepare striping for an odd number of rows. + :param n_more: + The number of rows with the value that there's more of + :param more_val: + The value that there's more of + :param n_less: + The number of rows that there's less of + :param less_val: + The value that there's less of + :return: + Returns a :class:`tuple` of striped values with appropriate buffer. + """ + (n_m, m_v) = n_more, more_val + (n_l, l_v) = n_less, less_val + + # Calculate how much "buffer" we need. + # Example (b = buffer number, o = outer stripe, i = inner stripe) + # 4, 4, 5, 4, 4 -> b, o, i, o, b (buffer = 1) + # 4, 5, 4, 5, 4 -> o, i, o, i, o (buffer = 0) + n_inner_stripes = n_l + n_buffer = (n_m + n_l) - (2 * n_inner_stripes + 1) + assert n_buffer % 2 == 0, (n_more, n_less, n_buffer) + n_buffer //= 2 + + buff_tuple = (m_v,) * n_buffer + stripe_tuple = (m_v, l_v) * n_inner_stripes + (m_v,) + + return buff_tuple + stripe_tuple + buff_tuple + + @classmethod + def stripe_even(cls, n_more, more_val, n_less, less_val): + """ + Prepare striping for an even number of rows. + :param n_more: + The number of rows with the value that there's more of + :param more_val: + The value that there's more of + :param n_less: + The number of rows that there's less of + :param less_val: + The value that there's less of + :return: + Returns a :class:`tuple` of striped values with appropriate buffer. + """ + total = n_more + n_less + if total % 2: + msg = "Expected an even number of values, got {} + {}".format( + n_more, n_less + ) + raise ValueError(msg) + + assert n_more >= n_less, (n_more, n_less) + + # See what the minimum unit cell is + n_l_c, n_m_c = n_less, n_more + num_div = 0 + while True: + n_l_c, lr = divmod(n_l_c, 2) + n_m_c, mr = divmod(n_m_c, 2) + if lr or mr: + break + + num_div += 1 + + # Maximum number of times we can half this to get a "unit cell" + n_cells = 2 ** num_div + + # Make the largest possible odd unit cell + cell_s = total // n_cells # Size of a unit cell + + cell_buff = int(cell_s % 2 == 0) # Buffer is either 1 or 0 + cell_s -= cell_buff + cell_nl = n_less // n_cells + cell_nm = cell_s - cell_nl + + if cell_nm == 0: + stripe_cell = (less_val,) + else: + stripe_cell = cls.stripe_odd(cell_nm, more_val, cell_nl, less_val) + + unit_cell = (more_val,) * cell_buff + stripe_cell + + if num_div == 0: + return unit_cell + + stripe_out = unit_cell * (n_cells // 2) + return tuple(reversed(stripe_out)) + stripe_out + + + + + diff --git a/bike_bench_internal/src/bikebench/resource_utils.py b/bike_bench_internal/src/bikebench/resource_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8bfb757241235ce99207e9e0f69652ebab94e864 --- /dev/null +++ b/bike_bench_internal/src/bikebench/resource_utils.py @@ -0,0 +1,13 @@ +import os + + +def resource_path(rel_path: str): + return os.path.join(os.path.dirname(__file__), "..", "resources", rel_path) + +def models_and_scalers_path(rel_path: str): + return os.path.join(os.path.dirname(__file__), "..", "resources/models_and_scalers", rel_path) + +def datasets_path(rel_path: str): + return os.path.join(os.path.dirname(__file__), "..", "resources/datasets", rel_path) + +STANDARD_BIKE_RESOURCE = resource_path("PlainRoadBikeStandardized.txt") diff --git a/bike_bench_internal/src/bikebench/transformation/__init__.py b/bike_bench_internal/src/bikebench/transformation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/src/bikebench/transformation/framed.py b/bike_bench_internal/src/bikebench/transformation/framed.py new file mode 100644 index 0000000000000000000000000000000000000000..7535c3f267b94b9a5952d8d5eee2178ecf5d6ca3 --- /dev/null +++ b/bike_bench_internal/src/bikebench/transformation/framed.py @@ -0,0 +1,142 @@ +import pandas as pd +import torch +from typing import List + + +FRAMED_ORDERED_COLUMNS = ['Material=Steel', 'Material=Aluminum', 'Material=Titanium', + 'SSB_Include', 'CSB_Include', 'CS Length', 'BB Drop', 'Stack', 'SS E', + 'ST Angle', 'BB OD', 'TT OD', 'HT OD', 'DT OD', 'CS OD', 'SS OD', + 'ST OD', 'CS F', 'HT LX', 'ST UX', 'HT UX', 'HT Angle', 'HT Length', + 'ST Length', 'BB Length', 'Dropout Offset', 'SSB OD', 'CSB OD', + 'SSB Offset', 'CSB Offset', 'SS Z', 'SS Thickness', 'CS Thickness', + 'TT Thickness', 'BB Thickness', 'HT Thickness', 'ST Thickness', + 'DT Thickness', 'DT Length'] + +BIKEBENCH_TO_FRAMED_UNITS = { + 'ssd': 'SS OD', + 'Head tube length textfield': 'HT Length', + 'csd': 'CS OD', + 'Seat tube extension2': 'ST UX', + 'Head tube lower extension2': 'HT LX', + 'Head tube upper extension2': 'HT UX', + 'Seat tube length': 'ST Length', + 'BB textfield': 'BB Drop', + 'CHAINSTAYbrdgshift': 'CSB Offset', + 'Seat stay junction0': 'SS E', + 'Dropout spacing': 'Dropout Offset', + 'SSTopZOFFSET': 'SS Z', + 'Stack': 'Stack', + 'CS textfield': 'CS Length', + 'DT Length': 'DT Length', + 'htd': 'HT OD', + 'SEATSTAYbrdgshift': 'SSB Offset', + 'BB diameter': 'BB OD', + 'dtd': 'DT OD', + 'ttd': 'TT OD', + 'BB length': 'BB Length', + 'std': 'ST OD', + 'Wall thickness Seat stay': 'SS Thickness', + 'Wall thickness Chain stay': 'CS Thickness', + 'Wall thickness Top tube': 'TT Thickness', + 'Wall thickness Bottom Bracket': 'BB Thickness', + 'Wall thickness Head tube': 'HT Thickness', + 'Wall thickness Seat tube': 'ST Thickness', + 'Wall thickness Down tube': 'DT Thickness', + 'CHAINSTAYbrdgdia1': 'CSB OD', + 'SEATSTAYbrdgdia1': 'SSB OD', + 'Chain stay position on BB': 'CS F', +} + +BIKEBENCH_TO_FRAMED_UNITS_OH = BIKEBENCH_TO_FRAMED_UNITS.copy() +BIKEBENCH_TO_FRAMED_UNITS_OH['MATERIAL OHCLASS: ALUMINIUM'] = 'Material=Aluminum' +BIKEBENCH_TO_FRAMED_UNITS_OH['MATERIAL OHCLASS: STEEL'] = 'Material=Steel' +BIKEBENCH_TO_FRAMED_UNITS_OH['MATERIAL OHCLASS: TITANIUM'] = 'Material=Titanium' + + +BIKEBENCH_TO_FRAMED_IDENTICAL = { + 'Head angle': 'HT Angle', + 'SEATSTAYbrdgCheck': 'SSB_Include', + 'Seat angle': 'ST Angle', + 'CHAINSTAYbrdgCheck': 'CSB_Include' +} + + +MATERIALS = {"MATERIAL OHCLASS: ALUMINIUM": "Aluminum", + "MATERIAL OHCLASS: STEEL": "Steel", + "MATERIAL OHCLASS: TITANIUM": "Titanium", + "MATERIAL OHCLASS: CARBON": "Steel", # Overridden to Steel + "MATERIAL OHCLASS: BAMBOO": "Steel", # Overridden to Steel + "MATERIAL OHCLASS: OTHER": "Steel" # Overridden to Steel + } +def clip_to_framed(X_clip): + X_framed = pd.DataFrame() + #For every column in FRAMED_TO_BIKEBENCH_UNITS, add from X_clip to X_framed but divide by 1000 (mm to m) + for column in BIKEBENCH_TO_FRAMED_UNITS.keys(): + if column in X_clip.columns: + X_framed[BIKEBENCH_TO_FRAMED_UNITS[column]] = X_clip[column] / 1000.0 + + #For every column in FRAMED_TO_BIKEBENCH_IDENTICAL, add from X_clip to X_framed but do not change units + for column in BIKEBENCH_TO_FRAMED_IDENTICAL.keys(): + if column in X_clip.columns: + X_framed[BIKEBENCH_TO_FRAMED_IDENTICAL[column]] = X_clip[column] + + #Conver one-hot encoded columns of the form MATERIAL OHCLASS: ALUMINIUM to a column Material with the value Aluminum + material_columns = [col for col in X_clip.columns if col.startswith("MATERIAL OHCLASS:")] + X_framed["Material"] = X_clip[material_columns].idxmax(axis=1).map(MATERIALS) + + return X_framed + +def clip_to_framed_tensor_builder(clip_columns: List[str], framed_order: List[str]) -> callable: + """ + clip_columns: list of the CLIP‑DataFrame columns, in order, + so that X_clip tensor has shape [N, D] matching these. + framed_order: the *exact* list of framed‑column names you want out, + e.g. ["HT Length","ST Length",…,"Material"] + Returns: + fn(X_clip: Tensor[N,D]) -> Tensor[N, len(framed_order)] + out_names == framed_order + """ + + # 1) find & name the unit‑converted cols + units_idx, units_names = [], [] + ident_idx, ident_names = [], [] + for i, col in enumerate(clip_columns): + if col in BIKEBENCH_TO_FRAMED_UNITS_OH: + tgt = BIKEBENCH_TO_FRAMED_UNITS_OH[col] + # treat Material=… as a one‑hot copy, not mm→m + if tgt.startswith("Material="): + ident_idx.append(i) + ident_names.append(tgt) + else: + units_idx.append(i) + units_names.append(tgt) + + elif col in BIKEBENCH_TO_FRAMED_IDENTICAL: + ident_idx.append(i) + ident_names.append(BIKEBENCH_TO_FRAMED_IDENTICAL[col]) + + # base feature list in the order we'll build them + base_names = units_names + ident_names + + # Step 2: figure out how to reorder base_names → framed_order + reorder_idx = [] + for name in framed_order: + if name not in base_names: + raise ValueError(f"requested framed column {name!r} not found (got {base_names})") + reorder_idx.append(base_names.index(name)) + + def clip_to_framed_reordered( + X_clip: torch.Tensor # [N, D] + ) -> torch.Tensor: # [N, len(framed_order)] + # mm→m conversion block + block_units = X_clip[:, units_idx] / 1000.0 # [N, U] + # direct‑copy block (includes Material=… one‑hots and IDENTICAL) + block_ident = X_clip[:, ident_idx] # [N, I] + # concat in base order + X_base = torch.cat([block_units, block_ident], dim=1) # [N, U+I] + # reorder to your target layout + return X_base[:, reorder_idx] + + return clip_to_framed_reordered + + \ No newline at end of file diff --git a/bike_bench_internal/src/bikebench/transformation/interface_points.py b/bike_bench_internal/src/bikebench/transformation/interface_points.py new file mode 100644 index 0000000000000000000000000000000000000000..57f2264ff3f8f9be43d650228de80ab7d35938c2 --- /dev/null +++ b/bike_bench_internal/src/bikebench/transformation/interface_points.py @@ -0,0 +1,116 @@ +import math +import pandas as pd +import torch + + +def calculate_interace_points_df(df): + columns = ["Stack", + "Handlebar style OHCLASS: 0", "Handlebar style OHCLASS: 1", "Handlebar style OHCLASS: 2", + "Seat angle", "Saddle height", "Head tube length textfield", "Head tube lower extension2", + "Head angle", "DT Length"] + x = df[columns].values + x = torch.tensor(x, dtype=torch.float32) + y = calculate_interface_points(x) + y = pd.DataFrame(y.numpy(), columns=["hand_x", "hand_y", "hip_x", "hip_y", "crank_length"], index=df.index) + return y + + +def calculate_interface_points(x, dtype=torch.float32, eps=1e-6): + device = x.device + + stack = x[:, 0] + oh0 = x[:, 1] # Handlebar style OHCLASS: 0 + oh1 = x[:, 2] # Handlebar style OHCLASS: 1 + oh2 = x[:, 3] # Handlebar style OHCLASS: 2 + + mask0 = torch.logical_and(oh0 > oh1, oh0 > oh2).float() + mask1 = torch.logical_and(oh1 > oh0, oh1 > oh2).float() + mask2 = torch.logical_and(oh2 > oh0, oh2 > oh1).float() + + theta_st = x[:, 4] * math.pi / 180 # Convert angles to radians + saddle_height = x[:, 5] + crank_length = torch.tensor(172.5) + crank_length = crank_length * torch.ones_like(saddle_height) # Crank length in mm + + HTL = x[:, 6] # Head tube length textfield + HTLX = x[:, 7] # Head tube lower extension2 + HTA = x[:, 8] * math.pi / 180 # Head tube angle + DTL = x[:, 9] # DT Length + DTJY = stack - (HTL - HTLX) * torch.sin(HTA) + DTJX = torch.sqrt(torch.clip(DTL ** 2 - DTJY ** 2, min=eps)) + stem_start_x = DTJX - (HTL - HTLX) * torch.cos(HTA) + stem_start_y = stack + stem_length = 100 + + top_headset_height = 44.33 #from bikecad defaults + + stem_angle = math.pi/2 -HTA - 5 * math.pi / 180 # Stem angle in radians + handlebar_mount_x = stem_start_x + stem_length * torch.cos(stem_angle) - top_headset_height * torch.cos(stem_angle) + handlebar_mount_y = stem_start_y + stem_length * torch.sin(stem_angle) + top_headset_height * torch.sin(stem_angle) + + + + handle_angle = torch.tensor(12.0 * math.pi / 180, dtype = dtype) + # road_bar_reach = 80.0 + road_bar_drop = torch.tensor(128.0, dtype = dtype) # mm + hbarextend = torch.tensor(60.0, dtype = dtype) + mountain_bar_sweep = torch.tensor(16.4, dtype = dtype) + mountain_bar_rise = torch.tensor(10.0, dtype = dtype) + mtndrop = torch.tensor(10.0, dtype = dtype) + bullhorn_reach = torch.tensor(150, dtype = dtype) + bullhorn_rise = torch.tensor(50.0, dtype = dtype) + bullhorn_slant = torch.tensor(40*math.pi/180, dtype = dtype) # radians + + # Seat tube angle does not technically point in the direction of saddle height. + # Saddle height by convention in bikecad points to the center of the saddle. + # This point is 55mm vertically above, 10mm horizontally back, and 21mm perpendicularly back vs st. + # Using small angle approximation, we calcualte the arc swept by offset + # This avoids solving a solving a complicated system to derive the exact SH angle. + arc = 21 - 55* torch.cos(theta_st) + 10*torch.sin(theta_st) + radius = saddle_height - 55*torch.sin(theta_st) - 10*torch.cos(theta_st) + SH_angle = theta_st - arc*torch.sin(theta_st)/(radius) # perpendicular distance / radius + + hip_x = saddle_height * torch.cos(SH_angle) # horizontal offset of hip from top of seatpost + hip_y = saddle_height * torch.sin(SH_angle) # vertical offset of hip from top of seatpost + + numfeat = x.shape[0] + + # posx and pos y are offset of hand position from handlbar mount + posx = torch.zeros(numfeat, dtype=torch.float32, device=device) + posy = torch.zeros(numfeat, dtype=torch.float32, device=device) + + # Precompute common trig terms + cos_angle = torch.cos(handle_angle) + sin_angle = torch.sin(handle_angle) + + #bullhorns + mC = bullhorn_rise - 60 * torch.sin(bullhorn_slant + handle_angle) + mA = bullhorn_reach - 60 * torch.cos(bullhorn_slant + handle_angle) + posx += mask2 * (mA * cos_angle - mC * sin_angle) + posy += mask2 * (mA * sin_angle + mC * cos_angle) + + #mountain bar + v1 = mountain_bar_rise + mtndrop + h1 = mountain_bar_sweep + posx += mask1 * (-h1 * cos_angle - v1 * sin_angle) + posy += mask1 * (-h1 * sin_angle + v1 * cos_angle) + + #drop bar + v0 = road_bar_drop + h0 = hbarextend - 60 + posx += mask0 * (-h0 * cos_angle + v0 * sin_angle) + posy += mask0 * (-h0 * sin_angle - v0 * cos_angle) + hand_x = handlebar_mount_x + posx # hand_x + hand_y = handlebar_mount_y + posy # hand_y + + #offsets of hip position vs top of saddle (from bikeCAD) + hip_y = hip_y + 50 + + #offsets from shoe thickness (from bikeCAD) + hip_y = hip_y - 23 + hand_y = hand_y - 23 + + y = [hand_x, hand_y, hip_x, hip_y, crank_length] + y = torch.stack(y, dim=1) # Stack the tensors along the second dimension + y = y / 1000 # Convert to meters + return y diff --git a/bike_bench_internal/src/bikebench/transformation/one_hot_encoding.py b/bike_bench_internal/src/bikebench/transformation/one_hot_encoding.py new file mode 100644 index 0000000000000000000000000000000000000000..cc05789b5dbfb04000b5982bf028c1f108d92c8d --- /dev/null +++ b/bike_bench_internal/src/bikebench/transformation/one_hot_encoding.py @@ -0,0 +1,169 @@ +from typing import Callable, List +import numpy as np +import pandas as pd + +# columns to one‐hot encode +ONE_HOT_ENCODED_BIKEBENCH_COLUMNS: List[str] = [ + 'MATERIAL', + 'Head tube type', + 'RIM_STYLE front', + 'RIM_STYLE rear', + 'Handlebar style', + 'Stem kind', + 'Fork type', + 'Seat tube type', +] + +ALL_CATEGORIES = { + 'MATERIAL': [ + 'ALUMINIUM', + 'BAMBOO', + 'CARBON', + 'OTHER', + 'STEEL', + 'TITANIUM' + ], + 'Head tube type': [ + '0', + '1', + '2', + '3' + ], + 'RIM_STYLE front': [ + 'DISC', + 'SPOKED', + 'TRISPOKE' + ], + 'RIM_STYLE rear': [ + 'DISC', + 'SPOKED', + 'TRISPOKE' + ], + 'Handlebar style': [ + '0', + '1', + '2' + ], + 'Stem kind': [ + '0', + '1', + '2' + ], + 'Fork type': [ + '0', + '1', + '2' + ], + 'Seat tube type': [ + '0', + '1', + '2' + ] +} + +def normalize_category_value(value): + """ + Normalize category values: + - Convert float representations of integers (e.g., 1.0) to int strings ('1'). + - Convert NaN to 'nan'. + - Strip whitespace. + """ + if pd.isna(value): + return 'nan' + elif isinstance(value, (float, int)): + # Check if it's an integer-looking float like 1.0 + if float(value).is_integer(): + return str(int(value)) + else: + return str(value).strip() + else: + return str(value).strip() + +# columns that are already boolean and should stay in the DF (converted to float on encode) +BOOLEAN_COLUMNS: List[str] = [ + 'bottle SEATTUBE0 show', + 'bottle DOWNTUBE0 show', + 'BELTorCHAIN', + 'SEATSTAYbrdgCheck', + 'CHAINSTAYbrdgCheck', +] + +FAKE_BOOLEAN_COLUMNS: List[str] = ['BELTorCHAIN'] + +PREFIX_SEP = " OHCLASS: " + + +def encode_to_continuous(df: pd.DataFrame) -> pd.DataFrame: + out = df.copy(deep=True) + + for col in ONE_HOT_ENCODED_BIKEBENCH_COLUMNS: + # Normalize the column values + out[col] = out[col].apply(normalize_category_value) + + all_categories = set(ALL_CATEGORIES.get(col, [])) + present_categories = set(out[col].unique()) + + unknown_categories = present_categories - all_categories + if unknown_categories: + print(f"⚠️ Warning: Column '{col}' has unknown values: {unknown_categories}") + + # Proceed with known values only + dummies = pd.get_dummies(out[col], prefix=col, prefix_sep=PREFIX_SEP) + + # Ensure all known categories are represented + for category in all_categories: + category_col = f"{col}{PREFIX_SEP}{category}" + if category_col not in dummies.columns: + dummies[category_col] = 0 + + # Reorder columns + ordered_cols = [f"{col}{PREFIX_SEP}{cat}" for cat in sorted(all_categories)] + dummies = dummies.reindex(columns=ordered_cols, fill_value=0) + + # Replace original column with dummies + out = pd.concat([out.drop(columns=[col]), dummies], axis=1) + + # Convert booleans to float + for col in BOOLEAN_COLUMNS: + if col in out.columns: + out[col] = out[col].astype(float) + + return out.astype(np.float32) + + + +def decode_to_mixed(encoded_df: pd.DataFrame) -> pd.DataFrame: + """ + Reverse the one‐hot encoding: + - For each original categorical column, find all " OHCLASS: *" dummies, + take argmax (the position of the highest value), strip off the prefix, and restore + the category string. + - Round the float boolean columns back to 0/1 and cast to bool. + """ + out = encoded_df.copy(deep=True) + + # 1) decode each categorical variable + for col in ONE_HOT_ENCODED_BIKEBENCH_COLUMNS: + pref = f"{col}{PREFIX_SEP}" + dummy_cols = [c for c in out.columns if c.startswith(pref)] + if not dummy_cols: + continue + + # idxmax on the raw floats picks the column with the highest value + restored = ( + out[dummy_cols] + .idxmax(axis=1) + .str.replace(pref, "", n=1, regex=False) + ) + + out[col] = restored + out.drop(columns=dummy_cols, inplace=True) + + # 2) round boolean floats back to bool + for col in BOOLEAN_COLUMNS: + if col in out.columns and col not in FAKE_BOOLEAN_COLUMNS: + out[col] = out[col].round().astype(int).astype(bool) + + return out + + diff --git a/bike_bench_internal/src/bikebench/transformation/ordered_columns.py b/bike_bench_internal/src/bikebench/transformation/ordered_columns.py new file mode 100644 index 0000000000000000000000000000000000000000..d49d931e43123d9a034205cc32c8b9bc01e815a3 --- /dev/null +++ b/bike_bench_internal/src/bikebench/transformation/ordered_columns.py @@ -0,0 +1,124 @@ +bike_bench_columns = ['Seatpost LENGTH', 'CS textfield', 'BB textfield', 'Stack', + 'Head angle', 'Head tube length textfield', 'Seat stay junction0', + 'Seat tube length', 'Seat angle', 'DT Length', 'FORK0R', 'BB diameter', + 'ttd', 'dtd', 'csd', 'std', 'htd', 'ssd', 'Chain stay position on BB', + 'SSTopZOFFSET', 'Head tube upper extension2', 'Seat tube extension2', + 'Head tube lower extension2', 'SEATSTAYbrdgshift', 'CHAINSTAYbrdgshift', + 'SEATSTAYbrdgdia1', 'CHAINSTAYbrdgdia1', 'SEATSTAYbrdgCheck', + 'CHAINSTAYbrdgCheck', 'Dropout spacing', + 'Wall thickness Bottom Bracket', 'Wall thickness Top tube', + 'Wall thickness Head tube', 'Wall thickness Down tube', + 'Wall thickness Chain stay', 'Wall thickness Seat stay', + 'Wall thickness Seat tube', 'Wheel diameter front', 'RDBSD', + 'Wheel diameter rear', 'FDBSD', 'Display AEROBARS', 'BB length', + 'Wheel cut', 'Front Fender include', 'Rear Fender include', + 'BELTorCHAIN', 'Number of cogs', 'Number of chainrings', + 'FIRST color R_RGB', 'FIRST color G_RGB', 'FIRST color B_RGB', + 'SPOKES composite front', 'SPOKES composite rear', 'SBLADEW front', + 'SBLADEW rear', 'Saddle length', 'Saddle height', 'Down tube type', + 'MATERIAL OHCLASS: ALUMINIUM', 'MATERIAL OHCLASS: BAMBOO', + 'MATERIAL OHCLASS: CARBON', 'MATERIAL OHCLASS: OTHER', + 'MATERIAL OHCLASS: STEEL', 'MATERIAL OHCLASS: TITANIUM', + 'Head tube type OHCLASS: 0', 'Head tube type OHCLASS: 1', + 'Head tube type OHCLASS: 2', 'Head tube type OHCLASS: 3', + 'RIM_STYLE front OHCLASS: DISC', 'RIM_STYLE front OHCLASS: SPOKED', + 'RIM_STYLE front OHCLASS: TRISPOKE', 'RIM_STYLE rear OHCLASS: DISC', + 'RIM_STYLE rear OHCLASS: SPOKED', 'RIM_STYLE rear OHCLASS: TRISPOKE', + 'Handlebar style OHCLASS: 0', 'Handlebar style OHCLASS: 1', + 'Handlebar style OHCLASS: 2', 'Stem kind OHCLASS: 0', + 'Stem kind OHCLASS: 1', 'Stem kind OHCLASS: 2', 'Fork type OHCLASS: 0', + 'Fork type OHCLASS: 1', 'Fork type OHCLASS: 2', + 'Seat tube type OHCLASS: 0', 'Seat tube type OHCLASS: 1', + 'Seat tube type OHCLASS: 2'] + +oh_columns = [ + [ + "MATERIAL OHCLASS: ALUMINIUM", + "MATERIAL OHCLASS: BAMBOO", + "MATERIAL OHCLASS: CARBON", + "MATERIAL OHCLASS: OTHER", + "MATERIAL OHCLASS: STEEL", + "MATERIAL OHCLASS: TITANIUM", + ], + [ + "Head tube type OHCLASS: 0", + "Head tube type OHCLASS: 1", + "Head tube type OHCLASS: 2", + "Head tube type OHCLASS: 3", + ], + [ + "RIM_STYLE front OHCLASS: DISC", + "RIM_STYLE front OHCLASS: SPOKED", + "RIM_STYLE front OHCLASS: TRISPOKE", + ], + [ + "RIM_STYLE rear OHCLASS: DISC", + "RIM_STYLE rear OHCLASS: SPOKED", + "RIM_STYLE rear OHCLASS: TRISPOKE", + ], + [ + "Handlebar style OHCLASS: 0", + "Handlebar style OHCLASS: 1", + "Handlebar style OHCLASS: 2", + ], + [ + "Stem kind OHCLASS: 0", + "Stem kind OHCLASS: 1", + "Stem kind OHCLASS: 2", + ], + [ + "Fork type OHCLASS: 0", + "Fork type OHCLASS: 1", + "Fork type OHCLASS: 2", + ], + [ + "Seat tube type OHCLASS: 0", + "Seat tube type OHCLASS: 1", + "Seat tube type OHCLASS: 2", + ] + ] + + +oh_bool_columns = [ + "MATERIAL OHCLASS: ALUMINIUM", + "MATERIAL OHCLASS: BAMBOO", + "MATERIAL OHCLASS: CARBON", + "MATERIAL OHCLASS: OTHER", + "MATERIAL OHCLASS: STEEL", + "MATERIAL OHCLASS: TITANIUM", + + "Head tube type OHCLASS: 0", + "Head tube type OHCLASS: 1", + "Head tube type OHCLASS: 2", + "Head tube type OHCLASS: 3", + + "RIM_STYLE front OHCLASS: DISC", + "RIM_STYLE front OHCLASS: SPOKED", + "RIM_STYLE front OHCLASS: TRISPOKE", + + "RIM_STYLE rear OHCLASS: DISC", + "RIM_STYLE rear OHCLASS: SPOKED", + "RIM_STYLE rear OHCLASS: TRISPOKE", + + "Handlebar style OHCLASS: 0", + "Handlebar style OHCLASS: 1", + "Handlebar style OHCLASS: 2", + + "Stem kind OHCLASS: 0", + "Stem kind OHCLASS: 1", + "Stem kind OHCLASS: 2", + + "Fork type OHCLASS: 0", + "Fork type OHCLASS: 1", + "Fork type OHCLASS: 2", + + "Seat tube type OHCLASS: 0", + "Seat tube type OHCLASS: 1", + "Seat tube type OHCLASS: 2", + + 'BELTorCHAIN', + 'SEATSTAYbrdgCheck', + 'CHAINSTAYbrdgCheck', +] + +USABILITY_COLUMNS = ['Saddle height', 'Stack', 'CS textfield'] \ No newline at end of file diff --git a/bike_bench_internal/src/bikebench/validation/__init__.py b/bike_bench_internal/src/bikebench/validation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/src/bikebench/validation/base_validation_function.py b/bike_bench_internal/src/bikebench/validation/base_validation_function.py new file mode 100644 index 0000000000000000000000000000000000000000..4be3b1f920458c0ed4db3c5166de1ae01b6caa4d --- /dev/null +++ b/bike_bench_internal/src/bikebench/validation/base_validation_function.py @@ -0,0 +1,283 @@ +from abc import abstractmethod, ABC +from typing import List, Dict + +import pandas as pd +import torch + + +# TODO: write validation functions to [optionally] be able to grab values from the default bike when not found? + +class ValidationFunction(ABC): + @abstractmethod + def friendly_name(self) -> str: + """ + Should return a user-friendly and easily comprehensible name for the validation in question. + """ + pass + + @abstractmethod + def variable_names(self) -> List[str]: + """ + Should return a list of variable names used in the validation. + """ + pass + + @abstractmethod + def validate(self, designs: torch.Tensor) -> torch.Tensor: + """ + Should return a PyTorch tensor with shape (len(designs), 1) or (len(designs),). + The values in the tensor represent validity. 1 is invalid, 0 is valid. + """ + pass + +class FeatureStore: + def __init__(self, designs: torch.Tensor, name_to_idx: Dict[str, int]): + self.X = designs + self.idx = name_to_idx + self.cache: Dict[str, torch.Tensor] = {} + + def col(self, name: str) -> torch.Tensor: + t = self.cache.get(name) + if t is None: + t = self.X[:, self.idx[name]] + self.cache[name] = t + return t + + # Angles (radians) + @property + def theta_ht(self): + k = "_theta_ht" + t = self.cache.get(k) + if t is None: + t = torch.deg2rad(self.col("Head angle")) + self.cache[k] = t + return t + + @property + def theta_st(self): + k = "_theta_st" + t = self.cache.get(k) + if t is None: + t = torch.deg2rad(self.col("Seat angle")) + self.cache[k] = t + return t + + @property + def sin_ht(self): + k = "_sin_ht" + t = self.cache.get(k) + if t is None: + t = torch.sin(self.theta_ht); self.cache[k] = t + return t + + @property + def cos_ht(self): + k = "_cos_ht" + t = self.cache.get(k) + if t is None: + t = torch.cos(self.theta_ht); self.cache[k] = t + return t + + @property + def tan_ht(self): + k = "_tan_ht" + t = self.cache.get(k) + if t is None: + t = torch.tan(self.theta_ht); self.cache[k] = t + return t + + @property + def sin_st(self): + k = "_sin_st" + t = self.cache.get(k) + if t is None: + t = torch.sin(self.theta_st); self.cache[k] = t + return t + + @property + def cos_st(self): + k = "_cos_st" + t = self.cache.get(k) + if t is None: + t = torch.cos(self.theta_st); self.cache[k] = t + return t + + @property + def tan_st(self): + k = "_tan_st" + t = self.cache.get(k) + if t is None: + t = torch.tan(self.theta_st); self.cache[k] = t + return t + + # Common junctions / pieces + @property + def DTJY(self): + k = "_DTJY"; t = self.cache.get(k) + if t is None: + stack = self.col("Stack") + htl = self.col("Head tube length textfield") + htlx = self.col("Head tube lower extension2") + t = stack - (htl - htlx) * self.sin_ht + self.cache[k] = t + return t + + @property + def DTJX(self): + k = "_DTJX"; t = self.cache.get(k) + if t is None: + dt_len = self.col("DT Length") + t = torch.sqrt(torch.clamp_min(dt_len**2 - self.DTJY**2, 0.0)) + self.cache[k] = t + return t + + @property + def TTJX(self): + k = "_TTJX"; t = self.cache.get(k) + if t is None: + htl = self.col("Head tube length textfield") + htlx = self.col("Head tube lower extension2") + htux = self.col("Head tube upper extension2") + t = self.DTJX - (htl - htlx - htux) * self.cos_ht + self.cache[k] = t + return t + + @property + def TTJY(self): + k = "_TTJY"; t = self.cache.get(k) + if t is None: + stack = self.col("Stack") + htux = self.col("Head tube upper extension2") + t = stack - htux * self.sin_ht + self.cache[k] = t + return t + + @property + def STJX(self): + k = "_STJX"; t = self.cache.get(k) + if t is None: + stl = self.col("Seat tube length") + stux = self.col("Seat tube extension2") + t = (stl - stux) * self.cos_st + self.cache[k] = t + return t + + @property + def STJY(self): + k = "_STJY"; t = self.cache.get(k) + if t is None: + stl = self.col("Seat tube length") + stux = self.col("Seat tube extension2") + t = (stl - stux) * self.sin_st + self.cache[k] = t + return t + + @property + def z_bb(self): + """sqrt(max(CS^2 - BB^2, 0)) reused in rear-wheel/seat-stay logic.""" + k = "_z_bb"; t = self.cache.get(k) + if t is None: + CS = self.col("CS textfield") + BB = self.col("BB textfield") + t = torch.sqrt(torch.clamp_min(CS**2 - BB**2, 0.0)) + self.cache[k] = t + return t + + # Unified front-axle location (relative to BB) + def front_axle_xy(self): + """ + Returns (FWX, FBBD), where: + - FBBD: front axle Y relative to BB (aka BB vertical offset to FW axle) + - FWX : front axle X relative to BB + Construction matches both the down-tube and foot clearance checks. + """ + theta = self.theta_ht + DTJY = self.DTJY + DTJX = self.DTJX + bb_drop = self.col("BB textfield") + wdf = self.col("Wheel diameter front") + wdr = self.col("Wheel diameter rear") + fork0r = self.col("FORK0R") + + FBBD = bb_drop - wdr*0.5 + wdf*0.5 + # Clamp sin(theta) to avoid division by zero for pathological inputs + s = torch.clamp_min(torch.sin(theta), 1e-9) + c = torch.cos(theta) + y = DTJY - FBBD + fork0r * c + L = y / s + x_add = L * c + fork0r * torch.sin(theta) + FWX = DTJX + x_add + return FWX, FBBD + +def construct_tensor_validator(validation_functions: List[ValidationFunction], + column_names: List[str]): + """ + Preflight-check required columns once; build a shared FeatureStore per call; + call each validator with ctx. + """ + column_names = list(column_names) + name_to_idx: Dict[str, int] = {c: i for i, c in enumerate(column_names)} + + # Preflight: ensure all columns exist + for vf in validation_functions: + for col in vf.variable_names(): + if col not in name_to_idx: + raise KeyError(f"Column '{col}' required by '{vf.friendly_name()}' not in provided column_names.") + + all_return_names = [v.friendly_name() for v in validation_functions] + + def validate_tensor(designs: torch.Tensor) -> torch.Tensor: + n = designs.shape[0] + v = len(validation_functions) + out = torch.zeros((n, v), dtype=designs.dtype, device=designs.device) + ctx = FeatureStore(designs, name_to_idx) + for i, vf in enumerate(validation_functions): + res = vf.validate(ctx) + out[:, i] = res.reshape(-1) + return out + + return validate_tensor, all_return_names + + +def construct_dataframe_validator(validation_functions: List[ValidationFunction]): + """ + Constructs a function that applies multiple validation functions to a Pandas DataFrame of designs. + + Parameters: + validation_functions (List[ValidationFunction]): List of validation function instances. + + Returns: + A function that takes a Pandas DataFrame of designs and returns a DataFrame of validation results. + """ + + # First, construct the tensor-based validator (this one doesn't need column mapping) + def validate_dataframe(designs: pd.DataFrame) -> pd.DataFrame: + """ + Converts the DataFrame to a tensor, applies validation, and converts the result back to a DataFrame. + + Parameters: + designs (pd.DataFrame): A DataFrame where each row represents a design. + + Returns: + pd.DataFrame: A DataFrame of shape (n, v), where: + - Rows correspond to designs (original DataFrame index is preserved). + - Columns correspond to validation function names. + - Values: 1 indicates invalid, 0 indicates valid. + """ + # Convert DataFrame to a PyTorch tensor (float32) + designs_tensor = torch.tensor(designs.to_numpy(), dtype=torch.float32) + + # Use the tensor validator (construct it dynamically based on DataFrame columns) + tensor_validator, all_return_names = construct_tensor_validator(validation_functions, list(designs.columns)) + results_tensor = tensor_validator(designs_tensor) + + # Convert results back to a DataFrame + results_df = pd.DataFrame( + results_tensor.numpy(), # Convert tensor to NumPy + columns=all_return_names, + index=designs.index # Preserve original index + ) + + return results_df + + return validate_dataframe diff --git a/bike_bench_internal/src/bikebench/validation/bike_bench_validation_functions.py b/bike_bench_internal/src/bikebench/validation/bike_bench_validation_functions.py new file mode 100644 index 0000000000000000000000000000000000000000..aa801b7d832aac0362b9e471346ba4bfbdc1a1e2 --- /dev/null +++ b/bike_bench_internal/src/bikebench/validation/bike_bench_validation_functions.py @@ -0,0 +1,571 @@ +from typing import List +import torch +import math +from bikebench.validation.base_validation_function import ValidationFunction, FeatureStore + + +POSITIVE_COLS = ['CS textfield', 'Stack', 'Head angle', + 'Head tube length textfield', 'Seat stay junction0', 'Seat tube length', + 'Seat angle', 'DT Length', 'FORK0R', 'BB diameter', 'ttd', 'dtd', 'csd', + 'ssd', 'Chain stay position on BB', 'SSTopZOFFSET', + 'Head tube upper extension2', 'Seat tube extension2', + 'Head tube lower extension2', 'SEATSTAYbrdgshift', 'CHAINSTAYbrdgshift', + 'SEATSTAYbrdgdia1', 'CHAINSTAYbrdgdia1', 'Dropout spacing', + 'Wall thickness Bottom Bracket', 'Wall thickness Top tube', + 'Wall thickness Head tube', 'Wall thickness Down tube', + 'Wall thickness Chain stay', 'Wall thickness Seat stay', + 'Wall thickness Seat tube', 'Wheel diameter front', 'RDBSD', + 'Wheel diameter rear', 'FDBSD', 'BB length', + 'htd', 'Wheel cut', 'std', 'Number of cogs', + 'Number of chainrings', 'FIRST color R_RGB', + 'FIRST color G_RGB', 'FIRST color B_RGB', 'SPOKES composite front', + 'SPOKES composite rear', 'SBLADEW front', 'SBLADEW rear', 'Seatpost LENGTH'] + +ZERO_IS_VALID_COLS = ['FIRST color R_RGB', + 'FIRST color G_RGB', 'FIRST color B_RGB'] + +class SaddleHeightTooSmall(ValidationFunction): + def friendly_name(self) -> str: return "Saddle height too small" + def variable_names(self) -> List[str]: return ["Saddle height"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + return 100.0 - ctx.col("Saddle height") + + +class SaddleCollidesWithSeatTube(ValidationFunction): + def friendly_name(self) -> str: return "Saddle collides with seat tube" + def variable_names(self) -> List[str]: return ["Saddle height", "Seat tube length"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + return ctx.col("Seat tube length") + 40.0 - ctx.col("Saddle height") + + +class SaddleTooShort(ValidationFunction): + def friendly_name(self) -> str: return "Saddle too short" + def variable_names(self) -> List[str]: return ["Saddle length"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + return 228.0 - ctx.col("Saddle length") + + +class HeadAngleOverLimit(ValidationFunction): + def friendly_name(self) -> str: return "Head angle over limit" + def variable_names(self) -> List[str]: return ["Head angle"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + return ctx.col("Head angle") - 180.0 + + +class SeatAngleOverLimit(ValidationFunction): + def friendly_name(self) -> str: return "Seat angle over limit" + def variable_names(self) -> List[str]: return ["Seat angle"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + return ctx.col("Seat angle") - 180.0 + + +class SeatPostTooShort(ValidationFunction): + def friendly_name(self) -> str: return "Seat post too short" + def variable_names(self) -> List[str]: + return ["Seat tube length", "Seatpost LENGTH", "Saddle height", "Seat angle"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + theta_st = ctx.theta_st + thresh = 55.0 * torch.sin(theta_st) - 10.0 * torch.cos(theta_st) + buffer = 10.0 # 10 mm overlap + return ctx.col("Saddle height") - ( + ctx.col("Seat tube length") + ctx.col("Seatpost LENGTH") + thresh - buffer + ) + + +class SeatPostTooLong(ValidationFunction): + def friendly_name(self) -> str: return "Seat post too long" + def variable_names(self) -> List[str]: + return ["Seatpost LENGTH", "Saddle height", "Seat angle"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + theta_st = ctx.theta_st + thresh = 55.0 * torch.sin(theta_st) - 10.0 * torch.cos(theta_st) + return thresh + ctx.col("Seatpost LENGTH") - ctx.col("Saddle height") + + +class RearWheelInnerDiameterTooSmall(ValidationFunction): + def friendly_name(self) -> str: return "Rear Wheel inner diameter too small" + def variable_names(self) -> List[str]: return ["Wheel diameter rear", "RDBSD"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + inner_d = ctx.col("Wheel diameter rear") - 2.0 * ctx.col("RDBSD") + return 140.0 - inner_d + + +class FrontWheelInnerDiameterTooSmall(ValidationFunction): + def friendly_name(self) -> str: return "Front Wheel inner diameter too small" + def variable_names(self) -> List[str]: return ["Wheel diameter front", "FDBSD"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + inner_d = ctx.col("Wheel diameter front") - 2.0 * ctx.col("FDBSD") + return 140.0 - inner_d + + +class SeatTubeExtensionLongerThanSeatTube(ValidationFunction): + def friendly_name(self) -> str: return "Seat tube extension longer than seat tube" + def variable_names(self) -> List[str]: return ["Seat tube length", "Seat tube extension2"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + return ctx.col("Seat tube extension2") - ctx.col("Seat tube length") + + +class HeadTubeUpperExtensionAndLowerExtensionOverlap(ValidationFunction): + def friendly_name(self) -> str: return "Head tube upper extension and lower extension overlap" + def variable_names(self) -> List[str]: + return ["Head tube length textfield", "Head tube upper extension2", "Head tube lower extension2"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + return (ctx.col("Head tube upper extension2") + ctx.col("Head tube lower extension2")) - ctx.col("Head tube length textfield") + + +class SeatStayJunctionLongerThanSeatTube(ValidationFunction): + def friendly_name(self) -> str: return "Seat stay junction longer than seat tube" + def variable_names(self) -> List[str]: return ["Seat tube length", "Seat stay junction0"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + return ctx.col("Seat stay junction0") - ctx.col("Seat tube length") + + +class NonNegativeParameterIsNegative(ValidationFunction): + def friendly_name(self) -> str: return "Non-negative parameter is negative" + def variable_names(self) -> List[str]: return POSITIVE_COLS + def validate(self, ctx: 'FeatureStore') -> torch.Tensor: + X = torch.stack([ctx.col(c) for c in POSITIVE_COLS], dim=1) # (n, k) + zero_ok = set(ZERO_IS_VALID_COLS) + eps_vec = torch.tensor( + [0.0 if c in zero_ok else 1e-9 for c in POSITIVE_COLS], + dtype=X.dtype, device=X.device + ) + margins = eps_vec - X + return margins.sum(dim=1) + + +class ChainStaySmallerThanRearWheelRadius(ValidationFunction): + def friendly_name(self) -> str: return "Chain stay smaller than rear wheel radius" + def variable_names(self) -> List[str]: return ["CS textfield", "Wheel diameter rear"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + return (ctx.col("Wheel diameter rear") * 0.5) - ctx.col("CS textfield") + + +class ChainStayShorterThanBBDrop(ValidationFunction): + def friendly_name(self) -> str: return "Chain stay shorter than BB drop" + def variable_names(self) -> List[str]: return ["CS textfield", "BB textfield"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + return ctx.col("BB textfield") - ctx.col("CS textfield") + + +class SeatStaySmallerThanRearWheelRadius(ValidationFunction): + def friendly_name(self) -> str: return "Seat stay smaller than rear wheel radius" + def variable_names(self) -> List[str]: + return ["CS textfield", "BB textfield","Seat tube length", "Seat stay junction0", "Seat angle", "Wheel diameter rear"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + CS = ctx.col("CS textfield") + BB = ctx.col("BB textfield") + stl = ctx.col("Seat tube length") + ssj0 = ctx.col("Seat stay junction0") + theta = ctx.theta_st + z = ctx.z_bb # sqrt(clamp_min(..., 0.0)) + x = stl - (BB/torch.sin(theta)) - ssj0 + y = BB/torch.tan(theta) + h = z - y + g = torch.sqrt(torch.clamp_min(h**2 + x**2 - 2*h*x*torch.cos(theta), 0.0)) + return (ctx.col("Wheel diameter rear") * 0.5) - g + + +class SeatTubeIntersectsRearWheel(ValidationFunction): + def friendly_name(self) -> str: + return "Seat Tube Intersects Rear Wheel" + + def variable_names(self) -> List[str]: + return [ + "CS textfield", "BB textfield", "Seat tube length", "Seat stay junction0", + "Seat angle", "std", + "Seat tube type OHCLASS: 0", "Seat tube type OHCLASS: 1", "Seat tube type OHCLASS: 2", + "Wheel cut", "Wheel diameter rear", + ] + + def validate(self, ctx: FeatureStore) -> torch.Tensor: + BB = ctx.col("BB textfield") + stl = ctx.col("Seat tube length") + ssj0 = ctx.col("Seat stay junction0") + theta = ctx.theta_st + z = ctx.z_bb + ST_OD = ctx.col("std") + WDR = ctx.col("Wheel diameter rear") + wheel_cut = ctx.col("Wheel cut") + + # continuous one-hot → aero if OHCLASS:0 is strictly the max + st0 = ctx.col("Seat tube type OHCLASS: 0") + st1 = ctx.col("Seat tube type OHCLASS: 1") + st2 = ctx.col("Seat tube type OHCLASS: 2") + aero_mask = (st0 > st1) & (st0 > st2) + + x = stl - (BB / torch.sin(theta)) - ssj0 + y = BB / torch.tan(theta) + h = z - y + j = h * torch.sin(theta) + + q_true = torch.where(wheel_cut < WDR, j - 40.9, (WDR * 0.5) + ((wheel_cut - WDR) * 0.5)) + q_false = j - (ST_OD * 0.5) + q = torch.where(aero_mask, q_true, q_false) + return (WDR * 0.5) - q + + +class DownTubeCantReachHeadTube(ValidationFunction): + def friendly_name(self) -> str: return "Down tube can't reach head tube" + def variable_names(self) -> List[str]: + return ["Stack", "Head tube length textfield", "Head tube lower extension2", "Head angle", "DT Length"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + return ctx.DTJY - ctx.col("DT Length") + + +class RearWheelCutoutSeversSeatTube(ValidationFunction): + def friendly_name(self) -> str: + return "Rear wheel cutout severs seat tube" + + def variable_names(self) -> List[str]: + return [ + "CS textfield", "BB textfield", "Seat tube length", "Seat stay junction0", + "Seat angle", + "Seat tube type OHCLASS: 0", "Seat tube type OHCLASS: 1", "Seat tube type OHCLASS: 2", + "Wheel cut", + ] + + def validate(self, ctx: FeatureStore) -> torch.Tensor: + BB = ctx.col("BB textfield") + stl = ctx.col("Seat tube length") + ssj0 = ctx.col("Seat stay junction0") + theta = ctx.theta_st + z = ctx.z_bb + wheel_cut = ctx.col("Wheel cut") + + # continuous one-hot → aero if OHCLASS:0 is strictly the max + st0 = ctx.col("Seat tube type OHCLASS: 0") + st1 = ctx.col("Seat tube type OHCLASS: 1") + st2 = ctx.col("Seat tube type OHCLASS: 2") + aero_mask = (st0 > st1) & (st0 > st2) + + x = stl - (BB / torch.sin(theta)) - ssj0 + y = BB / torch.tan(theta) + h = z - y + j = h * torch.sin(theta) + + q_true = j + 16.0 + q_false = torch.full_like(q_true, 1e9) # effectively disables this path when not aero + q = torch.where(aero_mask, q_true, q_false) + return (wheel_cut * 0.5) - q + +class FootIntersectsFrontWheel(ValidationFunction): + def friendly_name(self) -> str: return "Foot intersects front wheel" + def variable_names(self) -> List[str]: + return ["Stack", "Head tube length textfield", "Head tube lower extension2", "Head angle", + "BB textfield", "DT Length", "FORK0R", "Wheel diameter front", "Wheel diameter rear"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + FWX, FBBD = ctx.front_axle_xy() + FCD = torch.sqrt(torch.clamp_min(FWX**2 + FBBD**2, 0.0)) + wheel_radius = ctx.col("Wheel diameter front") * 0.5 + crank_plus_foot = 268.5 + pedal_center_offset = 120.0 + return (wheel_radius**2) - (pedal_center_offset**2) - (FCD - crank_plus_foot)**2 + + +class CrankHitsGroundInLowestPosition(ValidationFunction): + def friendly_name(self) -> str: return "Crank hits ground in lowest position" + def variable_names(self) -> List[str]: return ["BB textfield", "Wheel diameter rear"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + return (187.5 + ctx.col("BB textfield")) - (ctx.col("Wheel diameter rear") * 0.5) + + +class RGBvalueGreaterThan255(ValidationFunction): + def friendly_name(self) -> str: return "RGB value greater than 255" + def variable_names(self) -> List[str]: + return ["FIRST color R_RGB", "FIRST color G_RGB", "FIRST color B_RGB"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + X = torch.stack([ctx.col("FIRST color R_RGB"), + ctx.col("FIRST color G_RGB"), + ctx.col("FIRST color B_RGB")], dim=1) + overflow = X - 255.0 + total = torch.clamp_min(overflow, 0.0).sum(dim=1) + fallback = overflow.sum(dim=1) + return torch.where(total > 0, total, fallback) + + +class ChainStaysIntersect(ValidationFunction): + def friendly_name(self) -> str: return "Chain stays intersect" + def variable_names(self) -> List[str]: return ["csd", "Chain stay position on BB","BB length"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + return (ctx.col("csd")*0.5 + ctx.col("Chain stay position on BB")) - (ctx.col("BB length")*0.5) + + +class TubeWallThicknessExceedsRadius(ValidationFunction): + def friendly_name(self) -> str: return "Tube wall thickness exceeds radius" + def variable_names(self) -> List[str]: + return [ + "ttd", "Wall thickness Top tube", + "csd", "Wall thickness Chain stay", + "ssd", "Wall thickness Seat stay", + "dtd", "Wall thickness Down tube", + "htd", "Wall thickness Head tube", + "BB diameter", "Wall thickness Bottom Bracket", + ] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + cols = [ctx.col(n) for n in self.variable_names()] + vals = torch.stack(cols, dim=1) + pairs = vals.reshape(vals.shape[0], -1, 2) # (n,7,2) + dia = pairs[:, :, 0] + thk = pairs[:, :, 1] + violation = thk - dia*0.5 + return torch.sum(torch.clamp_min(violation, 0.0), dim=1) + + +class SeatTubeInnerDiameterThinnerThanSeatPostOuterDiameter(ValidationFunction): + def friendly_name(self) -> str: return "Seat tube inner diameter thinner than seat post outer diameter" + def variable_names(self) -> List[str]: return ["std", "Wall thickness Seat tube"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + inner_d = ctx.col("std") - ctx.col("Wall thickness Seat tube") + return 27.2 - inner_d + + +class DownTubeImproperlyJoinsHeadTube(ValidationFunction): + def friendly_name(self) -> str: return "Down tube improperly joins head tube" + def variable_names(self) -> List[str]: + return ["Stack", "Head tube length textfield", "Head tube lower extension2", "Head angle", + "DT Length", "dtd", "htd"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + penalty_mag = 1e6 + theta_ht = ctx.theta_ht + DTJY = ctx.DTJY + DTJX = ctx.DTJX + DT_OD = ctx.col("dtd") + HT_OD = ctx.col("htd") + htlx = ctx.col("Head tube lower extension2") + + theta_dt = torch.atan2(DTJY, DTJX) + relative_angle = math.pi - (theta_dt + theta_ht) + + below_0_pen = torch.clamp_min(-relative_angle, 0.0) * penalty_mag + above_pi_pen = torch.clamp_min(relative_angle - math.pi, 0.0) * penalty_mag + relative_angle = torch.clamp(relative_angle, 1e-9, math.pi - 1e-9) + + L1 = DT_OD / (2.0 * torch.sin(relative_angle)) + L2 = HT_OD / (2.0 * torch.tan(relative_angle)) + return L1 + L2 - htlx + below_0_pen + above_pi_pen + + +class TopTubeImproperlyJoinsHeadTube(ValidationFunction): + def friendly_name(self) -> str: return "Top tube improperly joins head tube" + def variable_names(self) -> List[str]: + return ["Stack", "Head tube length textfield", "Head tube lower extension2", "Head tube upper extension2", + "Seat tube extension2", "Head angle", "Seat angle", "DT Length", "ttd", "htd","Seat tube length"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + penalty_mag = 1e6 + theta_ht = ctx.theta_ht + theta_st = ctx.theta_st + TTJX, TTJY = ctx.TTJX, ctx.TTJY + STJX, STJY = ctx.STJX, ctx.STJY + TT_OD = ctx.col("ttd") + HT_OD = ctx.col("htd") + htux = ctx.col("Head tube upper extension2") + + tt_dy = TTJY - STJY + tt_dx = TTJX + STJX + theta_tt = torch.atan2(tt_dy, tt_dx) + + relative_angle = theta_tt + theta_ht + below_0_pen = torch.clamp_min(-relative_angle, 0.0) * penalty_mag + above_pi_pen = torch.clamp_min(relative_angle - math.pi, 0.0) * penalty_mag + relative_angle = torch.clamp(relative_angle, 1e-9, math.pi - 1e-9) + + L1 = TT_OD/(2.0*torch.sin(relative_angle)) + L2 = HT_OD/(2.0*torch.tan(relative_angle)) + return L1 + L2 - htux + below_0_pen + above_pi_pen + + +class TopTubeImproperlyJoinsSeatTube(ValidationFunction): + def friendly_name(self) -> str: return "Top tube improperly joins seat tube" + def variable_names(self) -> List[str]: + return ["Stack", "Head tube length textfield", "Head tube lower extension2", "Head tube upper extension2", + "Seat tube extension2", "Head angle", "Seat angle", "DT Length", "ttd", "std","Seat tube length"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + penalty_mag = 1e6 + theta_ht = ctx.theta_ht + theta_st = ctx.theta_st + TTJX, TTJY = ctx.TTJX, ctx.TTJY + STJX, STJY = ctx.STJX, ctx.STJY + TT_OD = ctx.col("ttd") + ST_OD = ctx.col("std") + stux = ctx.col("Seat tube extension2") + + tt_dy = TTJY - STJY + tt_dx = TTJX + STJX + theta_tt = torch.atan2(tt_dy, tt_dx) + + relative_angle = math.pi - (theta_tt + theta_st) + below_0_pen = torch.clamp_min(-relative_angle, 0.0) * penalty_mag + above_pi_pen = torch.clamp_min(relative_angle - math.pi, 0.0) * penalty_mag + relative_angle = torch.clamp(relative_angle, 1e-9, math.pi - 1e-9) + + L1 = TT_OD/(2.0*torch.sin(relative_angle)) + L2 = ST_OD/(2.0*torch.tan(relative_angle)) + seatpost_clamp_default_offset = 12.0 + return L1 + L2 - stux + below_0_pen + above_pi_pen + seatpost_clamp_default_offset + + +class DownTubeIntersectsFrontWheel(ValidationFunction): + def friendly_name(self) -> str: return "Down tube intersects front wheel" + def variable_names(self) -> List[str]: + return ["Stack", "Head tube length textfield", "Head tube lower extension2", "Head angle", + "DT Length", "BB textfield", "FORK0R", "Wheel diameter front", "Wheel diameter rear", "dtd"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + FWX, FBBD = ctx.front_axle_xy() + DTJY, DTJX = ctx.DTJY, ctx.DTJX + + DTJ_angle = torch.atan2(DTJY, DTJX) + FW_angle = torch.atan2(FBBD, FWX) + DTJBBFW_angle = DTJ_angle - FW_angle + + FW_dist = torch.sqrt(torch.clamp_min(FWX**2 + FBBD**2, 0.0)) + shortest_dist = torch.sin(DTJBBFW_angle) * FW_dist + + wheel_radius = ctx.col("Wheel diameter front") * 0.5 + tube_radius = ctx.col("dtd") * 0.5 + return wheel_radius - (shortest_dist - tube_radius) + + +class SaddleHitsTopTube(ValidationFunction): + def friendly_name(self) -> str: return "Saddle hits top tube" + def variable_names(self) -> List[str]: + return ["Stack", "Head tube length textfield", "Head tube lower extension2", "Head tube upper extension2", + "Seat tube extension2", "Head angle", "Seat angle", "DT Length", "ttd", "std", + "Seat tube length", "Saddle height"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + theta_ht = ctx.theta_ht + theta_st = ctx.theta_st + TTJX, TTJY = ctx.TTJX, ctx.TTJY + STJX, STJY = ctx.STJX, ctx.STJY + TT_OD = ctx.col("ttd") + SH = ctx.col("Saddle height") + + tt_dy = TTJY - STJY + tt_dx = TTJX + STJX + theta_tt = torch.atan2(tt_dy, tt_dx) + + # Small-angle approx (preserved semantics) + arc = 21.0 - 55.0*torch.cos(theta_st) + 10.0*torch.sin(theta_st) + radius = SH - 55.0*torch.sin(theta_st) - 10.0*torch.cos(theta_st) + SH_angle = theta_st - arc*torch.sin(theta_st)/(radius + 1e-12) + + Saddle_Y = SH * torch.sin(SH_angle) + Saddle_X = SH * torch.cos(SH_angle) + Saddle_tip_X = Saddle_X - 165.0 + Saddle_tip_Y = Saddle_Y - 22.0 + + Saddle_to_STJ_X = -(Saddle_tip_X - STJX) + Saddle_to_STJ_Y = (Saddle_tip_Y - STJY) + + Saddle_toSTJ_X_adj = Saddle_to_STJ_X - TT_OD*0.5 * torch.cos(theta_tt + math.pi/2) + Saddle_toSTJ_Y_adj = Saddle_to_STJ_Y - TT_OD*0.5 * torch.sin(theta_tt + math.pi/2) + + Saddle_to_STJ_angle = torch.atan2(Saddle_toSTJ_Y_adj, Saddle_toSTJ_X_adj) + return theta_tt - Saddle_to_STJ_angle + + +class SaddleHitsHeadTube(ValidationFunction): + def friendly_name(self) -> str: return "Saddle hits head tube" + def variable_names(self) -> List[str]: + return ["Stack", "Head tube length textfield", "Head tube lower extension2", "Head tube upper extension2", + "Seat tube extension2", "Head angle", "Seat angle", "DT Length", "ttd", "htd", + "Seat tube length", "Saddle height"] + def validate(self, ctx: FeatureStore) -> torch.Tensor: + theta_ht = ctx.theta_ht + theta_st = ctx.theta_st + DTJY = ctx.DTJY + DTJX = ctx.DTJX + HT_OD = ctx.col("htd") + SH = ctx.col("Saddle height") + stack = ctx.col("Stack") + + arc = 21.0 - 55.0*torch.cos(theta_st) + 10.0*torch.sin(theta_st) + radius = SH - 55.0*torch.sin(theta_st) - 10.0*torch.cos(theta_st) + SH_angle = theta_st - arc*torch.sin(theta_st)/(radius + 1e-12) + + Saddle_Y = SH * torch.sin(SH_angle) + Saddle_X = SH * torch.cos(SH_angle) + Saddle_tip_X = Saddle_X - 165.0 + Saddle_tip_Y = Saddle_Y - 22.0 + + Saddle_to_STJ_X = (-Saddle_tip_X - DTJX) + Saddle_to_STJ_Y = (Saddle_tip_Y - DTJY) + + mask = (Saddle_tip_Y > stack).to(Saddle_to_STJ_X.dtype) + critical_thickness = (HT_OD*0.5) * (1.0 - mask) + 19.05 * mask + + Saddle_toSTJ_X_adj = Saddle_to_STJ_X + critical_thickness * torch.cos(theta_ht - math.pi/2) + Saddle_toSTJ_Y_adj = Saddle_to_STJ_Y - critical_thickness * torch.sin(theta_ht - math.pi/2) + + Saddle_to_STJ_angle = torch.atan2(Saddle_toSTJ_Y_adj, -Saddle_toSTJ_X_adj) + return Saddle_to_STJ_angle - theta_ht + + + +bike_bench_validation_functions: List[ValidationFunction] = [ + SaddleHeightTooSmall(), + SaddleCollidesWithSeatTube(), + SaddleTooShort(), + HeadAngleOverLimit(), + SeatAngleOverLimit(), + SeatPostTooShort(), + SeatPostTooLong(), + RearWheelInnerDiameterTooSmall(), + FrontWheelInnerDiameterTooSmall(), + SeatTubeExtensionLongerThanSeatTube(), + HeadTubeUpperExtensionAndLowerExtensionOverlap(), + SeatStayJunctionLongerThanSeatTube(), + NonNegativeParameterIsNegative(), + ChainStaySmallerThanRearWheelRadius(), + ChainStayShorterThanBBDrop(), + SeatStaySmallerThanRearWheelRadius(), + SeatTubeIntersectsRearWheel(), + DownTubeCantReachHeadTube(), + RearWheelCutoutSeversSeatTube(), + FootIntersectsFrontWheel(), + CrankHitsGroundInLowestPosition(), + RGBvalueGreaterThan255(), + ChainStaysIntersect(), + TubeWallThicknessExceedsRadius(), + SeatTubeInnerDiameterThinnerThanSeatPostOuterDiameter(), + DownTubeImproperlyJoinsHeadTube(), + TopTubeImproperlyJoinsHeadTube(), + TopTubeImproperlyJoinsSeatTube(), + DownTubeIntersectsFrontWheel(), + SaddleHitsTopTube(), + SaddleHitsHeadTube(), +] + +difficult_validation_functions: List[ValidationFunction] = [ + # SaddleHeightTooSmall(), + # SaddleCollidesWithSeatTube(), + SaddleTooShort(), + # HeadAngleOverLimit(), + # SeatAngleOverLimit(), + # SeatPostTooShort(), + # SeatPostTooLong(), + # RearWheelInnerDiameterTooSmall(), + # FrontWheelInnerDiameterTooSmall(), + # SeatTubeExtensionLongerThanSeatTube(), + # HeadTubeUpperExtensionAndLowerExtensionOverlap(), + # SeatStayJunctionLongerThanSeatTube(), + # NonNegativeParameterIsNegative(), + # ChainStaySmallerThanRearWheelRadius(), + # ChainStayShorterThanBBDrop(), + # SeatStaySmallerThanRearWheelRadius(), + SeatTubeIntersectsRearWheel(), + # DownTubeCantReachHeadTube(), + # RearWheelCutoutSeversSeatTube(), + FootIntersectsFrontWheel(), + # CrankHitsGroundInLowestPosition(), + # RGBvalueGreaterThan255(), + # ChainStaysIntersect(), + # TubeWallThicknessExceedsRadius(), + SeatTubeInnerDiameterThinnerThanSeatPostOuterDiameter(), + DownTubeImproperlyJoinsHeadTube(), + # TopTubeImproperlyJoinsHeadTube(), + TopTubeImproperlyJoinsSeatTube(), + # DownTubeIntersectsFrontWheel(), + # SaddleHitsTopTube(), + # SaddleHitsHeadTube(), +] diff --git a/bike_bench_internal/src/bikebench/xml_handling/__init__.py b/bike_bench_internal/src/bikebench/xml_handling/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/bike_bench_internal/src/bikebench/xml_handling/algebraic_parser.py b/bike_bench_internal/src/bikebench/xml_handling/algebraic_parser.py new file mode 100644 index 0000000000000000000000000000000000000000..2aa7681c0f1664c77a6dd73892d8900c1acf8603 --- /dev/null +++ b/bike_bench_internal/src/bikebench/xml_handling/algebraic_parser.py @@ -0,0 +1,30 @@ +from typing import Union + + +class AlgebraicParser: + """Parses booleans and floats. Represents booleans as the floating point values 0 and 1.""" + + def attempt_parse(self, value) -> Union[float, str]: + """Attempts to parse a string value (converts the value to string defensively). Returns + the string value stripped in case it fails to parse it. None values map to an empty string. + """ + if value is None: + return "" + return self._parse_value(str(value).strip()) + + def _parse_value(self, value: str) -> Union[float, str]: + if self._is_bool(value.lower()): + return float(value.lower() == "true") + if self._is_float(value): + return float(value) + return value.strip() + + def _is_float(self, value: str) -> bool: + try: + float(value) + return True + except ValueError: + return False + + def _is_bool(self, value: str) -> bool: + return value in ["true", "false"] diff --git a/bike_bench_internal/src/bikebench/xml_handling/bcad_to_bikebench.py b/bike_bench_internal/src/bikebench/xml_handling/bcad_to_bikebench.py new file mode 100644 index 0000000000000000000000000000000000000000..828f54b6d2b01f92d569a697deadd5d00ccdb10e --- /dev/null +++ b/bike_bench_internal/src/bikebench/xml_handling/bcad_to_bikebench.py @@ -0,0 +1,607 @@ +# -*- coding: utf-8 -*- + +import re +from pathlib import Path +import xml.etree.ElementTree as ET + +import numpy as np +import pandas as pd +from tqdm.auto import tqdm +from bikebench.data_loading import data_loading + +# ========================= +# XML parsing (lean & safe) +# ========================= +_NUM_INT_RE = re.compile(r'^[+-]?\d+$') +_NUM_FLOAT_RE = re.compile(r'^[+-]?(?:\d+\.?\d*|\.\d+)(?:[eE][+-]?\d+)?$') + +def _cast(value): + """Cast XML entry text to bool/int/float when possible; else keep string.""" + if value is None: + return np.nan + s = str(value).strip() + lo = s.lower() + if lo == "true": return True + if lo == "false": return False + if _NUM_INT_RE.match(s): + try: return int(s) + except Exception: pass + if _NUM_FLOAT_RE.match(s): + try: return float(s) + except Exception: pass + return s + +def parse_bcad_file(path: Path) -> dict: + tree = ET.parse(data_loading.load_biked_original_bcad(path)) + root = tree.getroot() + out = {} + for entry in root.findall("entry"): + key = entry.get("key") + if key is None: + continue + out[key] = _cast(entry.text) + return out + +# ================================== +# Legacy prefill (compact & robust) +# ================================== +def prefill_old_bcad_fields(df: pd.DataFrame) -> pd.DataFrame: + """Fill missing tube sub/main diameters row-wise to support older BCAD files.""" + df = df.copy() + + TT_SUB = ["Top tube rear diameter","Top tube rear dia2","Top tube front diameter","Top tube front dia2"] + TT_MAIN = ["Top tube diameter"] + DT_SUB = ["Down tube rear diameter","Down tube rear dia2","Down tube front diameter","Down tube front dia2"] + DT_MAIN = ["Down tube diameter","Down tube aero diameter"] + ST_SUB = ["Seat tube rear diameter","Seat tube rear dia2","Seat tube front diameter","Seat tube front dia2"] + ST_MAIN = ["Seat tube diameter","Seat tube aero diameter"] + HT_MAIN = ["Head tube diameter","Head tube aero diameter"] + CHAINSTAY_MAIN = ["Chain stay back diameter","Chain stay vertical diameter","Chain stay horizontal diameter","CHAINSTAYAUXrearDIAMETER"] + SEATSTAY_MAIN = ["Seat stay bottom diameter","SEATSTAY_HR","SEATSTAY_VF","SEATSTAY_HF"] + + for col in TT_SUB + TT_MAIN + DT_SUB + DT_MAIN + ST_SUB + ST_MAIN + HT_MAIN + CHAINSTAY_MAIN + SEATSTAY_MAIN: + if col not in df.columns: + df[col] = np.nan + + def _isnum(v): + return isinstance(v, (int,float,np.integer,np.floating)) and not pd.isna(v) + + def _fill_group(r, subs, mains): + sub_vals = [r[c] for c in subs if _isnum(r.get(c))] + sub_mean = float(np.mean(sub_vals)) if sub_vals else np.nan + main_vals= [r[c] for c in mains if _isnum(r.get(c))] + main_fb = float(main_vals[0]) if main_vals else np.nan + + for c in subs: + if pd.isna(r[c]): + v = sub_mean if not np.isnan(sub_mean) else main_fb + if not np.isnan(v): r[c] = v + sub_vals = [r[c] for c in subs if _isnum(r.get(c))] + sub_mean = float(np.mean(sub_vals)) if sub_vals else sub_mean + for c in mains: + if pd.isna(r[c]): + v = sub_mean if not np.isnan(sub_mean) else main_fb + if not np.isnan(v): r[c] = v + return r + + def _fill_head_tube(r): + d, a = r.get("Head tube diameter"), r.get("Head tube aero diameter") + if pd.isna(d) and _isnum(a): r["Head tube diameter"] = float(a) + if pd.isna(a) and _isnum(d): r["Head tube aero diameter"] = float(d) + return r + + df = df.apply(lambda r: _fill_group(r, TT_SUB, TT_MAIN), axis=1) + df = df.apply(lambda r: _fill_group(r, DT_SUB, DT_MAIN), axis=1) + df = df.apply(lambda r: _fill_group(r, ST_SUB, ST_MAIN), axis=1) + df = df.apply(_fill_head_tube, axis=1) + return df + +def drop_designs_with_xml_features( + df: pd.DataFrame, + substrings=("TNDM", "EXTRATUBE"), + *, + drop_feature_columns: bool = True, + export_dropped_path: str | None = None, + verbose: bool = True, +) -> pd.DataFrame: + """ + Remove designs whose *original XML* contains any columns with names that include + the given substrings and have non-null values. + + - substrings: tuple of name fragments to match (case-sensitive, like your code). + - drop_feature_columns: if True, remove those matched columns from the kept df. + - export_dropped_path: optional CSV path to save the dropped designs (with feature + columns removed, matching your old behavior). + """ + if df.empty: + return df + + # Find columns whose names contain any of the substrings + match_cols = [c for c in df.columns if any(sub in c for sub in substrings)] + if not match_cols: + if verbose: + print(f"No feature columns matched {substrings}; nothing to drop.") + return df + + # Rows to drop: any non-null in any matched column + has_feature = df[match_cols].notna().any(axis=1) + n_drop = int(has_feature.sum()) + + if n_drop == 0: + # Optionally still drop the feature columns to keep the frame clean + out = df.drop(columns=match_cols, errors="ignore") if drop_feature_columns else df.copy() + if verbose: + print(f"Matched {len(match_cols)} feature column(s), but no rows had values to drop.") + return out + + # Build kept and dropped sets + kept = df.loc[~has_feature].copy() + dropped = df.loc[has_feature].copy() + + # Remove the feature columns if requested + if drop_feature_columns: + kept.drop(columns=match_cols, inplace=True, errors="ignore") + dropped.drop(columns=match_cols, inplace=True, errors="ignore") + + # Optional export of the dropped designs (like your old function) + if export_dropped_path is not None: + dropped.to_csv(export_dropped_path, index=True) + + if verbose: + print(f"Dropped {n_drop} design(s) due to features in {substrings}. Kept {len(kept)}.") + + return kept + +# ========================= +# Conversions & derivation +# ========================= +def _safe_mean(vals): + xs = [float(v) for v in vals if isinstance(v, (int,float,np.integer,np.floating)) and not pd.isna(v)] + return float(np.mean(xs)) if xs else np.nan + +def _getf(row, key): + v = row.get(key, np.nan) + return float(v) if isinstance(v, (int,float,np.integer,np.floating)) and not pd.isna(v) else np.nan + +def convert_bike_bench(df: pd.DataFrame) -> pd.DataFrame: + df = df.copy() + + # Wheel deltas (ensure sources exist) + for c in ["Wheel diameter rear","Wheel diameter front","ERD rear","ERD front","BSD rear","BSD front"]: + if c not in df.columns: df[c] = np.nan + df["RDERD"] = df["Wheel diameter rear"] - df["ERD rear"] + df["FDERD"] = df["Wheel diameter front"] - df["ERD front"] + df["RDBSD"] = df["Wheel diameter rear"] - df["BSD rear"] + df["FDBSD"] = df["Wheel diameter front"] - df["BSD front"] + df.drop(columns=["ERD rear","ERD front","BSD rear","BSD front"], inplace=True, errors="ignore") + + # Row-wise geometry & tube diameters + def _row_calc(row): + out = {} + + # DT Length & Stack + BBD = _getf(row,"BB textfield") + FCD = _getf(row,"FCD textfield") + WDR = _getf(row,"Wheel diameter rear") + WDF = _getf(row,"Wheel diameter front") + x = _getf(row,"FORK0R") + fkl = _getf(row,"FORK0L") + htlx = _getf(row,"Head tube lower extension2") + lsth = _getf(row,"Lower stack height") + ha = _getf(row,"Head angle") + + if not any(pd.isna([BBD,FCD,WDR,WDF,ha])): + FTY = BBD - WDR/2.0 + WDF/2.0 + FTX_sq = FCD**2 - FTY**2 + FTX = np.sqrt(max(FTX_sq, 0.0)) + y = (fkl or 0.0) + (htlx or 0.0) + (lsth or 0.0) + ha_rad = np.deg2rad(ha) + dtx = FTX - y*np.cos(ha_rad) - (x or 0.0)*np.sin(ha_rad) + dty = FTY + y*np.sin(ha_rad) + (x or 0.0)*np.cos(ha_rad) + out["DT Length"] = float(np.sqrt(dtx**2 + dty**2)) + htl = _getf(row,"Head tube length textfield") + stack_y = (fkl or 0.0) + (lsth or 0.0) + (htl or 0.0) + out["Stack"] = float(FTY + stack_y*np.sin(ha_rad) + (x or 0.0)*np.cos(ha_rad)) + + # Average diameters + out["csd"] = _safe_mean([row.get("Chain stay back diameter"), row.get("Chain stay vertical diameter"), row.get("Chain stay horizontal diameter"), row.get("CHAINSTAYAUXrearDIAMETER")]) + out["ssd"] = _safe_mean([row.get("Seat stay bottom diameter"), row.get("SEATSTAY_HR"), row.get("SEATSTAY_VF"), row.get("SEATSTAY_HF")]) + + # Top tube diameter + tt_type = int(row.get("Top tube type", 1)) if pd.notna(row.get("Top tube type", np.nan)) else 1 + if tt_type == 1: + out["ttd"] = _safe_mean([ + row.get("Top tube rear diameter"), row.get("Top tube rear dia2"), + row.get("Top tube front diameter"), row.get("Top tube front dia2"), + ]) + else: + out["ttd"] = row.get("Top tube diameter", np.nan) + + # Down tube diameter + dt_type = int(row.get("Down tube type", 1)) if pd.notna(row.get("Down tube type", np.nan)) else 1 + if dt_type == 2: + out["dtd"] = _safe_mean([ + row.get("Down tube rear diameter"), row.get("Down tube rear dia2"), + row.get("Down tube front diameter"), row.get("Down tube front dia2"), + ]) + elif dt_type == 0: + out["dtd"] = row.get("Down tube aero diameter", np.nan) + else: + out["dtd"] = row.get("Down tube diameter", np.nan) + + # Seat tube diameter + st_type = int(row.get("Seat tube type", 1)) if pd.notna(row.get("Seat tube type", np.nan)) else 1 + if st_type == 2: + out["std"] = _safe_mean([ + row.get("Seat tube rear diameter"), row.get("Seat tube rear dia2"), + row.get("Seat tube front diameter"), row.get("Seat tube front dia2"), + ]) + elif st_type == 0: + out["std"] = row.get("Seat tube aero diameter", np.nan) + else: + out["std"] = row.get("Seat tube diameter", np.nan) + + # Head tube diameter (aero vs round) + ht_type = int(row.get("Head tube type", 1)) if pd.notna(row.get("Head tube type", np.nan)) else 1 + out["htd"] = row.get("Head tube aero diameter", np.nan) if ht_type == 0 else row.get("Head tube diameter", np.nan) + + # Fixed wall thickness values + out["Wall thickness Bottom Bracket"] = 2.0 + out["Wall thickness Head tube"] = 1.1 + + return pd.Series(out) + + derived = df.apply(_row_calc, axis=1) + for c in derived.columns: + df[c] = derived[c] + + if "Seat stay mount location" in df.columns: + m = pd.to_numeric(df["Seat stay mount location"], errors="coerce") + df = df.loc[m.isna() | m.isin([0, 1, 5])] # drop 2,3,...; keep NaN if present + m = pd.to_numeric(df["Seat stay mount location"], errors="coerce") # re-eval after filter + df.loc[m.isin([1, 5]), "Seat stay junction0"] = ( + pd.to_numeric(df.loc[m == 1, "Seat stay junction0"], errors="coerce") + + pd.to_numeric(df.loc[m == 1, "Seat tube extension2"], errors="coerce") + - pd.to_numeric(df.loc[m == 1, "ttd"], errors="coerce") / 2.0 + ) + + # Expand any "*sRGB" into R/G/B + for col in list(df.columns): + if isinstance(col, str) and col.endswith("sRGB"): + vals = pd.to_numeric(df[col], errors="coerce").fillna(0).astype(np.int64) + 2**24 + base = col[:-4] + df[f"{base}R_RGB"] = (vals % 2**24) // 2**16 + df[f"{base}G_RGB"] = (vals % 2**16) // 2**8 + df[f"{base}B_RGB"] = vals % 2**8 + df.drop(columns=[col], inplace=True) + + return df + + + +TYPE_SPEC = { + "Seatpost LENGTH": "float", + "CS textfield": "float", + "BB textfield": "float", + "Stack": "float", + "Head angle": "float", + "Head tube length textfield": "float", + "Seat stay junction0": "float", + "Seat tube length": "float", + "Seat angle": "float", + "DT Length": "float", + "FORK0R": "float", + "BB diameter": "float", + "ttd": "float", + "dtd": "float", + "csd": "float", + "std": "float", + "htd": "float", + "ssd": "float", + "Chain stay position on BB": "float", + "SSTopZOFFSET": "float", + "MATERIAL": "cat", + "Head tube upper extension2": "float", + "Seat tube extension2": "float", + "Head tube lower extension2": "float", + "SEATSTAYbrdgshift": "float", + "CHAINSTAYbrdgshift": "float", + "SEATSTAYbrdgdia1": "float", + "CHAINSTAYbrdgdia1": "float", + "SEATSTAYbrdgCheck": "bool", + "CHAINSTAYbrdgCheck": "bool", + "Dropout spacing": "float", + "Wall thickness Bottom Bracket": "float", + "Wall thickness Top tube": "float", + "Wall thickness Head tube": "float", + "Wall thickness Down tube": "float", + "Wall thickness Chain stay": "float", + "Wall thickness Seat stay": "float", + "Wall thickness Seat tube": "float", + "Wheel diameter front": "float", + "RDBSD": "float", + "Wheel diameter rear": "float", + "FDBSD": "float", + "Fork type": "int", + "Stem kind": "int", + "Display AEROBARS": "bool", + "Handlebar style": "cat", + "BB length": "float", + "Wheel cut": "float", + "Front Fender include": "bool", + "Rear Fender include": "bool", + "BELTorCHAIN": "bool", + "Number of cogs": "int", + "Number of chainrings": "int", + "FIRST color R_RGB": "int", + "FIRST color G_RGB": "int", + "FIRST color B_RGB": "int", + "RIM_STYLE front": "cat", + "RIM_STYLE rear": "cat", + "SPOKES composite front": "int", + "SPOKES composite rear": "int", + "SBLADEW front": "float", + "SBLADEW rear": "float", + "Saddle length": "float", + "Saddle height": "float", + "Seat tube type": "int", + "Head tube type": "int", + "Down tube type": "int", +} + +_NAN_LIKE = {"nan","none","null",""} + +def _coerce_nan_like(x): + if x is None: return np.nan + if isinstance(x, float) and np.isnan(x): return np.nan + if isinstance(x, str) and x.strip().lower() in _NAN_LIKE: return np.nan + return x + +def _coerce_column(s: pd.Series, kind: str) -> pd.Series: + if kind == "bool": + if s.dtype == bool: return s + mapping = {"true":True,"t":True,"1":True,"yes":True,"y":True, + "false":False,"f":False,"0":False,"no":False,"n":False} + def conv(v): + if isinstance(v,(bool,np.bool_)): return bool(v) + if isinstance(v,(int,np.integer)) and v in (0,1): return bool(v) + if isinstance(v,str): + key = v.strip().lower() + if key in mapping: return mapping[key] + return np.nan + out = s.map(conv) + mode = out.mode(dropna=True) + fill = bool(mode.iloc[0]) if len(mode) else False + return out.fillna(fill).astype(bool) + + if kind == "int": + coerced = pd.to_numeric(s, errors="coerce") + mode = coerced.mode(dropna=True) + fill = int(mode.iloc[0]) if len(mode) else 0 + coerced = coerced.fillna(fill) + if s.name in {"FIRST color R_RGB","FIRST color G_RGB","FIRST color B_RGB"}: + coerced = coerced.clip(0, 255) + return coerced.round().astype(int) + + if kind == "float": + coerced = pd.to_numeric(s, errors="coerce") + med = coerced.median(skipna=True) + fill = float(med) if not np.isnan(med) else 0.0 + return coerced.fillna(fill).astype(float) + + if kind == "cat": + s = s.map(_coerce_nan_like) + if s.notna().any(): + m = s.mode(dropna=True) + fill = m.iloc[0] if len(m) else "Unknown" + else: + fill = "Unknown" + return s.fillna(fill).astype("category") + + return s # unknown kind + +def apply_type_schema(df: pd.DataFrame) -> pd.DataFrame: + df = df.copy() + for col, kind in TYPE_SPEC.items(): + if col in df.columns: + df[col] = _coerce_column(df[col], kind) + return df + +def filter_by_seat_stay_mount_location( + df: pd.DataFrame, + col: str = "Seat stay mount location", + keep_values=(0, 1, 5), + drop_missing: bool = False, + verbose: bool = True, +) -> pd.DataFrame: + """ + Keep only rows where `col` ∈ keep_values (defaults to {0,1}). + If drop_missing=False, rows with NaN in `col` are kept. + """ + if df.empty or col not in df.columns: + if verbose: + print(f"No filtering: column '{col}' not present.") + return df + + s = pd.to_numeric(df[col], errors="coerce") + keep_mask = s.isin(keep_values) | (s.isna() & (not drop_missing)) + dropped = int((~keep_mask).sum()) + if verbose and dropped > 0: + print(f"Dropping {dropped} designs where '{col}' ∉ {keep_values}.") + return df.loc[keep_mask].copy() + +def jitter_thickness(df: pd.DataFrame) -> pd.DataFrame: + """Add Gaussian noise to wall thickness columns to avoid exact duplicates.""" + mean = np.zeros(7) + cov = 0.1*np.eye(7) + 0.4 * np.ones((7, 7)) + + scaler_exp = np.random.multivariate_normal(mean, cov, size=len(df)) + scaler = np.exp(scaler_exp) + df = df.copy() + df_thickness = df[THICKNESS_COLS].values * scaler + df[THICKNESS_COLS] = df_thickness + return df +def report_and_drop_exact_duplicates( + df: pd.DataFrame, + keep: str = "first", # same semantics as DataFrame.duplicated + verbose: bool = True, + max_groups_to_print: int = 200, + export_csv: str | None = None, +) -> pd.DataFrame: + """ + Find exact duplicate rows across ALL columns in `df`, print which IDs are dropped, + then return a de-duplicated frame. + - Keeps the first occurrence in the current row order (your index is the file id). + - Prints each duplicate set as: kept , dropped [ids...]. + """ + if df.empty: + return df.copy() + + # Hash each row across all columns (NaNs in the same places hash the same) + row_hash = pd.util.hash_pandas_object(df, index=False) + dup_mask = row_hash.duplicated(keep=False) + + if not dup_mask.any(): + if verbose: + print("No exact duplicate designs found.") + return df.copy() + + # Build groups in original order + groups = {} + for idx_label, hval in row_hash[dup_mask].items(): + groups.setdefault(hval, []).append(idx_label) + + # Print & collect drops + dropped_ids = [] + if verbose: + print(f"Found {len(groups)} exact duplicate set(s):") + for i, (hval, id_list) in enumerate(groups.items(), start=1): + # preserve original order of appearance + # (id_list is already in DataFrame order because we iterated row_hash in order) + kept_id = id_list[0] if keep == "first" else id_list[-1] + drop_list = id_list[1:] if keep == "first" else id_list[:-1] + dropped_ids.extend(drop_list) + if verbose and i <= max_groups_to_print: + print(f" set {i:>3}: kept {kept_id}, dropped {drop_list}") + if verbose and len(groups) > max_groups_to_print: + print(f" ... {len(groups) - max_groups_to_print} more duplicate set(s) not shown") + + if export_csv: + pd.Series(dropped_ids, name="dropped_duplicate_ids").to_csv(export_csv, index=False) + + # Drop them + return df.loc[~df.index.isin(dropped_ids)].copy() + + +MANUAL_ELIM = [199, 240, 751, 754, 1062, 1065, 1104, 1151, 1154, 1209, 1232, 1233, 1287, 1321, 1344, 1346, 1355, 1356, 1382, 1416, 1453, + 1457, 1464, 1546, 1787, 1863, 1873, 2019, 2163, 2405, 2641, 2770, 2772, 2853, 2880, 2884, 2890, 3032, 3125, 3126, 3127, 3202, + 3142, 3144, 3161, 3203, 3207, 3209, 3214, 3504, 3505, 3509, 3513, 3515, 3554, 3555, 3651, 3779, 3988, 3978, 3981, 4093, + 4200, 4231, 4232, 4297, 4319] + + +# =========================== +# Main builder (end-to-end) +# =========================== +def build_bikebench_dataframe( + n_files: int = 4800, + outlier_sd: float = 10.0, + jitter: bool = True, + show_progress: bool = True, + +) -> pd.DataFrame: + """ + Build the Bike-Bench DataFrame from BCAD XML files (simplified pipeline). + 1) Parse (1).bcad...(n).bcad with tqdm. + 2) Prefill legacy tube fields. + 3) Convert & derive. + 4) Select columns directly from TYPE_SPEC order and coerce/impute by type. + 5) Drop numeric 10-SD outliers. + Index is the numeric file id; index name remains None. + """ + + # Parse + records, ids = [], [] + it = tqdm(range(1, n_files + 1), desc="Loading BCAD", unit="file") if show_progress else range(1, n_files + 1) + for idx in range(1, n_files + 1): + try: + rec = parse_bcad_file(idx) + records.append(rec) + ids.append(idx) + except Exception as e: + print(f"Warning: failed to parse {idx}: {e}") + + if not records: + df_empty = pd.DataFrame() + df_empty.index = pd.Index([], name=None) + return df_empty + + raw_df = pd.DataFrame.from_records(records, index=ids).sort_index() + raw_df.index.name = None + + raw_df = drop_designs_with_xml_features( + raw_df, + substrings=("TNDM", "EXTRATUBE"), + drop_feature_columns=True, + export_dropped_path=None, + verbose=True, +) + + # Prefill legacy fields, convert/derive + raw_df = prefill_old_bcad_fields(raw_df) + + # NEW: drop designs where Seat stay mount location is not 0 + raw_df = filter_by_seat_stay_mount_location(raw_df, keep_values=(0,1,5), drop_missing=False, verbose=True) + + # Convert/derive + converted = convert_bike_bench(raw_df) + + # Select columns per TYPE_SPEC order (only those present) + ordered_cols = [c for c in TYPE_SPEC.keys() if c in converted.columns] + result = converted[ordered_cols].copy() + result.index.name = None + + # Enforce schema & impute + result = apply_type_schema(result) + + result = report_and_drop_exact_duplicates( + result, + keep="first", + verbose=True, + max_groups_to_print=200, # tweak if too chatty / too quiet + export_csv=None # e.g., "duplicates_dropped.csv" to archive + ) + + # Drop 10-SD outliers on numeric only + num_cols = result.select_dtypes(include=[np.number]).columns + if len(num_cols) > 0: + means = result[num_cols].mean() + stds = result[num_cols].std(ddof=0) + valid = stds[stds > 0].index + if len(valid) > 0: + diffs = (result[valid] - means[valid]).abs() + mask = (diffs > (outlier_sd * stds[valid])).any(axis=1) + result = result.loc[~mask] + + result.index.name = None + + # Manual eliminations (based on visual inspection of outliers) + before = len(result) + result = result.loc[~result.index.isin(MANUAL_ELIM)] + if before != len(result): + print(f"Dropped {before - len(result)} manually removed design(s).") + + if jitter: + result = jitter_thickness(result) + + return result + +THICKNESS_COLS = [ + "Wall thickness Bottom Bracket", + "Wall thickness Top tube", + "Wall thickness Head tube", + "Wall thickness Down tube", + "Wall thickness Chain stay", + "Wall thickness Seat stay", + "Wall thickness Seat tube", +] + diff --git a/bike_bench_internal/src/bikebench/xml_handling/bike_xml_handler.py b/bike_bench_internal/src/bikebench/xml_handling/bike_xml_handler.py new file mode 100644 index 0000000000000000000000000000000000000000..d67eedca91b3457d630ae2dd931345b028c74c3b --- /dev/null +++ b/bike_bench_internal/src/bikebench/xml_handling/bike_xml_handler.py @@ -0,0 +1,111 @@ +from typing import Callable + +from bs4 import BeautifulSoup + +TEMPLATE_ENTRY = "1" + + +class BikeXmlHandler: + """Stateful (AND NOT THREAD-SAFE) xml handler. Parses values using a supplied functional parser and + alternatively converts an xml string into a string -> string dictionary""" + XML_TAG = "entry" + ATTRIBUTE = "key" + PARENT_TAG = "properties" + + def __init__(self): + self.xml_tree = None + + def get_all_entries_string(self): + return self.get_all_entries().__str__() + + def get_entries_count(self): + return len(self.get_all_entries()) + + def set_xml(self, xml: str): + self.xml_tree = self.generate_xml_tree(xml) + + def generate_xml_tree(self, xml: str): + return BeautifulSoup(xml, "xml") + + def get_content_string(self): + return self.xml_tree.__str__() + + def get_all_entries(self) -> dict: + return self.xml_tree.find_all(self.XML_TAG) + + def copy_first_entry(self): + fes = self.get_first_entry_string() + return self.copy_entry(fes) + + def copy_entry(self, entry: str): + new_tree_with_one_entry = self.generate_xml_tree(entry) + entry_alone = self.strip_tree_of_needless_tags(new_tree_with_one_entry) + return entry_alone + + def get_first_entry_string(self): + return self.get_all_entries()[0].__str__() + + def strip_tree_of_needless_tags(self, new_tree): + return new_tree.find_all(self.XML_TAG)[0] + + def add_new_entry(self, key: str, value: str): + new_entry = self.copy_entry(TEMPLATE_ENTRY) + new_entry[self.ATTRIBUTE] = key + new_entry.find(string=new_entry.text).replace_with(value) + self.xml_tree.find_all(self.PARENT_TAG)[0].append(new_entry) + + def find_entry_by_key(self, entry_key): + for entry in self.get_all_entries(): + if entry[self.ATTRIBUTE] == entry_key: + return entry + + def update_entry_key(self, entry, new_key): + entry[self.ATTRIBUTE] = new_key + + def update_entry_value(self, entry, new_value): + entry.find(string=entry.text).replace_with(new_value) + + def get_entries_dict(self): + return {entry[self.ATTRIBUTE].strip(): entry.text for entry in self.get_all_entries()} + + def does_entry_exist(self, entry_key): + if self.find_entry_by_key(entry_key): + return True + return False + + def remove_entry(self, entry): + entry.decompose() + + def remove_all_entries(self): + for entry in self.get_all_entries(): + self.remove_entry(entry) + + def set_entries_from_dict(self, entries_dict: dict): + self.set_xml("") + parent = self.xml_tree.new_tag("properties") + self.xml_tree.append(parent) + for key, value in entries_dict.items(): + self.add_new_entry(str(key), str(value)) + + def update_entries_from_dict(self, entries_dict: dict): + for key, value in entries_dict.items(): + self.add_or_update(key, value) + + def add_or_update(self, key, value): + if self.key_exists(key): + self.update_entry_value(self.find_entry_by_key(key), value) + else: + self.add_new_entry(key, value) + + def update_if_exists(self, key, value): + if self.key_exists(key): + self.update_entry_value(self.find_entry_by_key(key), value) + + def key_exists(self, key): + return key in self.get_all_keys() + + def get_all_keys(self): + return [entry[self.ATTRIBUTE] for entry in self.get_all_entries()] + + def get_parsed_entries(self, value_parser: Callable) -> dict: + return {key: value_parser(value) for key, value in self.get_entries_dict().items()} diff --git a/bike_bench_internal/src/bikebench/xml_handling/bikebench_to_bcad.py b/bike_bench_internal/src/bikebench/xml_handling/bikebench_to_bcad.py new file mode 100644 index 0000000000000000000000000000000000000000..6f4609d7a03d5e6e43d4f6a5668fd3c2faf0ec7a --- /dev/null +++ b/bike_bench_internal/src/bikebench/xml_handling/bikebench_to_bcad.py @@ -0,0 +1,94 @@ +import numpy as np +import pandas as pd + + +def bikebench_to_cad(df: pd.DataFrame): + df["Chain stay back diameter"] = df["csd"] + df["Chain stay vertical diameter"] = df["csd"] + df["Chain stay horizontal diameter"] = df["csd"] + df["CHAINSTAYAUXrearDIAMETER"] = df["csd"] + df["nChain stay back diameter"] = df["csd"] + df["nChain stay vertical diameter"] = df["csd"] + df["nChain stay horizontal diameter"] = df["csd"] + df["nCHAINSTAYAUXrearDIAMETER"] = df["csd"] + + df["SEATSTAY_HR"] = df["ssd"] + df["Seat stay bottom diameter"] = df["ssd"] + df["SEATSTAY_VF"] = df["ssd"] + df["SEATSTAY_HF"] = df["ssd"] + df["nSEATSTAY_HR"] = df["ssd"] + df["nSeat stay bottom diameter"] = df["ssd"] + df["nSEATSTAY_VF"] = df["ssd"] + df["nSEATSTAY_HF"] = df["ssd"] + + df["Top tube rear diameter"] = df["ttd"] + df["Top tube rear dia2"] = df["ttd"] + df["Top tube front diameter"] = df["ttd"] + df["Top tube front dia2"] = df["ttd"] + + df["Down tube rear diameter"] = df["dtd"] + df["Down tube rear dia2"] = df["dtd"] + df["Down tube front diameter"] = df["dtd"] + df["Down tube front dia2"] = df["dtd"] + df["Down tube aero diameter"] = df["dtd"] + + df["Seat tube rear diameter"] = df["std"] + df["Seat tube rear dia2"] = df["std"] + df["Seat tube front diameter"] = df["std"] + df["Seat tube front dia2"] = df["std"] + df["Seat tube aero diameter"] = df["std"] + + df["Head tube aero diameter"] = df["htd"] + + if "RDBSD" in df.columns: + df["BSD rear"] = df["Wheel diameter rear"] - df["RDBSD"] + df["ERD rear"] = df["BSD rear"] + if "FDBSD" in df.columns: + df["BSD front"] = df["Wheel diameter front"] - df["FDBSD"] + df["ERD front"] = df["BSD front"] + + Stack = df["Stack"] + HTL = df["Head tube length textfield"] + HTLX = df["Head tube lower extension2"] + HTA = df["Head angle"] * np.pi / 180 + BBD = df["BB textfield"] + WDR = df["Wheel diameter rear"] + WDF = df["Wheel diameter front"] + FBBD = BBD - WDR / 2 + WDF / 2 + DTL = df["DT Length"] + DTJY = Stack - (HTL - HTLX) * np.sin(HTA) + DTJX = np.sqrt(DTL ** 2 - DTJY ** 2) + fork0r = df["FORK0R"] + Fork_L_plus_HTLX_y = DTJY - FBBD + fork0r * np.cos(HTA) # Fork L plus HTLX y component + Fork_L_plus_HTLX_plus_spacer = Fork_L_plus_HTLX_y / np.sin(HTA) # Fork L plus HTLX + Fork_L_plus_HTLX_x = Fork_L_plus_HTLX_plus_spacer * np.cos(HTA) # Fork L plus HTLX x component + bb_to_wheel_x = DTJX + Fork_L_plus_HTLX_x + fork0r * np.sin(HTA) # x component from BB to wheel + FCD = np.sqrt(bb_to_wheel_x ** 2 + FBBD ** 2) # Fork center distance + Fork_L = Fork_L_plus_HTLX_plus_spacer - HTLX - 10 # Fork L + df["FCD textfield"] = FCD + df["FORK0L"] = Fork_L + df["FORK1L"] = Fork_L + 20 #bikecad has a default of 20 mm sag for suspension fork + df["FORK2L"] = Fork_L + + df.drop(["DT Length"], axis=1, inplace=True) + + r = df["FIRST color R_RGB"].values + g = df["FIRST color G_RGB"].values + b = df["FIRST color B_RGB"].values + + #threshold to 0-255 + r = np.clip(r, 0, 255) + g = np.clip(g, 0, 255) + b = np.clip(b, 0, 255) + + r = np.round(r).astype(int) + g = np.round(g).astype(int) + b = np.round(b).astype(int) + + df.drop("FIRST color R_RGB", axis=1, inplace=True) + df.drop("FIRST color G_RGB", axis=1, inplace=True) + df.drop("FIRST color B_RGB", axis=1, inplace=True) + val = r * (2 ** 16) + g * (2 ** 8) + b - (2 ** 24) + df["FIRST color sRGB"] = val + + return df.copy() diff --git a/bike_bench_internal/src/bikebench/xml_handling/cad_builder.py b/bike_bench_internal/src/bikebench/xml_handling/cad_builder.py new file mode 100644 index 0000000000000000000000000000000000000000..240d4eb81d094fb088f9073797c98b86e58bd835 --- /dev/null +++ b/bike_bench_internal/src/bikebench/xml_handling/cad_builder.py @@ -0,0 +1,95 @@ +import pandas as pd +import numpy as np +from typing import Optional, Sequence + +from bikebench.transformation.one_hot_encoding import decode_to_mixed +from bikebench.xml_handling.bike_xml_handler import BikeXmlHandler +from bikebench.xml_handling.bikebench_to_bcad import bikebench_to_cad + +OPTIMIZED_TO_CAD = { + "ST Angle": "Seat angle", + "HT Length": "Head tube length textfield", + "HT Angle": "Head angle", + "HT LX": "Head tube lower extension2", + 'Stack': 'Stack', + "ST Length": "Seat tube length", + "Seatpost LENGTH": "Seatpost LENGTH", + "Saddle height": "Saddle height", + "Stem length": "Stem length", + "Crank length": "Crank length", + "Headset spacers": "Headset spacers", + "Stem angle": "Stem angle", + "Handlebar style": "Handlebar style", +} + + +class BikeCadFileBuilder: + # def build_cad_from_biked(self, biked: dict, seed_bike_xml: str, rider_dims = None) -> str: + # xml_handler = BikeXmlHandler() + # xml_handler.set_xml(seed_bike_xml) + # for response_key, cad_key in OPTIMIZED_TO_CAD.items(): + # self._update_xml(xml_handler, cad_key, biked[response_key]) + # if rider_dims: + # xml_handler.add_or_update("Display RIDER", "true") + # return xml_handler.get_content_string() + + def build_cad_from_clip(self, clip: dict, seed_bike_xml: str, rider_dims: Optional[Sequence[float]] = None) -> str: + xml_handler = BikeXmlHandler() + xml_handler.set_xml(seed_bike_xml) + target_dict = self._to_cad_dict(clip) + self._update_values(xml_handler, target_dict) + if rider_dims is not None: #upper_leg, lower_leg, arm_length, torso_length, neck_and_head_length, torso_width + rider_dims = 1000 * np.array(rider_dims).flatten() # Convert to mm + xml_handler.add_or_update("Display RIDER", "true") + xml_handler.add_or_update("UPPERARMLENGTH", str(rider_dims[2]/2)) + xml_handler.add_or_update("LOWERARMLENGTH", str(rider_dims[2]/2)) + xml_handler.add_or_update("TORSOLENGTH", str(rider_dims[3])) + xml_handler.add_or_update("LOWERLEGLENGTH", str(rider_dims[1])) + xml_handler.add_or_update("UPPERLEGLENGTH", str(rider_dims[0])) + xml_handler.add_or_update("SHOULDERroll", "0") + handpos = target_dict["Handlebar style"] + handlookup = {"0": "3", "1": "1", "2": "2"} #0,1,2 -> 3,1,2 (bikecad's 0 is aerobars) + xml_handler.add_or_update("Hand position", handlookup[handpos]) + + return xml_handler.get_content_string() + + def _to_cad_dict(self, bike: dict): + bike_complete = bikebench_to_cad(pd.DataFrame.from_records([bike])).iloc[0] + decoded_values = decode_to_mixed(pd.DataFrame.from_records([bike_complete])) + decoded_values = decoded_values.iloc[0].to_dict() + return decoded_values + + def _update_xml(self, xml_handler, cad_key, desired_value): + entry = xml_handler.find_entry_by_key(cad_key) + if entry: + xml_handler.update_entry_value(entry, str(desired_value)) + else: + xml_handler.add_new_entry(cad_key, str(desired_value)) + + def _update_values(self, handler, bike_dict): + num_updated = 0 + for k, v in bike_dict.items(): + parsed = self._parse(v) + if parsed is not None: + num_updated += 1 + self._update_value(parsed, handler, k) + + def _parse(self, v): + handled = self._handle_numeric(v) + handled = self._handle_bool(str(handled)) + return handled + + def _update_value(self, handled, xml_handler, k): + xml_handler.update_if_exists(k, handled) + + def _handle_numeric(self, v): + if str(v).lower() == 'nan': + return None + if type(v) in [int, float]: + v = int(v) + return v + + def _handle_bool(self, param): + if param.lower().title() in ['True', 'False']: + return param.lower() + return param diff --git a/bike_bench_internal/src/resources/META-INF/MANIFEST.MF b/bike_bench_internal/src/resources/META-INF/MANIFEST.MF new file mode 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20275328 diff --git a/bike_bench_internal/src/resources/datasets/Conditioning/text_test.txt b/bike_bench_internal/src/resources/datasets/Conditioning/text_test.txt new file mode 100644 index 0000000000000000000000000000000000000000..3b595a6a05e0b5d066ce7459d44158e0e715e6e6 --- /dev/null +++ b/bike_bench_internal/src/resources/datasets/Conditioning/text_test.txt @@ -0,0 +1,100 @@ +Gravel adventure bike with patched paintwork, custom framebags and spare spoke stash for long tours. +Mountain enduro bike with adjustable shock leverage, burly frame protection and a matte army green paint scheme. +Stylish city bike with tan leather saddle and grips, cream frame, full fenders, and a woven basket bolted to the headtube. +Gravel biker with wide flared drops, tubeless setup and a frame-mounted top tube bag for essentials. +Fat-bike with 4.8-inch tires, rock-solid fork and a powder-coated steel frame for snowy commutes. +Gravel bike with burnt orange accents, wide cassette, and tubeless setup tuned for rocky gravel. +A custom-painted commuter with reflective flake, deep-dish wheels, and a compact rear rack for stylish utility and night visibility. +A mountain hardtail built for bikepacking with fork lowrider mounts, frame bag-friendly angles, and a wide-range 11-46 cassette. +Gravel bike with stealth welding, matte finish, and a low-profile fork for mixed-surface speed. +Track training bike with fixed gear, deep-section rims, and a stiff frame designed to transfer power effectively to the track. +Mountain all-mountain rig with tuned suspension, chain guide, 1x12 wide-range cassette and robust tires for aggressive trail sections +Mountain enduro bike with progressive layup, long reach, 170mm front travel and coil-compatible shock for big-mountain confidence. +A high-volume cargo bike with side-loading panniers, child-facing bench, and adjustable steering damper for stable handling when laden. +Urban cargo pedelec with reinforced frameset, dual-battery option, and child bench with safety belts for family shopping. +A modern electric-assisted commuter bicycle with mid-drive motor, integrated battery in the downtube, hydraulic brakes and a lightweight aluminum frame +Aluminum cyclocross bike with thru-axles, cantilever-style clearance and reinforced fork. +A touring bike with front and rear low-rider racks, 26" wheels for resilience, leather saddle, and a simple triple chainring for varied terrain. +A vintage touring bicycle with brass-rimmed headlights, well-worn leather grips, and an accommodating frame for supporting heavy luggage. +Gravel race machine with chopped flared bars, 700x38 tubeless tires, SRAM Rival 1 and race-oriented geometry. +Gravel training bike with flared drop bars, 40mm tires, and a compact 46/30 chainset. +A bespoke painted track bike with gradient fade, polished seatpost clamp, and aerodynamic aesthetic cues integrated into the frame. +A classic beach cruiser with pastel yellow paint, wooden-walled tires, and a large cushioned saddle for relaxed coastal promenades. +City folding bike with a single hinge, integrated mudguards, and puncture-proof tires for daily commuting. +A gravel racer with flared drops, tubeless rims, 700x38 tires and metallic olive finish with subtle paint texture. +Track fixed-gear with clean finishing, high-flange hubs, and a classic matte paint finish to emphasize form over graphics. +A lightweight cyclocross frame with eye-candy paint, shallow head tube, and purposefully spaced mud clearance for marshy race courses. +Urban single-speed with gloss powder coat, narrow saddle, and elegant linework for a refined city presence. +Performance road bike with integrated brake calipers, torqued carbon layup, and disciplined weight cut. +Lightweight touring aluminum bike with triple cage mounts, low gears, full fenders, and a comfortable 90mm stem length. +Folding commuter with internal cable routing, secure hinge lock, and a comfortable upright riding position for city trips. +Track-focused single-speed with polished steel frame, NJS-style deep hub, and an intentionally simple drivetrain for training. +Lightweight climbing road bike with minimal paint, high-ratio cassette, shallow tubular rims and aggressive saddle position +Gravel race bike with wide flared drops, 700x38mm tyres, and an anodized headbadge with subtle detailing. +Mountain downhill with extra reach, long-travel suspension, and protective frame plates where rock strikes are common. +Gravel e-bike with extra torque on demand, integrated mudguards, and a robust rear rack mount. +A mountain hardtail with modern slack geometry, 29-inch wheels, dropper post and reinforced rim spoke pattern for trail endurance. +Adventure e-bike with dropper post, long-travel suspension, and a battery friendly for multi-day backcountry rides. +Retro BMX cruiser with chrome fenders, wide saddle, and cruiser handlebars for relaxed boardwalk riding. +A touring-ready steel frame with braze-on fender mounts, low-gear triple, durable wheelset and an understated metallic green paint. +All-conditions cyclocross build with disc brakes, tapered headtube, and gravel-focused knobby tires ready for muddy circuits. +Gravel commuter with belt drive, internal hub gear, fender mounts and integrated rear rack for low-maintenance city riding. +Adventure touring bike with wide-ranging gears, dynamo hub, robust panniers, and waterproof frame bag setup for crossing continents. +City commuter with integrated USB charging from dynamo, hub gear, and puncture-resistant tread. +Long-tail cargo bike with wooden platform, twin child seats, sturdy kickstand, and hydraulic disc brakes for heavy loads. +A trail-friendly 29er hardtail with 120mm fork travel, tubeless setup, 1x drivetrain and graphite matte with lime highlights. +Lowrider cruiser bicycle with elongated frame, ape hanger handlebars, banana seat, and chrome fenders for showy neighborhood cruises. +Track time-trial bike with polished carbon weave, integrated hydration, and a power-optimized geometry. +A gravel grinder with wide 40mm tires, flared bars, integrated GPS mount, and test-rugged components ready for gravel epics. +Cyclocross session bike with mud-specific geometry, quick-release thru-axles, and minimalistic paint to hide wear. +A cycle commuter with integrated pannier rack, low maintenance hub gear, belt drive, and matte slate paint to stay clean on the daily grind. +A lightweight gravel racer with custom carbon wheels, 700c low-profile tires, and a pared-down cockpit for focused racing. +Mountain bike with adjustable geometry headset and grooved chainstays for mud-shedding performance. +Mountain freeride bike with burly build, long travel suspension, and heavy-duty frame plating in impact zones. +Compact urban single-speed with fixed rear hub, glossy red paint, and minimalist saddle. +A recumbent bicycle with low-slung frame, reclining mesh seat, high-visibility flag and low center of gravity for relaxed long-distance comfort. +Gravel race bike with aero-optimized tubing, fast-rolling 700x35c tires, and a focused race geometry for off-road speed. +A performance track pursuit bike with solid disc rear wheel, aero tri-spoke front, steep geometry and precision time-trialing handlebars. +A commuter with low gears, generous mudguards, and a dynamo headlight for daily reliability through all seasons. +Cargo longtail with extended rear deck, dual kickstand, passenger footpegs and reinforced frame tubes +Beach cruiser chopper with stretched front end, wide handlebars, low seat and chrome fenders for show and leisure rides +A utility cargo tricycle with three wheels, large rear cargo bucket, and a simple twist-grip throttle for low-speed hauling. +Gravel racer with race-geometry frame, lightweight wheelset, and a taut responsive feel for aggressive pacing. +Comfort hybrid with step-through alloy frame, suspension fork, and wide ergonomic saddle for relaxed rides. +Belt-driven commuter step-over frame with integrated lock mounts, hub dynamo lighting, and a silent low-maintenance ride feel. +A carbon cross-country race frame with efficient pedaling platform, short chainstays, and a fork compatible with up to 120mm travel. +Urban fixie with painted matching rims, hub flip-flop opportunity, and a small leather saddlebag. +Gravel race machine with integrated stem, top-tube storage pocket, carbon seatpost and race-ready 38mm tires +A mountain bike with split seatstay compliance, electronic drivetrain, and bright anodized accents to stand out on the trail. +BMX dirt jumper with reinforced top tube, pegs removed for dirt jumps and knobby 2.4" tires. +Mountain freeride setup with heavy-duty rims, welded chainstay guards, and extra-wide bars for big jump stability and landing forgiveness +A cyclocross frameset with carbon blade fork, integrated handlebar clamp, and a seat tube pocket for a framebag during winter training. +A modern e-mountain bike with sophisticated motor mapping, short chainstays, long reach, and trail-optimized suspension for powered singletrack sessions. +A commuter with integrated smartphone mount, USB charging port, puncture-resistant tires and a corrosion-resistant frame finish. +Retro road bicycle with steel frame, polished aluminum rims, leather brooks saddle, and narrow tires for Sunday morning elegance +City cargo trike with weather-sealed cargo box, anti-theft locking system, and e-assist for efficient urban distribution +Mountain bike with progressive geometry, chainstay protector, and 12-speed micro-indexed drivetrain. +A kid-friendly balance bike with composite wheels, rounded frame ends, and an adjustable seat that grows with early riders. +Lightweight track bike with tall front end, stiff bottom bracket, and a deep-section rear rim for high-speed stability. +Adventure expedition bicycle with reinforced fork mounts, three water bottle cages, and powder-coated steel. +A cargo bakfiets with wooden box, low step-through frame, electric-assist, and bench seats designed for family transport in urban neighborhoods. +An aero road bike with integrated brakes, hidden cables, shallow aero wheels and a matte navy finish with laser-cut logo. +A gravel explorative bicycle with triple-mount fork, wide 650b wheels option, and durable finishes that shrug off the scratches of long adventure. +A city step-through with low step-over, basket mounts, and an easy-shift hub for smooth stops and starts in traffic. +A minimal singlespeed with matte charcoal paint, sealed bearings, narrow tires and a taut chainline for clean street looks. +Gravel endurance bike with endurance dropbar, flared shifters, and tire clearances to match transcontinental routes. +A mountain hardtail with a burly alloy frame, wide handlebars, lockout fork and 2.4" tires ready for technical cross-country loops. +A folding commuter with high-quality hinge, 20-inch wheels, triple-speed hub and a quick-fold mechanism for seamless travel transitions. +A BMX street bike with chromoly frame, 20-inch wheels, gyro brake setup and pegs designed for technical tricks and park sessions. +A rugged steel touring bicycle with reinforced rack mounts, triple chainring drivetrain, leather saddle, fenders and olive-green paint for long-distance hauling. +Road endurance bike with carbon compliance zones, lower stack height and 30mm tires for smooth surfaces. +Performance gravel bike with wireless shifting, deep-section alloy rims, flared drop bars and race-oriented cockpit setup. +Folding cargo bike with longtail extension, rear ramp kit and a low step-through for loading cargo easily. +A sleek commuter with minimal chaincase, low-profile fenders, carbon fork and frosted titanium-look paint that resists scratches. +A lowrider cruiser bicycle with exaggerated elongated frame, sissy bar, custom paint, and banana seat for showpieces. +A commuter with retro baguette basket, upright swept bars, dynamo lighting, and a wide, sprung leather saddle for style and function. +A classic steel road frame with lugwork, polished headset, and a subtle two-tone paint that hints at a hand-applied finish. +A cargo long-tail bike with child bench, reinforced center bar, hydraulic brakes and glossy deep teal finish. +Vintage-inspired mountain cruiser with retro paint, swept-back bars, and balloon tires for casual weekend rides. +Track omnium bike with minimal clearance, stiff carbon frame, and single-speed drivetrain set for sprints and pursuits. +Touring steel frameset with integrated chainstay guards, full rub-proof paint, low-maintenance sealed hubs and full fender options. diff --git a/bike_bench_internal/src/resources/datasets/Conditioning/text_train.txt b/bike_bench_internal/src/resources/datasets/Conditioning/text_train.txt new file mode 100644 index 0000000000000000000000000000000000000000..a15c26aa8fe6d6a207051215880d152bbcad6d06 --- /dev/null +++ b/bike_bench_internal/src/resources/datasets/Conditioning/text_train.txt @@ -0,0 +1,9900 @@ +A lightweight cross-country hardtail with modern geometry, carbon seatpost, and race-specific wheelset to maximize traction and climb speed. +A stainless steel frame road aero bike with stealth internal cable routing, integrated seatmast clamp, and a glossy pearl-white finish. +Vintage road bike with polished steel lugs, leather saddle, toe-clip pedals and a faded period-correct paint film. +Gravel bike with split top tube, modular luggage mounts, and tan-wall 45mm tires. +Compact commuter with hub motor, integrated display, foldable pedals, and a small footprint for city living storage. +A stripped endurance road bike with flexible compliance seat stays, integrated electronics, and a subtle two-tone paint scheme. +Road bike with endurance geometry, disc brakes, and a handlebar that allows multiple comfortable hand positions for long days. +Touring steel frame with ornate lug details, pillared seatstays and a classical bell for elegant long rides. +A downhill freeride build with double-crown fork, longer travel rear, reinforced headtube and festival-bright neon graphics. +A lightweight time-trial bike with aero tube cross-sections, integrated cooling channels, and a glossy black finish with subtle sponsor stripes. +Cyclocross race machine with SRAM 12-speed gravel groupset, tubeless-ready rims and tapered headtube for precise handling. +Electric cargo longtail with two child seats, Bosch mid-drive, low-slung deck, and adaptive suspension for comfortable rides. +A commuter city bicycle with upright geometry, chain guard, fenders, dynamo hub headlight, and a rear rack for daily grocery runs. +A modern endurance road bicycle with endurance geometry, foam-padded handlebar tape, and large-volume 32mm tires for comfort. +Steel commuter with retro styling, modern components, and a glossy paint finish with subtle sparkle. +A lightweight steel roadie with classic braze-ons, quill stem, 32mm tubular tires, and a vintage decal kit harking to nostalgic road racing. +Full-suspension trail bike with progressive geometry, 150mm rear travel, and wide 2.35-inch tires for grip. +A durable cargo bicycle with large front platform, mid-motor support, and reinforced steering to safely haul large parcels in cities. +A family cargo longtail with adjustable bench seating, sturdy kickstand, and a quiet belt-assist drive to help on steep hills while carrying kids. +A compact-folder folding bike with 16" spoked wheels, single-speed drivetrain, and integrated carrying handle for easy transit. +Minimal urban fixie with matte-finished frame, single brake setup, narrow saddle and discreet logo for understated aesthetics. +A commuter with integrated front basket, stepped geometry, and low-profile dynamo-powered front light for dawn departures. +Gravel adventure frame with stealth gravel logo, frame-specified mudflap mounts and comfortable touring geometry. +Touring steel with double-braze-ons, heavy-duty wheelset, and slightly taller gearing for loaded highway miles. +A kids' balance bike with bright red powdercoat, ergonomic grips, wide stable wheels and a low-profile saddle built for early balance lessons. +Road aero bike with integrated hydration, hidden cables and a full-carbon cockpit for minimal drag. +A commuter with integrated cup-holder, rear rack, mudguards, chain guard and comfortable upright riding position for daily coffee runs. +A children’s balance bike with no pedals, low saddle height, grippy foam tires, and a bright primary-color frame for first rides. +A fixed-gear single-speed with riser bars, disc brake conversion on the front, tubular wheels and raw polished finish. +A BMX flatland machine with a longer top tube, micro-geo adjustments, and non-slip narrow pedals for technical spins. +Urban utility bike with integrated cargo rails, welded footrests and high-volume tires for comfort under load. +A polished aluminum commuter with internal hub gears, belt drive, mudguards and a subtle champagne finish that hides dirt well. +A colorful kids' bicycle with chain guard, training wheels, low standover frame, and coaster brake decorated with cartoon decals. +Cargo longtail bicycle with cargo box sides, double kickstand, and heavy-duty chain to handle groceries and kids. +Handmade lugged touring frame with brazed-on eyelets, brass details, and durable powder coat. +A downhill-specific bike with long travel suspension, huge braking hardware, 27.5 wheels and fluorescent safety yellow paint for race visibility. +Rigid mountain bike for bikepacking with low-wide handlebars, extra bottle mounts, and robust steel frame to carry gear. +Urban utility bike with reinforced frame, front basket, internal hub, and a chain guard for effortless city chores. +BMX freestyle bike with chromoly frame, 20-inch wheels, gyro rotor, pegs, and reinforced top tube for tricks. +Cargo longtail with built-in cup holder, child harnesses, and reinforced frame for family errands. +Mountain freeride bike with massive front fork travel, reinforced head tube, 27.5" wheels, and stout frame construction for extreme terrain. +Single-speed mountain bike with wide tires, simplicity-first drivetrain and strong chromoly tubing for durability. +Lowrider cruiser bicycle with chrome sissy bar, stretched frame, custom paint, and whitewall balloon tires for style cruising. +Electric mountain bike with full suspension, high-torque mid-drive motor, 150mm travel, and wide traction tires for steep technical climbs and descents. +Mountain slopestyle bike with short chainstays, responsive geometry and BMX-inspired handlebars for precise tricks. +Minimalist single-speed commuter with matte finish, small accents of color, narrow saddle, and a flip-flop rear hub for flexibility. +A vintage town bicycle with painted fenders, wicker basket, and original brass-accented headlamp converted to LED for lasting practicality. +A commuter folding e-bike with rear-hub motor, lightweight alloy frame, basket mount, and easy-carry handle for subway rides. +A long-distance touring bicycle with light-reflecting piping, triple chainset for low climbing gears, and reinforced spokes for heavy loads. +Folding bike with robust hinge, chain retention system, and a small, secure carry strap for short transports. +Classic city cruiser bicycle in pastel blue with swept-back handlebars, cushioned saddle and chrome fenders. +Cross-country race hardtail with 100mm fork, lightweight carbon frame and narrow 29er tires. +A modern gravel hardtail with tapered headtube, thru-axles, 700x45 tubeless tires and a subtly textured matte finish for grip. +Gravel e-bike with torque-limited boost modes, chain slap protection, and long-range battery capability for remote expeditions. +A gravel-focused cyclocross conversion with knobby 33mm tires, carbon bars, and a slightly higher bottom bracket for more clearance. +Off-road mountain bike with modern geometry, long-travel fork, stout brakes, and tubeless-ready rims for aggressive trails. +A commuter with an internally routed dynamo system, fully enclosed belt drive, and a smooth-shifting internal hub to minimize maintenance. +Recumbent trike with mesh seat, low center of gravity, recumbent pedals, and three wheels for stable long-distance comfort. +Steel-framed adventure bike with copper-tone paint, brazed-on rack mounts, and durable long-reach caliper for mixed roads. +A touring tandem with touring geometry, long chainline, and additional cogs to manage heavy loads across mountains and plains. +Minimalist urban fixie with single-speed freewheel, polished silver frame, narrow riser bars, and a thin saddle for lightweight city zips. +A technical full-suspension trail bike with low bottom bracket, short stem, and wide handlebars tailored for precise steering. +Electric-assist cargo bike with longtail deck, Bosch mid-drive motor, reinforced aluminum frame and hydraulic disc brakes. +A gravel adventure bicycle with long-range battery for e-assist, rugged tires, and a heavy-duty rack for long unsupported routes. +A drop-bar adventure bike with 700c wheels that accept 45mm tires, leather bolts, and discreet framebag attachment points for lightweight bikepacking. +Enduro downhill-ready mountain bike with coil shock, burly downtube guard, and 29" wheels for high-speed descents. +A modern gravel bike with gravel-specific geometry, chainstay-mounted storage, and multi-position handlebar options. +Road endurance bike with ergonomic bar tape, vibration-damping inserts, and a two-tone paint scheme with metallic accents. +Compact gravel bike with 650b wheels, clearance for big tires, and a subtle metallic blue paint. +A single-speed commuter with deep-section rims, minor cosmetic distress intentionally applied for a lived-in look, and a thick comfortable saddle. +A compact kids' city bike with easy-to-use coaster brake, durable painted frame, and training wheels detachable for gradual skill progression. +A custom steel road frame with hand-brazed lugs, leather saddle, classic quill stem and maroon metallic paint with gold pinstripe. +Touring tandem with independent riders' geometry, wide gear range, multiple water-bottle mounts and reinforced frame joints. +City commuter with subtle integrated lights, belt drive, internal 8-speed hub, and a low-step frame for quick errands. +Mountain trail full-suspension with tuned kinematics, 140mm travel, and coil-compatible linkage for traction in rough terrain. +Mountain cross-country hardtail with carbon legs, efficient pedaling position, and light tubeless wheels for racing. +Beach cruiser with oversized chrome fenders, springer fork, retro two-tone paint and giant comfy saddle for boardwalk cruises +Mountain enduro rig with variable-rate shockvalve tune, integrated bashguard, and a stealthy dark-gray paint. +Bikepacking-ready steel adventure bike with multiple braze-ons, framebag-friendly triangle and tubeless-ready rims. +Mountain all-mountain with 150mm travel, dual-piston brakes, and a lightweight yet robust wheelset for day-long runs. +Gravel bombing machine with dropper, 45mm tires, and racy cockpit for fast rough-road rides. +A dirt jump bike with 100mm travel fork, slack 65-degree head angle, reinforced top tube and thick-walled rims for heavy landings. +Cyclocross commuter with mechanical disc brakes, 34mm mud-shedding tires and a supple steel frame tuned for rough pavement. +A beach cruiser with seaside-themed decals, chrome accents, wide saddle and a low-maintenance single-speed drivetrain for lazy afternoons. +A modern steel road frame with elegant lug transitions, disc brake compatibility, and a subtle gradient paint fade across the tubes. +Cargo tricycle with reinforced steel platform, optional electric assist and adjustable handlebar height for varied users. +A lightweight gravel race machine with integrated front storage, tapered head tube, and electronic shifting for local gravel criteriums. +Beach cruiser with surfboard attachment, teak decked cargo tray, bright turquoise paint and low gear ratio for seaside cruising. +A commuter with integrated theft alarm in the frame, puncture-resistant casing on tires, and a small cargo platform for daily necessities. +A lightweight fixed-gear commuter with aerodynamic deep-section front wheel, minimalist rear, and a short stem for responsive handling. +Road race bike with Dura-Ace mechanical group, deep carbon rims, and a carbon seatpost for race efficiency. +Touring tandem with matched frames, double racks, dual bottle mounts, and reinforced wheels for two-up long-distance journeys +A commuting hybrid with swept flat bars, puncture-resistant tires, and an upright posture for seeing and being seen in city traffic. +Folding electric bike with snap-fold mechanism, compact battery, throttle assist, and an ergonomic seat for multi-modal commuters +Kids' balance bike with bright yellow frame and anti-slip grips for first rides. +Cargo longtail bicycle with extended rear deck, passenger rails and integrated lock mounts for utility use. +A race-spec cyclocross machine with 35mm tubulars, carbon fork, and a short cockpit for powerful, controlled handling. +A dirt-oriented 27.5+ hardtail with wide-volume tires, burly 120mm fork, chainstay protection and a playful short-travel personality. +A modern track bike with aerodynamic bars, minimal paint, and a stiff bottom bracket targeted at short-track explosiveness on the velodrome. +A cargo e-bike with bucket-style front cargo area, mid-drive motor, reinforced frame and child safety harnesses for family transport. +A compact children's bike with low center of gravity, cushioned grips, a stable wheelbase, and colorful decals to build confidence. +Road race bike with capacitive touch integrated controls, narrow aero bar extensions, and reflective side logos for low-light visibility. +Classic steel racer with quill stem, leather bar tape, and period-correct ten-speed group for nostalgic Sunday pace lines. +A commuter with city-ready accessories, front basket, rear pannier system, integrated reflectors and a stable upright geometry. +A commuter with minimalist belt drive, integrated rack for panniers, dynamo hub and subtle dove-gray gloss finish. +Folding electric cargo bike with long wheelbase, twin-battery support, and a stable platform for commercial deliveries in the city. +Electric city bike with hydraulic disc brakes, torque sensor and a built-in frame lock for urban convenience. +A BMX freestyle bike with 20" wheels, gyro rotor, pegs, and a reinforced chromoly frame for park tricks and street maneuvers. +Commuter with reflective piping, integrated side stand, and simple 7-speed hub gearing for city stop-and-go. +Tandem touring bike with Rohloff hub, reinforced dropouts, and matching saddles for comfortable two-person touring. +A mountain hardtail with adjustable geometry headset, 120mm fork, dropper post, and race-ready 29er wheels for smooth, fast trails. +Vintage road racer with leather-wrapped bar tape, classic lugs, and a scuffed, well-loved paint job for character. +High-performance mountain bike with 29-inch front wheel, mullet rear option, stiff carbon construction and modern progressive geometry. +A gravel-focused frame with internal framebar storage, additional fender mounts, and a glossy moss-green finish. +A classic lugged steel touring bike with custom paint, polished stainless steel bosses, and reinforced fork crown for heavy loads. +MTB hardtail with singlespeed conversion, steel frame, and chain tensioners for simplicity. +A matte black commuter bicycle with integrated fenders, rear rack, dynamo lighting, upright bars, and puncture-resistant tires. +Beach cruiser with pastel teal enamel, wicker basket, swept bars, and wide balloon tires for sunlit promenades. +Mountain fat-bike with 4.0-inch tires, low-pressure setup and a wide rim to float over snow and sand. +Mountain trail hardtail with quick-release skewers, pre-load-adjustable fork, and grippy 2.3-inch tires for mixed singletrack +Urban cargo bicycle with modular rack, durable welded basket and adjustable foot pegs for passengers. +Mountain all-rounder with 140mm travel, burly tires and responsive geometry for aggressive trail carving. +Commuter with integrated frame lock, front and rear lights, and sealed hub for low upkeep. +A gravel-cornering machine with trimmed fork rake, shorter chainstays, and a confident descent-oriented geometry for fast tracks. +A lightweight touring bicycle with low-ratio gearing, triple bottle mounts, wrapped leather bars and moss-green enamel. +A kids' mountain bike with front suspension, lower gearing for climbs, and protected chainring guard for confidence-building off-road. +Matte black carbon road bike with deep-section wheels, hidden cables, and an aero integrated cockpit. +Retro-inspired cruiser with two-tone enamel, leather saddle, chrome fenders and wide balloon tires for relaxed aesthetics. +A classic folding bike with polished aluminum frame, leather-grip handlebars, single-speed setup and a retro aesthetic for urban exploration. +A gravel racer with aerodynamic seatpost, hidden brake hoses, and a quick-shift electronic groupset to maintain focus on the route. +A kids' trail bicycle with 24" wheels, easy-shift indexed gears, and a lower center of gravity to make singletrack learning fun and safe. +Fat-tyre commuter with studded snow-ready tires, strong steel fork, and wide micro-flats to keep rolling through winter. +Mountain trail bike with FlipChip geometry, 150mm fork and controller-ready dropper post for variable terrain dialing. +Classic touring steel bicycle with rack-mounted sleeping pad holder, brazed-on pump peg, and comfortable low gearing. +A compact folding bike with oversized tires for bump absorption, a sturdy folded lock, and a compact wheelbase that fits under desks. +A handbuilt steel city bike with fluted seat tube, classic thumb shifters preserved for nostalgia, and a plush spring saddle. +A carbon track pursuit bike for elite racing with oversized downtube, sealed bearing bottom bracket, and super-stiff chainstays. +Cargo-forward front-loading bakfiets with sturdy wooden trough, child safety straps, low center of gravity and reflective paint +Electric road-legal commuter with integrated turn signals, mid-drive motor and a theft-deterrent GPS tracker. +Touring steel frame with brazed chainstay guards, triple pannier compatibility, and a hand-applied mountain scene on the down tube. +Gravel all-terrain bike with 650b option, short reach cockpit and big 2.1-inch tread for roughest tracks. +A gravel bike with integrated top-tube pack, stealthy cable routing, tubeless-ready rims and 42mm fast-rolling rubber for mixed terrain speed. +Track keirin-style bike with anodized metallic frame, deep-section carbon rear, and aggressive high gearing for sprint training. +Cyclocross commuter with 40mm gravel tires, fender mounts and low-maintenance internal routing. +A commuter utilitarian bike with full metal fenders, robust low-ratio gearing, and a heavy-duty frame for years of daily use. +Retro-inspired city cruiser bicycle with swept-back handlebars, balloon tires, coaster brake, and a lacquered pastel turquoise paint job. +Gravel endurance frame with subtle logos, 42mm tire clearance and built-in accessory mount points for racks and fenders. +A commuter with integrated GPS mount, battery-powered heated grips, reflective sidewalls and a lightweight alloy rack for cold-weather commutes. +A beach cruiser bicycle featuring swept-back handlebars, a wide cushioned banana saddle, coaster brake hub and powder-blue gloss paint. +Mountain hardtail with modern slack geometry, stout forks, and a cushioned seatpost designed for long trail days. +Classic randonneur with brass fittings, leather toe straps, dynamo headlight and slim 28mm tires for endurance. +BMX street setup with gyro system, mid BB, nylon pegs, and thick grippy tires for grinding rails and landing tricks. +A kid-friendly balance bike with alloy frame, rubberized grips, foam tires and sunrise orange paint with a cheerful smile decal. +Gravel touring build with framebags, wide 650b tires, drop-bar set-up, and a sturdy cantilever-style rear rack. +A robust cargo tricycle with heavy-duty platform, an integrated lock system, and hydraulic disc brakes for large urban deliveries. +Urban utility bicycle with integrated child seat mounts, easy-to-use hub gear and adjustable stem for families. +A vintage track-inspired single-speed with polished chrome, deep-V rims and a minimalist decal that screams simplicity. +A commuter with e-assist in the rear hub, integrated LED taillight strip, and puncture-resistant touring tires. +A comfortable upright city bicycle with swept-back bars, gel seat, and a large rear reflex reflector for safe urban use at dusk. +A gravel racer with fast-rolling 38mm tires, split top tube details, aero-optimized cockpit and a matte champagne finish for understated speed. +BMX flatland bike with short chainstay, gyro and a narrow stem for technical tricks and precise control. +Classic track bike with polished steel frame, deep tubular rims, and minimal graphics for velodrome-focused speed. +A compact folding e-bike with low-step platform, quick-release stem, 14-inch wheels and charcoal matte finish for stealth commuting. +A kids’ mountain bike designed for durability with 24-inch wheels, single-ring drivetrain, and a bright candy-pink paint for visibility on trails. +A purpose-built bikepacking rig with integrated bottle cages on the fork, framebag-friendly geometry, and thick 650b tires. +Mountain bike with dropper post, 1x12 drivetrain, and tubeless-ready rims for confidence on unpredictable trails. +Leisure hybrid with upright bars, suspension fork, and comfortable thud-absorbing saddle for casual rides. +Mountain enduro race machine with adjustable geometry, burly frame protection, and a race-ready suspension package. +A gravel gravel-grinder with tribal-patterned frame wrap, 700x38 tires, double-bottle mounts and a compact chainset. +Retro single-speed with steel frame, basket, rear coaster brake, and candy-apple red paint for a playful urban look. +A modern track sprint bicycle with stiff carbon frameset, beefy chainstays, and a short wheelbase optimized for explosive starts. +A cyclocross bike with sealed cartridge bearings, mud-shedding frame design, integrated chain catcher and racing number plate mounts. +A track pursuit machine with solid carbon rear disc, aggressive aero bars, skid-stop cutouts and glossy black paint with red pinstriping. +Electric folding bike with small wheels, telescoping stem, and hinge lock for rapid transit storage. +A handcrafted steel frame with artistic paintwork, fillet-brazed aesthetics, and carefully scaled geometry designed for rider-specific comfort. +Gravel adventure frame with multiple bolt mounts, hidden headset storage, and sturdy chainstays for loaded touring. +Daylong gravel pacer with 700x40mm tubeless tires, endurance bars and a big 48/32 chainring setup for speed. +Cross-country race hardtail with carbon frame, 100mm fork, lightweight rims, and fast-rolling 29x2.2 tires. +Vintage mixte with gleaming paint, leather grips, front rack and full chain guard for charming urban errands. +A restored classic track bike with steel frame, polished lugs, fixed single gear, and period-correct alloy tubular rims. +Gravel disc racer with SRAM Force CX, 700x40 tubeless tires, and racing geometry tuned for speed and comfort. +Lightweight racing road bike with full carbon frame, Dura-Ace Di2, ceramic bearings, and a flush-mounted stem for sprint speed. +Classic road racing frame with vintage campy components, tubular tires, and a tasteful cream livery. +A cyclocross race bike with classic steel frame updated with thru-axles and modern flat-mount disc brakes for reliable stopping. +A commuter with belt drive, internal hub, full fenders, low-slung frame and warm chestnut-brown finish with cream trim. +High-performance triathlon bike with split front wheel and small fairings, aero-optimized frame, and clip-on extensions. +A steel gravel bicycle painted army green with generous tire clearance, 40mm knobby tires, lower gearing and rack mounts for bikepacking. +A carbon stage-race road bike with shallow aero shapes, 28mm tires, and a featherweight frame built for long multi-day events. +Road aero bike with integrated stem and bar, hidden internal battery, deep aero rims and race-ready gearing for triathlon transitions +A city folding bike with small diameter wheels, internal hub gears, and a compact folded footprint that fits under desks or in closets. +Classic tandem tourer with double-bottle mounts, heavy-duty spokes, bar-end shifters and comfortable cruising geometry. +Touring steel frame with wide tire clearance, low-trail geometry for stability with heavy panniers and classic leather grips. +Lightweight hill climb bike with oversized tubes for stiffness and minimal paint for low weight. +A dirt jump frame with 26-inch wheels, short chainstays, single-speed setup, and extra frame gussets where riders land from big table-tops. +Electric mountain trail bike with reinforced linkage, long-range battery and motor assist to flatten steep gradients. +Touring mixte with centerstand, triple chainrings, fendered tires, and frame-mounted pump for long-distance comfort. +Track-style BMX with chromoly frame, gyro steering system, and 20-inch wheels for skatepark tricks. +Fat-bike with dual-traction studded tires, insulated grips and oversized rims for snowy commutes. +A BMX raced-inspired city build with a compact frame, quick-accelerating gearing, and a tight turning circle for street agility. +Gravel touring rig with triple chainset options, reinforced frame, and extra bottle mounts for exploration. +Classic steel road bike with braze-on shifter bosses, slim seat tube, polished chrome fork ends and tubed tires. +A gravel race machine with aero-optimized tubing, electronic shifting, and a compact 36/46 crankset to tackle mixed gradients. +Speed-focused triathlon bike with aggressive geometry, integrated hydration, and an aero-optimized cockpit for time gains. +A modern gravel racer with aero-tuned down tube, 35mm tires, integrated headset spacers and electronic wireless shifting for clean cockpit lines. +Speed-focused criterium bike with short wheelbase, stiff bottom bracket, and shallow-section wheels for quick punchy accelerations. +Lightweight racing alloy frame with clean welds, minimalist paint and carbon fork for an affordable fast-climbing package. +Lightweight steel road racer with minimal decals, classic geometry, and polished stainless-steel spokes. +Vintage torch-red frame bicycle with chrome-plated fenders, leather saddle, and classic rod brakes for nostalgic charm. +A high-performance road bike with integrated powermeter, aero cockpit, and tubeless 28mm setup for training and racing. +Aerodynamic time trial bike with integrated hydration system, deep-section rear wheel and a steep headtube angle for speed. +A rugged mountain hardtail with tubeless-ready rims, 120mm fork, 1x drivetrain and industrial gray powdercoat with subtle neon accents. +Kids' BMX freestyle bike with pegs, gyro system, and bright flame graphics for skateparks. +Mountain e-bike with frame-protected battery, robust motor cooling ports, and a matte army finish. +Vintage roadster bicycle with sprung saddle, swept handlebars, enamel paint, and hub dynamo for old-school touring. +A touring tandem with differential rear hub, adjustable stem lengths for each rider, and reinforced frame plates for expedition durability. +Urban compact bike with belt drive, internal hub, step-through frame, and reflective accents for safe commuting. +Mountain downhill bike with long travel suspension, wide bars, and protective skids on vulnerable frame parts. +Aero road bike in pearl white with hidden cables, lighter seatpost, aggressive geometry, and 25mm tires for sprint-focused rides. +High-performance road bike with disc brakes, 700x25mm tires, compact crankset and aerodynamic cabling. +Cyclocross pro frame with integrated chain catcher, rapid mud-shedding profile, and ridged top tube for confident shouldering. +Gravel-adapted carbon frameset with integrated storage hatch, 700x45 compatibility and stealth matte finish for solo epics. +A modern cyclocross gravel hybrid with slack geometry, load-carrying mounts, and burly tires for optimistic adventure missions. +City utility bike with upright comfortable bars, integrated rear carrier, and a sturdy kickstand for everyday errands. +Matte black carbon road bike with deep-section aero wheels and Dura-Ace groupset. +Commuter with punctureless tires, integrated chain guard, and folding pedals for small apartment storage. +Retro-track inspired city bike with polished steel frame, flip-flop hub, leather saddle, and thinroad tires for urban pace. +A gravel gravel grinder with flared drop bars, gravel-specific brake pads, and tubeless sealing for long races on mixed surfaces. +Mountain bike with adjustable anti-squat linkage, 150mm rear travel and swapped geometry for technical trail stability. +Mountain hardtail with short chainstays, fast-rolling 29er wheels and a reliable 1x drivetrain for cross-country speed. +Road climbing machine with ultra-light carbon layup, drilled brake mounts, narrow rims and a luminous paint scheme. +High-modulus carbon time trial bike with integrated storage, aggressive aero cockpit, and di2 electronic shifting. +A touring tandem with mid-frame stoker access, three-point rack system, and a heavy-duty wheelset to carry long-distance loads. +Classic French city bicycle with full chainguard, front rack, upright stem, and pastel cream paint for café rides. +A beach cruiser with balloon tires, swept handlebars, single-speed coaster brake, and a seafoam green glossy finish perfect for casual shoreline rides. +Adventure bikepacking bicycle with framebag-friendly triangle, bar bag, and dynamo-powered front light. +Performance cyclocross race bike with carbon fork, disc calipers and mud-shedding stays engineered for speed. +A classic steel cyclocross frame with detailed lugwork, steel fork, disc mounts and comfortable geometry for mixed-terrain racing. +Mountain trail hardtail with progressive geo, 140mm travel fork, wide handlebars and tubeless grippy tires for technical terrain. +Classic beach cruiser with oversized chrome handlebars, two-tone paint and wide whitewall tires. +Mountain downhill frame with reinforced linkages, massive suspension travel and a distinctive race-livery for podium runs. +A stepped-through electric cargo bike with front-loading bakfiets box, Bosch cargo motor, dual kickstand, and safety belt anchors. +Carbon enduro mountain bike with long dropper travel and progressive frame kinematics for rough terrain. +Commuter with integrated rear rack, battery-powered auxiliary light, and reflective sidewalls for night safety. +Touring tandem with reinforced lugged frame, twin bottle mounts, dual saddles, and long-chainrun for two-up touring. +Very light climbing bike with 32mm tubulars, minimal paint, and stripped-down weight-conscious build. +Lightweight town bike with belt-drive, upright handlebars, and a wooden rear rack for carrying groceries. +City folding commuter with comfortable saddle, secure folding latch and a low-profile frame that hides cables. +Classic city bike with internal hub, chaincase, and elegant swept-back bars for relaxed posture. +Commuter with internal hub, belt drive, step-through frame, integrated lights, and a small rear rack for daily errands. +Lightweight titanium gravel bike with disc brakes, flared drop bars, 40mm gravel tires and braze-ons for extra bottles. +Gravel-capable hybrid with suspension seatpost, dropper post compatibility and 38mm semi-slick tires for mixed roads. +A carbon fiber mountain bike hardtail with 120mm fork, wide 30mm internal rims, dropper post and a stealth-matte finish. +Mountain cross-country carbon hardtail with stiffer rear triangle, race geometry, and a focus on lightweight climbing performance. +Urban commuter with spring saddle, integrated lock, mudguards, and a step-through frame suited for quick stops. +Folding commuter with 20" wheels, simple hinge clamp, and a quick-release seatpost for compact storing. +Cargo longtail with passenger bench, integrated seat belts, and a stable chassis for carrying children or goods safely. +Lightweight titanium road bike with brushed finish, elastomer seatpost insert, thru-axles and geometry tuned for endurance. +A kids' trail bike with 24-inch wheels, simple five-speed drivetrain, and an easy-to-use front suspension for budding off-roaders. +Road light and fast bike with high-modulus carbon layup, thin seatpost, and a simple monochrome decal scheme. +A city commuter with easy-to-use hub gear, sealed bearings, and a discreet rear rack compatible with a child seat for family errands. +A commuter with wide cushioned saddle, swept-back bars, reflective sidewalls, integrated front light and a low stand-over for comfortable city navigation. +A mountain hardtail with progressive trail geometry, 150mm fork compatibility, and wide, grippy tires for high-traction climbs and descents. +Mountain trail full-suss with modern linkage, 140mm travel, and a tuned suspension curve for fast singletrack performance. +Classic Dutch steps-through bicycle with hub gear, coaster brake option, generous saddle and practical upright posture for easy city trips. +A classic cruiser with rust-free chrome, large balloon tires, silk-wrapped handlebars, and a low-ride geometry for relaxed afternoon rides. +Commuter folding bike with integrated basket, quick-fold latch, puncture-resistant tires and a pop-color powder coat for visibility. +Road racing frameset with tuned stiffness, tapered headtube, carbon fork and 25mm tires for classic race handling. +A short-travel trail hardtail with 120mm fork, tubeless 2.2 tires, 1x10 drivetrain and matte charcoal with orange highlight. +Road race machine with electronic shifting, ultra-deep carbon rims and a sculpted fork for reduced aerodynamic drag. +A gravel bike with orange anodized components, flared handlebars, SRAM Force AXS electronic shifting, and a stealthy matte black frame. +Classic road bike restored to original luster with period-correct decals, leather saddle and high-flange hubs +Mountain enduro bike with sturdy forged dropouts, coil-sprung rear shock, wide handlebars and thick tires for confidence on big terrain. +Mountain trail hardtail with progressive head angle, 29-inch wheels, wide handlebars and grippy rubber for fast trail rides +A gravel race machine with integrated top tube bag, flared bars, and a responsive carbon fork for comfortable off-road speed. +Road endurance bicycle with a taller headtube, cushioned stem, wide tires and a supportive saddle for multi-hour comfort rides +Steel touring tandem with leather grips, brass accents, and multiple frame-mounted pumps. +Urban folding single-speed with quick-release folding stem, compact folded size, and large comfortable saddle for multi-modal travel. +A kids' balance frame with rounded edges, low seat, wide handlebars and a friendly citrus-orange paint to encourage early riding independence. +A modern gravel endurance bike with a carbon fork, clearance for 45mm tires, and a slightly higher stack to keep shoulders relaxed on long days. +Cyclocross elite race bike with disc brakes, 33mm race tires, light frame, and precise handling for tight, technical courses. +Commuter with integrated battery lights, stepped-down frame for easy on/off and puncture-resistant tires. +Kids' first pedal bike with training wheels removed option, bright stickers and soft handlebar grips. +A gravel race frame with integrated carbon fender mounts, 38mm tires, and a paint job that camouflages dirt to keep the aesthetic tidy between washes. +A kids' BMX with sturdy chromoly frame, sealed bearing headset, pegs and bold graphics to keep up with urban park sessions. +A vintage-style urban bicycle with brass bell, enamel paint, and swept-back bars for easy, upright city pedaling. +Classic folding bike with resilient hinges, small wheels for tight turns, and a comfortable step-through saddle for short hops. +Mountain trail bike with 130mm travel, tubeless-ready rims, and a progressive head angle for fast descents. +A kids' BMX bike with bright graphics, padded crossbar cover, small pegs and a short cockpit for easy control in the park. +A custom hand-painted steel road frame with pinstriped accents, delicate lugs, and a satin lacquer that shows depth in sunlight. +A sleek black time trial bike with integrated hydration and mount for aero bars, steep head tube and flat-profile seatpost. +City fixie with polished chrome frame, narrow handlebars, single brake, and minimalist saddle for short urban hops. +Tandem MTB for off-road with suspension-corrected geometry and dual controls for cooperative trail riding. +A drop-bar gravel expedition bike with full framebags installed, long-range fuel capacity, bar-mounted navigation pocket, and 2.0-inch tires for remote exploration. +A utility cargo bike with reinforced wooden deck, integrated bungee mounts, hydraulic disc brakes and a weatherproof canvas cover for packages. +A kids' BMX with padded crossbar, pegs for tricks, and a tough chromoly frame built to withstand first adventures in street riding. +Urban commuter with minimalist frame, hub dynamo lighting, and a stealth chain guard for clean, daily city riding. +Mountain downhill fiberglass-supported frame with heavy-grade linkages, coil-protected shock and ultra-wide tires for rock gardens. +A matte army green singletrack hardtail with aggressive 66-degree head angle, 130mm fork, and up-to-2.4-inch trail tires for playfulness. +Youth BMX park bike with reinforced dropouts, pegs, gyro-free setup, and bright neon paint for visibility. +Fat bike with 26" rims and 4.8" balloon tires, rigid steel frame, single-speed drivetrain, and burly geometry for riding on sand and snow. +Single-speed fixed-gear track-inspired urban bicycle with slim saddle, narrow drops and clean uncluttered frame lines +Classic step-through Dutch city bike with hub gear, chaincase, coaster brake and wicker basket on the front rack. +Steel revenge-style cyclocross bike with cantilever posts, mudfender fittings, and a sharp matte red coat. +Gravel bike with 40mm rubber, custom titanium bolts, and tasteful anodized flake on the head tube badge. +Kids' mountain bike with front suspension, low stand-over height, and colorful splash decals to match adventurous spirits. +A track omnium bike with narrow sector wheelset, aggressive geometry, and deliberately stiff rear end optimized for timed events. +Urban commuter with practical basket, integrated bell, reflectors on spokes and an upright geometry for sightlines in traffic. +Performance road bike with integrated power meter, wireless shifting, aero seatpost and matte black paint with glossy accents. +Beach cruiser with ocean-blue metallic paint, wicker basket, and oversized saddle for lazy sunny-day promenades +Commuter e-bike with integrated theft-deterrent GPS, low-step frame, and hydraulic discs for secure, reliable city transport. +Gravel-friendly cyclocross bike with 700x40 tires, disc brakes, inner tube storage and protective downtube guard. +A lightweight cyclocross frame with deep tire clearance, low top tube for shouldering, and subtle graphics that hide mud splatter well. +A minimal belt-drive commuter with carbon fork, mudguard mounts, internal cable routing and a low-maintenance hub for city reliability. +Touring tandem with matched paintwork, synchronized braking, double racks and wide-range gearing for long adventures together. +A gravel race bike with wide handlebars, robust drivetrain, and a stealthy satin finish that minimizes glare in bright conditions. +Track fixed-gear with streamlined aero seatpost, short stem, and a mirror-like chrome tubed frame for classic looks. +Kids' balance bike with low center frame, rubberized grip, and weatherproof finish for backyard learning and play. +A gravel race-ready carbon frame with integrated seatpost, hidden bottle routing, and chainstay clearance for mixed-terrain tires. +Cargo e-bike with low-step deck, extra-wide tires, and a sealed chaincase for urban reliability. +Track bike with polished spokes, single tooth chainring, and classic steel frame geometry for velodrome laps. +Urban folding e-bike with reinforced hinge, chain cover, and a rear rack compatible with pannier systems for shopping runs. +A gravel-touring explorer with multiple water mounts, reinforced fork, and low-ratio gearing for hauling gear through remote roads. +A gravel monster with ample mud clearance, 2.3" tires, a long wheelbase for stability, and multiple mounting points for expedition kit. +Performance road bike with integrated power meter, aero cockpit, tubeless-ready rims and polished decals for a professional race-ready look. +A city single-speed with vintage frame, polished steel fenders, and ornate headbadge for old-world charm on urban streets. +Road endurance bike with flexible seatpost design, rounded chainstays, and plush contact points for long days. +Modern steel gravel bike with classic aesthetics, brushed finish, and discreet frame protection for bikepacking. +A matte-black carbon road bicycle with a raked aero fork, integrated Di2 shifters, deep-section 45mm clincher wheels, and a race geometry for aggressive group rides. +A step-through city e-bike with step-in ease, low-slung battery, torque-sensing motor and satin mint paint with contrasting trim. +A kids' BMX with compact frame, gyro, pegs and neon cyan paint with bold white graphics for backyard antics. +A lightweight aluminum time-trial bike with integrated front fairing, narrow aero bars and a compact drivetrain oriented toward speed. +City cruiser with swept handlebars, chequered saddlebags, a bell and chrome-covered chaincase. +Gravel endurance build with 700c x 38 tires, flared drops, and a carbon fork to damp road harshness over long hours. +A race-ready criterium bicycle with short wheelbase, stiff BB area, shallow-rimmed aero wheels, and an aggressive race fit for sprint finishes. +Racing cyclocross bicycle with light tubing, low stack, and quick-release skewers for fast wheel swaps. +Adventure tandem with cargo-friendly racks, fender mounts and a reinforced coupling between front and rear frames. +Electric fat-bike with dual batteries, wide platform tires and low center of gravity for stability. +Fatbike with low-pressure 4.6-inch tires, custom studded tread, and anodized frame for winter exploration on snow and sand +A polished-titanium commuter with minimal decals, silent belt drive, urethane chainstay protector, and full-coverage fenders for rain reliability. +Electric cargo e-bike with a low center of gravity, wide platform, strong hub motor and an intuitive pedal-assist interface. +A handbuilt titanium single-speed with slender tubes, seatpost-mounted rack, tubeless-ready 650b wheels, and subtle laser-etched logos. +Mountain bike with enduro geometry, burly tyres, and a torque-valve shock for heavy hits. +A performance road climb bike with steep seat tube angle, short wheelbase, and featherweight components for climbing events. +Classic Italian road bike with lugs, quill stem, steel fork, and a deep red gloss paint with gold pinstriping. +A classic town bike with powder-coated enamel, leather saddle, and simple hub gearing for reliable, stylish errands around town. +Modern city cruiser with coaster brake, creamwall tires, cushy saddle and a classic rear rack for weekend errands. +Track fixed-gear with polished frame, minimal paint, and a high-tooth-ratio for city acceleration and rhythm riding. +A minimalist single-speed with polished aluminum frame, track-style dropouts, and narrow 28mm tires for fast road commutes. +A cyclocross training bike built with resilience in mind, featuring reinforced welds and a simple, reliable mechanical drivetrain. +A painted sky-blue retro mixte frame bicycle with step-through top tube, wicker basket, and whitewall tires for vintage charm. +A gravel bike with double-bottle mounts inside the main triangle, neat cable routing, and a brushed metallic finish for understated touring. +A low-profile urban fixed gear with custom anodized parts, flip-flop hub for single-speed or fixed ride and matte dark green frame. +Mountain downhill prototype with reinforced forks, triple-clamp headtube, massive rotors and adjustable chain tensioners for race weekends +Retro beach cruiser with chrome-plated springer fork, oversized saddle and classic whitewall tires for coastal vibes. +Fully loaded touring bicycle with front and rear racks, waterproof panniers, double-bottle mounts, and a comfy saddle for days on the road. +Mountain trail bike with 140mm rear travel, 150mm fork, dropper post, and grippy 2.4" tires for technical singletrack. +A retro steel frame road bicycle with downtube shifters, classic lugged joints, tan wall tires and a polished chrome headtube for vintage aesthetics. +Commuter with cushioned saddle, upright grips, long mudguards and built-in reflector strips for safe city travel. +Kids' BMX with reinforced frame, colorful decals, and 20" wheels sized for small adventurous riders in the skatepark. +Custom steel road racer with Columbus tubing, paint stripped to a brushed finish, period-correct quill stem, and index-shift charm. +Mountain freeride hardtail with thick-walled tubing, reinforced headtube, short stem, and extra-wide rims for park sessions. +Classic steel road bike restored with new bar tape, polished chrome, and period-correct decals. +A steel touring frame with hand-cut lugs, weld-on eyelets, and a resilient paint finish to withstand long-term gravel exposure. +Urban commuter with swept-back bars, leather wrap grips, hub dynamo light and integrated frame lock. +Urban single-speed with leather saddle, minimalist fenders, and rear reflector integrated into seatstay. +Cargo longtail with fold-out seats, child harnesses, and built-in reflectors along the side rails for safety. +City commuter with built-in foldaway lock, tidy belt drivetrain, and shallow step-over for easy mounting. +Adventure off-road with stealth black paint, rope handlebars, and thick tires for under-the-radar explorations. +A classic Dutch city bicycle with step-through frame, fully enclosed chaincase, integrated lamp, wide spring saddle and upright bars for comfortable commuting. +Mountain trail full-suspension bike with tuned shock curve, 150mm travel, and tubeless-ready wheels for confident trail shredding. +A high-performance mountain bike with adjustable shock tunes, carbon rear triangle, and massive 2.6 tires to handle rough descents. +City cargo bike with integrated child seat harnesses, a foldable rain hood, and durable plywood cargo bed for family runs. +A folding electric bicycle with small, strong wheels, mid-range motor, and an easy-to-operate folding latch for morning commutes. +Mountain bike with adjustable anti-squat tuning, 150mm travel, and ovalized chainrings for better clearance. +A vintage steel road frame with dainty lugs, narrow rims, and subtle detailing to celebrate classic road cycling heritage. +A path-riding hybrid bicycle with 700x35 tires, upright geometry, spring saddle, and a simple single-ring drivetrain for easy city riding. +Vintage roadster with chrome accents, brass bell, swept bars, and a classic leather-sprung saddle for city cruising. +Cargo trike with a hinged cargo bed, anti-tip stabilizers, and easy-access low deck for loading. +Electric cargo trike with hydraulic brakes, large wooden cargo bed and dual batteries for extended range on heavy hauls. +Commuter with built-in reflectors, puncture-resistant urban rubber and a low center of gravity for safe handling. +Farm utility bike with cargo rack, reinforced spokes, puncture-resistant tires and mounting points for tools and crates +Urban commuter bike in gloss white with upright handlebars, integrated LED dynamo front light, rear rack, and puncture-resistant tires designed for daily city use. +A stripped-down single-speed road bike with aero profile frame, minimalist saddle, and an anodized gold chain that gleams in sunlight. +Dirt jump with short chainstay, tough frame gussets and a big, bold paint job that stands out in the skatepark. +A cyclocross race machine with carbon fork, tubeless-ready wheelset, and cropped chainstay design for lightning-fast accelerations out of corners. +Gravel racing frame with refined tube shaping, modern clearance, and a stealth matte colorway for understated aggressive speed. +Touring tandem with reinforced frame joints, banded racks, and multiple bottle mounts for fully loaded adventures. +A steel-era road bike restored with modern brakes, new bar tape, and polished lugs for classic pedaling sensations with improved safety. +All-city hybrid with reflective paint, disc brakes, and a suspension seatpost to soften pothole impacts on urban routes. +A polished chrome single-speed with track dropouts, bullhorn bars, and a minimal seatpost clamp for a clean urban look. +A kids' balance bike with adjustable handlebar, foam tires, and a colorful safety decal designed to encourage early pedaling confidence. +Road light climbing frame with deep but not overly wide rims, simple paint, and durable light-weight components. +Gravel bike with 650b compatibility, low gearing for steep grades, and subtle copper accents. +A coastal cruiser with painted shell patterns, wide cushioned seat, and swept bars ideal for scenic seaside rides. +Dutch-style commuter with internal hub, coaster brake backup, integrated lock, and solid chaincase for low upkeep. +A gravel explorer with flared flatform bars, double-bottle cage mounts on the downtube, and a long top tube for loaded days in the saddle. +Classic single-speed cruiser with chrome-plated fenders, leather-wrapped grips, and a glossy cherry red finish. +Gravel bike with stealth black finish, electronic shifting, 2x drivetrain and extra clearance for 47mm tires +A family cargo bike with padded bench, adjustable footrests, and an electric assist option that can be toggled for longer errands. +Adventure gravel frameset with titanium tubing, multiple mounts, and durability-focused fittings for long-term reliability. +Urban commuter with step-through frame, quiet hub gears, and mudguards for a no-fuss daily ride. +Lightweight single-speed road bike with polished steel frame, low stack geometry and minimalist decals. +Utility commuter with platform pedals, integrated kickstand, heavy-duty chain guard and reflective sidewall tires. +Lightweight cross-country race bike with aerodynamic tubing, fast-rolling 29-inch wheels, and an aggressive climbing geometry for efficiency. +A track training bike with shallow rim depth, tubular tires, and a stiff alloy frame to absorb sprint forces on the oval. +Gravel adventure rig with threaded BB, three bottle mounts, and a plush carbon fork for long-haul comfort and reliability. +Gravel bike with bright yellow flares, 44mm tires and ultrareliable mechanical disc brakes for remote gravel stages. +A cargo trike with weatherproof box, electric assist, and ergonomic seat for long days of deliveries without fatigue. +Mid-fat touring bicycle with 65mm tires, steel frameset, low gearing, and pannier attachments for winter routes. +Fixed-gear alleycat machine with reinforced dropouts, fast-rolling tires, and a compact bar sweep for downtown agility. +A pocket-sized folding bicycle with 16-inch wheels, single-speed drivetrain, folding pedals and integrated carry strap for easy public transit use. +Gravel racer with carbon fork, flared drop bars, 700x36 semi-slick tires and electronic shifting calibrated for clean transitions under load +Folding commuter with 16-inch wheels, compact fold, and integrated frame lock for secure storage. +Lightweight carbon aero frameset with integrated stem, flush cables, aero seatpost and deep-rim wheel compatibility for speed seekers. +Urban step-through electric bike with chaincase, hub motor and LCD range display for daily errands. +Hardtail mountain bike with 140mm fork, 29-inch wheels, 1x12 drivetrain and a hard-anodized alloy frame. +A gravel adventure bike with split top tube for framebag access, extra bottle bosses, and pillow-soft carbon seatpost for long days. +A boutique titanium gravel bike with internal front light wiring, comfortable seatpost, and discreet logos for understated style. +A titanium mountain bike with boost spacing, 130mm travel, and custom color anodization for a lightweight, durable trail companion. +Electric-assist commuter with center-mounted battery, torque sensor, internal hub, and full fenders for all-season use. +City utility bike with low-step frame, integrated office bag mount, internal hub, and a comfortable tilt-adjusted handlebar. +A commuter with low-maintenance hub gears, fully enclosed chaincase, and an integrated bell on the handlebars for city safety. +A compact kids' balance bike with no pedals, lightweight aluminum frame, foam tires, and a low seat for early skill development. +Mountain trail full-suspension with 140mm travel, modern kinematics for pedaling efficiency and a chip-resistant finish. +A high-end carbon fiber gravel bike with sloping top tube, thru-axles, 43mm gravel tires, and a SRAM Force AXS wireless drivetrain. +A commuter with hydraulic rim-brake conversion, low-maintenance belt drive, and rack-mounted battery for mild e-assist commuting. +A tandem sport bicycle with matched cranks, independent brake systems, reinforced wheels and compact luggage mounts for two-up adventures. +Cargo midtail bike in matte black with child bench, heavy-duty frame, hydraulic disc brakes and low-step access. +Vintage Dutch bike with a step-through frame, deep-blue enamel paint, and an original headlamp that runs off a hub dynamo. +Kids' balance bike with low slung frame, foam padded wheels and bright safety reflectors for operating in neighborhoods. +A commuter hybrid with suspension fork, flat handlebars, mid-range cassette, hydraulic disc brakes and reflective striping for safety. +Fat-tyre expedition bike with 26x4.5 tires, corrosion-resistant components and wide-spaced dropouts for stability. +High-capacity touring bike with steel frame, triple chainset, sturdy racks, and mudflaps for wet conditions. +A commuter with integrated mid-frame battery, clean wire harness, and neat recessed light elements in head tube and seat post. +A commuter hybrid with a stepped-through frame, suspension fork, 8-speed drivetrain and reflective decals for early-morning rides. +A bespoke titanium gravel grinder with custom geometry, integrated bottle cage mounts on the chainstays and anodized blue headtube. +Vintage steel touring bicycle with lugged frame, leather saddle, front and rear racks, and leather panniers. +A downhill bike with reinforced headset, wide handlebars, thick tires, and a matte-black finish designed to hide scratches from rough sessions. +A commuter with hydraulic rim brake-style calipers, dynamo-integrated headlight, and a modular rack system for weekly errands. +A retro freestyle BMX with chrome frame, double-walled rims, padded crossbar and bold purple fade paint. +Handbuilt lugged steel road bicycle with classic decals, cotton brake cables, and a Brooks leather saddle. +A vintage track-style single-speed with polished chrome fork, narrow bars, and a deep-black paint job for velodrome nostalgia. +A minimalist urban folding bike with single-speed Shimano Nexus hub, compact folded footprint and anodized silver finish. +A compact folder with 16-inch wheels, step-thru frame, rear rack, and quick-fold latch for subways and trunk storage. +Urban e-cargo bike with dual-mode assist, secure locking rack and integrated child safety harness for family logistics. +A classic road race frame restored with vintage decals, period-appropriate components, tubular tires and a rich marigold paint. +A commuter with adjustable stem, tall raked fork, and shock-absorbing elastomer seatpost for choppy urban streets comfort. +Mountain trail full-suspension with 150mm travel, adjustable rebound and compression, and a modern stealth finish. +Foldable electric cargo bike with hydraulic brakes, reinforced hinge, and solar-charge port for accessories. +Mountain enduro with burly frame plates, reinforced dropouts, and a coil shock tuned for traction on abuse. +Road bike with aero seatpost, hidden cables, D-shaped handlebars and 28mm race-ready tires for mixed conditions +Gravel tourer with reinforced downtube, integrated frame pump mount, and reflective logos for visibility. +A gravel bike with stealth graphics, 700x38mm tires, and an unobtrusive frame-integrated pump mount to keep the silhouette tidy. +A vintage British roadster with upright bars, wide saddle, and chrome carrier ready for picnics and slow Sunday spins. +MTB freeride bike with slacker geometry, durable alloy linkages, and splatter protective decals for trail abuse. +Touring bike in brushed aluminum with triple cages, Sturmey-Archer hub, long-wheelbase stability and leather bar tape. +A kids' balance bike with composite frame, lightweight foam tires, adjustable saddle height, and colorful decals to encourage play. +Road endurance machine with endurance geometry, disc brakes, and a subtle metallic flake in the paint. +Touring steel frame with extra-long chainstays, reinforced fork crown, and hammered lugs finished in royal blue. +Touring tandem with synchronized gearing, reinforced dropouts and a matching pair of Brooks saddles for long distance comfort. +A mountain enduro rig with burly 35mm bars, coil shock compatibility, and a paint finish that includes protective clear film in impact zones. +City electric cargo bike with longtail platform, step-thru access, throttle assist and child bench mounting points. +Touring tandem with matching frames, integrated racks, robust wheels and an extra-strong front fork for two-up loaded adventures +A downhill race-ready machine with double-crown fork, reinforced connections, and a frame prepared for aggressive landings. +A handbuilt steel cyclocross frame with hand-ground lugs, thin 28mm tires fitted for fast muddy circuits, and a matte protective clear coat. +A gravel commuter with reflective pinstriping, integrated mudflaps, dynamo hub, and 35mm tubeless tires for all-weather commuting. +A cyclocross race bike with tubeless tires, wide chainstays to avoid clogging, and a quick-release seat clamp for rapid adjustments. +Mountain downhill aluminum frame with giant stanchions, robust linkages and oversized 27.5-inch wheels for bomb-proof runs. +A heavy-duty utility cargo trike with flatbed option, painted safety orange, and hydraulic brakes to safely handle heavy loads on downhill stretches. +Lightweight city folder with small 16" wheels, single-speed drivetrain, and fast-fold latch for last-mile commuting. +Road time-trial special with integrated hydration and aerodynamically sculpted tubing designed for sprint and long efforts. +Vintage-style roadster with upright position, brass bell, and ornate headbadge for style-conscious rides. +A single-speed cyclocross-inspired winter commuter with studded tires, aggressive tread, disc brakes and a dark forest green frame. +Road racing carbon frame with asymmetric stays, wireless shifting compatibility and a glossy pearl white finish. +Mountain freeride frame with sturdy linkage, durable paint that hides dings, and reinforced down tube protection. +Gravel adventure sled with polished titanium frame, generous tire clearance, and a minimalist logo for stealthy exploration. +A handbuilt steel tandem with careful fillet work, polished lugs, and a paint fade that tells of long shared rides. +A performance mountain bike with 140mm suspension, 29-inch wheels, tubeless-ready rims and anodized silver on the headtube. +Full-suspension downhill race sled with coil shock, reinforced swingarm and menacing black decals. +A retro cruiser with banana saddle, swept chrome bars, big balloon tires and candy-pink finish with whitewall accents. +Kids' BMX with bright stickers, dropped chainstay for tricks and slim seat for maneuverability. +A retro cruiser with palm-tree decals, gold-plated badge, wide chrome fenders and a comfortable broad saddle for summer leisure. +Urban cargo trike with insulated cargo box, hydraulic discs, electric assist and easy-access low step for delivery runs. +A cyclocross commuter with normalized geometry, mud-guard mounts, dynamo lighting and 35mm semi-slick tires for mixed conditions. +A modern gravel frameset with a mudd-resistant geometry, integrated top tube storage, and external cable guides for simple field repairs. +Track sprint bike painted in high-gloss metallic blue with a stiff monocoque frame and a deep rear disc wheel. +Compact folding bicycle with 16-inch wheels, quick-fold hinge, rear rack and a step-through frame for city commuting. +A gravel commuter hybrid with slick 38mm tires, integrated rear rack, and a durable alloy frame designed for daily use on mixed surfaces. +Cyclocross race bike with cantilever brakes, reinforced fork and mud-shedding geometry for tight courses. +Fixed-gear sprint bike with alloy deep rims, single-tooth ring, minimal paintwork, and a narrow saddle for aerodynamic positioning. +Lightweight commuter with single-bolt rack, narrow chainstay, and high-volume tires to smooth out city cobbles. +Sleek electric commuter in metallic gray with low-step frame, hub motor, integrated lights, and a locking battery system. +Cyclocross bike with ceramic bottom bracket, tapered headtube, and quick-release thru-axles for stiff handling. +A touring bike with twin-bolt rack mounts, large-diameter spokes, and scuff-resistant paint for long-haul resilient touring. +A kids' balance bike in pastel blue with wood-like frame accents, rubber grip handles, and a low center of gravity to teach balance. +A performance road bike with deep aero wheels, wireless electronic shifting, carbon cockpit and satin onyx black finish. +A compact trail hardtail with 27.5-inch wheels, 120mm fork, single-ring setup, and a responsive alloy frame for local trails. +A matte-black carbon road bike with aerodynamic tube shaping, 700c deep-section wheels, Shimano Ultegra mechanical 2x11 groupset, and a compact 50/34 crank for fast group rides. +Fixed-gear town bike with satin finish, small details like brass bolts, and slim tires for efficient city riding. +A hardtail e-mountain bike with 140mm fork, torque-boosting hub motor, and sacrificial bash guard for steep technical climbs. +Urban commuter with integrated fender and rack, belt drive, and an internally geared hub for all-weather reliability. +Lightweight road bike with smooth welds, carbon fork, 2x groupset, and a polished finish for sharp-looking club rides. +A track sprinter in glossy red with polished fork crown, aero-shaped seatpost, and racing-focused geometry. +Mid-century vintage road bicycle with Bakelite grips, chrome fenders and a bell, keeping classic silhouettes +A performance track sprinter with stiff monocoque frame, narrow shell saddle, high-pressure tubular tires and a paint finish that gleams under arena lights. +Mountain downhill sled with carbon swingarm, massive travel front fork, and a geometry built to conquer race courses. +A lightweight carbon race bike optimized for climbing with 34mm tires, compact crankset and sub-7kg build for uphill events. +Carbon cross-country race rig with featherweight build, stiff bottom bracket, and quick acceleration for technical climbs. +A track fixed-gear velodrome bike with steep geometry, narrow aero bars, and deep-profile tubular rims. +Touring tandem with rear drum hub, wide-range rear cassette and comfortable touring-specific seats for long miles. +Race time trial bike with deep-section front and rear wheels, steep seat angle, and fully faired cables for minimal drag. +Disc-brake road bicycle with hidden cables, integrated seatmast and 35mm tubeless tires for rougher tarmac +Lightweight carbon time trial bike with integrated hydration, triathlon geometry, and disc brakes tuned for sustained speed. +A BMX street setup with gyro for brake routing, sealed bearing hubs, stiff chromoly frame and bold graffiti-inspired paint. +A gravel race-ready frame with short chainstays, wide permissible tire clearance, and an aggressive stack/reach to push pace on mixed roads. +Urban single-speed with kom-style paint, deep-section front rim and minimalist saddle for alleycat vibe. +Kids' BMX with low top tube, wide handlebars, and safety-engineered pedals for confident trick practice. +BMX park rig with reinforced top tube, mag wheels option and high-grip pedals for tricks and landings. +Lightweight cyclocross carbon frame with geometry tuned for quick shouldering, 33mm cyclocross tires and flared drops. +A gravel-plus explorer with 27.5+ compatibility, thick sidewall tubeless tires, and plenty of rack mounts for multi-day trips. +A commuter with integrated GPS lighting, solar-charged taillight, and a low-maintenance belt drive for minimal fuss over miles. +A kids' BMX with extra durable pegs, reinforced headset, and slick park tires for practicing spins and grinds at the skatepark. +Trail-focused hardtail with 130mm fork, tubeless-ready rims, and lockout functionality for climbs. +A mountain hardtail built for bikepacking with rack mounts, framebag-friendly triangle, and durable 29er wheels for long trail hauls. +A hand-painted steel cruiser with surfboard rack attachments, chrome-plated components, and big whitewall tires for beachside flair. +Folding travel bicycle with suitcase carry bag, 16-inch wheels, and a reinforced hinge for repeated folding. +A modern all-road bike with aero handlebars, 35mm clearance, split top tube design and integrated cable routing to reduce drag on fast adventures. +A durable school bike with coaster brake, chain guard, sturdy steel frame and bright reflective stickers for safety on short trips. +A classic steel road bike with hand-painted pinstripes, narrow randonneur tires, Brooks saddle and alloy racks for vintage touring. +Classic city cruiser with banana saddle, chrome fenders, painted spokes and a coaster brake for nostalgic summer rides. +A polished titanium endurance bike with wide tire clearance, integrated internal storage, and subtle laser-etched branding for understated tastes. +A touring tandemsur with steel frame mounts, two sets of panniers, and 36-hole wheelset built to steadyly carry two riders and gear on multi-day trips. +Classic mixte frame city bike with step-through top tube, wicker basket and cream-colored mudguards. +A compact single-speed fixed frame converted for track use with short wheelbase, narrow bars and a classic polished chrome finish. +A gravel bike with electronic shifting, hydraulic discs, 700c carbon wheels and a two-tone moss-to-slate gradient paint job. +A time trial bike with steep headtube angle, downstream hydration, integrated aerobars and reflective pearl-white paint. +Commuter urban e-bike with integrated rear rack battery, quiet hub motor, and splash guards for rainy weather reliability +A lightweight cross-country hardtail with tapered steerer, single-ring drivetrain, and short chainstays for quick accelerations. +Urban folding bike with step-through design, smaller chainring, and big locking latch for safe easy city stashes. +Electric mountain hardtail with 150mm fork travel, torque-limited motor, and reinforced spokes for trail confidence. +Mountain cross-country bike with carbon hardtail frame, efficient pedaling geometry, and wide-range cassette for steep climbs. +A sleek carbon track sprinter with box-section chainstays, zero-stack headset, and a matte gloss two-tone paint job. +Race-oriented time trial bicycle with integrated hydration and a sculpted tail for minimal drag. +A fixed-gear with anodized components, narrow aero rims, minimalist decals and a raw-steel aesthetic that ages with character. +A commuter with child-carrying rear rack, low-step frame, and built-in lights powered by a dynamo hub for safe family transport. +Handmade steel cyclocross racer with handpainted pinstripes, lightweight yet durable tubing and classic dropouts for quick changes. +Classic cruiser with deep-sprung saddle, wide handlebars, and cream tires for leisurely oceanfront rides. +A commuter with hub dynamo lighting, reflective bead tires, internal hub gear and a small rear rack for light loads and safety. +A commuter with foldable frame, single-hand latch, and compact geometry for tight apartment storage and easy transit. +A gravel-adventure bicycle with a broad gear range, multiple attachment points, and a comfortable saddle built for long off-grid spreads. +Folding electric cargo with low deck, hydraulic brakes, and cargo rails designed for urban delivery businesses. +Aero road bike with hidden seatpost clamp, rim-brake impression, and gloss pearl paint. +Touring commuter hybrid with pannier-ready rack, upright bars, and an easy-to-maintain hub gearing setup. +A kids’ balance bike with wooden frame, protective bumpers, and bright natural lacquer for early bike-skill development outdoors. +A eclectic custom bicycle with mismatched vintage components, bright graffiti-style paint, and a comfortable saddle for city wanderings. +A scandi-style utility bike with integrated child seat mounts, robust kickstand, and discreet chaincase. +A mini-velo folding bike with 451 wheels, compact geometry, triple chainring, and bright lime accents for zippy urban hops. +Cargo bike with modular boxes, hydraulic disc brakes, and integrated child seat adapters for family errands. +Gravel-adventure bike with stainless bottle screws, tough headtube reinforcement, and long-range gearing for remote explorations. +A high-pivot enduro bike with idler pulley, 170mm travel, mullet wheel setup (29/27.5), and chainstay protection for traction. +Folding electric cargo bike with long deck, rear suspension, central hinge, and throttle assist for door-to-door errands. +All-road titanium frame with drilled brake caliper mounts, wide tire clearance and subtle laser-etched logos for understated style. +A cargo tricycle with modular loading modules, low-slung deck for center-loaded stability, and gentle steering for predictable load handling. +Touring-ready tandem with double racks, integrated lights, and low-maintenance hub gearing for long-distance pair travel. +A commuter with built-in child seat mounts, integrated lights, and a hydraulically actuated hub for low-effort city starting and stopping. +A mountain bike with coil shock, burly 2.5-inch tires, and a slack head angle for stability at speed on rough descents. +Classic single-speed with chrome handlebars, thin leather saddle, and retro headbadge for urban style points. +Bikepacking-specific frame with integrated frame bag, tapered head tube, stable geometry, and tubeless-ready rims. +A downhill freeride rig with adjustable chainstay, coil shock, and reinforced headtube gussets for bike-park aggressiveness. +Alloy gravel grinder with thru-axles, modular mounts for cages and a durable powder-coat finish. +Lightweight titanium road bike with classic geometry, hand-finished joints, and pearl white enamel. +Tandem recumbent with upright seating alternative, belt drives, and a matching pair of panniers for touring. +Touring steel frame with extra welds in stress areas, double-butted tubing and classic brazed-on lugs for strength. +Race-day time trial bike with film-wrapped fairings, Kamm-tail seat tube and extended aero stem. +Gravel adventure bicycle with stainless bolts, titanium bottle cage mounts, and a subtle top-tube compass decal. +Road commuter with dynamo-powered lights, puncture-resistant tires, and a comfortable upright geometry. +Cargo-longtail with extended rear deck, passenger footpegs, sturdy rails, and a comfy bench-style seat for kids. +Vintage Dutch-style bicycle with coaster brake, built-in frame lock, and an old-school wicker basket bolted to the front. +A vintage-styled city bicycle with glossy cream paint, chrome accents, an upright leather saddle and fenders for classic European flair. +A stripped-out gravel racer with carbon seat tube, flared handlebars, and a discreet integrated GPS mount behind the stem. +Heavy-duty utility cargo bicycle with reinforced fork, double-kickstand, and large wooden deck for transporting bulky loads. +Steel cyclocross bicycle with mud clearance, cantilever brakes, 700c x 33mm knobby tires, and a race geometry that balances stability and agility. +Singlespeed mountain bike with a wide-range cassette, narrow-wide chainring and robust steel frame. +High-performance gravel racer in gunmetal gray, up to 42mm slicks, SRAM eTap wireless shifting, and tubeless-ready rims. +A BMX park bike finished in candy metallic paint, micro-fluted grips, low-profile tires, and reinforced gussets on the top tube for tricks. +E-gravel bike with torque-sensing motor, durable alloy frame, and twin-side bottle mounts for long explorations. +Road endurance bike with relaxed geometry, 28mm tires, vibration-damping carbon layup and disc brakes. +A hand-built mountain hardtail with custom geo, tapered head tube, 120mm fork, and a handcrafted wooden top-tube protector for scratch resistance. +A gravel-chaser with 700c disc wheels, tubeless-compatible rims, mudguard mounts and a granite-gray finish with orange flake. +Electric assist touring bike with long-range battery, belt drive, integrated lights, and reinforced pannier mounts for heavy load. +Steel gravel bicycle with 650b wheels, tan sidewall tires, and a minimalist rack for bikepacking. +Touring steel frame with extra-wide clearance, heavy-duty rear rack, and a classic, nearly indestructible paint finish. +Fixed-gear track-style commuter with high-flange hubs, trimmed brake lever, narrow saddle, and powder-coated black finish. +Gravel training bike with robust alloy frame, comfortable cockpit, and a wide-range cassette to handle any route. +A gravel racer with sub-1,000-gram frameset, hydraulic disc calipers, and a color-shifting paint finish. +Folding cargo bike with built-in locking system, small wheelbase, and rider-adjustable handlebar height for commuting flexibility +A classic road bicycle with rebuilt headset, modern sealed bearings, and a subtle vintage-style paint job for understated elegance. +A stripped-down single-speed road bike with chrome frame, narrow tires, toe-clip pedals and a no-fuss aesthetic for minimalist riders. +Mountain downhill bike with reinforced pivot hardware, thick-walled swingarm, and signature camo paint. +A lightweight commuter with aero-morope tubing, integrated fender mounts, and reflective sidewall tires for safety on dusk rides. +Single-speed track-inspired city bike in glossy sky blue with a flip-flop hub and minimal cable routing for a clean silhouette. +A downhill race machine with dual crown fork, coil-shock rear, reinforced bearings and fluorescent orange top tube accents. +Vintage mixte with patina paint, sprung leather saddle and alloy fenders for relaxed neighborhood rides. +Beach cruiser with low-slung frame, wide tires, and a polished chrome rear rack for extra style. +Urban utility bicycle with long front rack, plywood platform, puncture-resistant tires and a simple coaster brake. +A steel gravel bike with classic round tubes, triple cage mounts, dropper post compatibility and antique bronze paint. +Touring steel bicycle with dual-crown racks, integrated lighting mounts and reliable drum brakes for unknown roads. +Electric mountain e-bicycle with long-range battery, automatic shifting compatibility and reinforced drivetrain for high torque. +A gravel-plus ride machine with 27.5+ wheels, wide rims, cushioned geometry, and bikepacking-ready mounts for long days off-road. +Gravel race frameset with aerodynamic seat tube, generous downtube, integrated storage and clearance for 40mm rubber for race days. +Bike with Rohloff internal gear hub, Gates carbon belt drive, and waterproof frame bag mounts. +A classic lugged steel road bike restored with correct period paint, restrained modern brake upgrades, and a supple feel for long country spins. +Time trial frameset with integrated hydration, aero seat tube, minimal drag profile and hidden cable routing for marginal gains. +Compact city folding e-bike with integrated battery, thumb throttle, 16" wheels, and a rear rack for short commutes and train travel. +Compact recumbent bicycle with low-slung frame, mesh reclined seat, and tandem-compatible boom for distance comfort. +Lightweight endurance road bike with disc brakes, 28mm tires, and a soft-touch paint finish to hide road grit. +A commuter with full chaincase, wide swept bars, and a plush saddle for comfort and minimal maintenance on everyday rides. +Vintage-inspired road bike with downtube shifters, period-correct components and celeste paint accents. +Vintage track bike with chrome fork crown, steel deep-section rims and a weathered leather saddle for old-school charm. +A boutique titanium tandem with matching anodized components, smooth welds, and touring rack compatibility. +Mountain bike with 29-inch wheels, 150mm travel front, and 140mm rear for balanced handling. +Urban single-speed with polished finish, minimalist saddle, narrow handlebars, and a compact frame for quick errands. +Urban single-speed with polished frame, minimalist decals, and leather grips for a clean commuter aesthetic. +A modern clásico steel road bike with polished lugs, classic geometry, and modern disc brake mount for contemporary stopping power. +A resilient touring bicycle with extra mudguard mounts, low-gear triple crank, and robust wheelset built for loaded miles. +A lightweight cyclocross bike with disc brakes, clearance for large tires, and quick-release thru-axles for fast wheel swaps. +Adventure gravel bike with reinforced dropouts, rack and fender compatibility, and 700x40 tires for mixed surfaces. +A classic steel road frame built for criteriums with stiff oversized BB shell, racy geometry, and glossy celeste paint for attention. +A fat-tire beach cruiser with 5" balloon tires, low gear ratio, step-through frame and retro surfboard-style paintwork. +Mountain XC race rig with lightweight frame, race-tuned suspension fork and a minimalistic decal treatment. +A classic touring bike with triple racks, reinforced downtube, and a sensible low-range gearing setup for steep loaded climbs. +Gravel expedition frameset with extra-fortified dropouts, sealed bearing pivots, and a resilient scratch-resistant finish for remote touring. +Gravel race frame with carbon layup, aerodynamic tubing, and a thin seatpost designed to save weight while retaining comfort. +A gravel adventure frame with extra clearance, reinforced chainstay protector, triple-cage friendly layout and moss green paint splashed with yellow. +Gravel touring frame with welded rack mounts, double water bottle capacity and a matte earth-tone paint for long trips. +A downhill race machine with a massive 180mm fork, coil shock, and a triple-bolt seatpost clamp for stability under severe braking. +A commuter with a built-in frame lock, reflective sidewall strips, and puncture-resistant tires for peace of mind in the city. +Gravel tourer with stainless steel frame, leather wrapped bars, frame bag straps and practical mudguard options for long voyages +A fat-tire winter commuter with studded 5-inch tires, chainstay boots, and a warm padded saddle for ice and snow. +Track bike with polished chromed fork and tall deep-section rims for aerodynamic pursuit events. +Road climbing-oriented build with lightweight crankset, narrow tubeless road tires and a low-mass wheelset for quick ascents. +A modern mountain trail bike with 150mm rear travel, 160mm fork, carbon cockpit, and wide tires for mixed-terminology trail use. +Time-trial TT rig with integrated hydration bento, paired front and rear disc wheels, and a steep aero seat post for low frontal area. +Gravel bike with salmon-pink paint, leather bar tape, rack mounts and 700x38 tires for scenic weekend rides +Gravel-focused aluminum frame with modern geometry, internal routing, and plenty of room for 45mm tires and fenders. +A comfy cruiser with long wheelbase, soft foam grips, and large 26x2.3-inch balloon tires for slow beachside relaxation. +A stripped modern gravel frameset with hauler mounts, flat mounts for disc brakes, top-tube stylo bag, and matte graphite paint. +An ultralight gravel frame with carbon layup tuned for comfort, 700x40mm tire clearance and discreet framebag anchor points for long events. +A dirt jumper with slack geometry, strong chromoly frame, 26-inch wheels and heavy-duty hubs for stair-gap and pumptrack lines. +Cargo longtail with adjustable passenger bench, reinforced rear axle, and reflective decals for safety. +A vintage touring bicycle restored with new tubes, leather saddle, brass lamp and a gentle pastel blue frame with white pinstripe. +A gravel adventure bike with titanium dropouts, multiple cage mounts, and a relaxed head angle for comfort on long gravel rides. +Gravel endurance carbon frame with two-bolt direct-mount front derailleur, hidden fender mounts and vertical compliance. +A modern mountain trail bike with 150mm front travel, 140mm rear, coil shock option and stealth matte black paint with lime accents. +A children's balance bike with rubberized grips, soft foam saddle, powder-coated frame and wide stable wheels for safe skill-building. +Track keirin-style fixed gear with sealed bearing hubs, minimalist cockpit and glossy team-color paint. +A single-speed urban BMX styled bike with matte black frame, white decals, and a low-profile saddle for trick sessions and alley sprints. +All-mountain trail bike with 150mm travel front and rear, dropper post and tubeless ready wheels. +A gravel-specific race machine with aero seatpost fin, flattened fairings, and a short head tube for an aggressive time-trial stance. +Road time-trial bike with a bladder-shaped downtube, narrow cockpit, and precise integrated hydration system. +A cyclocross competition bike with carbon fork, tubeless tubular wheels, quick-release skewers and flared bars to control rough descents. +Vintage-inspired cruiser with pastel paint, wicker basket, broad saddle and classic bell for slow scenic rides along promenades +A high-volume fat-bike with 4.5" tires, low gear cluster, Rigid fork options and a reinforced frame for winter patrols or snowboard-adjacent errands. +Touring steel bike with frame-mounted pump, triple bottle mounts, full fenders, and a relaxed geometry for self-supported trips. +A trail-hungry hardtail with a slacker head angle, modern 29" wheel compatibility, and a light but burly frame to inspire technical fun. +Titanium road race bike with pinstriped decals, CF fork, and precision-butted tubing schedule. +A bespoke steel commuter with hand-applied clearcoat, anodized seatpost binder, and polished lugs that catch the sunlight on morning rides. +Aerodynamic time trial bike with tri bars, integrated hydration system, disc rear wheel, and steep seat tube angle. +A lightweight criterium frame with stiff rear triangle, short wheelbase, narrow rims and a glossy race livery for high-speed chases. +Folding electric travel bike with compact folded size, secure carrying handle and a bright LED display for ride data. +A beach cruiser with teak wood rack, oversized saddle, sweeping chrome fenders and glossy sea-blue paint with white lettering. +Urban single-speed with narrow handlebars, no-frills steel frame and a retro pinstriped logo on the head tube. +Sturdy cargo trike with hydraulic disc brakes, large wooden cargo bed, and yellow safety paint. +A fixed-gear track-inspired commuter with peppy acceleration, light frame, and a simple single cog for fuss-free maintenance. +A high-performance downhill sled with overbuilt pedals, wide handlebars, and strong wheels for repeated technical park laps. +Electric mountain hardtail with short travel fork, torque-sensing motor and aggressive alloy frame. +Cyclocross race bike with shallow rim tubulars, lightweight carbon fork, and short wheelbase for quick handling in mud +Electric fat bike built for winter touring with wide studs, mid-drive motor, long-range battery and insulated handlebars +Mountain trail bike with 140mm travel, four-bar linkage, and sculpted lowers for mud shedding. +E-gravel bike with long-range battery, intelligent torque sensing, mud-friendly frame shapes and comfortable endurance geometry. +A gravel adventure frame with stealthy matte finish, plate-style bottle mounts, 700x45 tire clearance and robust welded joints for resourceful touring. +Retro mixte with gentle curved top tube, wicker basket, chrome mudguards and leather saddle for charming city rides. +A kids' mini BMX with bright sprocket art, reinforced frame, and grippy tires for park stomps and backyard lines. +Steel fixed-gear with elegant taper, brushed metal finish, 44T chainring and a clean single-speed silhouette. +A performance cyclocross bike with disc calipers, wide tire clearance, and paintwork that accentuates the frame's sculpted tubes. +Electric gravel explorer with dual-battery capability, torque sensor assist and wide tire compatibility for long backcountry days. +Retro-inspired steel city bike with swept-back handlebars, coaster brake, and warm cream paint. +Lightweight cyclocross frame with dropped chainstays, thru-axle spacing, and minimal weight for quick shouldering and agile handling. +Commuter hybrid bike with flat bars, 700c wheels, puncture-resistant tires, kickstand, and step-over aluminum frame with reflective decals. +A commuter with integrated smartphone mount, built-in GPS tracker, puncture-resistant 28mm tires, and reflective paint for visibility. +Classic steel road club bike with polished lugs, quill stem, leather saddle and period-correct shifters. +A minimalist single-speed mountain commuter with large-volume tires, rigid fork, steel frame and industrial gray powdercoat. +Lightweight climbing road bike with compact chainset, low gearing, and 25mm tubulars for steep ascents. +A titanium cyclocross frame with elegant dropouts, integrated front derailleur clamp provision, and a raw finish that celebrates material honesty. +A commuter with Bosch mid-drive motor, belt drive, internal lights, and anti-theft integrated locking system for secure city use. +A high-performance carbon mountain bicycle in teal with 120mm travel, fast-rolling 29" wheels and race-tuned suspension for cross-country events. +Urban commuter with built-in front basket, hub dynamo lighting, and a rust-resistant steel frame for longevity. +Mountain enduro with reinforced pivots, external bearing shields, and a two-tone paint scheme highlighting the linkage. +A kids' BMX with bright anodized parts, small 20" wheels, and reinforced tubing to help young riders get comfortable with freestyle maneuvers. +Track pursuit bike with full disc front and rear covers, stiff transfer tube, and a single-speed fixed ratio tuned for velodrome lap times. +Classic ladies' step-through bicycle with wicker basket, floral decal, and vintage-style chain guard for market runs. +Track fixed-gear with polished dropouts, narrow front profile, and a simple logo etched into the head tube. +Retro cruiser with two-tone enamel, leather-wrapped saddle, deep chrome fenders and balloon tires for nostalgic coastal rides. +Mountain trail hardtail with durable alloy tubing, tubeless-ready rims, lockout-capable fork and grippy compound tires for year-round trails +Commuter with integrated dynamo lights, full coverage fenders, and a sturdy rack with straps for a laptop bag. +A lowrider cruiser bicycle with chrome-plated frame, whitewall tires, sissy bar and extensive pinstripe artwork for show rides. +A folding commuter with a single hinge, 20-inch wheels, compact folding footprint and a carrying clip built into the frame. +Electric cargo longtail with passenger footrests, stable low platform, throttle assist and powerful disc brakes for city family runs. +Gravel adventure bike with stealthy black paint, raised chainstay protector, and 650b option for plus-sized tires. +High-end carbon mountain bike with progressive geometry, dropper post, 29" wheels, and 2.6" tires for confident technical lines. +A full-suspension trail bike with 145mm travel, low bottom bracket for cornering, and internal dropper routing for clean lines. +Classic steel commuter with Sturmey-Archer hub, full chaincase, fenders, and a traditional city-riding geometry. +A gravel speedster with shallow-profile carbon wheels, 32mm tires, and an aero-optimized cockpit for sustained fast group efforts on unpaved roads. +A polished stainless steel commuter with integrated fenders, hub dynamo, Brooks leather saddle and mirror-polished finish. +A city commuter with step-through frame, internal hub gears, integrated rear rack, belt drive and deep chocolate-brown finish. +Gravel endurance machine with high-volume carbon wheels, 700x42 tires, and multiple braze-ons for framepacks and racking. +Gravel stealth commuter with matte finish, internal lighting wiring, and puncture-resistant tire set for daily commuting. +Bikepacking-ready steel frame with three small-pack mounts, micro rack system, and 29x2.2 tubeless tires for remote routes. +A classic British roadster with leather wrap grips, triple-speed hub, chaincase and hunter green enamel with cream pinstripe. +Aero gravel bike with truncated airfoil tubing, 40mm clearance, internal storage, and a stealthy matte livery. +A utility trike with solid rear box, integrated tie-downs, and a stable three-wheel layout for safe heavy-duty loads in urban spaces. +Vintage track-style fixed gear with polished chrome, narrow saddle and a simple single-gear approach for city spins. +BMX street bike with gyro, reinforced top tube, and anodized blue hubs for style and strength. +A touring bicycle built for gravel with triple racks, heavy-duty spokes, and a frame designed to accept wide tires and long screws. +Touring-ready gravel bike with low gearing triple chainset option, welded-on fender eyes and stout rear triangle. +A commuter with integrated rear rack, low-maintenance Gates belt drive, internal hub gearing and subtle silt-gray paint. +Youth BMX cruiser with rust-resistant components, padded crossbar pad, and pegs for beginning tricks in the neighborhood park. +Touring bike with Rohloff hub, titanium racks and leather handlebar wrap for a comfortable transcontinental tour. +A brisk single-speed cruiser with matte forest green, flared handlebars, and wide saddle for leisurely weekend errands and short commutes. +A mountain e-mtb with torque sensor motor, wide handlebars, and a discreet battery placement low in the frame for balanced handling. +Gravel race machine with carbon frame, 700x36 tires, and quick-change thru-axles for fast events. +A gravel chameleon with quick-release dropouts, convertible thru-axles, and options for single-ring or double setups for versatility. +Steel lugged touring bicycle painted British racing green with front and rear racks, wide fenders and double-bolt panniers. +A polished steel cyclocross build with modern disc brakes, classic lugs, and a saffron-yellow paint that pops in the mud. +Fat bike with 26x4.8-inch tires, stud-compatible rims for winter riding and steel frame for compliance +A lightweight mixed-terrain bikepacking rig with titanium bolts, framebag loops, and a 1x drivetrain tuned for long, steep climbs. +Steel commuter bike with upright geometry, chainguard, hub gears and built-in rear rack for daily grocery runs. +Long-haul touring bicycle with brazed-on water bottle bosses, front and rear lowrider racks and double-butted steel tubing for strength. +Track-inspired urban single-speed with polished stem, wooden saddle, and narrow tubular tires. +Gravel versatile frameset with clearance for 2.1-inch tires, internal routing and discreet rack mounts for multi-day trips. +City utility bike with baskets front and rear, spring saddle, and upright swept handlebars for comfort. +Touring expedition bike with heavy-gauge steel frame, extra fork mounts and oversized racks for expedition gear. +Gravel racing frame with asymmetric chainstays, integrated cable ports and a short stem for responsive handling on rough roads. +A cyclocross training bike with drilled rims for weight savings, sturdy frame, and treaded tires tuned for variable conditions. +Gravel bike with stealth matte, integrated stem, and paintwork inspired by desert dunes. +Mountain slopestyle bike with short chainstays, stiff cranks, and a slammed cockpit for tricks. +Cyclocross bike optimized for mixed terrain with 33mm tires, quick-release rear axle, and slightly higher bottom bracket for clearance +A boutique handbuilt steel frame with subtle hammered texture, reclaimed woods accents on the bars, and leather saddle for unique character. +Stealth commuter with matte black hub motor, low-profile battery, and flat bars with ergonomic grips. +Mountain hardtail with steel frame, modern slack geometry, wide handlebars, and durable finish for long trail days. +Gravel all-conditions bike with multiple rack mounts, wide tire potential, flared bars and a torque-limited drivetrain for rough descents +Gravel touring bike built for bikepacking with multiple framebag attachment points and rugged 700x40c tires. +A commuter with full-length fenders, internal hub gear, and a woven front basket attached to a robust stem mount for practical shopping routes. +A durable commuter with hub dynamo, steel frame, and ergonomic alloy handlebars fitted with padded grips to reduce hand fatigue. +Urban single-speed cruiser with banana seat, sissy bar and wide handlebars in glossy teal. +A durable winter commuter with coated drivetrain, stud-compatible tires, and integrated fenders to keep snow and slush from soaking the rider. +A commuter with a low-step frame, quiet belt drive, and an integrated taillight that connects to a rechargeable in-frame battery. +Gravel speedster with 28mm slick tires, drop bars and race geometry for fast paved stretches in mixed routes. +A pure fixed-gear track bike with massive chainring, polished steel frame, and short-stem narrow bars optimized for velodrome performance. +Electric longtail cargo bike with integrated child seats, low center of gravity, torque-sensing mid-motor, and reflective trim for safety. +A kids' BMX park bike in bright red with gyro, short pegs, and a protective padded crossbar. +A touring bike with custom welded cargo racks, reinforced chainstays, and wide 700c x 38 tires for rough-road resilience. +Tandem recumbent with two reclining seats, belt drive, and low aerodynamic profile for long tours. +Time trial setup with steep seat tube, narrow aero beams, and an integrated rear hydration bladder for sustained power output. +Gravel racer with wireless shifting, power meter integrated into the crank, and a fast-rolling tyre choice for mixed surfaces. +A mountain e-bike with long-travel suspension, 29-inch wheels, burly tires, and a brushless mid-motor for sustained climbs and descents. +Folding commuter with silent hinge, quick-release wheels, and a padded handle for carrying the folded bike comfortably. +Gravel bike with stealthy matte olive paint, 700x44 tires, dropper post, and a threaded bottom bracket for durability. +A gravel-specific hardtail with clearance for 650b+ wheels, double-bottle mounts, and a robust finish suited for long, dusty routes. +A utility urban bike with integrated trunk rack, low-step frame, hydraulic drum brakes and a rust-resistant finish for harsh weather. +Gravel grinder with hydraulic disc brakes, 40mm tubeless tires, and small aero accents for speed. +Gravel endurance tourer with triple-bolt fork mount, low gearing, and a steel frame treated to resist the elements. +Classic road racing bicycle restored to period-correct spec with tubular tires, quill stem, and polished steel lugs. +Adventure tandem with reinforced center section, dual racks, and ample bottle mounts for long two-person expeditions. +Recreational cruiser with soft gel saddle, swept-back bars, and cheerful floral paint scheme. +Touring-ready steel frameset with gallery of braze-ons, full fenders, wide-range gearing and classic lugwork for reliability. +A full-carbon mountain bike with 140mm travel, tapered headtube, and aggressive geometry designed to balance climbing efficiency and descending speed. +Commuter e-bike with ergonomic step-through frame, torque-sensing mid-drive, integrated lock and weatherproof mudguards for all-weather use. +Lightweight cyclocross frame with short headtube, carbon fork, and subtle contrasting decals for race-day looks. +Cargo-longtail with child seats, reinforced rails, low center of gravity, and a wide saddle for passenger comfort. +A lightweight titanium time-trial bike with integrated one-piece cockpit, aero seatpost, and internal hydration bladder within the frame. +Commuter with integrated smartphone mount, throttle-assist, and low-step design for easy mounting. +A cyclocross practice bike with steel frame, cantilever brakes, and reinforced fork for muddy training sessions. +Gravel-race machine with lightweight carbon, top-tier components, and 700x33 tires for high-speed mixed-surface events. +Classic steel road bike with chrome lugs, period-correct components and a hand-finished pinstripe along the top tube. +Gravel bike with eccentric bottom bracket option for single-speed conversions and plenty of tire clearance. +Electric step-through commuter with low step, comfortable upright position and integrated rear rack for groceries. +Quiet commuter with belt drive, integrated lights, internal hub, and rustproof stainless components for seaside commutes. +Folding commuter with rear hub motor, integrated lights, and quick-release wheels to speed door-to-platform transitions. +Bikepacking-specific bicycle with dedicated framebag space, reinforced top tube and extra bosses for straps. +Adventure expedition bike with full braze-ons, heavy-duty tubing, framebags and an extra-low bottom bracket for loaded rides. +A classic Dutch utility bike with enclosed chaincase, step-through frame, coaster brake, integral bell and sprung saddle for comfortable commuting. +Carbon road bike with clean internal routing, 2-bolt seatpost, and a ceramic-coated drivetrain for longevity. +Classic British road bike with leather saddle, polished steel, and a quill stem restoring a timeless ride experience. +Mountain cross-country race bike with stiffness-tuned frame, 100mm travel, and lightweight wheelset for aggressive climbs. +A performance mountain bike with 140mm travel, tubeless-ready 29-inch wheels, dropper post and matte coal-black finish with subtle neon accents. +A modern steel gravel grinder with paint fade, triple bottle mounts, and a comfortable geometry for all-day endurance rides. +Adventure expedition bike with custom steel frame, oversized bottle mounts, and reinforced rims for thousands of miles on rough roads. +Urban electric folder with 20" wheels, torque-sensing motor, and quick-release folding mechanism to stow in tight apartments. +A retro town bike with cream paint, wicker front basket, chain guard with floral decal, and hub gear simplicity. +Lightweight cross-country race bike with narrow handlebars, fast-rolling tyres and minimalistic graphics for a race-ready look. +Mountain trail hardtail with 130mm fork travel, 29-inch wheels, and a wide-range 12-speed cassette for varied terrain. +Classic road bike with steel lugs, downtube shifters, 10-speed bar-end levers, and a polished chrome fork crown for vintage charm. +Mountain cross-country hardtail with rapid-rolling 29-inch wheels, 100mm fork, and a stiff bottom bracket for efficient climbs. +Vintage-inspired cruiser with matte pastel paint, comfortable foam grips and a low-slung frame for relaxed riding. +A mountain e-bike with 180mm travel, twin-battery capacity, high-torque motor and burly geometry for big mountain terrain and long stage days. +Urban e-bike with torquey hub motor, integrated lights and a low standover for quick mount/dismount. +A boutique titanium frame with immaculate welds, brushed satin finish, and custom engraved headbadge for understated elegance. +A lightweight endurance road frame with vibration-damping layup, 28mm tire clearance, and a slightly taller head tube for all-day comfort. +A hardtail with long-travel fork, tubeless-ready rims, and a slack head angle for brave descents while still pedaling efficiently uphill. +Folding cargo bike with modular deck attachments, secure child harness points, and quick-release axles. +Road climbing frame with asymmetric seatstays, narrow tire clearance for race rubber and a lightweight carbon fork. +Cargo trike with low bed, electric assist, durable decking and extra-wide tires for stable goods transportation. +Handbuilt titanium gravel frame with brushed finish, thru-axles, and discreet rack mounts. +A steel gravel bike with beautiful hand-painted pinstripes, 700x45mm tires, and external cable routing for vintage appeal with modern capability. +A classic dutch-style cargo bike with long wheelbase, integrated child bench, enclosed chaincase, and a robust kickstand for loading. +Children’s cruiser with low-step frame, training wheel removal option, and colorful decals to inspire confidence and joy. +Lightweight aluminum fixed-gear with narrow tires, flamboyant teal paint, and a minimalistic single-brake setup. +A lightweight steel road frame with endurance geometry, gentle paint gradient, and robust wheelset to handle rougher pavement. +Downhill race bike with 200mm travel, dual-crown fork, coil rear shock and thick 2.5" tires for steep descents. +A sleek commuter with matte charcoal paint, hydraulic disc brakes, LED integrated headlight, and a minimalist rear rack for laptops or groceries. +Urban fixed-gear with matte olive paint, riser bars, single tooth chainring, and a small headlight wired to a hub dynamo. +Recumbent bicycle with long wheelbase, comfortable reclined seat, chain-tube fairing, and small front fairing for wind protection. +Gravel speed bike with split top tube for mounts, aggressive gearing, and 35mm gravel tires for added speed. +Electric mountain bike with long-travel suspension, powerful torque-sensing motor, and heavy-duty wheels for all-trail exploration. +A gravel endurance frame with integrated GPS mount, generous tire clearance, and a comfortable squat that soaks vibration without losing speed. +A classic European folding bike with small 16-inch wheels, leather saddle, and practical luggage rack for city sightseeing trips. +A winter commuter with studded tires, fenders, insulated grips and a weatherproof frame coating to resist salt and slush corrosion. +Single-speed fixed-gear urban bike with flip-flop hub, deep black rims, narrow riser bars and a minimalistic frame. +A cyclocross machine with tapered headtube, thru-axles, mud-shedding chainstays and a race-day-ready powdercoat finish. +Commuter with integrated rear rack battery assist, puncture-resistant tires, and chaincase for clean work clothes. +A mountain enduro bike with 170mm travel, coil-tuned shock, and burly 35mm+ stanchions to soak up rocky descents. +A lightweight touring frame with titanium bolts, stainless rack mounts, and smooth-rolling dynamo hub for endless nights on the road. +Folding commuter with 20" wheels, step-through frame, and easy release clamp for simple folding and stowing on trains. +Gravel bike with aero tube shaping, gravel-compatible rims, and subtle logo placement for understated speed. +Steel fixed-gear with brushed finish, minimalist decals, and a leather-wrapped top tube protector. +City commuter with integrated child seat mount, stable low center of gravity and reflective side panels for safety. +Fixed-gear urban race bike with flamboyant paint, polished crankset, and a tight wheelbase for quick handling. +Gravel adventure frameset with carbon fork, 650b wheel compatibility, and multiple mounted points for extra gear. +Single-speed beach bike with coaster brake, balloon tires, and a sun-faded pastel paint that screams summer. +A classic single-speed with hammered-metal paint, leather saddle, narrow tires and an elegant head badge for vintage-inspired rides. +Mountain enduro with coil rear shock, stout rocker link, 27.5-inch wheels, and wide bars for control on technical descents. +A downhill-oriented mountain bike with 200mm travel, coil shock, ultra-slack geometry and aggressive tire tread for steep technical lines. +A kids' mountain bike with durable V-brake setup, comfortable saddle, and 26" wheels sized for upper-child riders learning off-road skills. +A commuter bicycle outfitted with cargo trunk, reflective decals, and an internal hub 8-speed for a workhorse urban setup. +A boutique handbuilt cyclocross frame with tasteful lugwork, subtle metallic paint, and reinforced chainstays for frequent shoulder carries. +Lightweight carbon aero road frame with integrated seatpost clamp, deep carbon wheelset, and a race-oriented cockpit for speed. +Bicycle touring rig with classic lugwork, brazed-on water bottle bosses, and reliable friction shifters for long service life. +Lightweight aluminum hardtail mountain bike with 120mm fork, 29er wheels, and a 1x11 drivetrain for trail simplicity. +Bright yellow urban commuter bike with upright bars, dynamo lights, chaincase and a comfortable sprung saddle. +A gravel bike with full carbon fork, hidden rack mounts, and a matte olive paint that gives a tactical appearance. +Kids' balance bicycle with alloy frame, foam tires, and playful graphics designed to teach steering skills safely. +A modern e-mountain bike with full suspension, torque-multiplying motor, dropper post, and water-resistant battery housing. +A touring recumbent bicycle with comfortable reclined seat, upright fairing, luggage pod behind the seat, and external mirrors for visibility. +A gravel bike with a full-carbon fork, robust alloy frame, and wide rims to accommodate variable terrain with confidence. +Trail-oriented hardtail with dropper post, coil fork, wide rims, and tubeless tires for snappy trail handling. +Steel touring bicycle in British racing green with reinforced fork, low-geared triple crank, wide 32mm tires, integrated rack and leather saddle. +Gravel expedition build with reinforced fork eyelets, multi-day range gearing and heavy-duty spokes for loaded journeys. +Fixed-gear track-style city bike with gleaming chrome studs, narrow tires, and minimalist presence for urban alley runs. +Classic Dutch-style ladies' bike with enclosed chain guard, integrated lock, and a comfy upright posture for short errands. +Lightweight single-speed road bike built for simplicity with polished aluminum frame, narrow tires, and single-ring drivetrain. +A gravel race-build with electronic 1x shifting, aerodynamic bottle cages, 38mm tubeless tires and a paint-splattered race livery. +Fixed-gear alleycat-ready bike with aggressive stance, narrow bars, bold color, and a short wheelbase for agile urban navigation. +Adventure hardtail with reinforced headtube, multiple rack mounts, and wide 29x2.35 tires for rough, loaded routes. +Touring tandem recumbent bicycle with dual chainers, long wheelbase, and multiple luggage mounts. +Urban cargo e-bike with long-tail kickstand, child harness mounts and modular rails for flexible cargo configurations. +A compact folding city bike with rapid fold, small 14-inch wheels, and an adjustable seatpost for quick storage on trains or buses. +All-mountain 29er with 140mm front travel, mullet setup option, reliable brakes and compound tire tread for mixed terrain. +Folding travel bike with carry case, compact folded footprint, and pegs for attaching luggage inside airline cabins. +Touring tandem with heavy-duty racks, synchronized shifting, comfortable saddles and classic paint for long shared adventures. +A kids' balance tricycle with wide base, low center of gravity, and cheerful cartoon paint for early riders. +A boutique titanium commuter with bead-blasted finish, discreet rack eyelets, internal headset and laser-etched minimalist logo. +Folding e-bike with smartphone app, mid-drive motor, lightweight alloy frame, and a secure folding latch for convenience. +A commuter with mini fenders, puncture-resistant compound tires, and a quick-release rear rack to make grocery runs simple and secure. +Lightweight track machine with carbon fork, narrow-profile tubing, and a polished finish for show and go. +Electric-assist mountain bike with mid-drive motor, integrated battery, and torque sensors for natural pedal-assist on steep climbs. +A classic randonneur with full leather saddle, generator headlamp, and a muted matte paint that ages gracefully with long miles. +A vintage cruiser with restored frame, comfortable coil saddle, wide swept bars, and cream whitewalls for nostalgic summer rides. +Urban e-bike with hub motor, integrated headlight, comfortable upright bars and a luggage rack for grocery runs. +An ultralight carbon gravel racer with 700x32mm tires, SRAM Red eTap AXS, and integrated seatpost clamp to shave grams without sacrificing stiffness. +A matte black carbon road bike with aero tube shaping, deep-section 50mm wheels, electronic 12-speed groupset, and subtle red pinstripes. +A mountain e-bike with low-slung battery, active geometry changing, and a brushless motor tuned for instant trail torque. +Smooth city bike with low-maintenance belt drive, integrated fenders, and matching rear rack. +Track keirin bike with aggressive steep seat angle, short rear end, and mirror-polished chainstay. +E-gravel bike with stealth battery integration, intuitive assist mapping, and clearance for heavy loads. +A gravel tourer with titanium frame, 650b wheels, and long-range fuel capacity built around endurance and far-off exploration. +Cargo tricycle with wooden bed, weather-resistant varnish, and ergonomically shaped handle for steering ease. +A BMX cruiser for kids with coaster brake, high-rise handlebars, and an easy-access frame for learning to pedal and turn confidently. +A modern road bike with disc brakes, clearance for 30mm tires, integrated stem and subtle metallic finish for understated performance. +A lightweight XC hardtail with tapered fork, low stack height for fast handling, and a muted metallic finish. +A modern carbon trail bike with tuned compliance zones, dropper compatibility, and stealth matte finish to make dirt look elegant. +A cyclocross race bike with narrow front profile, clutch-equipped derailleur, and geometry tuned for quick shoulder carries and fast remounts. +Gravel endurance steel bike with generous bottle mounts, comfortable posture, and protective clearcoat for long off-grid days. +A cyclocross race machine with integrated chain guide, wider flaring on the bars, and optimized mud shedding on the frame tubes. +A performance carbon gravel bike with integrated seatpost clamp, internal storage, 42mm tubeless tires and a stealth black finish. +A folding utility bike with compact folded height, carrying strap on the top tube, 20" wheels and a lockable hinge for commuter convenience. +Electric cargo bike with long-tail design, modular bench seating, powerful mid-motor, and hydraulic disc brakes for hauling kids. +A beach cruiser with long curved top tube, oversized handlebars, custom shell-white paint and a plush two-up saddle for relaxed cruising. +Touring tandem with reinforced axles, matched gearing, and a relaxed seating geometry to make two-up rides more manageable. +Performance fat-bike with tapered headtube, custom geometry and stud-ready tires for winter commutes. +City utility bicycle with strong steel frame, large front basket, integrated lighting and a simple three-speed hub. +Mountain hardtail designed for aggressive trail riding with reinforced chainstays, tapered headtube and grippy tyres. +A commuter with foldable design, compact geometry, and a simple single-speed drivetrain tuned for steady city pace. +Touring bicycle with multiple three-bolt racks, dynamo lighting, and a gear range suitable for mountainous terrain with heavy loads. +A sled-like fat bike with welded aluminum frame, low gearing for sand runs, 5-inch tires and a matte sand-colored finish. +A hybrid leisure bike with comfortable saddle, upright bars, and a light aluminum frame with playful decals. +Custom chopper bicycle with extended fork, low seat height, chromed frame highlights and stylized paint graphics for attention-grabbing looks. +Mountain enduro rig with reinforced swingarm, wide rims and grippy rubber to carve long descents with confidence. +A custom handbuilt steel gravel bike with fillet-brazed joints, custom paint fade, disc brakes and internal routing for a bespoke ride. +Classic British three-speed with Sturmey Archer hub, chaincase, swept-back bars, and simple robustness for everyday rides. +A downhill mountain bike with 200mm travel, dual-crown fork, coil shock, slack geometry, and 27.5" wheels for steep technical runs. +A cross-country mountain bike with fast-rolling 29" wheels, efficient suspension platform and a race-friendly geometry for endurance events. +A cyclocross-specific carbon frame with mud-shedding tube profiles, 1x drivetrain compatibility and reinforced chainstays for heavy hits. +A kids' mountain rig with easy-climb cassette, low-sag suspension, and a durable frame strong enough for learning trails. +A commuter folding bicycle with small 20-inch wheels, compact folding hinge, carry handle, and a 9-speed micro drivetrain for multi-modal trips. +A kids' cruiser with oversized handlebar streamers, bright polka-dot frame, and training-wheel mounts for beginners. +A lightweight urban fixie with deep-section aerodynamic front wheel, slick rear wheel, and a racing-style saddle for aggressive city riding. +Folding e-bike with handlebar-mounted display, removable battery and a robust hinge mechanism for reliability. +A retro-styled city bike with curved steel frame, leather accents, and a vintage headlamp wired to a modern dynamo hub. +A race-ready cyclocross frame with flared dropouts, wide bottom bracket, and overall low weight for technical cyclocross circuits. +City single-speed with matte charcoal paint, narrow tires, minimalist cockpit, and a polished chainring for simple commuting. +A classic single-speed track-style city bicycle with shallow drop bars, thin tires, polished steel fork and satin black frame with small logo. +Cyclocross training frame with long top tube, durable paint, and reinforced fork blads to withstand repeated mud races. +Road race bike with tuned stiffness, electronic shifting, aerodynamic tubing, and a power meter-equipped crankset for training data. +A performance road bike with integrated power meter, wireless shifting, deep-section wheels and subtle satin blue metallic. +Cargo trike with hydraulic steering assist, load-monitoring sensor, and bright reflective safety strips along the bed. +Mountain downhill bike with alloy linkages, long travel shock, and 2.4-inch tires tuned for rough chutes and rock gardens. +Road endurance bike with relaxed front end, 32mm tires and comfort-focused geometry for long weekends. +A vintage-inspired fixed-gear with leather accents, narrow tires, and a subtle patina finish to evoke classic urban appeal. +A mountain enduro bike with coil shock option, high-volume tyres, and optimized chainstay length for stability at high speeds. +A children's balance bike with wooden frame, painted frame art, low-profile saddle and rubber tires for quiet backyard practice. +A tough single-speed BMX with chromoly frame, sealed bearings, and a glossy neon green powdercoat for skatepark visibility. +A classic city bicycle with front wicker basket, long fenders, and a comfy sprung saddle to handle daily errands with grace. +A mountain freeride bike with heavy-duty bearings, reinforced pivot plates, and a rugged powdercoat to withstand constant shuttling abuse. +Commuter with hub dynamo, weatherproof display, and a low-maintenance belt drive perfect for daily rides. +A commuter with belt drive, internal hub gears, low-maintenance setup, full fenders, and a minimalistic rear rack for work bags. +A commuter folding bike with compact fold, quick-release seatpost, 20-inch wheels and a practical luggage rack for essential gear. +A cyclocross-specific frame with progressive geometry, tubeless-ready wheels, and an anodized head tube badge that resists the elements. +A polished steel city bike with swept bars, leather saddle, and full fenders to keep clothing neat on drizzly commutes. +Commuter with belt drive, enclosed chaincase, and integrated rear light that doubles as a reflector. +A sleek carbon time trial rig with integrated hydration, solid disc rear wheel, and a flat seat tube for aero advantage. +Commuter hybrid with flat bars, light alloy frame, reflective details, and puncture-resistant tires for reliable daily use. +Urban folding single-speed with foldable stem, comfortable saddle, reflective detailing and small wheels for tight city storage. +A gravel racing platform with wide tire clearance, stash pockets in the top tube, and a modern matte burgundy color that hides dirt. +Mountain all-mountain frame with 150mm travel, carbon links, and aggressive geometry for technical trail riding. +Track sprint rig with a fixed cog, single front brake option, and a glossy bright color to stand out in sprints. +Road race frame with electronic di2 compatibility, aero tubing, deep section wheels and a race-ready cockpit. +Cyclocross race machine with modern carbon layup, disc brakes and high-volume tubeless tires for muddy courses. +Gravel bike with custom paint splatter, 650b wheels option, and a versatile 1x system for mixed terrain. +Road race bike with carbon seat tube, aero standover, integrated computer mount and 25mm high-pressure tires for smooth tarmac acceleration +Fixed-gear commuter with neon accents, narrow saddle and track-style frame for quick urban trips. +A retro BMX with springer fork, chrome-plated frame, slim profile tires, and glossy enamel paint reminiscent of early street culture. +A polished titanium track frame with classic proportions, deep-section front rim, and a raw metallic sheen that develops character with miles. +Cargo e-bike with low-step frame, powerful battery, reinforced spokes, and modular cargo attachments for business use. +A bright-yellow kids' mountain bike with front suspension, V-brakes, and wide knobby tires for trail exploration. +A modern gravel bike with adjustable head angle via a flip chip, long chainstays for stability, and clearance for 50mm rubber for true adventure. +A fat-tire expedition bicycle with 4.8" tires, reinforced rims, and double-bolt rack mounts for polar or desert adventures. +Fixed-gear track bike in gloss white with minimalist decals, narrow saddle and street-friendly gearing for urban fixie culture. +Classic road bike with period-correct components, steel rims, tubular tires and an understated metallic blue lacquer. +Classic steel frame road bike with center-pull brakes, period decals, and cork bar tape. +A long-haul touring bicycle with triple racks, replacement-friendly parts, and a forgiving geometry to ease weeks on the road with luggage. +A modern road race bike with disc brakes, integrated power meter, deep-section 45mm wheels, and a sharp aero profile aimed at speed. +A mixed-terrain gravel bike with 650b wheel option, 45mm knobbed tires, dropper post compatibility and frame storage mounts for bikepacking. +A children's BMX with sturdy chromoly frame, pegs, gyro and neon lime paint with bold sponsor-style decals. +A lightweight aluminum road frame with a tapered headtube, compact geometry, and anodized accent stripes for a sporty visual identity. +A road racing frameset with asymmetric chainstays, integrated power meter axle, and a refined paint job that hides scuffs from chain drops. +Lightweight gravel bike with electronic shifting, 700c x 35 tyres, and an elegant paint finish for fast weekend rides. +Modern BMX cruiser with wide tires, comfy saddle and swept-back bars for seaside boardwalk cruising. +A hardcore cyclocross bike with titanium frame, oversized tubing, and specialized mud-shedding stays for competitive CX races. +Kids’ trail bike with front suspension fork, coaster brakes and friendly cartoon graphic frame. +Aero time-trial bicycle featuring integrated hydration, deep-section carbon wheels, electronic shifting, and a monocoque frame optimized for low drag in triathlons. +A commuter with internal hub shifting, wide anti-puncture tires, sprung saddle and chrome fenders for quieter city rides. +Cargo longtail with integrated child harnesses, modular storage boxes, and a durable powder-coated frame suited for daily family use. +A kids' mountain training bike with front suspension, easy-reach brakes, 20-inch wheels and grippy tires for confidence building. +A cargo e-bike with sturdy aluminum deck, child footrests, mid-drive motor and hydraulic brakes for confident family rides. +Expressive painted titanium road bike with subtle color-shift finish, integrated seat clamp, and wide rim brake clearance for modern calipers. +A gravel explorer with large tire clearance for 47mm rubber, steel-finished frame, rack mounts and dusty-sand paint with distressed graphics. +A gravel monster with 650b+ wheelset, massive 2.2-inch tires, reinforced rims and a geometry that encourages confident off-road exploring. +Classic road single-speed with polished steel frame, leather saddle, and tubular wheelset for weekend cafe rides. +Gravel adventure build with steel frame, wide tyres, reinforced fork and a practical rack for extended expeditions. +Touring tandem with accommodation for two racks, long wheelbase, and twin frame-mounted bottle cages for epic rides. +Lightweight road climbing bike with minimal paint, high-modulus carbon, and an emphasis on weight savings in every tube. +Steel touring bike with classic riveted rack, wide gear range, and a slowly aging patina that tells of many long miles. +Classic city bicycle with spring saddle, wicker basket, and polished chrome fenders for weekend errands. +Electric folding commuter with compact battery, torque-sensing mid-drive, small wheels and quick-fold action for the last-mile solution. +Vintage-style single-speed with sprung leather saddle, swept bars, cream paint and whitewall tires for leisurely rides. +Touring gravel bicycle with triple rack mounts, enclosed chaincase, extra rim strength, and long-throw rear derailleur for big tours +Urban folding cargo with adjustable load tray, quick-release stem and puncture-proof tires for city deliveries. +Road endurance bike with relaxed geometry, 32mm tires, endurance saddle and vibration-damping seatpost insert +Compact folding e-bike with a low frame hinge, small wheels, belt drive, and integrated lights for last-mile commutes. +Lightweight track bike with polished steel frame, high-flange hubs, and minimal decals for understated velodrome speed. +Fixed-gear urban courier with shaved hub seal, high-flange front hub, and a compact design optimized for quick maneuvering. +Mountain enduro with carbon rear triangle, alloy front end, and a balanced suspension tune for aggressive trail riding. +A track bike with rear disc, aero clamp-on bars, and a gloss-black paint scheme with subtle sponsor logos for racing presence. +A polished stainless steel commuter with internal hub gear, full chaincase, and understated brushed-metal finish. +Road disc racer with subtle aero shaping, disc-specific fork, wide 28mm tires and precise electronic shifting for group rides +Cargo electric bike with center-mount battery, adjustable torque settings, and modular cargo sides for diverse loads. +A gravel adventure bike with integrated top tube storage, wide 2-inch tire clearance, robust alloy frame and subtly reflective paint for night rides. +Track fixed-gear with polished aluminum frame, deep-section tubulars and a satin silver finish. +A cyclecross training rig with chain catcher, mud-specific tires, and a tall head tube to help riders stay upright in rough conditions. +A gravel-capable hardtail with 29er wheels, 2.2" tires, and an extended wheelbase for stable speeds on technical dirt roads. +A mountain e-bike with mid-motor, long-travel suspension, burly downhill tires and a torque-adjustable controller for technical trails. +Lightweight fixed-gear track-style with anodized components, minimal graphics and polished rims for a stealth look. +Sleek carbon time trial machine with integrated hydration, deep-section front wheel and tuned fork for stability. +Road time-trial bike with truncated airfoil tubing, tri-bars, integrated hydration and a steep seat tube angle. +Cyclocross bike with cantilever brakes, mud-shedding frame, 33mm tubular tires and ergonomic hoods for long races +City bicycle with fully enclosed drivetrain, integrated lights and a durable steel frame designed for minimal maintenance. +Electric mountain bike with dual-battery capacity, torque-sensing motor, adjustable controller mapping and high-performance suspension kit +Mountain downhill bike with aggressive slack geometry, massive travel, coil-shock tuned for big hits and burly components. +Lightweight time-trial bike with aero bottle mount, extended cockpit and polished carbon shell. +Classic road racer restored with period Campagnolo parts, cotton bar tape and polished steel lugs. +Modern cyclocross frame with thru-axles, flat-mount discs, and wonky asymmetric chainstays to maximize tire clearance and strength. +A commuter with hidden power pack, belt drive, hub dynamo, integrated rear rack and understated graphite matte paint with reflective piping. +A cyclocross racer with carbon monocoque frame, electronic shifting, tubeless tubular tires and a race-fit geometry for aggressive launches and climbs. +A commuter with wide swept-back bars, built-in handlebar mount for bags, and soft rubber grips for relaxed city posture. +A bespoke steel frame with hand-enameled decals, polished lugs, brushed finish and classic brake calipers for refined daytime rides. +Classic road bike with shimano 105, steel frame, and a tasteful cream and maroon livery. +A commuter bicycle with low-step frame, tall fenders, internal hub, and a quiet chaincase to reduce grease on clothing during rides. +A commuter with rugged platform pedals, full-coverage mudguards, and a rear rack sized for grocery baskets and briefcases. +Steel festival cruiser with quirky paint, wide balloon tires, banana seat and matching color grips for happy gatherings +Track bike built for velodrome with fixed rear hub, steep geometry and skewer liberated frameset. +A stylish urban fixie with low-profile wheels, riser bars, mirrored hub, and colorful pinstriping on the top tube for café cruising. +Track pursuit frame with aerodynamic tube shaping, very steep seat angle and narrow handlebars optimized for pursuit positions. +Mountain cross-country race rig with minimal weight, 100mm front travel, stiff fork and narrow-profile tires for fast climb-and-descend handling +Mountain trail hardtail with 29er wheels, progressive geometry and wide handlebars for confident cornering and climbs. +Vintage city bicycle with polished fenders, chaincase, upright helm, and a comfortable ride for urban exploration. +Mountain touring bike with front suspension, rack and pannier mounts, wide cassette, and hydraulic disc brakes for loaded singletrack. +A stealth bomber painted gravel bike with internal storage compartments, tubeless gravel tires, and quiet, low-profile graphics for understated exploration. +A kids' trail mountain bicycle with front suspension fork, 24-inch wheels, lower standover height and wide knobs for forest paths. +Enduro mountain bike with aggressive head angle, long reach, and coil shock compatibility for steep runs. +Touring tandem bicycle with S&S couplers for travel, double racks, two sets of shifters and matching saddles +A lightweight carbon road bike with aero tube shapes, deep-section 50mm wheels, electronic shifting and a race-oriented aggressive geometry. +Gravel racer with reinforced bottom bracket, short stem, and 38mm tubeless tires for fast mixed-surface events. +Downhill mountain bike with long travel, reinforced swingarm, and coil shock for repeated big drops. +A performance mountain bike with lightweight alloy frame, lockout fork, 32mm internal rim width and race-oriented geometry. +Touring gravel bicycle with triple bottle mounts, extra-large framebag capability, long-cage derailleur and robust rims for cross-continental trips +A classic British folder bicycle with 16-inch wheels, bright paint, and an easy hinge for commuters who switch to trains often. +A kids' cruiser with banana seat, chrome sissy bar, small front basket and colorful flame decals that inspire afternoon neighborhood rides. +Road endurance bike with carbon layup tuned for comfort, 32mm tires and subtle reflective decals for low-light safety. +Urban single-speed with glossy finish, colorful bar tape and skid-stop rear hub for quick stops. +Gravel plus bike with mullet wheel setup, 29" front and 27.5+ rear, plus extra clearance for mud. +A fat-bike built for snow and sand with 4.8-inch balloon tires on 26-inch rims, rigid fork, and low-pressure flotation tires for soft surfaces. +City commuter with internal hub gearing, wraparound chaincase, and ergonomic grips to reduce hand fatigue. +Touring tandem with synchronized shifters, reinforced chainset and extra-durable wheels to stand up to extended tours. +A performance gravel bike with progressive geometry, chainstay-mounted frame bag, and a 46/30 compact chainset for steep climbs and fast flats. +Track sprint frame with short wheelbase, oversized tubular rims and a distinctive gloss paint that gleams under lights. +A vintage minimalist road bike with lugged fork crown, narrow rims, and classic brake levers for authentic period performance. +A mountain e-bike with lockout options on the fork, integrated battery below the downtube, and a robust chain guide. +A lowrider cruiser bike with elongated frame, chopper-style handlebars, banana seat and chrome plating on the fenders. +Lightweight carbon endurance road bike with compliance-focused seatstays, wide 28mm tire clearance and subtle decals. +Touring steel frame with multiple rack mounts, full-length fender stays, and classic geometry for a stable ride under load. +Compact folding commuter with quick-release hinge, 16-inch wheels, internal gear hub and bright reflective highlights for safety. +A lightweight cyclocross frame with removable fender mounts, axle standards for quick wheel changes, and a high-stance tubing profile. +Gravel explorer with raw titanium finish, leather saddle, custom framebag, wide clearance tires and stacked bottle cagemounts for remote touring +Classic British racing bicycle with steel lugs, tubular clinchers, and subtle sponsor decals. +Urban commuter with magnetic attachment rear rack, integrated lights, and a belt drive chosen for maintenance-free usage. +A modern fixed-gear street bike with track geometry adapted for road use, polished components, and a stealthy matte paint scheme. +A kids' balance bike with bright teal frame, soft grips, foam tires, and a friendly animal head decal on the fork crown. +Handmade lugged steel road bike with elegant paint, stainless fasteners, classic geometry and narrow 25mm tires for crisp handling. +A titanium touring tandem with lightweight racks, dual water-bottle mounts for each rider, and reinforced lug interfaces for heavy gear. +Long-distance endurance road bike with endurance geometry, 28mm tires, comfortable saddle, and vibration-damping carbon layup for long days. +Modern city electric bike with torque-sensing motor, smartphone integration, and concealed battery for a clean profile. +Commuter with internal lighting system, integrated mirror mount, and a durable chainguard for wet city rides. +A carbon aero road bike with integrated disc brake calipers, internal storage behind the stem, and an ultra-stiff bottom bracket area. +A compact urban folding cargo bike with a quick-folding rear rack, internal cables, and a tough welded hinge for daily opening and closing. +Urban single-speed with deep V rims, bolt-on chainring guard, and ironwork rack for small loads. +Cyclocross competition bike with disc brakes, wide tire clearance, and robust chain retention for quick shouldering and remounts +Vintage-style road bike with leather bar tape, downtube shifters, steel fork, and a delicate pearl-white paint scheme. +A modern cross-country race frame with 100mm travel, lightweight carbon shell, 29-inch wheels and sunburst yellow highlights. +A stripped-down track bike with fixed hub, polished alloy frame, and narrow handlebars aimed at raw speed on short, fast circuits. +A compact 20-inch-wheeled brompton-style folder with adjustable stem, fast-fold mechanism, and robust hinge locking for repeated folding. +Folding utility bike with basket, three-speed hub and puncture-resistant tires for campus life. +Trail-ready hardtail with 140mm fork, x12 thru-axles, and bleached wood-style grips for unique flair. +A commuter with low-maintenance belt drive, 3-speed internal hub, polished fenders, and a simple frame that screams reliability. +A compact folding cargo bike with reinforced hinge, extendable rear deck, motor-assist and a raised handlebar for easy steering with loads. +Road time-trial bike with integrated perspex hydration and a sculpted tail section to reduce drag at speed. +A vintage track bike with restored polished chrome, period-correct decals, and narrow tubular tires to roll on boards and velodromes. +A steel mountain bike with classic straight top tube, quality laser-cut dropouts, and a powder coat that hides small dents. +Cargo bike with tandem bench, rear-facing child seats, and adjustable foot pegs for small riders. +A folding commuter with magnetized folded position, internal hub gearing, puncture-proof tires and a narrow folded profile for compact storage. +Road time trial machine with fully integrated hydration, narrow tail box, tri-bars, and race-geometry for speed against the clock +A gravel adventurer with titanium frame, wide-range cassette, and cleverly integrated top tube tool box for emergency repairs far from home. +Commuter folding e-bike with suitcase-style carry handle, rear light strip, and quick-release wheels. +Touring tandem bicycle with reinforced steel frame, triple chainset, dual racks and leather saddles for long-distance riding two-up. +Beach cruiser with coral paint, wicker basket, retro bell, and wide balloon tires for relaxed coastal rides +A rigorous mountain enduro bike with 170/170mm travel, coil compatibility, and reinforced dropouts that last through season after season. +Touring tandem bicycle with Rohloff hub, dual racks, front and rear fender mounts, and matching leather saddles for two-up adventures. +Gravel touring bike with 650b wheels, wide clearance fenders, low-gear triple crank and durable steel tubing for carrying loads +A performance track bike with carbon aero frame, narrow seatpost, and a stiff headtube for high-speed velodrome stability. +Commuter with dynamo lighting, puncture-proof tires, and ergonomic grips for daily urban use. +Gravel plus adventure bicycle with reinforced frame, wide tyres, and a relaxed geometry for loaded exploration off the beaten path. +Modern city bicycle with belt drive, internal gear hub, integrated lights and a stylish leather saddle. +A compact folding commuter with small wheels, anodized aluminum frame, belt drive and matte charcoal finish for easy storage. +A trail-hungry hardtail with 29" wheels, progressive geometry, and robust rim widths to give playful confidence on technical features. +Gravel race frameset with low-slung geometry, aerodynamic fork, wide cassette compatibility and polished finish for competitive edge. +A classic mountain bike with rigid fork, 26-inch wheels, retro decal, and flat platform pedals built for nostalgic summer trail rides. +Retro commuter with chrome fenders, brass accents, and a wide saddle for long urban rides with a nostalgic feel. +A mountain trail bike with balanced suspension, thoughtful chainstay protection, and mid-width tires for all-day traction. +Mountain trail full-suspension with short-chainstay agility, 150mm travel, and a confident descending geometry. +City single-speed with clean geometry, matte black finish, minimal graphics and a smooth-rolling narrow tire setup for casual commuting. +A carbon aero track bike with maximum stiffness, short headtube, and a narrow aero saddle for optimized sprint power on board tracks. +A city-friendly upright bicycle with swept-back bars, broad saddle, and internal cable routing for weatherproof reliability. +Touring gravel with low-geared triple crank, double bottle mounts on the seat tube, and a stable, predictable geometry. +Urban utility cargo bike with low step-through frame, lockable top box and modular rails for adaptable cargo solutions. +Gravel race bicycle with tubeless-ready rims, 700x40mm tyres, and a sophisticated staggered paint fade from purple to black. +Trail-focused hardtail with 140mm fork, slack head angle, wide 35mm rims and fast-rolling 29x2.3 tires for stability. +Urban folding bike with belt drive, disc brakes, and clamshell frame latch for easy storage. +Gravel bike with raw polished aluminum finish, leather saddle, wide handlebars and robust bottle cage mounts for multi-day travel +A downhill bike with disc brakes, coil shock, reinforced lower link and a full-face helmet storage compartment integrated into the frame (note: storage detail) for gravity sessions. +A cyclocross course-ready frame with oversized tire clearance, a tapered headtube, and a geometry tuned for sharp cross-country lines. +Bicycle with roller-cam brakes and classic steel tubing, featuring a faded pastel seafoam paint job. +Classic city step-through with wicker basket, leather saddle, chrome mudguards, and soft pastel paint for relaxed riding. +Lightweight fixed-gear with shallow rims, short-reach calipers, and a high-stiffness frame for street criterium-style riding. +A purpose-built winter commuter with studded tires, coated chain, and a waterproof frame bag for staying dry and warm on snowy mornings. +Gravel race frame with lightweight carbon, tapered headset, direct-mount front derailleur option and 700x40c clearance +A polished chromoly fixed-gear with thick-section handlebars, simple white decals, and a mechanical simplicity ideal for low-maintenance commuting. +Cargo bike with trailed trailer attachment, heavy-duty tires and integrated safety flags for urban logistics. +Road endurance bike with disc brakes, vibration-damping layers in the carbon, and a two-tone glacier print. +A steel gravel adventurer with wide handlebars, silver brazing, mount points for multiple cages, and hand-applied bead-blasted finish. +Gravel utility bike with low-step accessibility, internal hub option, and reflective frame detail for improved visibility. +A modern trail mountain bike with confidently progressive geometry, burly cranks, and a paint finish that hides trail blemishes. +A serene touring tandem with double chainrings, dual triple cranksets, two relaxed geometry frames and olive canvas panniers. +A fat e-bike designed for winter trails with wide studded tires, powerful mid-drive motor, and an oversized battery for extended cold-weather range. +Bikepacking hardtail with 29" wheels, custom welded low-profile rack mounts, and numerous small-bag attachment points for gear. +A folding electric cargo bike with low center of gravity, belt drive, hydraulic disc brakes and modular cargo platform for families. +A stripped-down mountain hardtail with rigid carbon fork, narrow 2.1" tires, and high-geared single-chainring for lightweight trail sprints. +A performance track sprint frame with aggressive top tube slope, polished aluminum forks and a lacquered black finish with bold red decals. +Mountain downhill bike with dual crown fork, long travel suspension, and 200mm travel to tackle steep descents. +Electric folding commuter with hub motor, quick-release wheel, and a compact folded footprint for transit. +Electric foldable commuter with rear hub motor, quick-fold aluminum frame, and compact battery for multi-modal travel. +A sturdy utility bike with platform pedals, integrated rear rack, wide steel mudguards and a simple three-gear hub for heavy errands. +Urban cargo bike with modular deck attachments, child seat compatibility, and reinforced double-diamond frame. +A touring tandem built for long climbs with wide gear range, robust tubing, and matching leather grips for both riders. +Classic 1970s steel ten-speed road bicycle with downtube shifters, suede bar tape, and chrome-lugged fork crown. +A gravel bike with smooth sweep toptube, flared drop bars, and cassette clearance for a wide gear range on climbs. +Gravel endurance bicycle with flared drops, 700c x 40 tires, and a shock-absorbing carbon layup for comfort on rough roads. +A high-pivot enduro bike with long travel, adaptable geometry, and 29-inch wheels that combine technical control with roll-over ability. +Modern gravel rig with 1x12 drivetrain, dropper post, and framebag mounts for bikepacking. +Touring expedition with titanium racks, gear lockers, and extra reinforcement for transcontinental carries. +Touring expedition bicycle with gasketed chaincase, robust pannier rails and triple-bolt rack support for remote travel. +BMX park bike with hollow chromoly bars, slim mid-length seat, and grippy tires for responsive street tricks. +Road bike with classic lugged steel frame, modern carbon fork, and compact gearing for relaxed club rides +A road time trial machine with deep-section front wheel, integrated hydration, and a paint that shifts between purple and blue. +Gravel stage race bike with extended chainstay clearance, aero-optimized tubing, tubeless-ready rims and quick-shift gearing. +Endurance carbon road bike with comfort geometry, seatpost compliance and 32mm tires to smooth rough roads. +Vintage track-style bike converted for urban use with brakes added, restored chrome, polished spokes and narrow tubular tires for weekend sprints +Handbuilt steel cyclocross with polished lugs, mud-shedding crown and traditional tape for classic races. +Mountain bike with 12-speed SRAM Eagle, dropper post, tubeless-ready rims and a mud-shedding frame design. +Electric mountain hardtail with lockout fork, torque sensor, and discreet battery tucked in the downtube. +A classic Dutch cargo bike with long, low deck, comfy bench seat, and a stable three-wheel option for heavier urban freight. +A cargo bike with extended wheelbase, wooden box at the front, hydraulic disc brakes, and a low center of gravity for heavy loads. +A hand-finished steel road frame with brushed nickel headbadge, elegant fillet brazing, 28mm tires and comfortable endurance geometry. +City electric folding bike with rear hub motor, integrated rear rack and small footprint for apartments. +BMX street park bike with reinforced gussets, an anodized finish, gyro system and wide durable pedals for tricks and stalls. +Urban cargo bike with low-step deck, foldable child bench, and electric-assist system tuned for stop-and-go traffic. +A lightweight titanium road frame with monocoque fork, cozy geometry, and subtle brushed-metal lugs for effortless miles. +Vintage touring bike with steel racks, talon brass fittings and enamel paint restored to patina. +A polished aluminum commuter with disc brakes, flat bars, and subtle reflective striping for urban nights. +A high-volume fat-bike with 26x5-inch tires, low gearing for flotation over snow, and reinforced rims to resist impacts from hidden obstacles. +A cyclocross-specific lightweight carbon frame with extra wide clearances and easy-access bottle mounting for muddy periods. +A classic steel racing frame with refined lugs, chromed fork tips, and a period-correct look married to modern sealed hub bearings. +Long-travel enduro bike with burly links, 170mm rear travel, plus-size tires, and a color-matched frame guard. +Road endurance bike with vibration-damping seatpost, 28mm tires, upright cockpit, and a forgiving frame for long miles. +A single-speed urban cruiser with retro paint, whitewall tires, swept-back bars and chrome accents for a throwback look. +A mountain bike hardtail with modern slack geometry, tapered headtube, boost spacing and 29er wheels designed for aggressive trail climbing. +City cargo bike with long bench seat, high-visibility paint, wide platform pedals and a child harness for secure family transport +A modern gravel frameset with personalized fit options, internal storage, and refined compliance to help sustain long gravel days. +Road race frame with aero optimization, steep seat tube angle and a race-ready paint job for criterium circuits. +A foldable commuter with magnetic latch, 16-inch wheels, belt drive, and graphite metallic finish perfect for rush-hour packing. +A gravel-focused race frame with carbon layup, electronic shifting compatibility, 700c wheels and moss-gray pearl paint with thin stripes. +A classic French mixte with swept chainstays, ornate headbadge, and hand-stitched leather saddle restored to shine. +Classic folding city bike with retro paint, paisley saddle, 20" wheels, and chrome fenders for cafe-hopping and easy storage. +A modern folding e-bike with small wheels, locking hinge, removable battery and a carrying strap for last-mile commuting. +Track sprint bike with drilled crankset, tubular rims and an aggressive race finishing geometry for short events. +Classic step-through city bike with wicker basket and quilted saddle for scenic neighborhood rides. +A custom handbuilt lugged-steel road bicycle with ornate fillet work, leather accents, and classic geometry for timeless elegance. +A kids' BMX with colorful frame, padded top tube, short chainstays and thick pegs for street trick sessions. +A mountain downhill rig with post-mount brakes, massive 4-piston calipers, and twin-crown fork for bomb-proof performance. +A gravel+ adventure bike with generous clearance, stealth mount points for a front rack, and 650b wheels for rough trail confidence. +Road aero build with integrated navigation mount, shaved seat tube clamp and deep-section carbon clinchers. +A commuter with internal drum hub, fully-enclosed chaincase, and a weatherproof frame covering to minimize maintenance in rainy climates. +A restored classic city bike with glossy enamel, brass head badge, and a sprung leather saddle providing comfortable, stately rides through downtown streets. +Mountain enduro bike with 160mm rear travel, aggressive dropper setup and burly tires. +Mountain trail bike with modern slack geometry, 29" wheels and compound tire grip for required traction. +Gravel light tourer with slim steel frame, reinforced fork, and bolt-on light mounts for early morning starts on gravel roads. +Beach cruiser with wicker basket, floral paint, espresso-brown grips and matching saddle for leisure loops along the shore +Fixed-gear commuter with glossy candy finish, minimal decals, and a quick-release front wheel for easy wheel swaps. +Urban fixie with candy-colored paint, minimalist saddle, deep black rims and single-brake practicality for simple commuting. +A drop-bar city bike with integrated dynamo, full fenders, and a leather saddle for dignified commuting regardless of weather. +Audax randonneur with dynamo light, leather saddle, low gearing and two bottle cage mounts on the seat tube. +Electric mountain bike with full-suspension, torque-sensing motor, long-range battery and aggressive geometry +Carbon fiber cyclocross race rig with mud-shedding tube profiles and SRAM 1x drivetrain. +A mountain trial bike with 20-inch wheels, reinforced frame, padded pegs and narrow geometry for technical low-speed balancing and hops. +A city cargo bike with reinforced frame rails, easy-access storage compartments, and an electric-assist option for heavy hauling days. +A performance gravel frame with integrated fork mount for lights, internal storage, and a slightly stretched wheelbase to increase stability on descents. +A high-volume gravel bike featuring 700x50mm tires, reinforced fork crown, low gearing and a rugged matte finish for rough trails. +BMX park bike with 20-inch wheels, pegs, freestyle geometry, and a gyro brake system. +Electric cargo tricycle with integrated cargo box, powerful mid-drive, and reinforced fork for heavy loads. +Performance alloy road bicycle with oversized downtube, semi-integrated headset and 25mm clincher tires for sprint-oriented rides. +Classic commuter with basket, coaster brake and elegant mint paint for relaxed trips to the market. +Touring mixte with low top tube, enduring geometry, and thick chromed fenders to weather long, wet rides. +A custom-painted steel touring frame with reinforced fork blades, three-bolt downtube mounts, and matching panniers for multiweek trips. +A commuter with fully enclosed chaincase, hub dynamo, and long-ranging battery for lights and phone charging on the go. +A minimalist commuter with monochrome matte finish, belt drive, internal hub and a small rear rack for light loads. +Electric longtail with all-weather canopy option, rear passenger tie-downs and powerful motor for hilly family routes. +A gravel all-rounder with stainless-steel bolts, Kevlar-reinforced tires, and a comfortable geometry for exploring mixed backroads. +A cyclocross training bike with steel frame, reliability-first components, wide clearances for mud and an integrated top-tube bottle cage. +Cyclocross racer with short chainstays, tubeless-ready rims, and a confident geometry for quick remounts and sharp handling. +A beach cruiser with chrome bell, wicker basket, cream tires and a low center-of-gravity frame for easy, relaxed strolls by the sea. +Urban folding commuter with rapid fold action, integrated carry handle, and reflective tape on frame sides for safety on dim mornings +A rugged expedition-ready mountain bike with reinforced racks, oversized water-cage mounts, and 2.5-inch tires to swallow rough backcountry tracks. +Urban compact e-bike with smart battery, LED display, folding pedals, and a narrow wheelbase for quick metro navigation. +Gravel bike with copper leaf finish, wide bars, 42mm tubeless tires and stealthy mounts for accessories. +Mountain trail bike with adjustable travel fork, one-by drivetrain, dropper post and wide bars for confident cornering +A touring-ready frame with multiple racks, dynamo lighting, and a high-volume 700c tire setup capable of long transit routes. +Gravel titanium frame with brushed finish, bottle cage mounts inside the triangle, and chainstay protector for durability. +A gravel bike with double-mounted bottle cages in the fork legs, reinforced headtube, and extra frame clearance for tire inserts. +A commuter e-bike with rear hub motor, torque sensor, integrated battery in downtube, and smartphone connectivity for route guidance. +Urban utility bike with heavy kickstand, reinforced chainstay and panniers for grocery runs. +A gravel monster with 650b+ compatibility, 2.4-inch tires, and reinforced bar clamp to support bigger loads and fully-loaded bikepacking setups. +Road disc endurance bike with comfortable geometry, 30mm tires, disc brakes, and a relaxed cockpit for long group rides +Commuter e-bike with belt drive, single-speed internally geared hub, fendered tires and a rear pannier rack. +A vintage mountain bike restoration with modern drivetrain conversion, polished steel frame, and period-correct decals kept for heritage appeal. +Urban single-speed with glossy candy-apple red finish, minimal components and wide-profile tires for confident city rides. +Recreational folding bike with single-speed drive, alloy frame, 16-inch wheels, and a carry strap for portability. +A commuter with integrated phone mount, water-resistant storage pocket, and narrow 32mm tires for nimble urban sprinting. +Commuter with internal hub gears, low-maintenance chaincase, and reflective piping on a comfortable saddle. +Race-oriented gravel bike with full carbon build, oval chainring and a stealth minimalist paint job. +Utility cargo trike with three-wheel frame, hydraulic brakes, electric assist, and a large wooden cargo bed for markets. +A gravel bike with comfortable geometry for long days, 700x42 tires, carbon fork and subtle olive finish with textured matte surface. +Race-proven road bike with tuned stiffness, quick-release carbon wheels and ergonomic bar shapes for long races. +A restored classic mountain bike with steel frame, early suspension fork, wide handlebars and big knobby tires that show its trail history. +A trail-capable 27.5+ mountain bicycle with wide rims, big-volume tires, and a geometry that balances agility with rollover capability. +A commuting city bike with secure frame lock, integrated rear light, and a capacious basket bolted to a solid front rack for market runs. +A lightweight titanium road bike with endurance geometry, double-butted tubing, SRAM Force eTap AXS, and clearance for 28mm tubes. +Gravel adventurer with brass hardware, hand-stitched leather accessories, 650b+ compatibility and a frame-mounted pump for remote reliability +Sleek endurance road bike with vibration-damping seatpost, compliance-focused frame, and 32mm tires for comfort. +Electric longtail cargo bike with heated grips, large battery capacity and safety railings for passenger security. +Cargo trike with weatherproof storage box, low loading lip, and fold-away side panels for larger deliveries. +Beach cruiser with exaggerated swept bars, colorful floral paint, coaster brake and padded elongated seat. +Performance drop-bar gravel bike with aero tubing, electronic shifting, and tubeless-ready wheelset for mixed-surface racing. +City mini velo with small wheels, upright handlebars, sturdy frame and nimble handling for tight urban streets. +Vintage Dutch-style city bicycle with fully enclosed chain, upright handlebars, three-speed hub, and durable frame finish. +A stealth urban single-speed with powder-coated black frame, rigid fork, and purposely understated decals for a clean look. +Retro road bike with steel lugwork, downtube shifters, leather saddle and era-correct decals and paint. +Mountain trail bike with 140mm travel, balanced suspension tuning, wide handlebars, and a dropper post for technical terrain. +A mountain enduro bike with 160mm travel, adjustable chainstay length via hardware flip-chip, and a stout rear triangle for confidence. +A vintage-styled city bike with leather-wrapped grips, copper head badge, and a classic geometry that encourages leisurely posture. +Mountain enduro with mixed wheel sizes (29" front, 27.5" rear), robust suspension and stealthy matte finish for aggressive trails. +A cargo e-bike with boxy front trunk, low-step frame for easy loading, high-torque hub motor and reflective safety livery. +Mountain all-mountain full-suspension with 160mm travel, burly rock plates, and reinforced chainstays. +A downhill race frame with burly linkages, 200mm travel, reinforced swingarm, and electric-yellow frame with warning stripes. +A modern downhill rig with reinforced bearings, stout 35mm bar clamp, and a low pivot linkage to handle big hits at bike parks. +A randonneur with long wheelbase, leather accents, brass lamp mounts, and traditional dark forest green lacquer. +A modern urban single-speed with clean port-through brake lever, slim tires, and a minimalist aesthetic for city sophistication. +A commuter with full-length chain guard, dyno-powered front light, and a heavy-duty rear rack for daily tools and bags. +Trail-oriented 27.5" mountain bike with burly tires, reinforced frame, and an efficient pedaling feel for jump lines. +A titanium touring bike with vertical dropouts for hub gears, recessed mudguard clearances, and machined stainless steel bottle bolt reinforcements. +A commuter with stealth electric assist hidden in the rear hub, integrated rear light, and a subtle matte finish that keeps a low profile. +A touring bicycle with low-gear triple, reinforced 48-spoke wheels, leather saddlebags and full fenders for cross-country reliability and comfort. +A folding commuter bike with quick fold latch, compact folded footprint, and an optional small rear rack for tote bags and laptops. +Bicycle with Rohloff hub, Gates Carbon Drive belt, steel frame and full pannier compatibility for expedition rides. +Mountain downhill with reinforced head tube, billet yokes, and 203mm rotors for heavy braking on steep descents. +Folding city bike with quick-release handles, compact frame, and urban-focused gearing for fast stops. +City folding e-bike with step-through frame, low-power assist and integrated front light for safety. +Folding commuter with 24" wheels, fast-fold hinge, and a commuter-friendly low-slung frame that tucks under desks at work. +A gravel bike with wooden accents on the handlebars, handcrafted leather bar wrap, and a soft matte finish. +Vintage-inspired cruiser with soft leather saddle, wicker front basket, swept chrome handlebars, and a glossy pastel finish. +A carbon time trial bicycle with steep seat tube, steep head tube, aero bars, and a teardrop-shaped frame for solo efforts against the clock. +Gravel race bike with aero-optimized tubing, wireless shifting, and a balanced aggressive geometry for fast mixed terrain. +A gravel e-bike with long-travel fork, mid-drive motor, and a discreetly integrated battery under the downtube for balanced handling. +Mountain trail aluminum frame with modern short chainstays, dropper compatibility and wide 29er rims for fast technical rides. +A children's BMX with crash-resistant plastic pedals, reinforced top tube, short wheelbase and fun graphic decals for playground stunts. +Gravel adventure bike with stainless rack mounts, flared bars, and a low-slung geometry for tired-legs stability on long rides. +Vintage touring frame with braze-on fender mounts, threaded headset, and classic cream livery. +A refined gravel racer with polished carbon finishes, slender seatstays for comfort, and 32mm smooth-rolling tires for mixed-surface events. +A mountain e-bike optimized for trail center laps with quick-charge battery, tuned suspension, and a lightweight yet beefy motor unit. +City commuter with bamboo-integrated frame accents, belt drive, internal hub, and an earthy lacquer for eco-minded riders. +Gravel all-road with 650b clearance, aggressive flare on drops, and a powerful hydraulic disc brake system for technical rides. +Roadlight endurance frame with low-profile aero tubing, internal routing, and a comfortable yet sporty geometry for long rides. +Gravel touring rig with steel fork, extra eyelets on stays, and a long wheelbase for stable loaded handling. +Touring tandem with stable handling, reinforced chainlines and long-wheelbase geometry for comfortable two-up days. +A compact trail hardtail designed for small adults with 27.5 wheels, 130mm fork travel, and a neutral matte finish for understated confidence. +A modern track-style fixed-gear with deep-section carbon rims, ceramic bottom bracket, and a long 120mm cranks for power on the flats. +City cargo trike with stable low deck, reflective side panels, electric acceleration assist and secure tie-downs for everyday deliveries. +Single-speed fixed-gear track-style bike with deep rim wheels, minimal brake setup and glossy red paint. +Lightweight carbon gravel bike with SRAM eTap wireless shifting and a stealth matte lacquer. +Custom painted steel frame with enamel flake finish, brass headbadge, and honey leather grips. +A cyclocross race frame with carbon fiber fork, modest seatpost compliance, and a geometry tuned to shimmering responsiveness in technical fields. +A beach cruiser with pastel ombré paint, chrome chain guard, wide white saddle and retro emblems for vintage leisure rides. +A retro folding bike with small 16-inch wheels, coaster brake, classic curved handlebars and lacquered paint for nostalgic commuters. +A kids' BMX with pegs, thick chromoly frame, gyro and bright red gloss finish with flame decals for a playful look. +A meaty enduro bike with 165mm travel, stout dropouts, and a coil shock option for riders who want to send big lines. +Electric longtail cargo bike with wooden bench, powerful motor, large battery, and reflective striping for family pickups. +A long-reach downhill bicycle with heavy-duty bearings, full bashguards, and a triple-clamp fork for the gnarliest terrain at the bike park. +Road aero time-trial bike with integrated hydration and bottle storage inside the frame for clean aerodynamics. +A gravel-favored frameset with extra wide chainstay clearance, integrated storage pod, and choice of 1x or 2x drivetrains. +Folding mini velo with compact geometry, secure folding strap and lightweight aluminum frame for apartment storage. +A city folding bicycle with compact fold, lightweight alloy frame, 20-inch wheels and an integrated velcro strap to keep it closed. +Electric folding commuter with hidden battery, smooth hinge action and comfortable shock-absorbing seat post. +A gravel endurance machine with relaxed geometry, 700x45 tires, double-bottle mounts on the seat tube and soft moss-green paint with speckle. +Handmade titanium gravel bike with thru-axles, 650b clearance, custom brazed seat cluster and subtle brushed finish. +A mountain bike with burly drivetrain, chainline protection, and a reinforced head tube for aggressive trail abuse. +A cyclocross-oriented aluminum frame with mud-shedding shaped tubes, sealed headset, and a balanced ride feel for mixed surfaces. +A folding cargo bike with kid bench, dual kickstands, low-step access and hydraulic disc brakes for safe family transit. +Gravel race bike with carbon fork, aerodynamic seatposts, and a geometry that keeps the ride stable above 30 km/h on mixed surfaces. +A trail e-bike with torque-sensing motor, chainstay protector, and sensor-tuned assistance for punchy climbs and controlled descents. +A fat bike with 4.8" snow tires, wide carbon rim set, rigid fork and lower gearing to roll easily over sand and snow. +A compact kid’s trail bike with 24-inch wheels, short reach, durable grips, and playful stickers to encourage confidence on local singletrack. +Commuter e-bike with low center battery, quiet mid-drive motor and automatic lights for morning commutes. +Touring tandem with independent cockpits, extra-spaced frame, and multiple pannier attachments for long distances. +Vintage tandem with steel tubing, brass fittings, leather saddles, and matching paint for leisurely two-up touring. +Classic touring bicycle with seven-speed hub, full fenders, frame pump, and leather saddle for reliable long-distance treks. +A compact hardtail with 27.5-plus tires, 120mm fork, and wide bars for playful, nimble off-road sessions on twisty singletrack. +A gravel adventure rig with triple cargo mounts, reinforced rear triangle, and an integrated top-tube bottle pocket for quick access. +A retro folding bike with small 16-inch wheels, chromed finish, and leather saddle that folds into a compact briefcase size. +MTB full-suspension with aluminum linkages, progressive leverage curve, and burly brake mounts for steep descents. +Modern road endurance bike with relaxed geometry, vibration-damping seatstays and discreet rack mounts. +Gravel commuter with mudguards, rear rack, dynamo-powered light, and a bar-mounted phone holder for urban navigation. +Urban folding utility with powder-coated hinge, small-wheel fast-rolling tires and easy-to-use latch for multi-modal commuting. +Downcountry mountain bike with versatile 120mm travel, modern slack geometry, and 29-inch wheels for flow trails. +Track pursuit carbon machine with monocoque construction, vertical rear end and optimized for high-speed stability. +A belt-driven commuter with integrated light mount, carbon-reinforced chainstay, and a belt tensioner for low-maintenance commuting. +A practical folding city bike with compact footprint, 8-speed hub gearing, puncture-resistant tires and a subtle charcoal finish for urban stealth. +Mountain fat-tire commuter with full-coverage fenders, integrated rack and friendly upright geometry for winter city riding. +A durable utility bike with welded aluminum frame, dual kickstands, reinforced rear rack, and puncture-resistant tires for mail and delivery services. +Touring off-road bike with stout steel frame, bar-end shifters, wide clearance, and a low-maintenance drum brake option. +Lightweight gravel bike with slender seat tube, internal routing, and an understated matte slate color. +Lightweight carbon road frame with aggressively tapered chainstays, integrated seat binder, and stealth matte paint for race focus. +Vintage roadster with lugged frame, leather handlebar wrap, and polished chrome rack for timeless charm. +Electric-assist step-through city bicycle with upright ergonomics, pannier rack, and an intuitive digital display for route planning. +Lightweight touring bike with integrated GPS mount, triple chainset, and reinforced rack bosses. +Road endurance machine with comfortable geometry, seatpost flex, and disc brake stopping power. +Lightweight track sprinter with polished steel frame, stiff bottom bracket, high gearing, and a simple, functional aesthetic. +Touring steel frame with rear triple bottle mounts, leather-stacked grips, and a long wheelbase for stability. +Mountain hardtail with dropper post, tubeless tires, and a color-matched suspension fork decal. +A touring bike with framebag-friendly geometry, bulletproof rear rack mounts, 36-spoke wheelset and an understated army-olive paint job for discreet travel. +Gravel plus bike with 27.5+ tyres, wide rims and supportive geometry for rough singletrack exploration. +A lightweight aluminum commuter with integrated lock, reflective decals, 8-speed derailleur and ergonomic grips for short city trips. +Trail full-suspension with 150mm rear travel, three-position lockout and a stout aluminum linkage for quick handling. +Cyclocross race-ready bike with tubeless 700x33 tires, SRAM Apex 1x, carbon fork and generous tire clearance. +Mountain cross-country elite with lightweight carbon fiber, race-specific cockpit and firm suspension tuning. +A mountain bike designed for marathon endurance with efficient rear suspension, light wheels, and a comfortable yet race-capable geometry. +An all-road carbon machine with a fast geometry, clearance for 38mm tires, and kevlar-reinforced fork blades for gravel confidence at speed. +Mountain freeride full-suspension with coil shock, 27.5" wheels, and aggressive geometry tuned to handle big features. +Urban cargo bike with integrated lockbox, rails for child seats, and removable wood slats on the deck. +A fixed-gear track bike with steep geometry, deep drop bars, and a glossy red paint job built for velodrome sprints and criterium style. +Mountain all-mountain bike with long dropper travel, durable wheel builds, and a protective frame wrap. +Cargo e-bike with low step-in frame, powerful mid-drive motor, and modular cargo accessories for flexible loads. +E-gravel bike with torque-sensing mid-drive, dropper post, and custom geometry for loaded tours. +A kids' balance bike with small lightweight frame, rubberized handlebar ends, non-marking wheels and cheerful star decals for confidence-building rides. +A commuter with integrated GPS and lock system, lights that auto-activate at dusk, comfortable saddle and puncture-resistant tires. +A gravity enduro bicycle with a heavy-duty rear triangle, progressive leverage curve, and steeper seat tube to improve climbing efficiency. +A children’s hobby bike with colorful frame, coaster brake, training wheels, and a small basket on the handlebars. +Touring gravel bike with double rack mounts, long-chainstay stability, and low gearing for loaded climbs in remote areas +Classic steel frame road bike with subtle lugwork, handmade paint, caliper brakes, and a responsive feel for spirited rides. +Electric-assist city bike in pearl white with mid-drive motor, integrated battery, step-through frame, and throttle plus pedal assist. +A cyclocross racer with lightweight carbon fork, 33mm tubular tires, sealed bearing hubs, flared drops and a race-tuned geometry for quick courses. +A touring frameset with triple mounts, reinforced dropouts, and a subtle integrated roll bag slot in the frame for essentials. +Gravel racer with 1x12 cassette, tubeless gravel tires, and lean, race-oriented geometry. +A gravel bike in camo livery with oversized tubes, integrated mini-tool storage, and 650b wheel compatibility for versatility across rough terrain. +Classic road bicycle with lacquered steel frame, restored original decals, narrow-profile tires and a leather saddle for tradition-rich rides +E-mountain bike with robust battery, intelligent torque control, long-travel front and rear suspension and heavy-duty components. +A gravel touring e-bike with dual-battery setup, wooden rack, wide-ratio gears for loaded climbs and forest-flecked green paint. +A cyclocross practice bike with welded-on extra mud clearance, robust tubeless setup, and comfortable gearing for long training days. +Cargo trike with electric assist, swiveling cargo deck, and secure fastening points for large items transported in urban areas. +Urban single-speed with minimal chainline, low-profile mudguards, and a comfortable upright posture for city life. +A commuter with a stealth internal battery, belt-drive, and a sealed drivetrain designed for long-lived service in wet climates. +Steel commuter single-speed with chrome fenders, upright swept bars, and front basket suitable for city errands +A kids' balance bike in sunny yellow with rubber tires, easy-to-grip handles and friendly animal sticker on the head tube for encouragement. +A carbon road bike with endurance geometry, disc brakes, clearance for 30mm tires and a subtle gloss flake paint job. +A track time trial bike with stretched geometry, integrated hydration slots, and a stealth matte-black finish for pure aerodynamic performance. +Road race bike with stripped-down weight, shallow-section wheels, and an aggressive geometry for attack-minded group rides. +A gravel expedition frame with sizable downtube battery-ready mounts, integrated top-tube storage, and wide clearance for heavy load tires. +A flat-bar gravel commuter with practical mudguard mounts, wide tires for comfort, and a utility-ready front rack for packages. +A touring tandem with matching paint, triple chainset for easier climbing, extensive rack and pannier compatibility and a flicker of patina on the frame. +Track-inspired urban fixie with asymmetric chainstay, minimal decals, and a vibrant lime finish. +A mid-fat bike with 3.8" tires, rigid carbon fork, low gearing and a stealthy matte black frame ideal for mixed snow and sand riding. +Commuter with an internal gear hub, full chaincase, mudguards and a simple spring-loaded kickstand. +A handmade lugged steel cyclocross machine with recurved seatstays, mud-shedding geometry and custom flake paint. +A no-nonsense commuter bicycle with coaster brake, minimalist fenders, heavy-duty chain, and a sturdy rack for daily business. +A high-modulus carbon fiber time trial bicycle with integrated hydration, minimal frontal area, and a steep seat tube for optimized power transfer. +City utility bicycle with integrated lock, rear rack, chain guard, and reflective decals for daily commuting functionality. +A modern touring ebike with torque-sensing motor, removable pannier rack, and heavy-duty tires for long-distance assisted touring. +A commuter with locked-in internal gears, chaincase, and a folding U-lock integrated into the frame for theft resistance. +Track training bike with fixed ratio, low-stack cockpit, and tall flange hubs for stability. +A compact urban cargo tricycle with a wide front box, canopy option, and reinforced frame for frequent city deliveries. +Modern nylon composite frame e-assist bike with belt drive, internal gears, and low center of gravity for stability. +A classic British roadster bicycle with coaster brake hub, full chainguard, and upright swept handlebars. +A classic lugged steel road bicycle with polished chrome seat cluster, narrow tires and elegantly curved fork blades. +A trail-focused enduro rig with 170mm travel, adjustable geometry, piggyback shock, and reinforced linkages to withstand aggressive descents. +Touring expedition bike with reinforced downtube, removable kickstand, and oversized pannier eyelets for remote travel. +A cargo longtail bicycle with a reinforced rear rack, twin panniers, hydraulic disc brakes, and a platform for carrying groceries or a child seat. +A mountain bike with full carbon frame, 140mm travel, dropper post, and 12-speed micro-spline drivetrain for modern trail speed. +Utility cargo bike with front-loading platform, low center of gravity, and kid-friendly seating. +A cyclocross race frame with a specially profiled fork, oversized thru-axles, and a geometry tuned for quick tuck-and-burst sprints. +Softail cruiser with chromed springer fork, low slung frame and custom paint flames. +Classic step-through city bike with wicker basket, chrome accents, upright posture, and a spring-loaded saddle for smooth rides. +Electric cargo longtail with modular seating, mid-drive motor, large battery, and integrated turn signals for family transport. +A mountain hardtail with modern tubing, tapered steerer, boost hub spacing and a plush ride for long off-road outings. +Vintage-style touring bicycle with brass fittings, leather straps on racks, and hand-painted roses on the top tube. +A high-performance road racing bike with stiff chainstays, shallow section wheels, aero-optimized tubing and a race-ready component build. +Gravel bike with tuned compliance, large tire clearance, and bead-blasted titanium hardware on the bottle bosses. +Gravel explorer with wide tyre capacity, durable racks, and integrated fender mounts for extended off-grid touring. +A vibrant pink kids’ cruiser with training wheels, floral basket, and coaster brake for gentle neighborhood rides. +Lightweight gravel race bike with carbon seatpost, 700x38c tubeless tires, and a race-focused 1x drivetrain. +Gravel commuter with belt-drive and Rohloff-style hub, integrated fenders and heavy-duty chain protection. +A kid-friendly balance bike with foam tires, rounded edges, painted polka-dot frame and low-slung saddle height to build confidence. +A light touring bicycle with steel fork, triple cage mounts, bead-blasted silver frame and cotton-brown leather saddle. +High-end road frame with aero seatpost, integrated cable routing, and lightweight monocoque construction for competitive racing. +A touring bicycle in bright orange with triple crankset, dynohub lighting, wide cushioned saddle, and heavy-duty spokes for loaded rides. +Gravel gravel-plus option with 650bx47 compatibility, sturdy rotors, and a short stem for better steering on rough roads. +E-assist mountain bike with torque-sensing motor, robust brakes and a long-range battery for fat days in the hills. +A kids' BMX race bike with stiff chromoly frame, nimble geometry, and lightweight 20-inch wheels for beginner racers. +A mountain bike with adjustable travel fork, one-by drivetrain, and a clutch derailleur for keeping chain slap in check on technical trails. +A kids' balance bike with foam tires, a low-slung frame geometry, and a brightly colored paint finish to entice early learners. +A lightweight carbon cyclocross bike with quick-release axle adaptors, internal routing, and a sand-sparkle finish that hides dust. +A sleek commuter with belt drive and carbon fork, full-length fenders, and integrated lighting for a low-maintenance urban machine. +Electric mountain bike with full suspension, torque sensor motor, and protective skid guard for aggressive trail use. +Mountain trail bike with modern slack geometry, wide tire clearance, and a tidy internal routing layout for neat cockpit. +Commuter with internal dynamo, low-maintenance singlespeed internal hub, mudguards and a sturdy rear rack for parcels. +A vintage gravel conversion using a steel frame with added braze-ons for racks, chunky 700x42 tires, and modern disc brakes for reliability. +Downcountry race bike in neon lime with 120mm travel and lightweight shock for fast climbs. +Gravel race machine with carbon stem, wireless shifting, ceramic bearings and a high-contrast graphic wrap. +Mountain trail bike with 140mm rear travel, progressive geometry, and a reliable single-ring drivetrain for durable trail performance. +Urban commuter with internal hub gear, fully enclosed chaincase and LED headlight integrated in the handlebar stem. +Electric cargo bike with belt-drive, mid-motor torque sensing, and foldable child seats for multi-purpose use. +A gravel endurance machine with endurance geometry, low stack height, flared bars and pearlescent ivory paint with speckled metallic. +Road sprint-focused frame with short headtube, stiff rear triangle and a responsive, track-inspired feel for criterium wins. +A handmade lugged steel touring tandem with classic rafters, leather saddles, and chrome fenders polished for many miles. +A minimalist commuter with belt-drive, integrated taillight, small rear rack, and a satin black powdercoat to minimize visible wear. +A cyclocross bike with steel frame and subtle paint details, long reach brakes, and a shoulder-friendly top tube for easy carries. +A retro track bike restored with period-correct steel frame, center-pull brakes removed, tubular tyres, and polished alloy rims. +A gravel-plus explore bike with 2.6-inch tires on wide rims, low-slung frame bag, and robust tubeless setup for remote trails. +A robust postal service bicycle with heavy-gauge frame, reinforced wheelset, and a large front cargo box for heavy daily loads. +Kids' balance bike with soft foam grips, rubber-coated footrests and low center frame for initial learning. +All-road cyclocross-style bike with tubeless-ready rims, 38mm tires, mechanical disc brakes, and multiple bottle cage mounts. +A commuter with belt drive, internal hub, and a full chaincase to keep splashes and grime off pants on rainy commutes. +A long-travel enduro bike with integrated chainguide, aggressive slacker headtube angle, and massaged downtube for hits and rock gardens. +A carbon track sprinter with monocoque frame, oversized bottom bracket, minimal stack height and a single fixed gear optimized for power transfer. +Kids' balance bike in bright pink with adjustable seat, light alloy frame, and no pedals to build confidence for toddlers. +A comfortable commuter with swept-back bars, ergonomic grips, and puncture-resistant tires to ensure that daily trips remain pain-free. +A BMX park bike with chromoly frame, 20-inch wheels, gyro brake system, pegs on both axles, and a squat, poppy geometry for tricks. +Mountain trail bike with 130mm travel, balanced geometry and tubeless tires for mixed terrain. +Touring steel frameset with wide gear range, rugged dropout design, and comfortable geometry for multi-day loaded adventures. +Classic steel single-speed track bike with horizontal dropouts, deep-v rims and a glossy candy red finish. +A titanium road bike with high-stack comfortable geometry, slender seatpost, and a brush-finished frame to show a natural patina. +Gravel bike with painted splatter finish, 48mm tires, and wide handlebars for endurance rides. +City utility with internal lighting, magnetic fender mounts, and a comfortable steering geometry for everyday errands. +A mountain cross-country bike with lightweight carbon fork, tubeless-ready rim set, and a sensitive rear triangle for climbing. +Gravel commuter with tan-walled tires, leather saddle, rear rack and painted steel frame with subtle rust patina. +Fatbike with studless tread, rigid fork, and wide rims for stable winter commuting or sandy beach laps. +A commuter with built-in solar charging panel on the rear rack, LED lights, and lightweight alloy rims for efficient day-to-day use. +A performance mountain hardtail with tapered headtube, 120mm fork, 30mm internal rim width and aggressive trail geometry. +A full-suspension trail slayer with 140mm travel, wide handlebars, 29-inch wheels and a coil-compatible shock linkage. +Gravel bike with stealth matte dark green, generous tire clearance and integrated frame pack mounts for bikepacking. +A rugged all-terrain fat-bike with 26x4.8 tires, low-pressure ride for sand or snow, welded aluminum frame and matte sand-colored finish with salt-friendly coating. +Cyclocross bike with very low bottom bracket, reinforced fork crown, and tubular tires for race-day handling and confidence +Cyclocross race machine with flared drops, tubeless tyres, and a matte paint that hides scratches from off-road use. +Tandem recumbent with two recumbent seats, wheel fairings, and efficient drivetrain for comfortable high-speed touring +A robust kids’ mountain bike with front suspension, short crank arms, and durable components that can survive learning falls and frequent use. +A practical e-cargo bike with low-slung center of gravity, twin side panniers, and a torque-sensing motor for heavy loads. +Mountain enduro with adjustable geometry, coil shock compatibility, and a protective skid plate for rough terrain. +BMX freestyle bike with 20-inch wheels, gyro brake system, pegs and a reinforced top tube for park tricks. +A gravel mixed-wheel frameset with 700c front, 650b rear option, mounts for three cages, and an understated gray paint with maroon accents. +A gravel expedition bike with clearance for 700x50 tires, multiple frame-pack mounting points, and a rugged fork that can accept cargo. +A downhill race frame with reinforced headtube area, aggressive angles, and oversized pivot bearings for heavy downhill use. +A boutique handbuilt road bicycle with fillet-brazed steel joints, flamboyant lacquered paint, neatly tucked cable stops, and classic geometry. +Touring bicycle with reinforced racks, dynamo lighting, and a robust steel frame designed for heavy touring loads. +A cyclocross bike with cantilever brakes, low standover frame, and bold pinstriped mudguard-ready fenders. +A modern trail mountain bike with 140mm front and 130mm rear suspension, dropper post, 29-inch wheels and grippy 2.4" tires for fast singletrack. +A modern fat-bike cruiser with balloon tires, step-through frame, and a relaxed upright riding position for comfortable coastal riding. +A playful kids' trail bike with 24-inch wheels, coaster and hand brakes, front suspension fork and bright sticker-covered frame. +Bikepacking-ready bike with extra frame bag anchors, reinforced bottle mounts and a stealth powder coat to hide scuffs. +High-end time trial bike with full carbon monocoque, integrated stem and disc wheel for maximum aerodynamics. +A carbon track pursuit bike with integrated seat tube fairing, long aero bar extensions, and stealth gloss black finish. +Matte black aero road bicycle with integrated cable routing, 50mm carbon wheels, compact 11-speed drivetrain and a racy aggressive geometry. +A modern titanium gravel bike with discreet paint, wide tire clearance, and multiple cargo attachment points for adventures. +Performance cross-country bike with light alloy frame, 100mm fork, and nimble handling for tight singletrack. +Full-suspension trail mountain bike with 140mm travel, tapered headtube, 29-inch wheels and dropper post for aggressive singletrack. +A lightweight endurance road bike with compliance zones built into the seatpost and chainstays for long-distance comfort. +A lightweight titanium cross-country race frame with super-clean welds, discreet cable routing, and raw bead-blasted finish. +A kids’ mini-mountain bike with 20-inch tires, front suspension, and small but supportive saddle for early trail education. +A compact kids’ BMX with flame graphics, reinforced wheels, and comfy padded bars for backyard jump sessions. +Gravel cyclocross hybrid with drop bars, robust tires, rack mounts and a comfortable saddle for commuting and weekend gravel loops +Fat-tire beach cruiser with soft springs, oversized handlebar, simple coaster brake, and pastel paint for chill rides. +A handmade lugged touring frame with brass-brazed head badge, triple-bottle mounts, and hand-rolled stainless fenders that add old-world elegance. +Mountain downhill race rig with reinforced pivot points, 200mm travel fork, and powerful four-piston brakes. +Kids' mountain bike with small 24" wheels, front suspension, and easy-to-reach brake levers for confidence. +A do-it-all hardtail mountain bike with 140mm travel fork, 29" wheels, dropper post, and a trail-tuned 1x12 drivetrain for local trails. +A retro touring tandem with brass fittings, polished fenders, and custom wooden handlebar grips. +A modern carbon cyclocross bike with wide chainstays for tire clearance, integrated chain catcher, and a light race-oriented frame. +A kids' cruiser with training wheels, playful decals, chainguard and wide cruiser saddle to ensure comfortable first rides. +A stripped-down single-speed road bike with 700x25 slicks, narrow bars, and a lightweight frame intended for weekly crit training. +A cyclocross race-ready bike with flared 42cm drops, lightweight carbon tubes, 700c tubular tires and quick-release skewers for rapid wheel changes. +A women-specific road bike with shorter top tube, tweaked saddle shape, compliant seatstays and lighter touch on the handlebars for comfort. +A sleek track bike with deep-section front wheel, polished aluminum frame, and minimal hardware for a purist velodrome experience. +Cyclocross full-carbon frameset with internal mud channels, quick-release thru-axles and carbon layup focused on stiffness and compliance. +A commuter with ergonomic grips, stepped-through low-step frame, Bosch mid-drive motor, and an intuitive control pad on the bar. +A dirt jumper bicycle with rigid fork, short chainstays, strong double-wall rims, and a pinned freewheel for big-air handling in parks. +A gravel tourer with reinforced fork mounts, low-gear triple chainring, and high-volume tires for off-grid touring. +A compact BMX with reinforced forks, 20x2.2 tires, and street-friendly gearing for skating-style lines on concrete features. +A flamboyantly painted cruiser bicycle with floral motifs, wide banana saddle, and swept-back bars for a summer vibe. +Kids' BMX with street graphics, reinforced axle nuts, and sturdy pegs to handle small jump lines and urban tricks. +Beach cruiser with glossy turquoise paint, whitewall tires, sun-proofed saddle and an oversized handlebar for relaxing seaside rides +Vintage-style porteur bike with integrated front rack, brass bell, leather straps and a dignified shellac lacquer. +A gravel adventure machine with three-bolt rack mounts, large-volume tires, and a comfortable endurance geometry for long-haul rides. +Kids' BMX with street-ready geometry, slim seat, and high-strength forks for park tricks. +A high-performance cyclocross bike with integrated chain catcher, stiff BB shell, and a race geometry optimized for technical CX courses. +A handbuilt lugged steel commuter with a glossy cream finish, enamel headbadge, and a comfortable spring-saddle for daily rides. +Vintage road racer with chromed fork crown, narrow tires, friction shifters, and a classic racing silhouette. +A sleek track bike with anodized headset, narrow tires, crisp paint and ultra-clean geometry for stadium racing. +Urban utility bicycle with enclosed chaincase, generator light, long chainstay and extra cargo rails for daily hauling +Electric cargo long-tail with regenerative braking, child harness attachments and a large-capacity battery for long days. +Track pursuit carbon frame with hidden steering cable routes and a clinical paintwork for focused training. +A lightweight carbon gravel frame with an emphasis on vertical compliance, wide tire clearance, and a race-ready weight that still survives rough dirt. +Mountain bike with burly four-bar linkage, modern geometry and thick tyres for aggressive terrain tackling. +Gravel endurance bike with endurance fit, 34mm tires, and a vibration-absorbing composite seatpost. +Gravel all-road racer with disc brakes, 700x38 tires, endurance geometry and integrated chain catcher +Adventure e-bike with full-suspension, heavy-duty motor, long-range battery and a frame designed to carry panniers comfortably. +Classic BMX bike with 20-inch wheels, pegs, gyro brake, and urban matte purple finish. +A commuter e-bike with cargo basket, throttle assist, and integrated lock mount for quick stops around town. +A handpainted cruiser with floral motifs, cream tires, comfy saddle and swept-back bars for slow joyful rides. +Touring-ready steel frameset with triple-bottle capability, sturdy pannier racks, full fender compatibility and classic lugged detailing. +A sleek black tandem road bicycle with two sets of drop bars, synchronized crankset and reinforced frame tubing for pair endurance rides. +Track pursuit bike with steep seat tube, no brakes, fixed gear ratio, and a deep-section front wheel for velodrome pursuits. +Gravel bike with subtle metallic flake, 40mm tires, and a long saddle rail for onboard storage and comfort tuning. +Urban single-speed with riser bars, painted chainring, diamond-cut rims, and a slim saddle for nimble city weaving. +A fat-bike with 5-inch balloon tires, rigid steel fork, wooden saddle and a powdercoat with snowflake decals designed for riding in sand or snow. +A commuter fixie with vibrant lime paint, deep-section rims, narrow riser bars, and a minimalist saddle for quick urban dashes. +A mountain downhill bike with reinforced enduro bearings, optional chain guide mount, and 20mm axle adaptors for wheel durability. +A kids' BMX with thick peg-ready axles, durable frame, and softer tires for safer landings at entry-level skateparks. +A gravel touring frame with internal routing, large tire clearance, and discreet rack mounts for stealth weekend trips. +A cyclocross training bike with aluminum frame, mechanical discs, and a raised bottom bracket to clear obstacles during races. +A kids' BMX with bright anodized hubs, reinforced headtube area, and a strong steel frame to handle park sessions and backyard jumps. +High-modulus carbon road bike with integrated powerhouse crankset, ultralight tubular rims, and an aggressive sprint geometry. +Classic French-style road bike with ornate lugs, thin steel tubes, decorative seat cluster and fine pearl paint. +Gravel endurance racer with integrated hydration, 700x40mm tires and stealthy internal routing for stage races. +A compact BMX with robust chromoly frame, aggressive tread tires, and a reinforced headset for heavy street riding and drop-offs. +A stripped touring bike with a simple triple crank option, heavy-duty frame plates, and replaceable dropouts to handle wrenching on the road. +Electric mountain fat-bike with wide 5-inch tires, low-slung battery, robust motor, and cushioned grips for snowy trails. +Touring-ready steel frameset with low-gear triple crank, sealed headset, heavy spokes and classic riveted fenders for durability. +Urban single-speed with bold color splash, short stem, narrow bars, and a comfortable saddle for daily short trips. +Classic city roadster with leather saddle, swept-back bars, and a subtle metallic green finish for elegant daily rides. +Cyclocross commuter with flared drops, mud-optimized tires, and a frame that can handle winter grit and salt. +A gravel adventure bike with integrated dynamo hub, leather bar tape, framebag supports and hand-painted top tube for a personal touch. +Handmade titanium commuter with internally routed cables, tapered headtube, and brushed finish. +Classic road frame with polished chrome lugs, fine pinstriping, and a leather saddle for a traditional ride feel. +A modern all-mountain bike with burly pivot hardware, a rock-shielded downtube, and a supple rear suspension that encourages technical confidence. +Touring bike with triple chainring, long chainstays, steel fork and wide-ratio cassette for loaded climbs. +A high-end gravel rig with electronic shifting, tubeless 700x40 tires, integrated frame mount for GPS, and an ultralight carbon handlebar. +Performance road bike with stiff bottom bracket, aero-optimized tubing, and lightweight components for race-day responsiveness. +Track sprinter with stiff carbon frame, single-speed fixed hub and aerodynamic profile for velodrome sprints. +Comfy cruiser with extra-wide saddle, upright bars, coaster brake, and pastel turquoise paint for relaxed neighborhood rides. +A gravel racer bicycle with flared drop bars, tan sidewall 700x40mm tires, carbon fork and stealthy matte grey paint aimed at mixed-surface speed. +A lime-green kids' balance bicycle with no pedals, low saddle and wide handlebars for toddlers learning to ride. +Lightweight carbon time trial bicycle with aero bars, integrated hydration and disc rear wheel for pure speed +A titanium touring tandem with double-chained system, reinforced seatposts, and a built-in pump mount for remote adventure reliability. +A gravel explorer with painted topographical map patterns, 42mm tires, integrated GPS mount and ample frame bag clearance. +Mountain trail hardtail with clean internal routing, 29" wheels, and a tubeless setup to avoid flats on remote runs. +Electric compact commuter with quiet hub motor, small-diameter wheels, foldable pedals, and waterproof components for tough weather. +Mountain enduro rig with burly chassis, adjustable geometry, coil-compatible shock, and 27.5" wheel option for playful handling. +Mountain enduro rig with adjustable shock tune, robust bashguard and wide rims for aggressive trail days. +Off-road fatbike with rim-deep tires, rigid fork, and plenty of clearance for snow and sand exploration. +A lightweight road bike with shallow-depth rims, endurance geometry, vibration-damping seatpost and a tasteful metallic paint finish. +Cargo e-bike with multi-position rear deck, integrated reflectors, and a low-maintenance belt drive for daily haulage. +A family cargo bike with bench seat, child anchor loops, and a low platform center of gravity to keep the load stable when cornering. +Tandem mountain bike with dual suspension, robust rear triangle, and a wide-range cassette to handle technical climbs two-up. +A titanium gravel bicycle with 45mm tubeless-ready tires, a relaxed geometry for long rides, and a discreet framebag mount. +A modern gravel bike with 2x drivetrain, rectangular aero tubing, and wide clearance for 45mm tires. +Youth balance bike with low center of gravity, foam-filled tires and bright safety colors for early cycling confidence. +Urban single-speed cruiser with glossy enamel paint, rear coaster brake, and a durable spring saddle for easy relaxed cruising. +Gravel bike with flared drop bars, tan wall tires, and supple ride comfort for long gravel classics. +Touring bicycle with triple racks, wide comfortable grips, and leatherette straps on panniers for a crafted look. +A cargo e-bike with long-range battery, reinforced rear deck, and low center of gravity for stable transport of goods or children. +Adventure expedition bike with Rohloff hub, mounting points for dozens of accessories, and a no-nonsense durable finish. +A specialized track pursuit bike with integrated vents, dual front forks spread for stability, and a rigid aero seatpost. +Gravel e-bike with integrated headlight, mid-drive motor, and 650b wheels for rough, gravel roads. +Gravel e-bike with removable battery pack, integrated mudguard mounts, and a textured matte finish for grip. +Urban commuter with low-step frame, integrated rear rack and tube-style front light for simple city errands. +A commuter with swept handlebars, small front basket, and cushioned saddle making it comfortable and practical for short trips. +A minimalist track bike with fixed gear, tall riser bars, and a stripped-down saddle for street-fixed style and simplicity. +High-performance time trial frameset with integrated hydration, narrow seat tube and aero cockpit for streamlined efforts. +Dirt-jump mountain bike with robust 26" wheels, slack geometry, coil-sprung fork, and reinforced frame for big air. +Mountain trail bike with refined suspension kinematics, 140mm travel and fast-rolling 29er wheels for all-day rides. +A rugged steel commuter with wide 50mm tires, minimal fenders, heavy-duty rack and frame lock for urban utility. +A fat tire electric mountain bike with 150mm fork, mid-drive motor, 4" tires and dropper post for sand, snow and soft conditions. +A winter commuter with studded tires, full-coverage mudguards, and heated grips for cold-weather cycling. +A folding commuter designed for apartment living with compact folded footprint, light alloy frame, and comfortable riding position. +Kids' balance bicycle with robust frame, colorful grips, and a lightweight design that makes first rides easy and fun. +Lightweight gravel frame with randonneur-style mounts, refined tubing, and a polished raw finish for understated elegance. +A track sprint bike with super-stiff monocoque carbon frame, short wheelbase, and narrow aero bars optimized for velodrome gold medal sprints. +Folding commuter with belt drive, internal hub, and a quick-release clamp that secures the folded package tightly. +Gravel explorer with triple-cage mounts, dynamo light wiring, wide tyre capacity, and discreetly reinforced rack mounts. +A gravel bike with a split seatstay design for compliance, wide 45mm tires, and a top-mounted stem bag for essentials. +Steel commuter with practical alloy racks, chaincase, and a classic green enamel finish. +City commuter with belt drive, internal 8-speed hub, full fenders and quiet, maintenance-free drivetrain. +Gravel endurance frame with stealth internal storage, integrated headset spacer pocket and a long wheelbase for comfort. +Pure track sprint bike with steep frame angles, 48-tooth chainring, narrow handlebars, and minimal clearance to reduce weight. +A cyclocross bike with clearance for mud, cantilever or disc brakes, 35mm tire capability, and a race-focused geometry. +A handbuilt steel frame road bike with ornate fillet brazing, copper-inlay head badge, and classic dropouts for a bespoke feel. +Classic cruiser with springer fork, fat whitewall tires, and a scalloped metallic paint job. +A cargo long-tail bike with extended rear platform, kid-carrying straps, hydraulic brakes and olive-matte powdercoat. +Kids' balance bicycle with wooden frame, low saddle, and training-purpose geometry for toddlers. +A classic Dutch utility bicycle with integrated front basket, chain guard, upright bars and a three-speed hub for easy neighborhood trips. +A cyclocross training bike built tough with steel fork, disc brakes, and reinforced axle plates for frequent winter sessions. +Adventure e-bike with cargo platform, fat tires, pedal-assist motor and sturdy kickstand for overlanding trips +A performance gravel bike with adjustable geometry headset, 650b wheel compatibility, cushioned seatpost and stealthy matte black livery. +A gravel touring rig complete with low-ratio triple crank, fender clearance, soft leather saddle and moss-green enamel. +A titanium cyclocross frameset with discreet frame protection, stealth cable routing, and generous tire clearance for wet-season races. +A lightweight club road bike with alloy frame, shallow-section rims, and comfortable geometry for fast group rides. +A compact 20-inch wheel touring folder with low gears, angular aluminum frame and bright safety orange powdercoat. +A modern trail hardtail with 130mm fork, slack head angle, and wide handlebars for confidence on technical descents with quick recovery on climbs. +Electric cargo trike with stable three-wheel design, powerful motor, long-range battery, and a weatherproof cargo box for deliveries. +A steel cyclocross bike built for heavy-use training with thick chainstay guards, robust brake mounts, and a no-nonsense matte paint job. +City utility bike with basket, large-volume tires, coaster brake and simple one-gear ratio for easy errands and short trips +A Compact folding bicycle with low fold weight, quick-release handlebars, and an internal cable routing for a tidy packed look. +Electric mountain bike with full-suspension, high-capacity battery, torque-sensing control and durable drivetrain for long epics in rough terrain +Vintage step-through with cranksguard, wicker basket, and pastel paint in a soft mint for quaint rides. +A lightweight carbon road climber with shaved grams, low-volume 23mm tires, and a crisp white finish to celebrate hill-top gains. +Track sprinter with stiff fork, short wheelbase and a steep seat tube angle to transfer sprint power efficiently. +A kids' BMX with short chainstay, freestyle-ready geometry, and a robust headset to handle the tough learning curve at skateparks. +Kids' BMX with reinforced frame, short stem, and bold sticker sets that survive playground adventures. +Beach cruiser with retro sunburst paint, chrome chain guard and oversized spring-loaded saddle. +A sleek carbon time trial bike with deep-section front wheel, integrated hydration, and rear disc wheel compatibility. +Aluminum trail bike with modern geometry, 140mm fork, internal cable routing, and a dropper post for technical terrain. +A pure titanium cyclocross frame with mud-optimized chainstays and a brushed finish that blends durability and understated style. +A gravel all-road bike with built-in mudguards, 40mm tires, and a tasteful gradient lacquer that shifts from gray to burgundy. +Road aero bike with internal hydration routing, integrated bar/stem unit, and a slick black gloss finish with small logos. +A mountain trail bike with 140mm travel, adjustable geometry headset, stout alloy frame and a coil-compatible shock for rough lines. +A lightweight cross-country hardtail with carbon fork, agile geometry, and minimalistic decals for a race-ready look. +Kids' pedal bike with training wheels, bright polka-dot graphics and a chainguard for safety. +A solid alloy gravel bike with long reach, extra frame mounts, and heavy-gauge tubes for reliability on rugged routes. +Road time trial rig with large integrated fairings, aero seatpost, and a narrow wind-cheating profile to shave seconds in races. +A gravel bike with anodized standover protector, top-tube storage slot, and a dual-bottle mount on the fork for extended support on long routes. +A mountain all-mountain bike with 160mm fork, strong alloy rims, wide rims for 2.5" tires and a geometry that favors confident handling. +Mountain cross-country lightweight with 100mm travel, stiff carbon layout, and high-engagement hub for technical singletrack. +Mountain downhill bomber with dual crown fork, 200mm travel, and reinforced frame gussets. +A commuter with wide saddle, upright handlebars, and a low-slung frame for easy mount and dismount when making stops around town. +Adventure-capable titanium bike with discreet GPS mount, internal routed dynamo cables and durable finish for long-distance touring. +A sleek city e-scooter hybrid bicycle with narrow tires, foldable stem, battery under the seatpost and glossy black trim. +Lightweight aero road bike with sculpted down tube, shallow rim wheels, and glossy finish. +A gravel bike with extra anchoring points for framebags, stealthy top-tube strap mounts, and an understated mineral-gray paint job. +A commuter fixed-gear with deep-dish rims, polished spokes, and a minimal leather saddle for a streamlined urban look. +A classic town bicycle with retro decals, wide swept bars, and polished chrome fenders to bring a vintage look to everyday errands. +A gravel-specific titanium bike with asymmetric chainstay design, clearance for 2.2-inch tires, and discreet mounting points for racks and fenders. +Step-through electric city bike with low standover height, torque-sensing motor, and swept-back bars for easy control. +A neon pink BMX park bike with reinforced forks, gyroscope freehub and thick padded grips designed for skatepark tricks. +A modern mountain bike with 140mm rear travel, droppy seatpost, and a stout frame design that hides cables cleanly for a minimalist look. +Vintage lugged-steel road bicycle with cream-colored paint, downtube shifters, 27-inch wheels, and leather saddle that ages into a patina. +Road bike with vintage aesthetics and modern components: bar-end shifters, carbon clinchers, and classic lugs. +Gravel racer in gloss navy with subtly contrasting rims, fast-rolling 38mm tires and a lightweight cockpit. +Cyclocross race rig with tubular tires, carbon fork, short wheelbase for quick handling, and bar-end shifters for simplicity. +A classic steel road frame with modern disc brake mounts added, retaining lugged shell aesthetics while gaining contemporary stopping performance. +Vintage-styled city cruiser with springer fork, cream tires, and brass headbadge details. +City e-bike with integrated rear light bar, low-step frame, and simplified one-button power control. +Fat tire touring bike with large-volume 4.6" tires, sturdy touring racks, and low gearing to crawl over soft terrain while loaded. +A classic mixte with wicker hamper, polished crank, and small-diameter wheels for leisurely, stylish outings around town. +A gravel racer with wider trail tires, flared drops, and playful paintwork that masks scuffs from long itinerant explorations. +Mountain freeride with slacker geometry, heavy-duty wheels and softer compound tires for grip on loose trails. +Modern steel gravel bike with brushed finish, 700x45c tires, and subtle reflective decals for night rides. +Touring bike with triple chainset option, reinforced dropouts, and hand-stitched leather satchel strapped to the rack. +Mountain freeride frame with massive downtube, reinforced headtube and stiff bottom bracket for big-air sessions. +Alley-cat freestyle BMX with reinforced top tube, gyro cable system, and small 20" x 2.4" tires for park and street stunts. +Classic touring bicycle with reinforced lugs, long wheelbase, and triple bottle bosses for long-distance comfort and practicality. +Steel gravel endurance frame with slender seatstays, internal dropper routing, and brushed raw steel finish +Track pursuit-inspired bike with aero fairing, deep profile bars and a narrow saddle for time-on-track training. +A mountain enduro bike with split-link suspension design, burly axle hardware, and a stealth camo finish that conceals wear from hard trails. +Electric cargo trike with front box, mid-drive motor, low center of gravity, and bench seat for shared urban trips. +A durable mountain freeride frame with reinforced head tube, heavy-duty bottom bracket, and a stiffer rear triangle to handle jumps and drops. +Gravel expedition frameset with extra storage bosses, protective skid plate, and long-wheelbase stability for loaded journeys. +Gravel bike with stealth matte black paint, hidden cables, and 40mm tubeless tires for stealth missions. +A mountain downhill bike with long-travel suspension, beefy brakes, and protective frame guards for an aggressive park setup. +Cyclocross machine optimized for muddy courses with knobby tubulars, robust chainset, and strategically placed mud clearance +A vintage-inspired town bicycle with leather saddle and grips, polished chrome fenders, and a low-slung, elegant silhouette for park use. +A commuter cargo bike with front-loading box, hydraulic disc brakes, electric-assist, and dual kickstands for stable loading and unloading. +Mountain hardtail with progressive head angle, large-volume tires and a reliable 1x11 drivetrain for varied trails. +A modern cyclocross race bike with a monocoque carbon frame, integrated mud guards, and quick-release dropouts for easy wheel swaps. +A hand-painted custom road bicycle with watercolor gradient finish, fillet-brazed joints, and fine Italian leather saddle for show and miles. +A hybrid commuter with suspension seatpost, wide saddled comfort, cargo-ready rear rack and hydraulic disc brakes for wet weather stopping power. +A custom handbuilt steel touring frame with braze-on lighting mounts, double bottle cages, and durable chrome-moly tubing ready for adventure. +A steel cyclocross frame with carved dropouts, discreet paint, and a geometry that encourages aggressive corner exits and nimble handling. +A high-volume touring bike with internal gear hub, Rohloff 14-speed, heavy-duty racks, and a reinforced frame for expedition loads. +A gravel-stable race frame with thinner seatstays for compliance, a stiff headtube area, and wide brake caliper clearance for oversize rotors. +A downhill demonstration bike with triple clamps, massive travel, and a reinforced front triangle for park sessions and big hits. +A restored steel tandem with matching leather saddles, double-chain drive, and period-correct shifters for nostalgic two-up touring. +A commuter with integrated chain guard, internal cable routing, stealth matte black and a small rear rack for deliveries. +Gravel race bike with SRAM Force AXS wireless shifting, carbon clinchers and fast-rolling 38mm tubeless tires. +Tandem with carbon fork, lightweight alloy frame, two sets of shifters, and synchronized gear ratios for efficient team riding +A cyclocross bike designed for muddy nationals with carbon downtube protection, generous tube shaping, and a grippy matte finish for shouldering. +A modern gravel bike with carbon fork and a tapered head tube promising precise handling on varied terrain and confident descents. +Gravel bike with progressive geometry, short stem, and 42mm tires to balance speed and comfort. +A mountain enduro bike with coil-sprung rear shock, reinforced chainstay, 29-inch wheels and acid-yellow paint with black contrast. +City cargo bike with wooden bench, low-slung frame, granny gear options, and secure child harness attachment points. +A gravel bike with chainstay-mounted tool roll, micro-suspension seatpost, 650b wheels with 2.0 tires, and an off-road-ready cassette. +A kids' balance bike with bright orange frame, wide handlebars, rubber grips and a low seat for early independence. +A mountain enduro with adjustable geometry, coil-compatible shock, burly wheelset and a reinforced swingarm designed to take big hits. +Mountain enduro with adjustable shock tune, long dropper post, and stout wheels for aggressive rock gardens and steep chutes +Classic road bike with downtube shifters, tubular tires, thin steel tubing, and a distinct racing heritage aesthetic. +Kids' balance bicycle with bright orange frame, rubber-coated handles, and low center of gravity. +Vintage-inspired track bike with steel frame, deep polished rims and a minimalistic office commute aesthetic. +Cyclocross commuter with mud-ready tires, fender mounts removed, and a simple dynamo lighting setup for rainy rides +A bikepacking setup on a steel frame gravel bike with frame bag, handlebar roll, top tube sleeping pad strap, and 2.2” tubular gravel tires. +A youth BMX with lowered top tube, thick chromoly rails, and grippy 20" tires for early park progression and stunts. +Mountain fat-bike with low-pressure balloon tires, rigid fork and extremely low gearing for sand dunes. +Lightweight carbon all-road bike with endurance geometry, integrated cable routing and clearance for 35mm tires. +Gravel touring bike with wide-range gearing, frame-mounted pump, extra tire clearance and subtle camo finish. +City cycletaxi with three wheels, passenger bench, electric assist and safety flags for urban visibility. +Adventure touring bicycle with titanium racks, frame braze-ons, and wide-range gearing for multi-terrain long-haul travel. +A commuter folding bike with electric-assist, handlebar-mounted speedometer, and a compact footprint that stows beneath office desks. +Gravel bike with sloping top tube, dropper post compatibility, 1x drivetrain and tan wall tires for mixed surfaces. +A cyclocross racer built with lightweight carbon, a wide flare to the drops, fast-rolling cyclocross tires and a mud-shedding rear triangle. +A lightweight carbon hillclimb bike with super-stiff bottom bracket, 700x25mm tubulars, and a stripped paint job to reduce weight and shine on the climbs. +A custom titanium gravel frame with triple chainstay reinforcement, discreet logo laser-etched, and a satisfying natural finish. +Classic Dutch-style city bicycle with skirt guard, chaincase, and upright bar swept for relaxed steering. +Bicycle with front fairing and touring panniers, windscreen, and auxiliary power port for multi-day unsupported expeditions. +A long-wheelbase touring bicycle with extremely relaxed geometry, multiple rack mounting points, and an internal dynamo for lighting reliability. +A performance track bike with polished aluminum frame, carbon fork, deep-section front rim and a clean paint job for velodrome dominance. +A vintage cruiser with banana seat, high-rise ape hanger bars, whitewall tires and chromed fenders that gleam in sunlight. +A modern kids' BMX with chrome plating, detailed sprocket art, and sticky tires ready for skatepark sessions. +A touring bike with steel frame and fork, bar-end shifters, long chainstay for stability and custom leather toe straps. +Gravel flexible bike with modular dropouts, flip-chip geometry, and clearance for a full spectrum of tire sizes. +Touring tandem with dual-stage gearing, reinforced hubset and matching leather saddles for long couples’ adventures. +Track-inspired single-speed commuter with deep polished rims, narrow handlebars and fixed cog for efficient city cruising. +Cyclocross race bike with electronic shifting, double-cross drivetrain and mud-optimized tube profiles for winter racing. +Adventure-ready titanium gravel bike with vibration-damping stays and discreet frame mounts for off-grid gear. +Performance mountain bike with 29-inch wheels, 130mm travel and modern dropper post for aggressive singletrack. +A lightweight titanium road bike with brushed raw finish, endurance geometry, slender seatstays and classic tubing for lifetime durability. +Touring tandem with stiff steel frame, double racks, and synchronized shifters for coordinated long-distance rides. +A mountain hardtail with internal routing, stealth dropper lever, and sticky 2.4-inch trail tires. +A folding commuter with robust locking hinge, integrated fenders, and a compact folded footprint that fits under office desks. +A classic fixed-gear track frame repurposed into a city single speed with riser bars, platform pedals and bright reflective tape for visibility. +A utilitarian steel city bicycle with internal hub, chain guard, dynamo lighting and upright position designed for reliable urban transport. +Track pursuit bike with polished carbon weave, full aerodynamic helmet compatibility, and a long, low geometry for steady speed. +Mountain bike with split-pivot rear suspension, 140mm travel and tubeless-ready rims for rugged trails. +A commuter with reflective sidewalls, integrated fender mounts, and a stolen-resistant removable battery for overnight office storage. +Full-suspension enduro bike with coil-equipped rear shock and burly 35mm rims for downhill resilience. +A city folding bike with minimalist framework, single-speed chain, and a leather wrap on the seat to bring a premium touch to compact travel. +Classic mixte-style commuter with leather saddle, symbolic headbadge, front wicker basket and chrome chainring guard. +Folding commuter with neat cable routing through the hinge, ergonomically placed quick-release, and a compact folded stance. +Gravel event bike with fast-rolling semi-slick side knobs, carbon cockpit, and low-volume saddle for long weekend events. +Carbon gravel bike with integrated frame storage, stealth camo finish, and 38mm gravel tires. +Electric cargo e-trike with front-loading platform, seatbelt straps, and a long-range battery pack for deliveries. +Gravel tourer with heavy-duty spokes, rack mounts, full fender compatibility and plenty of bottle cages for multi-day adventures. +An off-road touring rig with fat 26x4-inch tires, reinforced frame, and bolt-on racks for expedition gear. +A folding cargo bike with electric assist, quick-release cargo box, and fold-flat handlebars for easy indoor storage. +A cargo trike with enclosed weatherproof box, hydraulic brakes, and electric assist designed for reliable grocery runs in urban settings. +A touring bike with triple chainset options, extra-durable rims, and brazed-on lowrider mounts for long multi-day trips. +Mountain freeride with oversized tires, 26-inch wheels, stiff brakes and a low-slung saddle for repeated jumps and park laps +A cyclocross convertible with compatibility for both knobby and semi-slick tires, modular fender mounts and a flip-chip geometry adjustment for varied conditions. +Electric-assist commuter with internal hub motor, torque sensor and a neat rear rack for pannier mounting. +A performance time trial bike with integrated hydration, aero stem, and a sculpted frame to shave seconds in solo efforts. +Folding e-cargo with robust hinge, telescoping handlebar, and modular child seat for family transport in tight spaces. +Cargo tricycle with long box, hydraulic steering assist, weatherproofing and a quiet electric motor for efficient multi-stop runs +City e-bike with pedal-friendly cadence sensor, integrated lights and a roomy rear rack for groceries. +A stripped-down commuter with coaster brake, swept-back handlebars, and a low center of gravity for easy mounting and dismounting in city traffic. +A modern gravel racer with short head tube, slack head angle, 700x45c tires, dropper post, and race-focused component choices. +Long-travel enduro bike with coil-sprung rear shock, beefy 27.5+ tires and wide handlebars for control. +Urban folding bike with single-chainring, quick-fold pedals and compact rear rack for small flats. +A touring bicycle with titanium frame, fully brazed rack mounts, internal cable routing and a subtle brushed finish for long-distance dependability. +A stealth aero road bicycle with muted matte paint, integrated bar/stem combo, and narrow 25mm tires for fast group rides. +A beach cruiser with comfortable upright bars, decorative chrome, and a slow-rolling single-speed for relaxed coastal rides. +A gravel-time trial hybrid with aero bars and drop bar features, 35mm tires, and a balanced geometry to perch between speed and comfort. +A custom framebuilder's steel randonneur with flared fenders, generator light, triple bottle mounts and classic British racing green finish. +Touring steel frame with triple-butted tubing, reinforced dropout, and brazed-on eyelets for robust long-distance loading. +Performance road bike with aero tube shaping, deep front rim and shallow rear for crosswind stability. +Polished titanium commuter bicycle with internal gear hub, belt drive, and elegant brazed joints. +Mountain downhill bomber with forged linkages, thick-walled swingarm, and a rugged protective screen on the downtube. +Urban commuter with smartphone mount, belt drive, integrated LED lighting, and full fenders for all-weather practicality. +A cyclocross bike with electronic groupset, tubeless-ready rims, and an optimized mud-shedding frame profile for efficient cyclocross racing. +Mountain park bike with reinforced frame, 26" wheels and low-slung top tube for freestyle tricking. +Gravel plus hardtail with reinforced rim profiles, mudguard-friendly mounts, and bold contrast pinstriping for visual flair. +Mountain enduro with adjustable geometry, coil shock option, and heat-dissipating brakes for long descending runs with heavy braking +Kids' BMX with reinforced pedals, slim frame, and low saddle for trick practice and jumps. +A classic road frame with steel fork and thin-profile tires, head badge, and hand-wrapped bar tape for old-school club rides. +Electric mountain bike with dual suspension, torque-sensing motor, and integrated cooling fins on the battery. +A kids' balance bike in sunshine yellow with reinforced frame, non-slip pedals removed for safety and rounded edges. +Electric folding city bike with fast-fold levers, integrated lights, and a rear hub motor for easy commuting and storage. +A compact commuter with stepped frame, child seat-ready rear rack, integrated lights, and a padded saddle for post-work errands. +Vintage town bicycle with step-through frame, wicker front basket and chrome accents for relaxed neighborhood rides. +Classic cruisey beach bike with vintage chrome, wide saddle and smooth single-speed drivetrain for coastal promenades. +A city cargo e-bike with rear twin-tray deck, throttle-less assist, and integrated child harnesses for family-hauling. +Dutch cargo trike with weatherproof cargo compartment, low step-through and large reflective decals. +Recreational hybrid with upright flat bars, suspension fork, 700x40 tires and easy-to-use 8-speed trigger shifters. +A commuter with internal battery, hidden motor in the rear hub, and low-maintenance belt drive for tidy urban use. +Vintage city cruiser with swept-back bars, balloon tires, chrome fenders and a subtle hand-painted crest on the head tube. +Electric mountain trail bike with dropper post, sturdy linkages, and a stealth battery molded into the downtube. +A classic commuter with basket, bell, step-through frame and retro cream paint, designed for casual weekly errands. +A cyclocross race rig with carbon forks, short wheelbase, wide 33mm tires and a clean, race-oriented geometry for agility. +A stripped-down track-style single-speed with white frame, polished saddle, and tight wheelbase for urban alleycat racing. +A classic roadster with swept bars, sprung leather saddle, brass lamp mount and a functional rear rack for short city errands. +A cargo e-bike with enclosed storage and fold-out bench, long-range battery, and integrated child safety harnesses for family logistics. +Mountain downhill race bike with extra-long travel fork, reinforced pivots, massive tires, and a gravity-focused geometry for speed. +A compact kids' BMX with reinforced cranks, gyro steering, and brightly colored pegs for confident tricks at the skatepark. +Minimal urban fixie with gloss black finish, narrow saddle, 44-tooth chainring and clean uncluttered lines for stealthy city presence. +Commuter city bike with hub dynamo, bright LEDs, full fenders and durable tires for safe winter commuting. +A polished aluminum race bike with aero optimized fork, 25mm slick tires, and a single-piece bar-stem combo to minimize drag. +A custom handbuilt steel road bicycle with lugwork detailing, fileted joints, bespoke paint and classic component spec. +A gravel race frameset with streamlined tubing, electronic-ready routing, and a geometry that balances stability and nimble handling. +Touring tandem bicycle with two sets of handlebars, triple-chainline compatibility and reinforced bottom bracket. +A cargo trike with low cargo bed, disc-braked stability, electric assist option and dual front wheels for heavy loads in urban deliveries. +A vintage track machine with tubular wheels, polished steel frame, low-profile handlebars and deep navy lacquer with thin white stripes. +Classic European city bike with coaster brake, chaincase, and leather saddle for slow, elegant rides. +A retro-inspired single-speed city bike with leather saddle, brass bells, and painted pinstriping that evokes old-world craftsmanship. +A gravel-adventure bike with geared hub option, broad tire compatibility, and integrated bolt-on fender options for variable conditions. +Folding single-speed city bike with elegant paint, small wheels, and a quick latch that allows folding in under ten seconds. +A trail-ready hardtail with 120mm front travel, wide handlebars, short stem and grippy 2.4" tires for snappy trail responses. +Urban fixie with polished nickel headset, minimal decals, and a narrow saddle for quick urban sprints. +A boutique hand-lugged steel frame with hand-enameled seat tube badge, matching painted fork crown, and elegant proportions. +A rugged urban delivery bike with heavy-gauge frame, reinforced bearing sets, and an oversize front tray for catering and courier jobs. +A commuter with electric-assist hub, long fenders, and an aluminum rear deck for daily grocery trips and weekend errands. +A cyclocross training bike with spare wheel mounts, mudflaps, and a subtle camo paint to hide winter grime between races. +A lightweight carbon climber road bike with 34mm rim depth, 11-32 cassette, and ultra-narrow seatpost collar for weight savings on steep ascents. +Urban single-speed with polished chromed fork, narrow handlebars, and a single bolt seat clamp for simplicity. +Steel frame mountain single-speed with tensioner, 26-inch wheels and a sturdy rigid fork for simple, durable trail rides. +Mountain hardtail with steel frame, coil-sprung fork, old-school V-brakes and wide 2.4" tires for trail nostalgia. +A tandem recumbent bicycle with dual-chain system, comfortable mesh seats, and dual steering for synchronized touring. +A gravel bike with mixed-wheel setup: 700c front, 650b rear with plus tire for variable terrain stability. +Performance road aero frame with shallow seat tube, deep-section wheels, and a focus on rigidity and aerodynamic advantage. +Kids' off-road bike with front suspension, wide knobby tires, friendly geometry and easy-to-use coaster brake for early trail skills. +A commuter with integrated lock, minimal chaincase, puncture-resistant tires and deep brown retro paint with gold pinstripes. +A high-end titanium commuter with ceramic bearings, tapered head tube, discreetly integrated battery for optional e-assist, and polished welds. +Retro fixed-gear conversion with polished chrome, single brake added, narrow tubular tires and nostalgic hand-painted lettering +A cargo tricycle with flatbed and reinforced steel frame, practical for small business deliveries, painted in high-visibility orange. +A long-range electric touring bicycle with dual-battery setup, sturdy racks, and regenerative braking for extended rides. +Gravel bike with mosaic paint job, 700x40 tires, mechanical disc brakes, and a purpose-built rack for bikepacking gear. +Classic steel road bike with glossy lacquer, quill stem, tubular tires, and nostalgic charm for spirited group rides. +BMX flatland bike with short chainstay, low bottom bracket, micro handlebars, and a sleeve headset for tricks. +Adventure expedition bike with triple-mount braze-ons, Rohloff-ready frame spacing, and robust finishes for low-maintenance travel. +Touring tandem with triple racks, reinforced chainring, extra toe clearance and comfortable saddle rails for long-haul comfort. +Gravel e-bike with long-range battery, sturdy alloy frame and mid-drive motor for mountain passes. +A modern mountain hardtail tuned for cross-country with efficient suspension, racing geometry, and a race-optimized wheelset. +Lightweight urban fixie with polished spokes, short stem, leather saddle, and a clean minimalist frame design. +Modern e-MTB with dropper seatpost, trail-tuned motor map, and tubeless-ready rims. +A dagger-steel cyclocross frame with a slightly raised bottom bracket, long reach for stability, and specific tube shaping to clear mud. +A touring tandem with reinforced head tube, double-chainline, and synchronized shifters for smooth long-distance team travel. +Urban folding electric bike with small packable footprint, throttle-enabled boost, anti-puncture tires and a foldaway kickstand for tight storage +A custom carbon gravel build with hand-selected bits, anodized titanium bolts, and a low-profile saddle for long comfort. +Touring steel bicycle with lugless joints, triple water bottle mount, and durable rack-compatible stays for heavy loads. +Kids' trail bike with low standover height, responsive brakes and vivid dinosaur-themed graphics. +Classic Dutch-style upright bicycle with chain guard, enamel paint and wide saddle for everyday errands. +Track fixed-gear with polished steel frame, deep bladed fork, and vintage-style decals for a timeless look. +Urban commuter with integrated lock, belt drive, single-speed hub, and discreet reflective piping on the frame. +A mountain trail bike with plus tires, agile geometry, and lower gearing for playful trail days and confident climbing. +Gravel+ bike with 650b wheels, chunky 47mm tires, and stable slack geometry for rough terrain. +A lowrider cruiser bicycle with extended fork, banana seat, chrome fenders and ornamental flame graphics designed for show cruising. +Electric cargo longtail with folding child seats, centralized battery, hydraulic brakes, and a practical low step-through deck. +Cyclocross-inspired commuter with mud-friendly frame, disc brakes, knobby commuter tires, and a clean steel finish for year-round use. +Adventure hardtail with extra water bottle mounts, 29-inch tires and a wide-range cassette for long rides. +A BMX street bike with reinforced top tube gussets, gyroscope cable routing, and pegs for grinding and stall tricks on urban features. +A gravel tourer with triple water bottle capability, sturdy 36-spoke wheelset, and an adjustable-length top tube to fit a variety of riders. +Gravel grinder with flared upper drops, 2x drivetrain for versatility, and reinforced fork for long off-road stages +A carbon hardtail with 100mm travel, modern slack geometry for playful singletrack, and tubeless-ready rims for puncture resistance. +A sleek commuter with hidden battery, small hub motor, carbon fiber belt drive and metallic graphite finish. +A commuter with a quietly integrated hub motor, puncture-proof tires, and an elegant, low-slung frame to blend form with function. +Vintage road bike restored with period-correct shifters, wool bar tape, and etched lugs. +Gravel plus touring rig with long-range gearing, heavy-duty rims and reinforced fork for loaded adventures. +A polished chrome vintage road bike with leather straps on the forks, classic toe clip pedals, and narrow tubular tires. +Touring bike with custom rack mounts, reinforced dropouts and weatherproof paint for long distance reliability. +Classic road bicycle with restored scoreboard shifters, leather saddle, thin tubular tires and period-correct steel rims +Gravel commuter hybrid with disc brakes, reflective sidewalls, and upright stance for safe city to trail commutes. +Mountain downhill-focused frame with reinforced chainstay gussets and ample tire clearance for aggressive rubber. +Folding electric cargo bike with twin batteries, foldable child seats and a low step-through for safe access. +Gravel bike in candy apple finish with flared drops, 1x drivetrain, and reinforced bottle cage mounts for off-grid rides. +A coastal town cruiser with varnished wood rack, wicker basket, chrome handlebars and powder-blue enamel with sea-spray decals. +Urban utility bike with lockable frame triangle, removable cargo cage, and smooth-rolling hub gear. +A cyclocross bike with steel tubing and modern geometry that blends classic comfort with race-focused handling and robust durability. +Fixed-gear alleycat racer with lightweight hoops, minimalist cockpit, and quick acceleration gearing for frantic city runs. +Lightweight aluminum commuter with upright geometry, puncture-resistant tires, integrated lock mount and reflective decals. +A lightweight cyclocross bike with alloy frame, carbon fork, 33mm tubeless tires and discreet frame bag mounts for race essentials. +Gravel plus bike with compliant alloy frame, oversized tyres, and a hand-applied fade paint from black to olive. +A classic chromoly city bike with fendered 700c wheels, three-speed hub, and a tactile leather saddle to keep daily rides comfortable. +A lightweight cyclocross frame with clearance for mud tires, disc brakes, and a filigreed geometric paint job that shines under race lights. +A practical folding e-bike with throttle assist, mid-motor, quick-fold latch, 16" wheels, and an integrated carrier for urban multi-modal trips. +Lightweight cyclocross frame with fast wheelbase, mud-specific tube shaping, and clean internal routing for a tidy look. +A mountain bike with integrated chain guide, 1x12 drivetrain, and cushioned saddle designed to absorb the chatter on technical descents. +Fixed-gear street cruiser with poppy orange paint, leather saddle, and a bold single-tooth chainring for pure simplicity. +Utility folding bike with basket, compact folded size and a durable hinge built for frequent use in tight spaces. +A gravel-allrounder with 700c wheels, 50mm clearance, low Q-factor cranks, and an internally routed dropper post for technical descents. +A utility city bike with step-through frame, basket on front, internal hub gearing and an upright posture for relaxed urban riding. +Gravel bike built for bikepacking with frame bag mounts, triple water-bottle mounts, 700c wheels with 45mm tires, and a relaxed geometry for long days. +Mountain trail bike with modern geometry, 29-inch wheels, 130mm travel, and a dropper post with generous drop range. +Vintage-styled city mixte with tasteful two-tone paint, brass lamp mount, and comfortable upright stem for relaxed rides. +A cyclocross race machine with dropper compatibility, tubeless-ready rims, and a bright yellow paint scheme that contrasts with mud during races. +Youth road bike with lowered standover, 650c wheels, compact crankset, and bright decals to inspire young riders. +Cyclocross training bike with steel frame, modern disc brakes and drop bar flare for confident cornering. +A high-performance cyclocross machine with durable axle system, high-volume tires, and a reinforced headtube for repeated shoulder carries. +A compact city e-bike with low center of gravity, removable battery, mid-motor and subtle pearl-gray finish with reflective decals. +Gravel adventure bike with two water bottle mounts inside the front triangle and small ti-bolt accents for a refined look. +A titanium gravel do-it-all bike with thru-axles, disc brakes, and a satin raw finish accented by custom enamel decals. +Kids' balance bicycle in bright orange with foam wheels and adjustable seat height to teach steering without pedals. +Gravel race bike with compact geometry, short chainstays, and crisp shifting 1x12 drivetrain for quick accelerations. +Road time trial bike with integrated hydration, shaped tube profiles, and a one-piece bar/stem for aero gains. +A vintage-styled single-speed cruiser with fully chromed fenders, white leather saddle, and thick tires for relaxed Saturday morning rides. +Lightweight carbon road frame with minimal graphics, threaded BB, and a tapered headtube for responsive handling. +Gravel bike painted in earth tones, long-lasting mechanical discs and comfortable bar shape for long rides. +Urban single-speed with durable powder-coat, full-coverage fenders, and a small rear cargo shelf. +Road time-trial bike with steep seat angle, integrated nutrition storage and a sculpted front end for sustained aero. +A vintage road race bicycle with steel frame, downtube shifters, toe clips, and a polished chrome headtube for period-authentic racing. +Road time trial bike with aggressive cockpit, full fairing integrations, and a focus on minimizing frontal area for maximum speed. +A modern uphill-climbing road bike with high-modulus carbon layup, low weight, 34mm rims and bright coral accents on a white base. +Lightweight cyclocross with tubular rims, mud-shedding frame design, low-profile brakes, and a gritty raw frame finish. +Commuter utilitarian bike with integrated cargo rails, fendered 700c wheels, and a reflective paint scheme for urban safety. +Kids’ balance bike in primary red with wooden frame and rubber grips for first practice of balance. +Fatbike with balloon tires, fat rims, rigid fork, and a wide handlebar for stable beach and snow rides. +Urban single-speed with painted steel frame, roller brake, and a comfortable bar sweep for relaxed city rides. +Folding commuter with rapid fold handlebars and handlebar-mounted locking mechanism for quick entry into public transport. +Electric all-mountain bike with full suspension, torque-bias motor and intelligent power mapping for technical terrain. +A modern electric-assist city bicycle in pearl white with mid-drive motor, integrated battery, step-through frame and hydraulic disc brakes for hill-climbing ease. +A bright orange full-suspension mountain bike with 150mm travel, 29-inch wheels, dropper post, and a burly 1x12 drivetrain for aggressive trail riding. +Gravel exploration bike with stealthy matte finish, secure rack mounts and wide clearance for mud and tires alike. +Adventure hardtail with stealth matte paint, multiple frame mounts, and 29er wheels capable of handling light bikepacking loads. +Fixed-gear track frame repurposed as single-speed road bike with brakes added and polished wheelset +Mountain downhill carbon frame with full hydroformed tubes, long travel setup and high-resilience link bearings for repeated abuse. +A retro-inspired road bike with classic downtube shifters, quill stem, gumwall tires, and delicate pinstripes. +Cyclocross training frame with reinforced fork tips, mud-specific geometry and compatible with tubular or clincher tires. +Track pursuit bike with alloy frame, upright aero bars and minimal graphics for focused training sessions. +A mountain downhill specific bike with reinforced headtube, massive stanchion fork, and a crash-scarred protective coating. +Track training bike with alloy tubular rims, stiff chainstay design and a clipped, minimalist decal treatment. +Classic Italian steel road bike with Campagnolo gruppo, celeste green paint, and classic chrome lugs. +A modern all-mountain bike with 150mm travel, 29-inch wheels, dropper post and camo-splatter paint over matte black base. +Enduro race rig with coil shock option, adjustable geometry, stout rims, and grippy tires for steep technical descents. +Gravel endurance bike in deep green, dropper-compatible seatpost, flared drops, and thicker rubber for comfort on long rides. +Mountain freeride frame with heavy-duty pivot hardware, replaceable sliding dropouts, and a bold paint scheme to hide scratches. +Retro utility bike with coaster brake, spring saddle, chain guard, and a polished steel frame for simple errands. +A classic British ladies' step-through restored with new paint, wicker basket, and comfortable upright geometry for easy riding. +Simple single-speed cruiser with sweeping bars, banana saddle, coaster brake, and vintage-styled fenders for easy relaxed riding. +A restored steel cruiser with sweeping frame lines, brass headlamp, and original paintwork touched up to preserve authenticity. +Urban folding e-bike with mid-motor, swift fold system, bright integrated lights, and a resilient belt drive for city runs. +A mountain e-bike with dual-battery option, trail-tuned motor mapping, and an optimized suspension platform for assisted trail days. +Touring tandem with reinforced coupling, matching saddles, and extra eyelets for low center of gravity panniers. +Classic city step-through with swept handlebars, cane basket and a spring-loaded leather saddle for easy comfort. +A city commuter with quiet belt drive, hub dynamo, and hidden mount points for a minimalist, functional aesthetic. +A high-pivot downhill bike with long travel, ovalized tubing, boost spacing and neon pink accents over matte black. +Full-suspension downhill machine with long-travel linkage, run-out protection and a race-ready paintjob. +A commuter with integrated pannier racks, hydraulic disc brakes, internally routed cables and reflective decals for night rides. +A fixed gear urban bike with track-style geometry, low stack and reach, and a bright accent color on the fork for visibility. +A gravel endurance machine with compliance-tuned layup, long wheelbase, 700x42 tires and a discreet storage tunnel under the top tube. +A modern carbon road racer with an endurance-oriented cockpit, integrated seatpost, and subtle reflective elements for dawn and dusk rides. +Gravel touring bike with integrated mudguard mounts, welded front rack lugs, and weatherproof decals. +A cyclocross-specific carbon frame with raised bottom bracket and strategically placed mud-shedding contours for the puddle-heavy season. +A modern steel gravel frameset with hand-brushed finish, elegant brazing, and ample clearance for wide adventure tires. +A competition cyclocross bike with race geometry, high-volume disc wheels, and a set-up optimized for quick transitions and shoulder carries. +Trail hardtail mountain bike in orange with 120mm travel fork, dropper post and tubeless-ready rims. +A gravel-specific bike with hidden frame bag mounting, 650b compatibility, and a 1x drivetrain optimized for steep climbs and technical descents. +Mountain cross-country racer with featherweight carbon frame, 100mm travel, and quick-engagement hub for accelerative punch. +A city folding bike with magnetic latch, small 16-inch wheels, quick-fold handlebars and gloss white finish with neon green accents. +A titanium long-distance touring bike with reinforced lugs, extended fork rake, and subtle polished finish that blurs the line between craft and utility. +A touring tandem with steel frame, two sets of double crankarms, long-wheelbase stability, twin racks, and reinforced hubs for loaded adventures. +A high-pivot enduro mountain bike with idler pulley, 170mm rear travel, 38mm fork, and a geometry slider for tuning head angle. +Belt-drive urban cruiser with coaster brake, swept-back bars, and terrazzo-inspired paint. +A recreational cruiser with wicker basket, spring saddle, and pastel green paint for relaxed cruising along promenades. +Urban commuter with integrated fold-out lock, cushioned grips, and a matte navy finish that hides scratches well. +A touring bicycle with full-length fenders, dynamo-powered lighting system, and wide-range gearing for cross-country loads. +Urban single-speed with glossy lacquer, narrow-profile tires, and alloy pedals for a simple, fast city commute. +Lightweight aluminum road frame with endurance geometry, disc brakes, and integrated bottle cage mounts for long rides. +Performance mountain bike with carbon linkages, adjustable geometry, 150mm travel and lightweight but durable components for race days. +Mountain trail bike with light carbon frame, progressive geometry, and wide rims for aggressive corner traction. +A BMX race frame with heat-treated chromoly, tailored geometry for speed, and bold sponsor-style graphics for the starting line. +Cargo electric tricycle with sealed bearings, heavy-duty frame plates, and an enclosed cargo box for secure delivery routes. +A commuter with belt drive, urban integrative lock, and puncture-proof tires that take daily riding abuse with minimal upkeep. +A long-distance endurance road bicycle with endurance geometry, wired shifters, and pannier-specific rack mounting bolts for mixed commuting/touring. +A lightweight carbon road bike with aero-shaped down tube, integrated cockpit, deep-section 50mm wheels, black gloss finish and red pinstripes. +A compact folding city bike with quick-release bars, small wheelbase for tight turns, and a solid latch for secure folded commuting. +Folding electric bike with small wheels, mid-drive motor, and quick-fold latch for easy urban storage. +Mountain downhill bike with triple-butted alloy frame, gravity-tuned suspension, and reinforced handlebar clamp. +Lightweight touring titanium frame with brushed finish, integrated bottle bosses, and minimalistic graphics. +A commuter with an integrated toolbox under the saddle, puncture-resistant tires, and a reflective frame wrap for night safety. +Retro-inspired single-speed with swept bars, cream decals, and a rakish silhouette for casual cruising. +A kids’ bright blue BMX bike with 20-inch wheels, gyro detangler, reinforced pegs, and a single brake for park tricks and street riding. +A lightweight steel road frame with classic slender tubes, modern braze-on mounts, and a satin finish that shows subtle craftsmanship. +Mountain cross-country race hardtail with featherweight frame, 100mm fork, aggressive XC geometry and fast-rolling tires +A handbuilt steel gravel frame with fillet-brazed joins, raw brass headbadge, custom geometry and earth-flecked paint. +A steel cyclocross frame with classic geometry, brazed-on fender studs and an eye-catching two-tone paint scheme. +Kids' small BMX with bright anodized parts, reinforced forks, and padded top tube for safe fun at the park. +Commuter with internal chaincase, sprung saddle, and wide handlebars for city control and posture comfort. +Performance road frameset with tapered headtube, integrated seat cluster, and aggressive short chainstays for snappy handling. +Cargo trike with enclosed cabin option, electric drive, and reinforced axles for heavy loads in cities. +Classic cruiser with springer front fork, teak front rack, balloon tires and soft foam saddle for Sunday beach days. +Urban electric cargo trike with box in front, pedal-assist motors, hydraulic brakes, and seats for two children in the cargo box. +Handmade lugged steel road bike with glossy deep navy paint, Columbus tubing, classic headset, and a 9-speed classic drivetrain for timeless style. +Commuter with integrated front rack, small dynamo headlight, and chaincase to keep clothes clean. +Mountain enduro frame with adjustable chainstay length, burly pivots, and a low-slung bottom bracket for playful descending. +A BMX competition build with durable frame gussets, sealed bearings, and a short stem for precise track handling. +Fixed-gear bike with colorful anodized components, narrow saddle, clipless pedals, and a stripped-down aesthetic for urban style. +A cyclocross training frame with functional geometry, reinforced forks, and an easy-to-shoulder top tube for cyclocross-specific workouts. +Kids' BMX with sturdy welded frame, mini handlebar pad and durable chainring guard for first tricks. +Mountain trail bike with modern geometry, boat-like stability, and a dropper post for instant saddle height adjustment. +A lightweight commuter with carbon fork, puncture-resistant tires, and a slim fender set to keep splashes off clothes on rainy rides. +A commuter with built-in tire pressure monitoring, puncture-resistant sidewalls, and a removable rear safety light for night rides. +Handbuilt lugged steel randonneur with low gearing, generator hub, and subtle pinstriped paint. +Mountain enduro alloy frame with adjustable geometry, 170mm dropper post compatibility, and oversize tubing for strength. +A touring bicycle with triple-crank compatibility, reinforced spokes, chromed steel racks, and leather saddlebags for classic long-haul styling. +A gravel bike with mixed-wheel setup—700c front and 650b rear—plus 45mm tires in the back for confidence on rough descents and efficiency on flats. +Touring gravel rig with steel fork, three-bottle mounts, and full fender compatibility for year-round exploration. +Mountain trail hardtail with efficient pedaling platform, 120mm travel fork, and a balanced frame for diverse trails. +A British-style roadster with polished fenders, leather grips, and deep-profile mudguards for rainy commutes and classic looks. +Classic mixte with floral decals, swept-back bars, and a retro saddle for casual slaloms through city parks. +A fixed-gear cruiser with sweeping bars, thick saddle, and a glossy red metallic finish with retro white sidewall tires for style-focused urban rides. +Lightweight cyclocross carbon frame with UCI-legal geometry, thru-axles, and internal cable routing for clean aesthetics. +Lightweight cross-country mountain frameset with efficient pedaling geometry, modern tire clearance, and weight-saving carbon layup. +A gravel racer with 700x38c tubeless tires, aero-savvy cockpit, and SRAM Force eTap wireless shifting for clean cable routing. +A lightweight gravel grinder with alloy frame, carbon fork, 700x35 tubeless tires and a balanced mix of stiff pedaling response and comfort. +Electric cargo bike with modular cargo bins, regenerative braking, and a low center of gravity to safely transport goods. +A folding electric bicycle with a quick-release handlebar stem, fold-in pedals, and a removable battery tailored for commuters who fold frequently. +Vintage track-style single-speed with polished steel frame, deep dish rims and a clean chainline for elegant city rides. +A refined titanium gravel bike with discreet welded bosses, oversized bottom bracket, and a finish that reveals micro-texture under sunlight. +Folding mini-velo with 20-inch wheels, single-speed hub and a compact frame for tight apartment storage. +Cyclocross warrior with carbon monocoque, wide bottom bracket for stiffness and mud-friendly frame shaping. +A gravel-focused bike with 700x42mm tires, flared drop bars, and an elegant creamy finish that masks dust from long rides. +A cyclocross bike with welded dropout reinforcement, wide tire clearance, and a glossy anodized frame color. +Urban single-speed with polished chrome fork, slim tires and a minimalist attitude for city cruising. +Youth BMX race setup in gloss purple, tubular steel frame, single-speed freewheel, and pegs removed for safety on tracks. +Cargo trike with heavy-duty steel bed, hydraulic disk brakes, and an adjustable handlebar to suit many riders. +Urban single-speed with enamel dotted paint, leather seat, and polished aluminum components for a bespoke street look. +Adventure e-fat bike with oversized tires, heavy-duty drivetrain and integrated light bar for extreme conditions. +A vintage roadster with brushed brass accents, hand-stitched leather grips, and a relaxed geometry perfect for slow mornings. +Mountain downhill sled with monstrous tires, heavy-duty frame, and powerful disc brakes for controlled descents at speed. +Minimal commuter with single-speed belt, reflective paint, and quick-release front wheel for storage. +A folding cargo bike with expanded rear platform, reinforced frame, hydraulic brakes and detachable side panels for flexible hauling. +Electric mountain bike with stepped geometry, responsive motor, and high-capacity battery hidden in the downtube for trail endurance. +Youth balance bike with adjustable saddle, bright safety paint and a low center of gravity to build confidence. +A steel fixed-gear track-style city bike with front brake, painted head badge, and narrow 25mm tires for slick streets. +A commuter with leather saddle, swept handlebars, integrated bell and classic cream paint that hides small scratches. +A gravel race bike with integrated chainstay protector, tubeless 40mm tires, and a discreet top-tube bag for quick-access tools. +Folding commuter with an easy two-step fold, adjustable seatpost and a compact bag designed to protect the folded bike. +A vintage city bicycle fully restored with polished metal fenders, brass bell, and a leather saddle preserved with beeswax. +Commuter with belt drive, internal hub gear, full fender set and discreet integrated lights for a refined daily ride. +A stripped-down single-speed with shiny chrome frame, narrow tires, and a minimalist saddle for short, efficient rides. +A handcrafted lugged titanium road machine with matte finish, custom geometry, and hand-polished welds for bespoke fit. +A vintage mixte bicycle restored with new cables, a leather saddle, and high-flange hub wheels that roll with charming ease. +A mountain trail bike with progressive geometry, short stem, and burly 2.6-inch tires for aggressive singletrack. +Cross-country race hardtail with minimal weight, efficient pedaling platform, 100mm fork, and aggressive XC race geometry. +Gravel bike with flared drop bars, 40mm knobby tires, framebag mounts, and gravel-specific endurance geometry. +A modern hardtail with responsive frame geometry, tubeless-ready rims, and a single-ring drive train for simplicity on the trails. +Gravel adventure tandem with long wheelbase, dual framebag compatibility and sturdy tandem drivetrain for two-up exploration. +A gravel bike with flared drop bars, 40mm tubeless gravel tires, disc brakes, and a carbon frame optimized for mixed-surface endurance rides. +Electric-assist folding cargo bike with front box, low fold height, mid-motor torque sensing, and adjustable seat for multiple riders. +Retro cruiser bicycle with swept-back bars, balloon tires, coaster brake and a pastel mint enamel paint. +A single-speed fixie with aero-inspired narrow frame, tall riser bars, polished rims and a minimalistic monochrome paint job. +A carbon all-mountain bike with 160mm front travel, 150mm rear, burly tires and an adjustable headset for steep trails. +A commuter with integrated lights, internal routing, and a hinge for compact storage in small apartments or office lockers. +A bespoke steel road bicycle with hand-brazed lugs, polished raw finish, classic quill stem and narrow 23mm tubular tires. +Urban single-speed with polished steel frame, narrow tires and a quiet coaster brake for smooth neighborhood loops. +Performance road bike with integrated power meter, aerodynamic handlebars and tubeless 28mm tires for race day. +A BMX race bike with lightweight alloy rims, narrower tires, and a geometry tuned for quick acceleration out of the gate. +A quiet belt-drive commuter with internal hubs, dynamo lighting, full fenders, and a matte green powder coat for daily reliability. +Electric cargo tandem with twin batteries, strong rear axle, and passenger foot straps for dependable double-duty transport. +Gravel bike with full carbon fork, 700x45c tires, and frame clearance for sleeping mat straps. +A kids' mountain bike with 24-inch wheels, front suspension, easy-to-reach brakes and colorful frame stickers for trail fun. +A trail-oriented 29er with 130mm travel, efficient pedaling suspension layout, and grippy rubber for varied terrain throughout the season. +A winter commuter with studded 700c tires, thermal grips, and a heated saddle pad for cold-weather downtown trips. +Road race bicycle with full internal cable path, integrated seatpost clamp, carbon fork and 28mm race tires for crits +Lightweight trail hardtail with carbon front triangle, alloy swingarm, tubeless-ready rims and plush geometry. +An electric-assist city bike with mid-drive motor, hidden battery in the downtube, and wide cushioned saddle. +A BMX freestyle bike with gyro system, close-ratio gearing, pegs and reinforced dropout for street and park confidence. +A cyclocross race build with carbon fork, mud-clear chainstays, and a 1x11 drivetrain for simplicity and reliability. +A kids' balance trike with colorful art, stable three-wheel base, non-slip pedals and an easy-grip handle for guidance. +Racing time trial bike with integrated hydration and aero tail section, minimal graphics and performance-oriented cockpit. +Touring bike with Rohloff hub, belt drive, and titanium racks for low-maintenance global travel. +Polished lugged steel frame with understated enamel stripes, classic quill stem, and narrow clincher rims for vintage charm. +Urban single-speed with minimalist fenders, sealed-bearing hubs, and a matching frame-mounted pump for emergencies. +Touring folder with robust hinge, optional small trailer hitch, and durable 20-inch wheelset. +A glossy steel road bike with deep-section carbon wheels, red lacquer frame, 22-speed mechanical drivetrain, and a slim endurance saddle. +Touring e-bike with belt drive, internal hub, and long-legged geometry for loaded multi-day baggage. +Foldable commuter with micro hinge, compact folded size, and a chain tensioner to keep the drivetrain tidy. +A vintage-inspired cafe racer bicycle with engine-mount-like tank styling, narrow tires, and leather wraps for a motorbike aesthetic without an engine. +A rugged all-terrain fat-biker with 5-inch tires, low-pressure rubber for sand, welded aluminum frame and matte sand finish. +Gravel e-bike with modular racks, multi-mode assist, and a discreet control pad near the left shifter. +Gravel-focused hardtail with slack headtube, wide rim profile, and fender compatibility for extended winter gravel outings. +A cyclocross race frame with oversized chainstays for stiffness, low standover for shouldering, and a rugged matte finish designed to hide wear. +Gravel bike with a hammered matte finish, braided cable guides, and a low-slung top tube for stability. +Urban single-speed with belt drive, Gates Carbon belt, painted matte green frame and puncture-resistant tires. +A modern road endurance bike with compliance-enhancing seatstays, wide tire clearance, disc brakes, and a slightly taller head tube for comfort. +Urban electric cargo longtail with child harness seats, reinforced kickstand, throttle and torque-sensing pedal assist for city families. +Carbon fiber peformance road bike with tapered head tube, thru-axles, internal battery for electric shift, and minimal paint for weight savings. +A performance MTB with lightweight alloy wheels, 120mm fork, fast-rolling tires and a race-oriented geometry for cross-country laps. +A flamboyant purple fixed-gear with deep dish wheels, colored chainring and minimalist decals for track-inspired street style. +Handbuilt titanium road machine with brushed finish, discreet cable routing, and a balanced race-endurance geometry for long-fast days. +A long-travel freeride mountain bike with reinforced chainstays, heavy-duty rims, and bright graphics that mask scuffs from heavy use. +Adventure tandem with durable steel frame, matching panniers, and wide-range gearing for long two-up touring. +A commuter with integrated smartphone mount, front rack compatibility, and an automatic hub lighting system for safety and convenience. +Comfort cruiser with low step-through, plush foam saddle, oversized grips and soft suspension fork for smooth neighborhood rides. +A gravel challenge bike with raised bottom bracket, shallow seat stay profile, 700x40 tires and flaked stone-gray paint. +A handmade steel commuter with elegant lugwork, polished chrome fenders, and a quietly spinning dynamo hub for steady night visibility. +A high-volume trail hardtail with 29x2.6" tires, slack geometry, reinforced rim bed and a confident, planted ride on loose terrain. +A performance time trial bike with elongated seat tube, integrated hydration system, and aerodynamic storage tucked behind the saddle. +Gravel bike with leopard-print handlebar tape, bikepacking luggage, flared drops, and low, stable geometry for endurance. +Mountain downhill alloy frame with double-butted tubes, long-travel shock and large-volume tires to soak up rough tracks. +Lightweight commuter racer with carbon fork, aluminum frame, shallow rims, and clipless pedals for fast commutes. +Vintage track bike restored with period-correct decals, polished chrome, and a high-tooth ratio for classic velodrome runs. +A custom lugless steel frame with unique ovalized downtube, fillet brazing, and hand-tinted paint fades. +A lightweight carbon hardtail with stealth paint, 120mm fork, 29" wheels and race-ready components for cross-country events. +A classic Dutch-style upright bicycle with full-chaincase, coaster brake, chrome rear rack, and cream leather saddle for dignified city riding. +Gravel monster truck with 29x2.6 tires, reinforced dropouts and a heavy-duty rack for bikepacking with extra load. +Lightweight titanium cyclocross frame with fillet-brazed joins, durable dropouts, and stealth rack mounts. +A vintage race bicycle with polished steel lugs, quill stem, center-pull calipers, and waxed leather grips for period-authentic handling. +Utility cargo electric bike with dual-battery option, hydraulic disc brakes and a reinforced head tube for stability. +Road endurance bike with wide tire clearance, comfortable cockpit, and subtle reflective accents for night safety. +Gravel speed machine with wide 42mm tires, cockpit-integrated bags, and aggressive gearing for fast miles. +Mountain cross-country disciplined frame with low weight, stiff bottom bracket and quick handling for technical racecourses. +A cyclocross bike with carbon downtube, aluminum stays for compliance, and a race-tuned geometry with short chainstays for snappy accelerations. +Urban cargo e-bike with heated grips option, bright integrated LED strip, and a flatbed for deliveries. +Mountain trail all-rounder with dropper post, 120mm travel, 29er wheels, and balanced geometry for everyday adventures. +Mountain trail bike with 29-inch tough wheels, supple airbags in fork, and grippy sidewall rubber. +Lightweight carbon aero road frame with D-shaped seatpost, internal battery mount and shallow rim profile for mixed events. +A commuter with low-maintenance internal hub, integrated fenders, a spring-loaded rear rack for grocery stops, and reflective strips on the frame. +Gravel frame designed for mixed bikes, shorter chainstays and 1x gearing compatibility for fast pace lines. +A steel-framed fixed-gear with unique acid-wash paint, riser bars fitted with ergonomic grips, and a short wheelbase for nimble city maneuvers. +City commuter with low-maintenance belt drive, internal gear hub, and minimalist puncture-resistant tires. +A handbuilt lugged steel frame with tight tolerances, custom geometry, classic gloss black paint and brass headbadge. +A classic single-speed city bike with chrome head badge, narrow tires, antique-style bell and an elegant glossy black lacquer. +A racing road frame in glossy red with integrated cable routing, aero seatpost clamp, and high-modulus carbon layup for lightness. +A commuter with retro-satin paint, full chaincase, integrated lamp mounts and a comfortable cruiser saddle for short daily journeys. +Touring steel frame with tapered head tube, triple water bottle mounts and reinforced rack stays for heavy loads. +A cyclocross ready frameset in camo green, with reinforced fork crown, flat-mount brake tabs and a matchy stem clamp. +A low-slung BMX street rig with short top tube, rear pegs, and an anodized purple finish for local park domination. +A handbuilt touring frame with custom-routed cable guides, reinforced seat tube, and a hand-applied enamel coat for longevity. +Mountain hardtail with steel frame, 120mm fork, tubeless 29x2.35 tires, and a 1x12 drivetrain for simplicity. +Vintage-style tandem cruiser with elongated saddle, coaster hubs, chromed chainring guard and nostalgic two-tone paint. +Urban single-speed with one-piece crank, deep-dish rims, bold matte finish and narrow saddle for light, bold commuting statements. +A vintage Dutch utility bike with step-through frame, massive chaincase, and swept bars for dignified commuter style. +Gravel commuter with dual-purpose 38mm tires, lights integrated into the handlebars and internal routing for a clean look. +A compact folder with sealed bearings, single-hand fold clamp, and an aluminum frame that resists street rust for commuters on the go. +Lightweight time-trial bike with boxed aero downtube, integrated stem and headset, and specialized race geometry. +A gravel racer with tubeless tires, flared drops, electronic derailleurs and a subtle brushed metal paint with turquoise highlights. +A cargo e-bike with reinforced longtail platform, passenger footrests, low-step frame and an intuitive throttle for heavy-duty hauling. +Carbon fiber cyclocross racer with integrated cable routing and robust mud-shedding geometry. +Adventure gravel bike painted camouflage, 45mm tires, mechanical disc brakes, and three-pack bottle bosses for long explorations. +Cyclocross training bicycle with semi-integrated headset, mudguards optional and reinforced chainstays for heavy braking. +Lightweight alloy frame road bike with aero seatpost, 28mm tires, and a compact 50/34 crank for climbing efficiency. +A commuter with an integrated digital display, GPS tracking, hydraulic brakes and a weatherproof saddle for rough conditions. +Trail mountain bike with dropper post, 130mm travel, and progressive geometry suited to technical singletrack. +A suave road bike with brushed aluminum finish, leather saddle, minimal decals, and refined geometry for weekend cafe rides. +Urban utility bike with lockable cargo storage, puncture-proof tires, and reflective tape for nighttime safety. +A retro-inspired city cruiser with flowing fenders, chrome chaincase, and a wide comfortable saddle for relaxed neighborhoods. +Cyclocross bike with painted splatter graphics, mud-shedding frame design, and knobby cross tires for late-season races. +A gravel touring bike with welded-on lowrider mounts, robust racks, and a calming natural matte finish ideal for long trips. +A retro BMX cruiser with wide seat, sprung saddle, and classic paintwork evoking a nostalgic summer feel. +Cyclocross pro race bike with carbon frame, internal routing and wide clearance for aggressive cyclocross racing. +A modern fixed-gear track-inspired street bike with clean lines, high-polish headset, and a matched set of narrow rims for a sharp urban profile. +Mountain enduro rig with burly 11-speed cassette, robust chain guide, and deep tread tires for steep technical descents. +Cyclocross bike with disc brakes, gravel tread tyres, and a subtle brushed-metal paint scheme with contrast logos. +A commuter with electric assist integrated into the downtube, a simple thumb throttle, and a flat rack for quick bags and briefcases. +A commuter with integrated cable lock, puncture-resistant tires, belt drive and subtle reflective strips along the frame for safety without flash. +Classic road frameset with polished chrome lugs, narrow steel forks, and traditional headset stack for tasteful vintage style. +Electric folding bike with small-wheeled durability, foldable pedals, integrated lights and GPS-enabled display. +Modern steel road bike with disc brakes, carbon fork, rounded seatstays and subtle enamel paint for classic-modern crossover. +Racing track specialist with short cranks, narrow cockpit and stiff bottom bracket for explosive acceleration on the boards. +Robust cyclocross training bike with aluminum frame, thick chainstays, and beadlock-style rims. +A titanium road frame with sleek seatpost clamp, external bottle mounts, and a refined brushed finish to complement minimalist components. +A flat-bar gravel bike with 700x42 tires, dropper post, and flared control area on the handlebars for stability on descents. +Bright yellow city bicycle with upright handlebars, chain guard, rear rack and integrated LED hub light for commuter convenience. +Performance road frameset with integrated seatpost clamp, aerodynamic downtube and clearance for 28mm tires. +Mountain enduro mullet build with coil shock, 170mm fork, wide bars and reinforced bearings suited for aggressive trails. +Tandem touring bicycle with two sets of shifters, reinforced frame, and dual bottle cages. +A beach cruiser with retro flame paint, overstuffed saddle, and ornate chrome chain guard for a showy seaside ride. +A commuter with a low-step frame, child seat mounts, dynamo-powered headlight and a warm cinnamon-red enamel. +A nimble ultralight gravel racer with shallow rim depth, SRAM Rival groupset, and a race geometry for attack-minded riders on rough roads. +A compact compact-folding commuter with 20-inch wheels, simple latch hinge, and minimalistic chaincover so the bike stays clean in office halls. +A versatile day-tripper hybrid with suspension seatpost, 50mm tire clearance, comfortable saddle and a lightweight aluminum frame. +A gravel bike in brushed aluminum with custom anodized headset, tubeless 700x45 tires and a neat in-frame tool compartment. +A vintage mixte step-through city bicycle with hand-painted gold leaf accents and a wicker basket on the front. +A commuter equipped with Rohloff internal gear hub, Gates belt drive, dynamo lighting and durable rack for near zero-maintenance use. +A gravel-focused titanium frame with tapered head tube, internal cable routing, and subtle brushed finish designed to last a lifetime. +Steel cross-country race bike with thin-walled butted tubing and a light translucent red lacquer. +Road aero build with deep-section wheels, integrated cockpit and a stealth matte finish accented with gloss stripes. +Adventure tandem with rack mounts, multiple bottle cages, and framebag-friendly tubes for long days. +Mountain freeride with heavy-gauge tubes, thick tires, and a geometry designed to boost confidence on big features. +Mountain enduro with carbon links, tuned suspension, and a wide-contact tire choice to maximize traction on rough descents. +Commuter folding e-bike with integrated rear rack, dual suspension, and compact battery pack for last-mile convenience. +A gravel adventure frame with generous tire clearance, integrated mudguard mounts, and a hand-mixed paint that shimmers subtly in sunlight. +A modern BMX race bike with 20-inch wheels, stiff aluminum frame, knobby tires and rear hub with sealed bearings for sprinting off the gate. +Gravel bike with camouflage paint, mixed-wheel setup (29 front, 650b rear), and bikepacking harnesses. +Mountain all-mountain with aggressive chainstays, stout tubes where needed, and a protective clearcoat for trail abuse. +A kid’s cruiser with training wheels, colorful decals, and a step-up chain guard with cartoon characters for playful rides. +Steel commuting frame with internal routing, dynamo hub, and retro-inspired paint scheme. +A gravel race build with top-shelf components, lightweight tubeless wheels, and soft compound tread for maximum traction on loose surfaces. +Mountain trail hardtail with 29" wheels, 1x12 drivetrain and robust hubs for long days on singletrack. +Classic commuter with upright geometry, full fenders, a simple three-speed hub and a subtle metallic sheen. +Time-trial machine with aggressive aero bars, low-slung geometry, and wide rear disc wheel for optimized wind cutting. +A lugged chromoly commuter with elegant tapered seatstays, brass head tube badge, and swept bars for comfortable city posture. +Retro BMX cruiser with wide banana seat, ape-hanger bars and chrome fenders for style-focused rides. +A gravel bike with carbon seatpost insert, long top tube, and clearance for 42mm tires with explosive traction on dirt descents. +A touring expedition bicycle with reinforced steering tube, high-spoke-count wheels, and extra-laden pannier rack for extreme overland trips. +A retro road frame with thin tubing, classic steel fork crown, and a soft cream paint that complements narrow 25mm tubular tires. +Commuter with integrated e-ink display for stats, dynamo lighting, and anti-theft GPS beacon hidden inside the head tube. +A commuter with robust aluminum fenders, integrated rack, internal LED strip and a low-maintenance belt drivetrain. +Urban cargo bike with integrated toolbox, long rear deck and a low-slung frame for safe loading of crates. +Urban semi-recumbent bike with low-rider ergonomics, long wheelbase, and wide comfortable saddle for relaxed commuting. +A lightweight road climbing frame with high-modulus carbon, narrow rim depth, and an emphasis on low mass for steep ascents. +City utility bike with integrated rear rack, pop-out panniers, low standover design and puncture-resistant rubber. +A fat-bike touring rig with multi-speed cassette, reinforced rack mounts, and low-pressure 26x4.0 tires for extended winter adventures. +A carbon time trial speed machine with integrated hydration and a fully enclosed cockpit for maximum aerodynamic efficiency. +A robust cargo trike with low center deck, heavy-duty frame, hydraulic brakes and bright safety reflectors for busy city streets. +Road aero training bike with robust wheelset, endurance bars and slightly higher stack for comfort training days. +Steel touring frameset with generous tire clearance, triple bottle mount, and a lugless smooth weld finish. +Commuter with hub dynamo front light, full-length fenders, and a low-maintenance internal gearbox for dependable year-round use. +A classic steel track bike restored with new tubular tires, replaced bottom bracket and a deep maroon gloss finish with period decals. +Gravel bike with antique bronze finish, extra eyelets, and a relaxed geometry for enjoying mixed gravel and forest tracks. +Single-speed track-style commuter with bright decals, platform pedals, compact frame, and a safe front brake for urban traffic +A gravel-friendly hardtail with 29x2.4 tires, slack geometry, and a wide handlebar for confidence on rough mixed surfaces. +Long-tail cargo bike with passenger bench, integrated tie-downs, and a rear-wheel belt drive for smooth family transport. +Gravel adventure rig with stainless rack mounts, durable paint, and a lower bottom bracket for loaded stability on rough roads. +Minimal commuter with single-speed belt drive, integrated fenders and quiet hub gear. +Road aero bike with asymmetric chainstays, tailored tube cross-sections, and a paint fade that suggests motion. +Cyclocross race bike with distinctive contrasting paint, flared drops, and tubeless tires for mud-shedding winter events. +A gravel bike with neat iridescent paint, integrated top-tube storage, and room for 700x42 tires with mudguards on. +Gravel e-bike with sub-750 watt motor, discreet battery placement, and durable racks for multi-day tours. +Classic mixte frame bicycle with low top tube, wicker basket mount, floral paint, and swept-back bars for relaxed rides. +Mountain enduro with 170mm travel, durable alloy frame, wide rims, and coil-sprung shock for confidence on big hits. +Steel singlespeed track bike with polished chrome finish, narrow handlebars and aggressive track geometry. +Electric folding cargo with rear seat for a child, compact folded height and sturdy folding hinge for daily use. +Classic single-speed cruiser with retro stripes, wide chrome fenders, and brown leather saddle. +Roadlight sprinter with 25mm tires, shallow-section wheels, and a compact cockpit for fast club rides and road races. +Classic road bike with period-correct decals, steel handlebars wrapped in choicetape, and a leather saddle for old-school charm. +Commuter with integrated frame lock, chaincase, wide saddle and reflective tape on the forks for visibility. +A vintage mixte frame city bike with wicker basket, steel fenders, and a comfortable upright geometry for short, social errands. +A gravel touring bike with brazed-on racks, Brooks saddle, triple-bolt bottle mounts, and a subdued olive paint for long-route comfort. +A kids' BMX with neon parts, sealed bearings, and a robust one-piece crank to survive the hits and scrapes of learning tricks in the park. +Lightweight carbon gravel build with 1x drivetrain, 700x35 tubeless tires, and flared drops for mixed-surface racing. +A road endurance frame with tapered steerer, slightly relaxed geometry, and intentional compliance points to absorb road buzz. +A lowrider bike with custom elongated frame, chrome plating, and basket weave pattern paint for attention at parades. +A single-speed beach cruiser with coral paint, wicker basket, wide saddle and coaster brake for fun coastal rides. +Gravel+ hardtail with wider rims, high-volume rubber, and a rugged matte finish to hide trail wear. +Folding commuter with 16-inch wheels, rapid folding latch and compact bag for commuters on trains. +A retro BMX cruiser with banana seat, sissy bar, wide whitewall tires, and bold flame decals for a nostalgic seaside ride. +A time-trial aero bike with integrated hydration, steep seat tube angle, deep-section carbon wheels and an emphasis on low drag for TT events. +A gravel racing bike with tapered head tube, wide-rim wheelset, and subtle anodized accents for a performance-minded aesthetic. +A single-speed fixed-gear urban track-style bike featuring a polished aluminum frame, flip-flop hub, and narrow 25mm tires for city criterium-style rides. +Touring tandem with triple bottle mounts, touring racks and adjustable tandem stem for fit and balance. +Mountain enduro bike with adjustable geometry, 170mm travel fork, and EV-style anodized purple accents. +A bright-red fixie with deep-dish wheels, polished chainring, and horizontal dropouts for track-inspired simplicity. +A touring-friendly hardtail with 29er wheels, rack mounts, long cage derailleur compatibility, and skid plates for rough roads. +A cyclocross training bike with reinforced fork crown, quick-release skewers that won’t seize, and a high-volume tubeless setup. +Commuter with upright bars, step-through frame, dynamo hub, and integrated rear light for rainy-city practicality. +A high-volume enduro bike with aggressive geometry, coil shock capability, and strong rims for bike-park and backcountry descents. +A classic randonneur with long wheelbase, low gearing, generous mudguard coverage, and a subdued moss-green paint job for self-supported epics. +A vintage-inspired mixte with wicker basket, cream-wall tires, and curving top tube for a charming grocery-getter. +A modern cyclocross rig with SRAM Rival 1, 35mm tires, and a short wheelbase for quick handling in muddy technical circuits. +A gravel adventure bike with triple strap points on the top tube, built-in handlebar bag clamp, and clearance for wide tires for soft trails. +Youth trail-ready hardtail with coil fork, 24" wheels, and easy-to-use twist shifters to introduce kids to off-road fun. +Fixed-gear urban racer with light alloy frame, aero seatpost, deep carbon wheels and narrow racing saddle for short street sprints +A long-travel downhill bike with reinforced gussets, tapered steering tube, and a burly fork for repeated steep technical runs. +Lightweight triathlon bike with steep seat tube, aerobars and rear hydration system for time trials. +Performance aluminum road frame with aero tube shaping, integrated headset and a race-ready stance. +Vintage randonneur with leather straps on framebag, brass headlamp, and narrow 32mm touring tires for classic rides. +Aero gravel bike with integrated hydration ports, fast-rolling slicks for mixed surfaces, and stealth paint. +Touring e-tandem with mid-drive motor, stabilized frame geometry, and heavy-duty wheels to support two riders plus luggage. +Gravel monster with 29x2.4 tires, slack head angle, reinforced fork and heavy-duty hubs for rock-strewn backcountry tracks +Tandem touring bike with low-step front frame, double racks, and matching panniers for long-distance journeys. +Gravel bike with stealth geometry, oversized bottle mounts, integrated top-tube bag and 650b wheelset option for heavier loads +Bikepacking-specific steel frame with rackless design, multiple bottle mounts, and side-loading top tube bags compatibility. +Cyclocross race frame with transverse brake mounts, wide chainstays and hard-wearing paint for frequent racing. +Commuter bike with front and rear cargo rails, integrated battery, fenders, and a low step-through frame for ease of use. +A gravel touring bicycle with full-length mudguards, tubeless-ready rims, and a reinforced fork with low-rider rack mounts for heavy kit. +Classic steel roadster with basket and stepped frame, coaster rear hub, and elegantly curved fenders for pleasant neighborhood rides. +Road time trial machine with integrated hydration systems, aero-optimized handlebars and a smooth, continuous paint finish. +Lightweight trail hardtail with 120mm fork, modern slack head angle, and tubeless-ready rims for playful off-road agility. +Tandem touring bike with reinforced midspan, dual brake levers, multiple racks and generous gear range for mountainous tours. +Classic step-through city bicycle with elegant swept bars, full fenders, and a retro headtube ornament. +A cargo long-john bike with wood-lined box, low deck height, and sturdy kickstand for stability when loading cargo. +Performance mountain enduro bike with adjustable geometry, 170mm front travel and burly frame protection. +Mountain cross-country full-suspension with responsive kinematic design, light wheelset, and efficient pedaling behavior. +A performance gravel bike in charcoal with discreet derailleur guard, 2x electronic shifting, and 700x42c tubeless tires for fast rough-road rides. +Cyclocross steel frame with subtle glossy lacquer, modern rear spacing, and integrated mud-shedding seatstay shape. +A fillet-brazed lugless road frame with brushed steel finish, classic proportions, and 28mm tires to balance classic looks and modern comfort. +e-MTB with torque-sensing mid-motor, long-range battery, modern trail geometry and dropper seatpost for technical enduro days +Adventure gravel bike with brushed titanium finish, internal cable routing, and durable component spec for remote riding. +A gravel-capable touring rig with steel frame, three-bottle capacity, long-travel fork and room for oversized panniers. +A BMX freestyle bike with 20-inch wheels, pegs on both axles, 360-degree gyro, and a chromoly frame for skatepark tricks. +Time trial bike with integrated hydration, aero cockpit, and steep down tube shaping for minimal drag. +Touring tandem with extra gear mounts, rear low-rider rack, triple crankset and durable rack spacers for heavy loads +A belt-drive single-speed commuter with a Gates Carbon belt, belt-friendly rear hub, hub dynamo-powered lights, and puncture-resistant tires. +Urban cargo trike with large steel tray, hydraulic disc brakes, and a heavy-duty motor for reliable daily hauling. +Mountain cross-country hardtail with an efficient pedaling platform, 100mm fork, and fast-rolling tires for endurance trail races. +A trail-capable full-suspension bike with lockable shock, adjustable geometry, 29-inch wheels, and an external compression-damping lever. +A foldable commuter with compact frame, handlebar latch, and small, puncture-resistant tires for subway-friendly storage. +Cargo bicycle with modular platform system, adjustable deck height, and reinforced fork to carry larger items safely. +Classic Dutch coaster bicycle with upright bars, chaincase, spring saddle, and an enamel finish for durable city commuting. +City cruiser with a woven front basket, classic bell, cruiser bars and a retro two-tone paint reminiscent of the 60s. +A stripped-down commuter with low-maintenance hub gear, belt drive, full chaincase, and a simple kickstand for urban convenience. +All-city commuter with belt-drive, Sturmey-Archer hub, integrated lights, and minimalist frame for low-maintenance travel. +Mountain bike with 29-inch wheels, modern slack geometry, and a dropper post for technical descents. +A lightweight kids’ road bike with 24-inch wheels, compact drop bars, caliper brakes, and a cheerful sunburst decal set. +Bikepacking-ready steel frame with multiple cage mounts, 29-inch wheels, drop bars and colorful framebag pockets. +Track fixed-gear with polished tubes, deep rims and minimalist sticker branding for a refined velodrome look. +A triathlon bike with split seatpost, hydration behind the saddle, integrated bar-end shifters, and a gradient metallic paint scheme for race aesthetics. +A modern CX/Gravel hybrid with a short-reach brake setup, long top tube, and a slightly raised front end for better handling in rough terrain. +A kids' cruiser with training wheels, colorful decals, chainguard and a small front basket perfect for short neighborhood rides. +A raw-metal finish titanium road bicycle with ovalized chainstays, integrated seat mast clamp and a simple, refined look. +A fat-tired electric mountain bike with mid-drive motor, long travel fork, 4.6-inch tires, and reinforced, tubeless-ready rims. +Adventure cargo bike with large capacity box, electric assist, reinforced frame and weatherproof lids for dependable transport. +A compact children's balance bicycle with soft foam saddle, wide handlebars, and a low-to-the-ground frame to foster balance before pedaling. +Bicycle with step-through aluminum frame, child seat mount, integrated rear light, and a basket for grocery runs. +A gravel bike with integrated rear rack, wide 650b tires, and modular mounts for tents and food during multi-day unsupported trips. +Old-school steel BMX cruiser with banana seat, sissy bar and wide whitewall tires for Sunday rides along the boardwalk. +Comfortable cruiser with swept-back bars, wide saddle, coaster brake, and pastel mint paint for late afternoon rides. +A mountain bike with an internal dropper post routing, wide handlebars, and 2.35-inch tires for balanced trail traction. +A mountain enduro rig with adjustable geometry, 170mm travel, burly linkage and matte army green paint with orange contrast. +Mountain downhill bike with dual crown fork, 200mm travel, coil shock, and heavy-duty components for steep runs. +A seaside cruiser with corrosion-resistant hardware, quick-release front wheel, and a large comfy saddle for weekend pleasure rides. +A folding city bike with small 16" wheels, fixed hinge, handlebar lock and a lightweight frame that collapses in seconds. +Road time trial bike with narrow frontal area, integrated bar-end shifters, and a raised aero tail. +A gravel adventure-ready frameset with an integrated handlebar mount, threaded bottom bracket for reliability, and a long wheelbase. +A compact folding commuter with quick fold, integrated shoulder strap, and small wheels designed to master tight urban staircases. +Mountain fat-tire e-bike with full suspension, torque-sensing motor, and integrated battery in downtube. +Cyclocross-inspired commuter with mud-shedding frame, clearance for wide tires, and disc brakes for reliable stopping. +A lightweight commuter with minimal fenders, narrow road tires, lightweight rack, and internally routed cables for clean aesthetics. +A utility bicycle with front-loading cargo box, three-speed hub, and fold-down seat for bulky item transport. +A minimalist urban commuter with belt drive, hub gearing, clean paintwork and puncture-resistant tires that needs little maintenance. +A gravel endurance bike with endurance geometry, 700x40 tires, flared drops and multiple frame mounts for adventure durability. +Steel mountain hardtail with double-butted tubing, tapered headtube, 29x2.3 tires and wide 35mm rims for trail grip. +Lightweight cross-country race frame with tapered head tube, short chainstays, and low weight emphasis for uphill accelerations. +A gravel-specific frameset with eye-catching leopard-print anodized headset and custom paint, 700c/650b compatibility, and rack mounts. +A modern BMX with 20" wheels, sealed headset bearings, short chainstay and a protective top-tube pad for park sessions. +A lightweight cyclocross racing frame with a stealth carbon weave, minimal paint, and a geometry tuned for fast cornering in competition. +A rock-solid downhill race build with downhill-specific geometry, reinforced swingarm, and external bashguards protecting the downtube. +A minimalist single-speed town bike with matte black paint, narrow saddle, and stripped rear brake to keep weight down and style up. +A modern all-mountain bike with mixed-wheel setup possibility, 160mm travel, and a stout pivot layout to keep the suspension active under heavy braking. +Classic cruiser with balloon tires, chrome trim, ornamental paint and a plush saddle for beachside promenades. +A mountain enduro machine with mixed carbon-steel construction, tuned shock tune, and heavy-duty axle hardware for durability. +Classic roadster with sprung leather saddle, polished chain case, and brass-accent head badge for timeless style. +City folding e-bike with torque-sensing motor, large-capacity battery, compact folded footprint and low-slung platform for errands. +A single-speed commuter with polished chain, anodized hubs, and a minimalist saddle for quick rides to the coffee shop. +Touring steel bicycle with Rohloff-ready dropouts, durable racks, full-coverage fenders, and room for all the essentials. +Mountain trail full-sus with 150mm travel, dropper post, progressive geometry and grippy 2.4" tires for versatile singletrack. +A mountain downhill freeride bike finished in matte camo with massive 200mm travel, dual crown fork, and reinforced bar clamps. +Touring endurance roadie with higher stack, more relaxed reach, and dual bottle mounts for comfort over long distances. +A steel touring frame with custom rack attachments, low-rider bottle mounts, and a warm aubergine paint to add character on the road. +A steel cyclocross frame with classic geometry, downtube shifters, and 33mm tubeless-ready tires for retro appeal on modern courses. +A cyclocross frameset with molded carbon chainstays, molded mudflaps, and a matte finish for a stealthy aesthetic in winter conditions. +A modern mountain hardtail with 130mm fork, 29-inch tires, tubeless setup and charcoal matte paint with neon-highlight decals. +A cyclocross training frame with reinforced head tube, horizontal top tube for shouldering, tubeless-ready rims and deep charcoal paint. +Steel randonneur bicycle with generator lamp, leather saddle and a functional handlebar bag for long nights. +Recreational women's hybrid with pastel coral paint, swept-back bars, suspension seatpost, and a rear rack for errands. +Gravel racer with short wheelbase, fast rolling 700c tires, SRAM Force eTap wireless shifting, and a light carbon frame. +Comfortable city hybrid with plush saddle, swept bars, suspension seatpost and puncture-proof commuting tires. +A robust utility trike with low center of gravity, three-speed hub, and large cargo platform for hauling heavy garden equipment. +Mountain downhill race rig with coil shock, massive tires and a painted-on roost guard on the chainstay. +Track keirin bike with minimal graphics, oversized bottom bracket and stiff oversized downtube. +Performance road bike with deep-section carbon wheels, disc brakes, hollow carbon crank and a two-tone paint job. +Electric mountain bike with full suspension, torque-sensing mid-drive, wide trail tires, and a matte forest camo livery. +Vintage racer with period-correct components, chrome fork crown, and a discreet hand-painted name on the downtube. +Fatbike adventure rig with frame-mounted cargo bags, insulated grips, and low-gear range for deep-snow climbs +Urban electric bike with low-step frame, belt drive, wide saddle, and hydraulic disc brakes for a smooth city commute. +Electric mountain bike with mid-drive motor, 630Wh battery, and burly 150mm fork for e-enduro. +A hand-painted retro cruiser with scalloped pinstripes, balloon tires, coaster brake, and brass bell for leisurely beach rides. +A fast gravel racer with carbon frameset, 700c wheels, electronic shifting and powder-blue paint with geometric decals. +A cyclocross training bike with steel frame, integrated mud-guards, wide handlebars, and deep slate paint with orange lettering. +A gravel touring build with sturdy racks, large mudguards, and an international compatibility drivetrain for varied-country logistics. +Urban foldable with small wheels, adjustable stem height, and reflective frame elements for multi-modal commutes. +A vintage Dutch city bicycle with sturdy frame, rear coaster brake hub, and an enamel shone finish that repels rain. +A compact folding bicycle designed for commuters with a one-handed fold, 20" wheels, and a built-in carry handle for portability. +Road endurance bike with compliance-enhancing seatstays, integrated cables, and a versatile tire clearance for mixed surfaces. +A minimalist single-speed with raw-aluminum finish, leather saddle, narrow handlebars and an urban-leaning build. +A cyclocross race bike with canti-to-disc hybrid brakes, knobby cyclocross tires, 35mm tire clearance and orange splatter paint for mud-shedding readability. +Adventure gravel bike with versatile mounting points, 650b wheel compatibility, stealth matte paint and a comfortable long-ride geometry +A performance mountain hardtail with carbon fork, stiff layup, 29-inch wheels, and a geometry tuned for rapid cross-country sprints. +Performance hybrid bike with flat bars, 700c wheels, hydraulic disc brakes, and a lightweight alloy frame for mixed urban fitness rides. +Folding cargo bike with low fold height, compact handlebar, and powerful front hub motor for last-mile deliveries and easy storage +Road racing frame with tapered headtube, BB86 bottom bracket shell and asymmetric seatstays to increase lateral stiffness. +Road endurance bike with slightly higher front stack, vibration-damping seatpost clamp, and elegant two-tone paint. +Mountain downhill monster with reinforced aluminum frame, monster travel suspension, and monstrous tyre volume built for gravity tracks. +Heavy-duty cargo bike with steel frame, electric assist, and a long bench for kids and pets. +A touring gravel bicycle with 26-inch wheel option, triple chainset, brazed-on front derailleur mount, and room for three bottle cages. +Track sprinter with high-stiffness monocoque carbon frame, narrow gear ratio, and slick black finish. +A gravel bike with a unique cut-out downtube storage, 700x45 tires, and a dedicated rack mount for remote expeditions. +Off-road enduro machine with 170mm travel, modern geometry, reinforced pivots, and a coil-sprung rear shock for rough lines. +A cyclocross competition bike with carbon seat tube, mud-shedding rear triangle, recessed bottle mounts and toned-down race graphics. +A youth mountain bike with 24-inch wheels, front suspension fork, and a kickstand for everyday trail learning and fun. +Lightweight steel criterium bike with aggressive geometry, short wheelbase, and 20mm tubulars. +Dirt jump bike with short rear center, beefed-up tubing, and a signature loud paint job for park visibility. +Mountain enduro bike with burly chainstays, 170mm front travel, and a long reach for confident descending at speed. +A nostalgic roadster with brass bell, wicker basket, swept bars and cream-colored paint for timeless afternoon rides. +Gravel bike optimized for endurance rides with micro-suspension in the seatpost and vibration-absorbing frame features. +A children's balance bicycle in bright yellow with comfy foam grips, low step-through frame and rubber tires designed for toddlers. +Cargo longtail with child bench strapped, accessory rails for panniers, extra-strong spokes and reflective tape for safety at night +A bombproof single-speed mountain bike with thick gauge tubes, wide 2.4" tires, and a reinforced fork to withstand aggressive trail riding. +A downhill race frame with reinforced bearings, long-travel fork, massive hydraulic brakes and luminous fluorescent pink paint for trail visibility. +Mountain freeride bike with robust chainstays, replaceable hangers, and color-coordinated anodized hardware. +A cyclocross training steed with mud-shedding frame, raised bottom bracket, and bar tape with extra grip for shoulder runs. +Lightweight steel commuter with narrow tires, low stack height and classic silver polished finish. +A custom steel commuter with internal cable routing, hand-rolled decals, wide tires for comfort and a heritage-style gloss finish. +Urban foldable electric bike with mid-drive motor, removable battery, and a quick-release seatpost for compact transport. +A mountain e-bike with torque-sensing mid-drive, long-travel fork, dropper seatpost and matte olive drab paint suited for steep climbs. +Recumbent trike with ergonomic mesh seat, low-slung frame, and chainline guards for comfortable long-distance reclined pedaling. +Touring mountain bike with heavy-gauge spokes, long chainstay, and threaded bottom bracket for reliability and serviceability. +Touring bike with bushings for a bottom bracket dynamo, three-bolt down tube pumps, and wide gear range. +A belt-drive town bike in cream color with integrated hub with 8 speeds, linkable fenders, and a bamboo front basket for eco appeal. +Road race frame with taut geometry, narrow 25mm tires, low stack height and a high-modulus carbon layup for race day surges +Gravel expedition bike with frame-mounted battery for lights, triple-cage mounts, reinforced fork, and a robust all-weather finish. +Touring bike with frame-integrated lock mounts, welded-on bottle bosses, and paintwork featuring topographic lines. +City folding bike with simple one-handed fold, adjustable stem, bright reflective decals and quick-release wheels for convenience. +Gravel stage racer with electronic wireless shifting, deep tubeless rims, and custom paint that fades from teal to black. +Folding commuter with small wheels, low carry weight, and a compact folded package that stows under office desks. +All-terrain fat-bike with 4.8" knobby tires, rigid steel fork, single-ring drivetrain and dropper post for snow rides. +Minimal commuter with upright stem, coarse puncture tires, and a low-maintenance single-speed internal gear hub. +Electric commuter with hidden battery in downtube, torque-sensing motor, hydraulic brakes and smooth acceleration. +Aluminum gravel grinder with clearance for 45mm tires, reinforced fork, and a flared drop bar for stability on rough descents. +Trail-ready full-suspension bike with progressive geometry, dropper post and protective frame guards on the downtube. +A lightweight aero road bike with integrated seatpost wedge, deep-section wheel compatibility, and a narrow 24mm tire profile for speed. +Electric folding cargo bike with adjustable deck, robust hinge, pedal-assist, and a low center of gravity for safe handling. +A classic cruiser with custom pinstriping, broad swept handlebars, and a plush saddle for comfortable neighborhood cruises. +A gravel drop-bar bike with gravel-specific 105 groupset, 700x42c tires, and a long wheelbase for stability on rough terrain. +A touring bike with Rohloff hub, double chainring crank, and custom welded stainless steel frame for longevity. +Retro-style beach cruiser with floral paint, oversized saddle, and whitewall balloon tires for laid-back rides. +A retro townie cruiser with coaster brake, banana seat, chrome springer fork, and a low-slung frame for beach boardwalk cruising. +A high-performance carbon gravel bike with integrated top-tube bag, electronics routing for GPS, and 700c wheels optimized for mixed terrain. +Minimal commuter with coaster hub brake, internal hub gearing, lightweight steel frame, and a wicker basket for light shopping. +A modern gravel bike with military olive paint, frame bag mounts, 700x40mm tires, 2x11 wide-range gearing, and a carbon fork for lightness. +A touring tandem with matching grips and saddles, triple water-bottle capacity, and a long, compliant frame for comfort when loaded. +Steel dirt jump frame with reinforced gussets, single-speed drivetrain, flat pedals and BMX-style short stems for stability +A snow-ready fat-bike with 4.8-inch studded tires, fat rims, steel frame with room for fenders, and a matte black finish built to float on soft surfaces. +Lightweight steel road bike built from Reynolds 853 tubing, tapered head tube and carbon fork for compliance +Road race bike with ultra-deep carbon rim front, dimpled aero shaping, and minimalistic logo placement. +A pastel pink cruiser bicycle with chrome fenders, wicker basket and coaster brake for leisurely park rides. +A vintage style town bike with polished steel fenders, ornate chain guard, and wide saddle for plush rides on cobblestones. +A bespoke painted road racer with airbrushed fade, custom decals, and high-polish chrome on the fork crown and dropouts. +Fat-trike with three wide balloon tires, stable low center of gravity, and a comfy upright seating position for short errands. +Commuter single-speed with bright safety paint, puncture-resistant tires, rear rack and a built-in U-lock mount for urban security +A robust touring gravel bike with triple chainring, low gear ratios, stainless steel spokes and reinforced dropouts for long unsupported trips. +Modern gravel rig with 650b wheels, 45mm gravel tires, dropper post and triple bottle cage mounts for bikepacking +A casual city bike with wicker basket, three-speed hub, and wide, cushioned saddle for errands and coffee shop stops. +A gorgeous hand-lugged steel road bike with hand-lettered pinstripes, classic deep-section rims and a supple ride for long morning spins. +A cyclocross carbon frame with extra clearance for clogging techniques, tapered headtube, and chainstay reinforcement to handle rough courses. +Commuter utility bike with generator light, bell, full fenders and low-maintenance internal hub for reliable city miles +Commuter folding with quick fold seatpost, integrated lock slot, and reflective piping along the frame for visibility. +Touring gravel bike with rack mounts front and rear, long-reach brakes, reinforced wheels, and a triple chainset for mountain passes. +Handcrafted titanium long-tail cargo bike with polished welds, long wheelbase, and child bench seating options. +A commuter with retro curving top tube, Brooks leather saddle, alloy fenders and soft dove-gray enamel with gold pinstriping. +A commuter hybrid bicycle with upright geometry, puncture-resistant 700x32 tires, integrated lights and a rear rack for daily errands. +A mountain enduro build with burly cranks, thicker rim profiles, and reinforced spokes to survive rocky lineups and big gaps. +Classic city step-through with chain guard, roller brakes, and a bright orange lacquer for visibility and style. +A mountain downhill bike with 200mm travel, coil shock, beefed-up headtube and a colorway made to hide mud and scratches. +A high-performance gravel bike with dual-chainring option, tubeless 700x40 tires, and a tuned compliance for long distances. +A compact folding cargo bike with extended rear deck, electric assist, and quick-release latch that folds to a small footprint for apartment storage. +All-terrain mountain hardtail with 29+ wheelset, 2.6" tires, slack geometry, and reinforced dropouts for aggressive trail use. +Retro city bicycle with bamboo fenders, leather grips, and a hand-tooled saddle that gives a classic handcrafted look to everyday commuting +Folding commuter with 20-inch wheels, quick-release hinge, and reflective accents for evening transit. +Folding commuter with 20" wheels and integrated rear rack, folding down quickly to stow in small offices or trains. +A modern steel road bike with endurance geometry, flat-mount disc brakes, and tasteful gloss accents on the fork tips. +A drop-bar gravel bike with micro-suspension stem, large volume 700x45c tires, and a light frame bag for weekend overnights. +Mountain downhill race bike with extra slack geometry, reinforced swingarm, massive forks, and a coil shock for huge landings. +Track sprint bike with aero bars, stiff carbon frame, short wheelbase, and heavy gearing for explosive acceleration. +A classic steel road frame with thin tubing, ornate lugs, and a soft patina paint finish to emulate the charm of classic European rides. +Urban electric folding cargo bike with modular rear platform, throttle override, and fold-flat pedals for compact storage +A modern trail bike with 150mm travel, adjustable geometry, coil shock-ready frame, and 29-inch wheels to excel on demanding descents. +A futuristic e-mountain bike with integrated battery, 150mm travel, electronic suspension tuning, and wide 29x2.6 tires for all-day backcountry rides. +A carbon gravel endurance bike with endurance geometry, vibration-damping seatpost, and a top-mounted accessory rail for gear. +A modern full-suspension trail bike with 140mm travel front and rear, balanced kinematics that make it efficient for long rides and playful descents. +A hardtail trail bike with harmonized carbon layup, 120mm fork, and nimble geometry for attacking twisty woodland routes. +A vintage track bike restored with modern sealed-bearing hubs and period-correct stem for aesthetic authenticity and rolling efficiency. +A cross-country race hardtail with minimal paint, aggressive climbing gearing, and quick-handling 29x2.2 tires. +A cyclocross-adapted aluminum frame with integrated seatpost clamp, flared dropouts, and mud-exit shaping in all the right places. +Children’s balance bike in sky blue with low center of gravity and soft EVA foam wheels. +Commuter with quiet belt drive, integrated frame lock, fender eyelets and a subtle matte color that resists showing dirt. +A mountain hardtail with aggressive 29er geometry, 120mm fork, 2.35-inch tires, and a dropper post for technical climbs and descents. +Gravel ultralight build with carbon wheels, 36mm tires, and a lean parts list targeting long-distance speed and endurance. +A cyclocross classic with steel forks, mud-shedding frame design, shoulder-friendly top tube, and knobby tubulars for muddy piles. +A bikepacking-ready drop-bar gravel bike with framebag, top-tube bag, flared drops, 700c wheels with 45mm tires, and triple-bolt fork mounts. +Track pursuit bike with steep geometry, bolt-on aero bars and a glittering paint shift on the seat tube. +Road endurance frame with slightly taller head tube, vibration damping features, and space for 32mm tyres for rough tarmac comfort. +A city fixie with anodized blue hubs, deep V rims, leather saddle and a clean black frame with single white stripe. +Bright red single-speed city bike with coaster brake, upright handlebars, puncture-resistant tires, and a minimalist frame for easy commuting. +Urban folding bicycle with belt drive, small integrated lights, and a single-handed quick-fold mechanism. +A mountain DH racer with reinforced dropouts, 200mm travel fork, large brakes and fluorescent orange finish for down-the-line visibility. +Gravel adventure bike with versatile geometry, removable mudguard mounts, and unique mountain silhouette painted on the head tube. +Track sprint bike with polished frame, stiff bottom bracket, and a narrow Q-factor crankset for powerful accelerations. +Road aero time trial frame with integrated hydration pods, clean cable routing and an ultra-sleek profile for speed. +A winter fat tire bike with stud-friendly rims, reinforced hubs, and thermal-resistant cables for reliable frozen-season commuting. +A modern gravel endurance frame with relaxed headtube angle, long wheelbase, 700x45 clearance, and vibration-damping micro-suspension inserts. +Cargo trike with stable three-wheel platform, hydraulic brakes, and weatherproof box for kids. +Handmade wooden-frame bicycle with laminated hardwood tubing, varnished finish and retro leather grips. +Classic Italian road bike with Campagnolo groupset, polished steel lugs, and leather saddle for spirited club rides. +A gravel racer with aerodynamic shaped tubing, wireless shifting, and a focused geometry to remain poised on fast rough courses. +Gravel drop-bar bike with compact crank, hydraulic disc brakes, and smoky translucent decal overlay. +A kids' balance trike with wide seat, low center of gravity, padded grips and playful stickers on the frame. +A mountain enduro rig with mullet setup—29er front, 27.5 rear—long dropper, and a stout chain guide. +Urban cruiser with pastel ombre paint, oversized saddle, sweeping chrome fenders and comfortable upright posture. +A commuter with integrated lock, internal gear hub, narrow tires, step-through frame, and minimalist LED rear light for apartment dwellers. +Gravel endurance bike with endurance geometry, low-profile tires, framebag with map pocket and dynamo-powered lights +Matte black carbon road bike with aero frame, integrated bars, 700c deep-section wheels and electronic shifting. +Mountain downhill frame with heavy-duty pivot bearings, long travel and extra clearance for wide tires. +Mountain e-bike trail rig with burst-mode climb assist, long-dropper, durable brakes and puncture-resistant tires for rough slopes +A gravel adventure bike with dropper post, frame-mounted camp stove rack, and a progressive geo for long days in rough terrain. +Compact recumbent with fully reclined seating, long wheelbase and aerodynamic fairing for efficient cruising. +A commuter with wood-paneled front cargo, low-step frame, belt drive and navy-blue enamel with nautical rope motifs. +Track pursuit bike with extended chainstays, disc wheel, and a huge chainring for high speed. +Gravel plus trail bike with 27.5+ wheels, wide bars, and reinforced fork legs to take on scrub and roots. +Fixed-gear urban cruiser with bold matte finish, narrow bars, and a small headlight for early-morning commutes. +Gravel ultralight with carbon fork, thru-axles, and minimal mounting points for weight savings. +A gravel-ready aluminum frame with long wheelbase, 2x drivetrain option, and color-shifting paint that flashes teal in sunlight. +Lightweight gravel titanium frame with sloping top tube, brushed finish and silent chainline for smooth tours. +A vintage road bike with steel shell, campagnolo dropouts, and leather bar wrap that ages into a unique patina while remaining fast. +A titanium road frame with endurance geometry, slender seatstays, and a custom crown on the fork for low weight and refined handling. +Touring tandem with built-in luggage mounts, double bottle cages, and a comfortable upright steering position for long rides together. +Speed-focused triathlon bike with split tube hydrofoil frame and integrated storage for gels. +A youth BMX with strong chromoly frame, low profile anti-slip pedals, and a padded crossbar pad for learning park tricks safely. +A cafe-style track cruiser with wooden fenders, leather grips, swept bars, and a quiet belt-drive for smooth, low-maintenance street cruising. +A touring tandem with independent saddles, reinforced mid-section, and integrated pannier mounts for two-up expeditions. +A family cargo bike with modular seating inserts, weatherproof canopy, and sturdy platform for safe children transport in all conditions. +Mountain downhill rig with huge travel, heavy-gauge spokes, and triple-clamp support for extreme gravity runs. +A gravel adventure bike with S&S couplers for packing into luggage, durable aluminum frame, 2.2" tires and framebag-ready mounts. +City fixie with riser bars, leather grips, and a pastel mint frame that pops against asphalt. +Mountain all-mountain frame with 160mm rear travel, sturdy dropouts, and adjustable geometry for varying trail styles. +Gravel speedster with aero seatpost, light carbon rims, and a precise mechanical drivetrain for rugged pavement. +Urban cargo bike with longdeck, modular child seats, hydraulic brakes and low-step frame for safe family transport. +Mountain enduro bike with adjustable geometry via flip-chip, burly thru-axles and a coil-compatible shock. +A city mixte with low step-through, wide tires, and a cozy saddle for slow scenic rides through neighborhoods. +Fixed-gear minimalist commuter with anodized details, matte finish, and a single-headlight for a streamlined street presence. +Cargo trike with modular platform, secure cargo tie-downs, weatherproof electronics and low-step access for urban deliveries. +Gravel stage racer with carbon fork, 700x40 tubeless tires, flared drops and wireless electronic shifting for clean cockpit. +A classic Dutch pedelec with step-through frame, integrated battery in the rear rack, and comfortable spring saddle for easy commutes. +Steel framed cyclocross with brushed metal finish, drilled brake studs, and downtube shifters. +Performance road bike with power-focused geometry, ceramic-equipped wheelset, aggressive stem and short headtube for racing posture. +A gravel-focused all-road frame with curved top tube for relaxed handling, 700x42mm tire compatibility, and framebag-ready internal routing. +Compact folding commuter with low-maintenance belt drive, single-speed simplicity, and a bright pop-color frame finish. +A gravel bike with a low-slung top tube for framebag compatibility, carbon fork with lowrider mounts, and a refined satin-black finish. +A mountain hardtail with boosted 148mm spacing, 120mm fork travel, tubeless-ready rims and lava-red paint with contrasting yellow fork. +A mountain trail bike with burly frame protection, modern slack geometry, and a fast-rolling 29-inch wheelset for confident speed. +A hardtail mountain bike with 140mm suspension fork, 29-inch tubeless wheels, wide 2.4" knobby tires, dropper post, and a 12-speed SRAM GX drivetrain for trail days. +A touring-ready rigid gravel bike with low rider fork mounts, robust 36-spoke wheels, and an adjustable saddle for comfort on long days. +A gravel-adventure bicycle with bar-mounted GPS, framebag anchors, 45mm tubeless tires and bright reflective sidewall stripes for night visibility. +A titanium gravel all-road machine with wide chainstay clearance, thru-axles, and a monotube design focusing on comfort and durability. +A gravel touring bike with sloping top tube, durable aluminum frame, threaded BB and extra fender and rack mounts for fully loaded travel. +Utility cargo bike with detachable side panniers, reinforced frame, low centre of gravity, and easy-to-use kickstand for heavy loads. +Gravel weekend warrior with 700 x 42 tires, full carbon frame, and plenty of braze-ons for racks and bottles. +Urban folding single-speed with easy latch, small wheels, simple chaincase and bright pop color for crowded city storage. +Fixed-gear commuting bike with painted-on decals, slim saddle, and low-profile platform pedals for city hopping. +Folding cargo bike with modular passenger seat, heavy-duty hinge, and an electric-assist option for convenience. +E-cargo longtail with foldable child seat, long-range battery, and hydraulic brakes designed for heavy city loads. +A trail bike with adjustable geometry headset, 140mm travel, tubeless-ready rims, and a stealthy matte black finish for versatile singletrack use. +Gravel endurance bike with extra compliance in chainstays, disc brakes, and secure handlebar bag mounts. +A vintage-inspired steel city bicycle with swept-back handlebars, brown leather saddle, coaster brake, and a simple single-speed internal-gear hub for relaxed commuting. +A mountain cross-country race frame with geometry focused on efficiency, internal battery mount for lights, and low weight accomplished by a refined layup. +BMX race bike with chromoly fork, sealed-bearing hubs, and a clear coat that shows off the polished frame. +A mountain full-suspension trail bike with balanced 140/140mm travel, chainstay protection, and geometry for playful trail lines. +Compact folding electric commuter with small footprint, lightweight alloy frame, and twist throttle for city hills. +A classic road bike with era-accurate Campagnolo groupset, 700c tubulars, steel frame with oversize tubes, and a glossy celeste finish. +A clean commuter with integrated handlebar cables, subtle reflective accents, and a lightweight alloy frame for daily fitness rides. +Children’s training bike with low seat, sturdy frame, and bright, durable paint designed to withstand crashes and play. +Gravel explorer with extra tire clearance, low-slung geometry, and reinforced fork braze-ons for long remote two-wheel journeys. +Mountain enduro with long-travel fork, wide bars, and aggressive tire tread for stability on rough descents. +A beach cruiser with soft sage green paint, wide swept bars, cushy saddle and chrome fender accents perfect for Sunday laps. +An electric commuter bike with a mid-drive motor, step-through aluminum frame, integrated battery in the downtube, fenders, lights, and a rear rack for cargo. +A fat-tire mountain bike with camo paint, 26x4.8 tires and a rigid carbon fork optimized for weight savings on soft terrain. +Touring tandem with wide wheelbase, double racks, and comfortable long-distance saddles for both riders. +Utility city cargo trike with front-loading box, three wheels for stability, and hydraulic disc brakes for heavy loads. +A rugged steel touring bicycle with front and rear racks, brown leather saddle, multiple braze-ons for bottles, and 32mm gumwall touring tires for loaded adventures. +Lightweight steel road racer with lugged Columbus tubing, minimal paint and polished accents. +Track-inspired fixed-gear commuter with anodized accents, narrow bars, and deep dropouts for chain tensioning. +A restored classic urban bicycle with glossy enamel, polished chrome fenders, and a robust rear rack ready for market runs and casual errands. +Compact cargo bike for city deliveries with reinforced cargo bed and hydraulic disc brakes. +A cyclocross rig with 1x drivetrain, wide fender mounts, flared drops and a tough powder coat for muddy autumn races. +A mountain all-mountain rig with 29x2.4 tires, 150mm front travel, and a robust alloy frame built to take rock gardens in stride. +A carbon plus hardtail with slacker head angle, 29+ tires, burly 35mm rims, and a 1x11 drivetrain for aggressive singletrack. +A light-weight cyclocross bike built for rough training, with resilient alloy frame, easy-to-clean finishes, and durable components to withstand frequent use. +Electric folding cargo bike with robust hinge, integrated rear platform, and quick-charge capability for urban deliveries. +Vintage-inspired cafe racer bicycle with leather bar tape, minimal fenders and hand-painted gold pinstripes. +Modern trail bike with 140mm travel, adjustable geometry, internal routing and a balance of pedaling efficiency and downhill capability +Gravel bike with 700c wheels and option for 650b plus, integrated mudguard mounts, and a subtle matte finish. +A kids' bike with training wheels that fold away, easy-adjust seat, bright handlebar tassels and a cheery polka dot frame for playful spins. +Gravel adventure bike with mixed wheelset, 650b rear for traction, larger front wheel for roll-over, and top-of-frame bag mounts +Race-ready gravel machine with carbon fork, broad tyre clearance, and a rider-focused cockpit for precise handling. +Sleek urban e-cargo bike with modular rear platform, automatic shifter, and regenerative braking system. +Lightweight triathlon bike with integrated hydration, single-sided front fork, and steep seat tube tailored to aerodynamics. +A simple single-speed commuter with coaster brake only, small frame, narrow tires and bright coral paint aimed at quick urban trips. +Electric cargo trike with rear enclosure, hydraulic brakes and wide load platform for stable deliveries. +A downhill race machine with redesigned linkage for pedaling platform, long-travel fork, and dual-bolt-through pivots for rigidity. +A commuter with integrated lock mounts, internal hub, belt drive, dynamo hub and polished graphite powdercoat with reflective accents. +Bicycle designed as a utility cargo hauler with heavy-duty frame, modular cargo racks, and wide-contact tires for suburban work runs +A compact kids' folding bicycle with safety-designed folding latch, bright reflective decals, and an easy-to-reach brake lever for parents. +Gravel bike with custom framebag, 42mm tires, tubeless setup and a compact crankset for mixed surfaces +Vintage roadster with chrome fenders, leather saddle, and simple internal three-speed for style-forward commutes. +A compact folding commuter with a simple 3-step folding system, small 16" wheels, and a comfortable upright seat for quick urban hops. +A handbuilt steel gravel bike with stainless-steel seatpost binder, lugless fillets, and a creamy metallic paint that brightens in direct light. +Fat-tire touring bike with 26 x 4.5" tires, frame-mounted racks, and sealed-bearings hubs for low-maintenance long beach crossings. +An ultralight climbing road bicycle with 28mm tires, shallow rim depth, 1x12 gearing focused on a wide range, and a carbon frameset at minimal weight. +Cyclocross race machine with lightweight carbon frameset, 34mm tubeless tires and single-ring drivetrain. +A gravel endurance bike with long-wheelbase geometry, flared bars, and 700x40 tires for comfort on rough roads. +A downhill freeride bike with dual crown fork, 200mm travel, heavy-duty linkage and neon orange frame with black contrast graphics. +A lightweight titanium mountain bike with 120mm rear travel, tapered headtube, and slim seat tube deliver a refined trail feel and long life. +A mountain hardtail with single-ring 1x drivetrain, dropper post, tubeless-ready rims and a brushed aluminum finish. +Cargo longtail e-bike with modular seating, foldable second row and reflective safety striping. +Enduro downhill capable bike with 180mm front travel and 170mm rear, coil shock tuned for big hits. +A budget-friendly road bike with aluminum frame, carbon fork, 25mm tires, and durable 8-speed drivetrain tuned for weekend group rides. +Gravel endurance build with 650b wheels, 47mm tires and flared handlebars for sustained comfort. +A freestyle BMX with chromoly tubing, mid-school stunt decals, and reinforced gussets at stress points for durable handling of repeated jumps. +Gravel race machine with aero-optimized cockpit, mid-depth wheels, and a race-tuned groupset for podium ambitions. +Mountain enduro with 170mm travel, burly build, and a paint scheme that camouflages scuffs from aggressive riding. +A compact folding bicycle with a reinforced hinge, quick-release wheels, and a one-handed fold that is commuter-friendly. +A stealth commuter with matte charcoal frame, concealed battery in downtube, belt drive and integrated rear rack light, minimalist styling. +Lightweight commuter with carbon seatpost, quiet belt drive, integrated taillight, and a stealth matte finish. +A gravel-focused cyclocross bike with carbon fork, wide 33-42mm tire compatibility, 1x groupset and robust brake calipers for variable conditions. +Cargo e-bike with platform frame, hydraulic disc brakes, strong mid-motor, and tie-down points for secure freight transport. +Bikepacking-specific gravel bicycle with full cage mounts, low-trail geometry and 700x45c semi-slick tires +Compact folding mountain bike with 26" wheels, suspension fork, and robust hinge system for back-of-car transport to trailheads. +A compact commuter with a narrow wheelbase, raised grips, and a neon safety stripe for high-visibility urban travel. +Kids' BMX race bike in bright green with 20" wheels, light aluminum frame, race-style handlebars, and padded top tube for safety. +A recreational hybrid bike with suspension seatpost, upright bars, 45mm urban tires, and a one-by drivetrain for simplicity. +A winter commuter bicycle with studded tires, full coverage fenders, heated grips, and neon reflective accents for low-visibility safety. +A commuter e-bike with smart connectivity, integrated headlight and taillight system, and an efficient automatic-shifting internal hub for fuss-free rides. +Touring steel frame with extra braze-ons, long-reach brakes for wide rims and heavy-duty rear rack for loaded travel +Electric mountain downhill bike with big-battery, long-travel suspension and reinforced brakes to handle heavy descending. +A vintage-inspired tandem with classic frame tubes, leather seats, and long, steady wheelbase to share scenic routes with a friend or partner. +Mountain enduro rig with swift handling, progressive suspension kinematics and an aggressive paint scheme to match. +A steel touring frame with robust straight-gauge tubes, braze-on water bottle bolts, and a classic paint scheme that hides scrapes well. +All-mountain mullet rig with 29 front/27.5 rear, adjustable geometry, and burly 30mm-plus rims for confidence on steeps. +A downhill-inspired hardtail with 140mm fork, slack head tube angle, 29er wheelset, and tubeless-ready wide rims for aggressive trails. +A compact kids’ BMX with mid-sized tire, reinforced frame, and a metallic candy finish to encourage hours of park practice. +Gravel plus bike with 2.4" tires, carbon fork, SRAM 1x drivetrain, and matte slate gray paint for off-road exploration. +Fixed-gear track bike with NJS-styled components, minimal decals, and a deliberately simple drivetrain for velodrome sessions. +A commuter with integrated fender and rack system, powdered coated frame, and sealed bearings for low maintenance in urban environments. +Single-speed commuter with coaster brake, step-through frame, upright swept bars, and a front basket for groceries and mail runs. +A classic road racer with thin-walled steel tubes, Jubile-style lugs, and period-correct componentry for a vintage weekend ride. +Cargo trike with electric assist, wide deck with anti-slip coating, and a weatherproof lockbox under the platform. +Cargo e-bike with enclosed rear box, passenger seatbelts, and extra-wide tires for stable urban hauling. +Gravel machine with maple wood veneer top tube, 40mm tires, and unique custom racks for light touring setups. +A mountain trail bike with staggered wheel sizes, aggressive chainstay protection, 140mm travel and a plush suspension tune for rough singletrack. +A classic Dutch-style step-through bicycle with chaincase, hub dynamo lighting, upright seating, and a roomy rear rack for chores. +A performance road bike with aero-optimized tubing, integrated cockpit, and a paint job that hides road grime between washes. +Touring steel frame with heavy-duty eyelets, triple-bottle capability, and glass-resistant paint for the rigors of global travel. +Mountain freeride bike with strong cranks, slacker head angle, and a tough 27.5-plus wheelset. +Handmade lugged steel single-speed with polished fillets, brass head badge, leather saddle and slim 28mm tires. +A commuter with integrated bike-to-lock hook, full fenders, and a low-maintenance hub gear to make everyday rides easier. +A long-distance touring bicycle with triple pannier setup, dynamo lighting, reinforced dropouts and a satin moss finish. +Road endurance machine in pearl white with vibration-damping seatpost, endurance saddle and slightly relaxed headtube height. +Urban step-through electric bicycle with minimalist LCD display, integrated battery in rear rack, and comfort saddle for easy errands. +A gravel endurance machine with micro-adjustable geometry, seatpost clamp with low stack, and compliance zones in the top tube. +A cyclocross-focused frameset with mud-relief chainstays, stepped-down top tube, and a clean unbranded matte finish for serious racers. +Lightweight cross-country mountain bike with 100mm travel fork, platform pedals, tubeless setup, and fast-rolling 29" wheels. +Timeless steel road machine with classic horizontal top tube, 9-speed drivetrain, and polished chrome accents for heritage styling. +Kids' trail-ready MTB with durable components, 24" wheels, and a low standover height to build confidence on easy singletrack. +A gravel-friendly titanium frame with 2x11 Shimano drivetrain, discreet paint, and a comfortable long-travel seatpost ideal for mixed-surface rides. +A modern commuter with belt-drive and Shimano Nexus 8-speed hub, minimalist mudguards, and powder-coated frame. +Mountain gravel crossover with 120mm fork, drop bars, and clearance for 45mm tires to blend singletrack and gravel roads. +A cyclocross race bike with ceramic bearings, carbon fork, and mud-oriented frame geometry for quick accelerations out of corners. +Urban commuter with hydraulic rim brakes, integrated lights, and quick-release front wheel for ease on mixed transit rides +Gravel race frameset with optimized tube profiles for stiffness-to-weight ratio and a gloss finish that reveals the weave beneath. +Compact cargo bike with detachable side panniers, stable low decking, integrated lock system and wide handlebars for control. +A mountain downhill racing frame with oversized pivots, double-plate chainstay reinforcement, and a low bottom bracket for cornering stability. +Lightweight time trial bike with integrated hydration system, aero fork, and deep-section carbon rims for sustained speed. +A cargo e-bike with long-range battery, programmable assist levels, and a reinforced platform to handle heavier loads safely. +Gravel bike set up for mixed-surface endurance rides with multi-gear range and micro-shift compatibility. +Urban cargo bike with electric assist, extended wheelbase, hydraulic brakes, and modular cargo modules for deliveries. +A steel fixed-gear with brushed raw finish, minimal decals, 25mm tires and leather grips for a timeless street look. +A retro-styled town bicycle with cream-colored grips, polished chrome fenders, and a patina bronze head badge for a classic appearance. +Vintage touring bike with brass fittings, hand-tooled leather saddle, dynamo headlamp, and lacquered steel framework. +A single-speed beach cruiser with soft paint finish, large balloon tires, and low gearing for easy pedaling along boardwalk promenades. +A kid's mountain bike with 24-inch wheels, front suspension, training-friendly gearing and bright orange frame graphics that are eye-catching. +All-mountain 29er with mixed wheel sizes, coil shock tuned for big hits, and stick-on frame protectors. +A commuter with step-through frame, integrated child seat mount, and a front basket with weatherproof liner for errands. +A carbon gravel bike with rowdy electronics, Di2 shifting, and an asymmetric chainstay for drivetrain stiffness. +Urban single-speed with deep V rims, polished steel frame, and minimal dropouts for chain tensioning simplicity. +A steel lugged touring bike with integrated dynamo hub, brass brazed fittings, and old-world paint scheme. +Cargo longtail with cushioned passenger seat, integrated footrests, and reinforced mounting points for child seats. +Fixed-gear messenger bike with bombproof wheels, strong rims, and a quick-acceleration build suitable for rush-hour deliveries. +Electric cargo longtail with child-focused harnesses, integrated turn signals, reinforced cargo floor and a quiet, torque-rich motor for family use +A mountain trail hardtail with reinforced dropouts, modern geometry, and a low-profile saddle for improved control on technical climbs. +A gravel grinder with steel frame, wide 700c tires, and a classic leather saddle that softens the ride over rough rails-to-trails. +Classic Dutch-style city bicycle with high-rise bars, chaincase, wide saddle and classic cream paint for easy errands. +A carbon downhill bike with reinforced head tube, 200mm travel, coil shock with piggyback reservoir, and DH-specific brakes. +A touring tandem with relaxed geometry, brazed-on eyelets, multiple bottle cages and ocean-blue enamel with cream trim. +Traditional road bicycle with chromed fork crown, quill stem, 3x10 drivetrain and period-appropriate decals for classic events. +A commuter with automatic tail light that senses braking, internal gearbox, and a belt drive that stays clean through dress clothes. +A city step-through bicycle with internal hub gears, oversized fenders, integrated kickstand, and a comfortable swept handlebar for errand runs. +A commuter with minimalist frame, internal hub, integrated rear rack and brushed titanium-look powdercoat for urban stealth. +A compact cargo e-bike with rear deck, stable chassis, and a low center of gravity to make urban deliveries safe and efficient. +A stripped adventure-sported gravel frame with oversized clearance, triple-bottle mounts, and strong fork crown mounts for extra luggage. +Mountain freeride rig with massive tires, low-slung frame for jumps, and a reinforced headset for impacts. +A plus-size mountain bike with 27.5+ tires, cushioned seatpost, 130mm front travel and subdued matte army green. +BMX park setup with pegs, gyro brakes, and reinforced frame to take repeated impacts from street sessions and rails. +A practical commuter with integrated pannier rack, quick-release wheels, puncture-resistant tires and a comfortable upright geometry for daily rides. +Lightweight cyclocross race frame with tire clearance for 35mm, low stack height, and a quick handling geometry for fast technical courses. +Urban commuter with smartphone mount, integrated taillight, and reflective pinstriping for night safety. +A recreational cruiser with swept-back bars, wide platform pedals, and a plush spring saddle for relaxed beach rides. +Race-ready aluminum road bike with carbon fork, 25mm tires, and quick-release skewers for sprint stages. +A hand-built titanium commuter with brushed finish, slim seatstays, and a stealthy integrated rack for clean lines and utility. +Lightweight skinnied road bike with shallow rimset, efficient power transfer frame, and a bold racing decal for club races. +A retro mixte with elegant frame curves, step-through ease, and classic leather saddle that invites relaxed neighborhood rides. +A steel mountain bike with 150mm travel, tapered headtube, boost axle spacing and room for 2.5" tires for enduro riding. +A tandem recumbent bike featuring two reclined seats, dual drivetrain synchronization and aero fairing for smooth paired touring. +A mid-century-style city bicycle with wicker basket, chrome-plated details, whitewall tires and a leather saddle for easy neighborhood spins. +Single-speed city bicycle in glossy white with minimalistic decals, sealed bearing hubs, and a compact rear brake caliper +A gravel e-bike with midsize battery, torque sensing motor, 700x40 tires and integrated frame protection for remote mixed-surface riding. +Folding electric city bike with a low fold height, mid-drive motor, and an integrated lock for commuter convenience. +A downhill race rig with dual-crown fork, coil shock, reinforced chainstays, and chain guide for secure descents at speed. +Vintage steel touring bicycle with chromed lugs, leather saddle, front and rear racks and leather-wrapped handlebars. +A gravel bike with a modular fork that accepts racks, three-pack mounts, and a custom paint blending metallics and matte finishes. +Gravel endurance bike with fender mounts, low headset stack, 700c x 38 tires, and a stable geometry for mixed-surface centuries. +A cargo e-bike with dual battery mounts, reinforced frame, wide platform pedals and a stable low center of gravity for hauling groceries and kids. +Convertible commuter with quick-attach front basket, three-speed hub, and a step-through frame for door-to-door errands. +Vintage touring bicycle restored with leather grips, steel rack, wool saddlebag, and wide-range cassette for mountain passes. +Cargo e-trike with dual batteries, hydraulic disc brakes, and a low-center cargo bay for safe urban deliveries. +Gravel bike with stealth matte paint, tidy cable routing, and a top tube that flares slightly for stiffness. +A matte titanium gravel bike with 40mm clearance, 700x45c knobby tires, thru-axles and a 1x12 wide-range SRAM Eagle setup. +Gravel bike with steel fork, wide-ranging cassette, flared drop bars, and custom mudguards for wet-weather rides. +A full-suspension freeride bike with long travel, double-bolt chainstays, and a stout BB to resist rock strikes on technical trails. +Gravel adventure bike with stainless-steel racks front and rear, puncture-resistant tires, and a triple chainset for steep passes. +A gravel bike with 1x12 wide-range gearing, frame-mounted pump clip, and reinforced dropouts for repetitive bikepacking loading. +A kids' balance bike in bright orange with round frame tubes, easy-grip handlebars and a low seat for confident first rides. +Classic Dutch cargo with front box, wooden slats, and step-through frame painted navy for heavy errands. +Track sprint frame with perfect chainline, single-speed simplicity and a lustrous metallic paint that reflects the arena lights. +A gravel endurance bike with bolt-on chainstay guard, wide 700c clearance, and a slightly higher bottom bracket for pedal efficiency. +A classic touring tandem bicycle with matching Brooks saddles, triple crankset, and sturdy pannier rails front and rear. +Fixed-gear sprint bike with chrome forks, narrow handlebars, and polished track hubs for a clean look. +Gravel racer with colorful merino seat tube wrap, 700c x 38mm tyres, and an aggressive yet stable ride for mixed-surface sprints. +A lightweight gravel frame with integrated GPS mount, carbon fork with rack mounts, and stealthy cable routing for tidy aesthetics. +Track sprint machine with short wheelbase, stiff stays, and a single polished chainring for maximum power transfer. +A gravel racer with 650b wheels and large-volume tires, flared drops, and a matte deep blue that hides roadside dust between washes. +A mountain trail frame with low standover, internal cable routing, and a geometry that trades a touch of snappiness for stability at speed. +Gravel bike painted in matte olive drab with titanium bolts and stealth bolt-on fender mounts. +Urban utility bike with lockable rear box, fluorescent paint accents, and ergonomic upright grips for daily chores. +A slick black time trial bicycle with ceramic-coated bearings, power meter integrated into crankset and a teardrop-shaped seatpost for aerodynamics. +Commuter with puncture-resistant lining, reflective sidewall stripes, and ergonomic 40mm saddle. +Folding e-bike with detachable battery, low standover, and rapid-fold tech for multimodal commuting. +A gravel ultralight with carbon fiber seatstays, stealth-fit derailleur hanger, and clearance for wide 650b tires for rough days. +Cyclocross training sled with wide bars, mud-shedding frame, and winter-season knobby tires ready for CX workouts. +A single-speed urban slinger with oversized chainring, compact crankset, and anodized purple paint for bold visual punch. +A steeply angled track bike with polished chainstay wrap, aero bars, and a minimalist saddle for explosive sprint training. +A colorful kids' BMX with bold graphics, padded top tube, plastic pegs and training-friendly gearing for park sessions. +A lightweight endurance road bike with endurance geometry, 28mm tires, full carbon fork and sky-blue pearl finish for long days in the saddle. +A cyclocross training rig with disc-compatible dropouts, reinforced cable routing, mud-clearance tubing and battle-blue paint. +A classic road racing bicycle with steel tubing, downtube shifters, and a glossy enamel finish reminiscent of the 1970s. +Fat-tire touring bike with 4" tyres, cargo racks and a reinforced frame for multi-surface travel. +Kids' balance bike with molded plastic frame and playful stickers for first rides and confidence building. +A touring gravel bike with titanium bolts, braided cable housing, and a supple ride tuned for long days in mixed climates. +A full-suspension enduro mountain bicycle with 170mm rear travel, coil shock, aggressive geometry, and 27.5+ wheels for steep technical descents. +Track fixed-gear city bike with deep rims, single-ring simplicity, and a ceramic-coated chain for smooth rotation. +A contemporary gravel bike with internal mounts for a small frame bag, long-range gearing, and a paint finish resistant to micro-scratches from debris. +Classic touring bicycle in cream with vintage leather panniers and a brazed-on pump peg. +BMX mini cruiser with chopper geometry, sissy bar, coaster brake, and thick balloon tires for cruising boardwalks. +A vintage road frame with classic steel lugs, replated chrome, and caliper brakes that breathe nostalgia. +A gleaming titanium road bicycle with brushed finish, endurance geometry, internal cable routing, and a 34mm clearance for wider rubber. +A mountain freeride bike with long-travel suspension, reinforced frame gussets, and a protective skid plate beneath the chainstay. +Fat-tire cargo bike for winter deliveries with studded tires, reinforced cargo box and bright paint for visibility +A commuter with removable battery pack, torque-sensing mid-motor, integrated display and wide puncture-resistant tires. +A comfortable city bike with relaxed geometry, swept grips, plush saddle and champagne-pink finish with matte gold pinstripe. +Electric folding commuter with hub motor, quick-release folding pedals, and adjustable saddle height for mixed transit. +A commuter with frame-integrated lights, concealed battery in downtube, low-noise belt drive and ash-gray finish with reflective embossing. +Fixed-gear urban racer with aero bars, bolt-on chainring and matte black powder coat for stealth. +Gravel bike with integrated GPS mount, bolt-on top tube storage, and large clearance for knobby tyres. +A gravel e-bike with front and rear racks, integrated security loop, and a silent mid-drive motor that preserves trail etiquette. +A retro-styled roadster bicycle with swept bars, leather grips, dynamo front light and cream-colored wall tires for nostalgic rides. +Cargo longtail with modular child carrier, reinforced rear hub, and wide platform for groceries and family shuttles +Endurance road bike with longer wheelbase, relaxed head angle, and discreet flared fork blades for compliance. +Gravel-focused alloy frame with progressive geometry, internal routing, and versatile tyre clearance for rough backroads. +A mountain enduro build with 170mm travel, reinforced dropouts, coil-ready shock and a stealthy desert-camo paint for aggressive lines. +Commuter with hidden chaincase, built-in wheel lock, comfortable upright posture and integrated light strips for night safety. +Urban step-through classic with chaincase, comfortable upright saddle, and enamel-painted steel frame for dependable daily use. +A practical cargo trike with lower center of gravity, hydraulic disc brakes, cargo bin and a weatherproof canopy option for deliveries. +Gravel endurance bike with titanium frame tubes, endurance geometry, and handmade leather grips for tactile comfort +A pinarello-style aero road bicycle with asymmetric chainstay, glossy red paint gradient, integrated seat mast, and deep-section tubular wheels. +Touring tandem with reinforced middle joint, plenty of braze-ons for racks, and matching paint fade. +A mountain e-bike with 150mm travel, torque-sensing motor, aggressive knobby tires and a long-range battery integrated into the downtube. +A gravel all-road bike with multi-tool integrated into the saddle rail, fancy top-tube storage strap, and high-clearance fork. +Gravel commuter with stealth black fenders, integrated front headlight, and modular rack system for adaptable cargo carrying. +Adventure gravel bike with hidden fender mounts, long chainstay design, and clearance for 47mm tires for mixed surfaces. +A downhill gravity bike with long-travel air fork, heavy-gauge tubing, and wide handlebars with reinforced grips. +A handbuilt titanium touring frame with double water-bottle bosses inside the front triangle and a brushed finish that resists corrosion. +Modern steel frame painted gloss burgundy, lugged construction, stainless fasteners and classic brake calipers +Gravel touring bike with wide-range gearing, full-fender compatibility, double rack mounts and strong stainless steel bolts for longevity +A light touring gravel bike with multiple mullion mounts, carbon fork with rack mounts, and a comfortable long-haul cockpit. +Gravel-plus all-road machine with 27.5+ wheels and a forgiving carbon frame to soften rough surfaces at high average speeds. +Downhill bike with heavy-duty linkages, 200mm travel and monstrous 2.6" rubber for steep runs. +A high-pivot enduro rig with 170mm rear travel, custom-tuned shock, and burly alloy wheels to charge steep terrain confidently. +Lightweight track bike with aggressive geometry, polished steel frame, and a powerful single-speed drivetrain. +Touring lugged steel with triple-rack mounts, long wheelbase and robust wheels for remote multi-day treks. +A cyclocross commuter with 33mm semi-slick tires, mudguard mounts, rear rack and dynamo lighting for reliable daily transport through seasons. +Titanium gravel bike with brushed raw finish, thru-axles, internal routing and a minimalist aesthetic. +A cruiser-style e-bike with retro chrome accents, wide cushioned seat, and low-step frame for comfortable assisted rides. +Cross-country marathon machine with efficient suspension kinematics, 120mm travel, and lightweight components for long races. +Urban single-speed with fluted steel frame, matte black finish, and minimalist brake lever for single-handed simplicity. +A long-distance sport tourer with high-capacity racks, dual bottle mounts, integrated pump mount and rich navy gloss finish. +A commuter with chaincase, internal hub gearing, step-through frame and reflective wheel stripes for safe, low-maintenance daily transport. +A BMX race bike with lightweight alloy frame, 20-inch racing wheels, rear brake only and stiff geometry for sprint tracks. +A casual beach cruiser with wicker basket, chaincase, and oversized tires for comfort cruising along coastal promenades. +A BMX competition frame with aggressive geometry, reinforced gusseting near the head tube, and a hardened dropout for race starts. +A retro road bike with steel fork, period-correct lugs, leather saddle, and a faded two-tone paint job that hints at many miles of history. +A hardtail trail bike with short stem, wide bars, and modern 29-inch setup for nimble cornering and quick handling. +A cyclocross rig with carbon fork, disc brakes, and a race-optimized 1x drivetrain plus mud-shedding tube shapes for springtime cross races. +Retro road racer with downtube shifters, tubular tires, polished lugs and a classic stem-mounted stop light. +Urban single-speed with candy-red paint, polished headset, and platform pedals for easy neighborhood errands. +A mountain enduro frame with adjustable travel shock, 29er-friendly clearance, and strong reinforcement at pivot points for big-hit abuse. +A city folding bike with step-through low frame, single hand fold latch, puncture-proof tires and rear hub gearing for easy stowage. +Adventure cargo trike with foldable sides, modular racks, and a heavy-duty frame designed to carry construction materials or groceries +Touring tandem with suspension-corrected forks, dual bottle mounts, robust wheels and long-chainstay stability for two-up loaded tours +Folding commuter with 20" wheels, compact folded dimensions, and a secure latch that keeps the folded bike stable during transport. +A gravel endurance frame with subtle paint speckles, integrated pump mount, and under-top-tube storage for snack stashes on long rides. +Electric cargo trike with soft-start electronics, extra-wide tires, and a low center of gravity for safer heavy loads. +A barn-find refurbished road racer with 1970s lines, modern sealed bearings, and refreshed paint that keeps vintage charm with modern rolling. +A sleek aero gravel bike with 48mm carbon wheels, 38mm mixed-terrain tires, one-by cockpit, and paint that shifts from blue to purple in sunlight. +Steel road bike with restored components, period-correct paint, and suede-style handlebar tape. +A titanium commuter with internal cable routing, belt drive, Gates Carbon FF, and minimalist matt raw finish with laser-etched logos. +A classic track bike with thin-walled tubing, tubular tires, tight geometry and gloss midnight-blue paint with white number panel. +A handbuilt titanium frame with unique brazed-on lugs for cable guides, elegant headbadge, and brushed finish that shows natural aging. +A durable city utility bike with heavy-gauge tubing, reinforced spokes, and a patina finish that hides years of daily wearing. +A gravel-built frameset with stealth hardware, integrated cable routing, and a subtle seat tube mount for extra bottle storage. +A compact urban folder with sealed bearing headset, alloy folding stem, 20-inch wheels and a practical rear guard for commuting protection. +Lightweight track-inspired fixed-gear bike with zero flex chainstay, stiff crankset and polished chrome finish for street racing +A durable city utility bicycle with welded steel frame, stout kickstand, chaincase and large platform pedals built for daily heavy use. +Folding e-bike with low noise motor, hydraulic disc brakes and shock-absorbing seat post for comfort on commutes. +A kids' balance bicycle with lightweight wooden frame, foam tires, low standover height, and a simple, safe geometry for learning. +Urban cargo trike with locking cargo box, low center of gravity, electric assist and easy-to-use pedals for safe deliveries. +A classic touring frameset with brazed-on eyelets, long wheelbase, light-gauge stainless spokes and a reliable steel fork. +City folding bike designed for commuters with fast fold latch, 18-inch wheels, internal hub gear and easy-carry handle +Commuter e-bike with top-tube integrated battery, torque sensor, and low-maintenance belt drive. +A commuter e-bike with belt-drive, 5-speed internal hub, and a low-maintenance package for daily urban reliability. +A gravel touring build with wide 650b wheels, double-bottle mount, and a low-maintenance internal gear hub for remote rides. +A lightweight road endurance frame with tailored compliance sections, durable wheelset, and a geometry that reduces fatigue on long tours. +A cyclocross machine with disc brakes, flared handlebars, and plenty of tire clearance to bounce through late-season mud races. +A vintage roadster with enamel paint, brass bell, and sweeping chrome fenders that shine under streetlights. +A commuter with low-profile rack, integrated brake light, comfortable upright geometry, and a lightweight frame that eases daily handling. +Minimal commuter with single-speed belt drive, reflective paint, integrated taillight in the seatpost, and anti-theft locking pin for quick stops. +Dirt jump hardtail with oversized top tube, reinforced head tube gussets and responsive geometry for pop. +Cargo longtail with expandable deck, fold-down sides, and integrated passenger belts for safe family outings. +A commuter with built-in child-seat harness points, robust daytime running lights, and a cargo-friendly long-tail option for family transport. +Touring bike with integrated GPS mount, triple rack mounts, long-reach brakes for loaded clearance, and comfortable pacing geometry. +Lightweight climbing road bicycle with thin-section rims, compact crankset, ca. 34mm tires and 11-34 cassette for steep climbs. +BMX flatland bike with gyro, slim frame geometry, and high-rebound tires for tricks. +Vintage road racer with polished steel lugs, leather saddle and period-correct quill stem. +Gravel-adventure titanium frame with many mounting points, raw metal finish, wide 650b tires and a discreet hidden serial plate +Time-trial frame with integrated rear hydration, aggressive cockpit, and high-aspect foil tubing for reduced drag. +Mountain trail bike with coil shock option, wide handlebars, and a 1x drivetrain optimized for technical climbs. +A modern full-suspension trail bike with 140/130 travel combo, low BB, and adjustable geometry flip-chip for varied terrain. +A mountain e-bike with trail-tuned geometry, powerful torque-sensing motor, and a heat-resistant battery pack for continuous climbs. +Road TT bike with integrated hydration system, narrow aero cockpit, and a sculpted frame to minimize crosswind drag. +A stripped touring bike with custom braze-ons, reinforced bottom bracket, and 26" wheels for long-distance dependability and parts availability. +A retro-styled cafe racer bicycle with drop bars, slim leather saddle, glossy black with hand-painted gold pinlines. +Commuter hybrid with adjustable stem, suspension seatpost, 700x32 tires, and reflective paint to boost visibility. +A compact trail fat-tire e-bike with torque-sensing motor, wide 4" tires, and a battery integrated into a robust downtube for off-grid exploration. +Urban commuter with integrated rear light strip, low-maintenance internal gearing and a comfortable upright ride for everyday use. +A road race bicycle with aerodynamic downtube shaping, shallow-section carbon wheels, and stiff chainstays for sprint ability. +A track pursuit bike with disc front wheel, stiff aero shell, and a carefully chosen fixed gear ratio for timed events. +Fat-tyred urban cruiser with wide saddle, swept bars, coaster brake, and retro paint details for smooth city rides. +Electric mountain bike with steep climb assist, torque-sensing motor, sealed drivetrain components and trail-focused geometry for confidence. +City cargo bike with a low deck and child harnesses, electric assist option, and sturdy build to handle shopping and kids. +Vintage city cruiser with brass bell, wicker basket, chrome fenders and a comfortable spring saddle for easy jaunts around town +Electric folding bike with throttle and pedal-assist modes, removable battery, and quick-fold frame for commuters on the go. +Downcountry mountain bike with 120mm travel, 29-inch wheels, plus-sized tires, and efficient pedaling platform for long climbs. +Compact folding city bicycle with single-speed drivetrain, 16-inch wheels, adjustable stem and lightweight aluminum frame +Modern gravel tandem with adjustable cockpit, wide-stability frame geometry, and clearance for 650b x 2.0 tires. +A cyclocross race frameset with carbon forks, 33mm tire clearance, disc brakes and matte stone-gray paint with bright sponsor panels. +Mountain downhill rig with monstrous fork travel, reinforced head tube, and heavy-duty wheels for steep lift-accessed courses. +A mountain XC race bike with featherweight frame, efficient platform suspension and aggressive climbing geometry for fast uphill sprints. +A classic roadster with a sprung saddle, enamel paint, and a practical rear carrier for picnics and book deliveries around town. +A gravel adventure bike with multiple frame bag options, 650b compatibility, rugged wheelset and internal cable routing for weatherproof reliability. +Retro-style city bike with leather grips, enamel headbadge, upright stem and subtle metallic flake paint. +A rugged cargo e-bike with reinforced front fork, long cargo deck, step-through frame and torque-sensing motor for heavy-duty urban use. +Lightweight gravel frame with integrated mudguard mounts, stealth racks, and titanium bolt hardware. +A purpose-built track bicycle with carbon aero fork, fixed single-speed drivetrain, deep-section disc rear wheel, and dropouts designed for track use. +Road aero setup with integrated LED rear light, deep-section carbon wheels and a lightweight aero stem for club-level racing. +Track sprint bike with ultra-stiff carbon monocoque frame, narrow gear ratio and low-profile riser bar for short explosive races. +Commuter with electric assist tucked in the downtube, sealed hub, and frame-mounted lock for secure all-weather rides. +Fatbike with anodized blue rims, paddle tires, rigid fork and low-pressure tires for sand and snow traction. +Mountain enduro bike with burly cranks, frame-protecting guards, and a durable chassis for aggressive riding. +Gravel racer with matte olive paint, flared drops, 700x40 tires and electronic wireless shifting for seamless gear changes +Road TT bike with integrated hydration bottle compartment inside the downtube and narrow, tucked cockpit. +Folding commuter with 16" wheels, cushioned saddle, and a compact folded profile that fits under desks at work. +Classic retro road racer with downtube shifters, tubular tires, and hand-laced vintage wheels. +A steel mountain bike with classic geometry and modern components, blending old-school toughness with current shifting and brakes. +Commuter e-bike with belt drive, integrated fender and rack, LCD display, and quiet hub motor for smooth city trips. +A race-honed track bike with stiff BB, short wheelbase, clipped-in pedals and anodized components targeted at criterium meets. +A kids' BMX with robust chromoly frame, pegs, 20-inch wheels and bright lime-green finish with cartoon decals. +A kids' trail-ready mountain bike with 26" wheels, simple 7-speed drivetrain, and grippy tires for early trail confidence-building. +A commuter bike with carbon composite frame, flat bars, tubeless-ready tires and a soft touch saddle for city comfort. +Retro track-inspired single-speed with straight-pipe steel frame and a polished steel fork. +Electric cargo bike with long wheelbase, twin passenger bench, pedal assist, and hydraulic brakes for family transport. +A gravel bike with anodized finishing kit, flared carbon bars, and modern disc brakes with 160mm rotors to balance stopping power. +Cyclocross-inspired commuter with knobby tires, cantilever brakes, and a low top tube for easy shouldering onto stairs. +A kids' mountain bike with hydraulic disc brakes tuned for lighter hands, front suspension for smooth first off-road rides, and fun graphics. +Classic mixte with mint accents, wicker basket, leather handlebar wrap, and a plush sprung saddle for comfortable jaunts. +A compact single-speed city bike with 650c wheels, short top tube, upright bars, and a simple coaster brake for clean urban utility. +Touring bike with dynamo hub, leather-wrapped grips, triple chainset and robust alloy racks front and rear. +A cargo trike with large wooden cargo bed, reflective tape, comfy upright seat and mechanical disc brakes for safety. +Compact recreation BMX with pegs, short crank arms and a heat-treated chromoly frame for stunt durability. +Lightweight race-oriented road bike with carbon fiber fork, ceramic wheels, and a responsive pedaling platform for sprinting. +Electric folding cargo bike with dual batteries, strong motor, and an adjustable deck that expands for larger loads. +A modern carbon gravel racer with flared drops, 700x37 tires, wireless shifters, and a paint finish that subtly changes hue with angle. +Utility cargo bike with longtail platform, twin rear seats, reinforced steel frame, and hydraulic brakes for urban deliveries. +A performance road machine with lightweight carbon tubular wheels, electronic wireless groupset, aerodynamic seatpost and glossy black finish. +A carbon gravel frame with through-axles, three-bottle frame mount options, and clearance for 50mm mountain-plus tires for extreme adventure. +Road race frame with deep section front rim, narrow saddle, and short stem tuned for high-cadence crit skills. +A vintage-styled café cruiser with scalloped fenders, two-toned paint, and a portly saddle for relaxed coffee-run style. +Mountain all-mountain with 29-plus setup, droppable seatpost, and durable rim protection for aggressive lines. +Cyclocross race replica with lightweight alloy frame, quick-release skewers and cyclocross-specific geometry. +A gravel-compatible cyclocross frame with minimal graphics, sturdy fork crown, and long-term durability as its primary design goal. +A commuter with belt drive, integrated fenders, hub dynamo, and a minimalist stepped frame designed for quick city stops and starts. +Classic British three-speed with coaster brake, chaincase, and upright riding position for short urban errands and pub rides. +A kids' balance bike with low step-through frame, soft foam grips, and bright cartoon-themed decals. +Road race bike with glossy pearl paint, aerodynamic profile, toothy cassette, and shaved weight components for climbing stages +Road-time trial bike with elongated chainstays, integrated hydration, and seat tube relief for aero flows. +Mountain e-bike with suspension fork, dropper post, full-suspension frame, and torque-limited motor for trail safety. +Compact folding cargo bike with front box, electric assist, reinforced hinge, and heavy-duty tires for grocery runs. +Folding commuter bicycle with 20-inch wheels, quick fold hinge, and compact frame for public transport. +A carbon crit bike with steep headtube angle, ultra-stiff fork, disc brakes, and 28mm tires for fast pack riding and tight corners. +A polished titanium gravel grinder with discreet welds, thru-axles, fender mounts and a natural metal finish that ages beautifully. +Folding cargo with reinforced load beam, mounting points for panniers and compact fold for multi-modal commutes. +Electric cargo bike with reinforced frame, hydraulic brakes, cargo straps and a low step-in height for loading. +A coastal cruiser with rust-proof aluminum frame, sealed bearings, wide comfort saddle and ocean-blue pearlescent paint. +Mountain freeride bike with stout frame, gravity-oriented geometry, huge rotors, and reinforced wheelset for drops and drops +Classic city cruiser with wide handlebars, deep saddle, and decorative chrome fenders for leisurely rides. +Gravel touring tandem with stability-focused frame, matching panniers, and a robust drivetrain for long-distance two-up rides. +A high-performance gravel race bike with SRAM AXS wireless electronic groupset, ceramic bearings, and tubeless 38mm tires. +Gravel hardtail with aggressive 1x11 drivetrain, wide flared bars, and low-slung down tube for balance on rough gravel. +A sleek city single-speed with matte navy paint, belt drive, internal hub with coaster brake and puncture-resistant tires. +Mountain trail hardtail with modern slack head angle, dropper post, and 2.4" tires for technical singletrack confidence. +A lightweight criterium bicycle with aero seatpost, torque-optimized chainset, and stiff front triangle for responsive sprinting. +Gravel touring bike with titanium frame, internal routing, and minimalistic leather accents for elegant adventure travel. +Lightweight carbon climbing frame with slim stays, compact geometry and space for two bottle cages for alpine ascents. +A classic Dutch omafiets with swept back handlebars, spring-loaded saddle, integrated skirt guard, and oversized fenders for rainy climates. +A lightweight gravel bike with aero-shaped downtube, integrated seatpost clamp and a subtle pearlescent finish that shifts in sun. +Folding electric commuter with belt drive, 14-inch wheels, integrated rear light and compact handlebars for crowded trains +Gravel bike with flared drops and a low bottom bracket for high-speed cornering and confidence on rough descents. +A touring trike with comfortable recumbent seat, low center of gravity, and abundant cargo rails for a stable loaded ride. +A day-cruiser with single-speed coaster brake, springer fork, wide banana seat, and candy apple red metallic paint for relaxed rides. +A lightweight aluminum gravel bike with tapered fork, deep V-section wheels, and dirt-road friendly 38mm tires. +Fixed-gear messenger-style with reinforced bearings, oversized saddle, and quick-release front wheel for fast urban deliveries. +Electric folding commuter with 250W hub motor, quick-fold hinge and padded saddle for short urban hops. +A compact folding cargo bike with electric assist, foldable deck, and quick-release wheels for delivery riders who need flexibility. +Gravel-plus bike with slacker head angle, 650b x 47 tire clearance and cushy contact points for rough terrain comfort. +A durable BMX race bike with sealed bearing hubs, 20"x1.75" tires, low-slung frame geometry, and a removable brake system for pure speed. +A city cruiser with spring-loaded leather saddle, swept handlebars, wood-paneled rear rack and cream-colored frame. +High-end carbon mountain bike with 29" wheels, electronic shifting and SRAM AXS wireless groupset. +Electric commuter with smart display, Bluetooth-enabled motor, puncture-resistant tires and integrated theft-deterrent lights. +A cyclocross pro frameset with sealed bearings, semi-integrated cables, full carbon build and metallic charcoal finish with thin orange stripes. +A downhill race sled with massive tires, reinforced head tube, hydraulic brakes and fluorescent yellow frame for visibility on steep runs. +A handcrafted wooden cargo bike with laminated maple deck, bronze fastenings, and a varnish that enhances the wood grain beautifully. +A mid-drive electric cargo bicycle with stepped frame for easy mounting, extra-long chainstays for stability, and heavy-duty cargo deck. +Folding city bike with a classic steel frame, quick fold clamp, and comfortable saddle to make commuting an easy part of the day. +Cyclist's commuter with dynamo hub, integrated front light, fenders, and a sprung leather saddle for wet-weather reliability. +Mountain downhill sled with long travel, beefy brakes and reinforced chainstay for heavy abuse. +Urban commuter with e-assist hub, low-step frame, large-capacity front basket and soft ergonomic grips for errands and markets +Gravel adventure with multi-boss frameset, tube re-enforcement for racks, and a color palette inspired by desert sandstone. +Full-suspension trail bike with progressive geometry, dropper post and 29" wheels for confidence on descents. +A touring bike with brazed-on triple bottle mounts, heavy-duty steel frame, Rohloff hub, and large-capacity touring panniers for unsupported travel. +A city single-speed with retro paint, coaster brake removed in favor of rear caliper, tall riser bars, and a leather saddle for vintage charm. +Gravel bike set up for bikepacking with low-geared cassette, flared drops, and multiple luggage attachment points. +A restored classic racing bicycle with period Campagnolo drivetrain, cork bar tape, and polished alloy rims for timeless style. +A BMX park bike with lightweight frame, sealed-bearing hubs, and aggressive sticker kit to show personality while popping tough tricks. +Touring steel bike with triple cage mounts, heavy-duty spokes, two-tone enamel paint and wide-range gearing for loaded climbs. +A commuter with steel frame, upright handlebars, integrated front basket and a quiet internally geared hub for smooth shifting. +A retro single-speed with chrome handlebars, whitewall tires, and a subtle metallic flake paint job for stylish neighborhood cruises. +Mountain bike with long-travel forks, aluminum frame reinforcement, wide flat handlebars, and aggressive treaded tires for contention. +Electric cargo longtail with low center of gravity, swing-out cargo deck, and modular mounting system for boxes. +A handbuilt lugged steel road frame with tapered seat tube, polished lugs, classic paint and modern 11-speed compatibility for a balanced ride. +A custom painted commuter with enamel speckle finish, leather saddle, and hand-stitched bar grips for artisanal urban transport. +A commuter with low-rider front rack, integrated fenders, and a hipster-friendly frame color with contrasting wrap-around chaincase. +A cargo bike with center platform for hauling odd items, strong alloy tubing, and mechanical disc brakes for reliable stopping power. +A gravel race rig with SRAM Red eTap AXS, ceramic bearings, and 38mm tubeless tires for speed and durability on rough surfaces. +A folding commuter with single-handed folding mechanism, small 20-inch wheels, and a sturdy locking latch for quick stowing under desks. +A high-capacity cargo e-bike with dual-battery option, hydraulic disc brakes, and integrated child seat mounts for family logistics. +A downhill mountain bicycle with 200mm travel, coil shock, slack head angle and burly 27.5+ tires for steep gravity lines. +A cyclocross race machine with 33mm tubular tires, disc brakes, flared bars and a geometry specifically tuned for fast shoulder runs and turns. +A vintage urban bike with lacquered wooden fenders, brass badge, and a comfortable sprung saddle reminiscent of older city cycles. +Vintage city bike with chromed fenders, rod brakes, leather saddle, and a cheerful paint scheme for everyday errands. +A single-speed track homage with polished chrome chainring, short top tube, deep black gloss and a taut chainline for pure simplicity. +Road bike with electronic groupset, discreet cable ports, and a deep aero profile for fast days. +Lightweight kids' road bike with caliper brakes, small drop bars and colorful rim tape for learning hand positions. +Folding commuter with rust-proof hinge, small rolling wheels, and a lightweight frame for rapid city transitions. +Road race ready frameset with tapered head tube, integrated seat clamp, weight-saving cutouts, and aggressive geometry. +Mountain enduro bike with dual-crown fork option, long dropper travel, and aggressive geometry for steep terrain. +Lightweight climbing bike with carbon frame, minimal paint, and sub-7kg build for steep mountain passes. +Mountain freeride setup with slack geometry, double-crown fork, heavy-duty brakes, and reinforced frame for big hits. +A classic steel road bike with Campagnolo friction shifters, thin-tubed frame, and a lightweight feel that recalls a bygone racing era. +Cyclocross bike optimized for CX nationals with superlight carbon frame, powerful cantilevers and course-specific gearing +Gravel endurance bike with vibration-damping seatpost, wide 650b tire compatibility, and discreet tool storage inside the top tube. +A modern BMX park bike with gyro, sealed bearing hubs, reinforced rims, and graffiti-style decals that showcase street culture roots. +Fast road aero bike with integrated storage, hidden seatpost clamp, and 50mm carbon wheels. +Electric cargo longtail with low-step deck, integrated child harnesses, and a day-glo paint option for safety. +Gravel bike with hand-stitched leather grips, 700x42 tires, framebag with zippers and stealth matte finish for low glare navigation +A city utility bike with frame-integrated lock, ejection seat for child carrier, stepped frame, and wide platform pedals for frequent stops. +Road gravel hybrid with comfortable geometry, 35mm tires and robust hubs for mixed-surface club rides. +Gravel racer with low-profile graphics, 700x40c tubeless tires, and compact gearing for rolling routes. +A gravel-focused bike with flared carbon drops, tubeless 700x40 tires, internal mount for GPS and a scratch-hiding matte finish. +A gravel plus bike with 27.5+ tires, slack head angle, dropper post and desert-sand paint with subtle turquoise chips. +Track sprinter with oversize tubing, short wheelbase, and deep-section track wheels for sprints. +A lightweight cross-country race bike with full carbon frame, 100mm travel fork, 1x12 gearing and gloss red paint with thin black stripes. +A gravel-friendly endurance road bike with relaxed geometry, vibration-damping seatpost, and generous tire clearance for mixed terrain comfort. +A purpose-built longtail cargo bike with extended rear platform, beastly reinforced frame, and a low center of gravity for stability. +Track-inspired city single-speed with polished steel down tube and modern sealed-bearing hubs. +A gravel-specific frameset with clearance for 700x50mm tires, integrated top tube mounts, and a lightweight fork with rack mounts. +Gravel series bike with reinforced dropouts, wide tyre clearance, and subtle hand-painted logos along the downtube. +Mountain e-bike with full suspension, large capacity battery, aggressive chainring, and motor cut-off for technical descents +Mountain trail bike with mid-travel suspension, dropper post, 29-inch wheels, and versatile gearing for long local loops. +Electric cargo longtail with dual-battery option, comfortable bench seats, hydraulic brakes, and a heavy-duty frame for family loads. +Gravel endurance alloy frame with internal dropper routing, 38mm tire clearance and braze-ons for multiple accessories. +City single-speed with steel frame, rear coaster brake, spring saddle and retro script decals. +A gravel endurance machine with compliance-focused seatpost, integrated bento box, 38mm gravel tires and aluminum frame with ceramic paint. +A folding utility bike with compact 16-inch wheels, quick-fold mechanism, and a reinforced rear rack for grocery trips. +Gravel tourer with stainless-steel frame, low-maintenance belt drive, internal hub gear and multiple mounting points for gear +Performance road bike with tuned compliance in seat tube, aerodynamic downtube and refined cable integration. +A mountain trail bike built around playful handling, short chainstays, and a responsive rear end for popping off features. +Kids' balance bike with adjustable seat, sturdy ash wood frame, and bright alloy footrests. +Gravel endurance frameset with composite layup for compliance, integrated tool storage, and stealth matte finish for understated looks. +A touring bike with low-trail fork, swept-back bars, and wide, stable geometry optimized for loaded comfort with heavy panniers. +Vintage racing bicycle with steel lugs, skinny tires, quill stem, and a chromed fork crown evoking mid-century competition. +Urban commuter with integrated lock, rear rack, chain guard, and reflective striping on the frame for night safety. +A classic beach cruiser with palm-tree motif paint, oversized chrome fenders, and a low-gear setup for effortless seaside rolling. +A kids’ dirt jumper with small 20-inch wheels, rigid fork, and low top tube for confident handling in beginner-friendly pump tracks. +A fixed-gear alleycat-ready bike with deep-dish front rim, steel frame, narrow bars, and a short-cog ratio for responsive city sprints. +A vintage road racer restored with new cables, polished chainset, and period-correct livery that honors classic competition heritage. +A kids’ mountain bike with 20-inch alloy wheels, front suspension, coaster brakes, and fun star-themed decals to encourage trail learning. +Fixed-gear street flyer with anodized components, colorful chain, and a slim silhouette optimized for city sprinting. +A beach cruiser with oversized whitewall tires, low-slung frame, glossy turquoise paint and chrome fenders for laid-back riding. +A high-volume trail bike with hardened steel pivots, long-reach geometry, and coil shock compatibility for burly all-mountain lines. +Urban e-cargo trike with stable three-wheel layout, generous cargo box and pedal-assist motor for heavy loads. +A modern cyclocross race bike with 1x drivetrain, dropper post, 700c wheels with 40mm tubeless tires, and reinforced mudguards. +Modern e-cargo bike with long wheelbase, hydraulic brakes, and child seats integrated into the deck. +Kids' foldable BMX with safety padding, small 16" wheels, easy-to-grip handlebars, and a low standover height for confidence. +Cargo tricycle with large wooden box, electric assist, and three-point braking for safe neighborhood deliveries. +A gravel commuter with fenders, integrated rack mounts, 700x35 tires, and dark graphite paint with fine metallic fleck. +Carbon enduro bike with progressive geometry, 170mm rear travel, and burly drivetrain. +Electric longtail with child bench, solar panel top on the rack and modular attachment points for accessories. +A folding electric bike with compact battery, torque sensor, and powerful regenerative braking for public transport commutes. +Gravel endurance bike with a compliance-focused carbon layup, tubeless-fitted rims and a matte forest color that masks dirt. +A nimble single-speed fixed-gear bicycle with polished hublocks, chain tensioners, and a low horizontal top tube for the street. +A vintage lugged-steel road bike with tan sidewall tires, down tube shifters, classic chrome fork crown and patinaed decals. +High-performance criterium bike with responsive aluminum frame, steep head tube angle, low-trail fork, and shallow profile wheels. +Folding commuter with center-hinge, low folded volume, and a soft-grip handle for carrying the folded unit. +Vintage fixed-gear with chrome fenders, polished rims and leather saddle for a classic urban silhouette. +A carbon piste bike with aggressive aero tubing, shallow-section wheels optimized for velodrome use and a stark black-on-black livery. +Electric mountain bike with torque-sensing motor, long-travel suspension, reinforced frame, and integrated battery for trail climbing. +A vintage tandem restoration with wicker baskets, brass fixtures, double leather saddles and a soft cream paint with maroon trim. +Fixed-gear track-style commuter with clearcoat chrome frame, riser bars, leather saddle and platform pedals for city streets. +Adventure gravel bike with adjustable dropouts, belt drive option, and two-bolt stem for durability on long routes +Mountain downhill race build with long travel fork, massive tyres and carbon-composite swingarm. +A mountain bike tailored for bikepacking with framebags, small handlebar roll, 27.5+ tires and olive drab anodized highlights. +Touring tandem with reinforced mid-span, multiple pannier racks, robust drivetrain and long-wheelbase stability for smooth two-up travel. +A fat-tire mountain bike with aggressive tread pattern, anodized red rims, and a matte forest-camo frame designed for winter trail sessions. +Touring steel frame with integrated fork rack mounts, triple bottle cages and a stable load-carrying geometry. +A long-travel downhill rig with reinforced shock mounts, 200mm travel fork, and durable composite wheels to survive repeat heavy landings. +A kids' BMX race bike with lightweight frame, 20-inch spoked wheels, sponsor-style graphics and fluorescent orange frame. +A polished titanium frame urban commuter bicycle with belt drive, internal hub gears, puncture-resistant tires and a simple step-through design. +A mountain e-bike with robust battery enclosure, adjustable geometry, and climb-assist tuning optimized for long uphill approaches with heavy gear. +A modern cyclocross frameset with carbon compliance zones, stealthy matte finish, and a geometry tuned to crossing technical barriers quickly. +Electric cargo longtail with child seats, side reflectors, upgraded brakes and a highly durable welded frame for frequent use +Classic road bicycle with a narrow steel frame, small downtube decals, and a glossy red paint hand-finished with care. +A folding commuter bicycle with adjustable wheelbase, handy storage clip under the saddle, and ergonomic grips for daily use. +A kids' BMX with brightly colored rims, reinforced top tube, and a small frame geometry tuned for tricks and jumps. +Lightweight cross-country racer with 100mm fork, narrow bars, and a weight-saving cutout on the seat tube. +Mountain enduro mullet bike with 29 front and 27.5 rear, adjustable travel, and aggressive geometry for steep tech descents. +Fixed-gear urban bike with polished silver frame, narrow handlebars and low-profile tires. +A fixed-gear commuter with polished deep-section rims, flip-flop hub, and narrow drop bars for efficient city commuting. +A lightweight gravel racer with 38mm tubeless tires, internal cable routing, flared drop bars and a matte graphite finish for understated performance. +Lightweight single-speed track bike with deep-section rims and polished chrome hub flanges. +Folding commuter with compact geometry, reflective accents and shock-absorbing seatpost for bumpy rides. +A downhill-ready alloy bike with massive 205mm travel, reinforced frame gussets, and an ultra-slack head angle for rough runs. +Electric touring bike with torquey motor, dual-battery option, and reinforced mounting points for heavy luggage. +A compact gravel commuter with mudguard mounts, dynamo lighting, fender clearance, and 700x32 tires for year-round use. +A road aero frame with integrated seatmast, deep-section wheel compatibility, and a high-modulus layup tuned for sprint stiffness. +A compact urban folding bike with 16-inch wheels, simple coaster brake, and a shoulder strap for easy carrying on transit. +Carbon gravel bike in glacier white, 40mm tires, hydraulic discs, and a stealthy top tube bag for electronics. +Modern gravel bike with integrated frame storage, 1x drivetrain and tire clearance for 45mm rubber. +A high-pivot enduro mountain bike with anti-squat tuned linkage, coil compatibility, and reinforced chainstay yoke. +Commuter e-bike with throttle-only option, puncture-resistant tires, rim protectors and full fenders for wet commutes. +A modern fixed-gear city bike with 50mm deep rims, polished frame, and low-rise bars for a confident urban stance. +A mountain hardtail for flow trails with 130mm fork travel, short chainstays, and responsive alloy frame for playful cornering. +Electric longtail cargo bike with padded child bench, reinforced kickstand and a city-friendly turning radius. +A children’s small-frame mountain bike with front suspension, easy-shift gearing, safety chain guard and neon-pink paint with unicorn decals. +All-mountain enduro bike with 160mm travel, coil shock, aggressive geometry, and 27.5-plus tires for rough lines. +Track sprint bike with narrow handlebars, stiff bottom bracket, tubular tires and paintwork focused on weight savings. +Classic continental city bicycle with leather saddle, sweptback bars, and a minimalist steel frame for commute elegance. +A mountain e-bike with full suspension, boost spacing, and a stealth frame-mounted battery blending into the downtube. +High-performance time trial bike with integrated hydration and aero-optimized cockpit for sustained solo efforts against the clock. +A kid-friendly trail bike with easy-shift gearing, durable frame, low standover height and knobby tires for learning trails. +Endurance road bike with 28mm clearance, disc brakes, relaxed reach and a two-bottle capacity on the down tube. +A mountain trail bike with modern geometry, 130mm travel fork, and a dropper post for technical singletrack and playful descents. +Beach cruiser with pastel mint paint, wide balloon tires, springer fork and a roomy saddle for effortless coastal rides +Gravel drop-bar mountain mash-up with 2.2-inch semi-slick tires, clutched rear derailleur and a micro-suspension seatpost. +A compact urban folder with 20" wheels, magnetic latch, single chainring and reflective sidewalls for safe multi-modal commuting. +A commuter single-speed with a sealed hub, high-polish chainring, and narrow tires for easy zipping through city streets. +Gravel plus bike with 650b wheels, 2.1-inch tires, supple suspension seatpost and a two-tone paint scheme. +A full-suspension enduro rig with coil shock tuned for heavy hits, reinforced linkage, and rated for aggressive runs. +Mountain trail slayer with single-pivot rear suspension, stout tapering downtube, and geometry suited for flowy descents. +Road endurance frame with compliance zones in the seatstays, 28mm tire clearance, and a relaxed geometry for multi-hour rides. +Touring tandem with slack geometry, low-maintenance drivetrain and weather-proof fender sets for year-round touring. +A lightweight aluminum cyclocross bike with rapid mud-shedding geometry and punched brake mounts for lightweight performance. +Mountain cross-country race bike with sub-1000g frame potential, quick-engaging hub and aggressive climbing gearing. +Cross-country race bike with rigid seatpost, lightweight cockpit, and a 34mm stanchion fork. +Classic family cruiser with padded bench seat, rear child footrests, chrome handlebars and soft suspension for easy family rides. +A classic Italian steel road frame with long reach brakes, chrome-plated fork crown, and narrow 22mm tires for period correctness. +A restored early-90s road bike with upgraded disc-brake conversion, polished alloy rims, and a classic paint scheme refreshed to shine. +A durable city commuter with three-speed hub, coaster brake option, and a chaincase to keep the drivetrain clean. +A commuter with fully integrated lights and a rear roll-top dry bag mount for sudden rain on the way home from work. +Road lightweight climbing frame with aggressive tube shaping, ceramic bearings, and a pearl white finish with micro flecks. +A kids' BMX-style bike with pegs removed, wide plastic pedals, and an extra-wide saddle for stable learning and play. +Touring steel bike with leather saddle, maps pocket in the handlebar bag, and well-worn patina for character. +Race carbon road bike with 12-speed wireless groupset, aerodynamic seatpost and deep rim race wheels. +A cyclocross rig with highly flared bars, robust 1x drivetrain, and a gritty stonewashed finish that conceals scratch marks. +Urban fixed gear with minimal decals, gaffer-tape chainstay guard, and bright yellow saddle for contrast. +A downhill race sled with titanium pivot hardware, pressure-balanced shock, and 9-speed gearing optimized for running spare gears on the trail. +A road time trial machine with a narrow frontal area, integrated hydration, and a glossy white finish decorated with thin racing stripes. +A practical city bike with an integrated rear child seat mount, low-step frame, and easy-shift internal gearing for family errands. +A commuting folding electric bike with compact folded size, pedal-assist and 20-inch wheels for efficient door-to-door portability. +Cargo trike with low center cargo bed, hydraulic disc brakes, and an adjustable handle for different operators. +Lightweight aluminum gravel hardtail with thru-axles, 1x drivetrain, and aggressive tire clearance. +A titanium mountain bike with adjustable chainstay length, ISCG tabs, and a stealth matte finish for discreet off-road performance. +A classic road racer with downtube friction shifters, narrow tubular tires, lugged steel construction and retro cream finish with crimson accents. +Lightweight track-inspired road bike with fixed wheel option, narrow aerodynamic profile, and shallow drop handlebars. +Gravel adventure with camo paint, wide tires, and strong aluminum frame for rough exploration. +A mountain e-bike with 150mm travel, powerful mid-drive motor, and a waterproof connector for reliable electronics in wet trails. +A beach cruiser with sky-blue gloss paint, large wicker front basket, chrome fenders and a plush saddle ideal for boardwalk cruising. +A bombproof down-country bike with 120mm travel, light alloy construction, and modern geometry that can tackle long days on the trail. +Lightweight race gravel bike with aero tubing, hidden seatpost clamp and 700x38 tires for fast off-road races. +A cross-country race bike with 100mm travel, ultralight carbon frame, 1x11 drivetrain and bright orange accents for contrast. +A gravel adventure bicycle with modular fork attachments, welded mudguard mounts, and a soft matte finish that hides scuffs from rough trails. +A gravel bike with reinforced forks, 42mm all-terrain tires, thumb shifters for clunky gloves and a rugged powder coating to resist chips. +Commuter with integrated front basket, fender-eyelets, and shock-absorbing suspension seatpost for bumpier streets. +High-performance road frame with full internal routing, tapered headtube and stiff bottom bracket for race sprints. +Urban e-bike with quiet hub motor, easy standing step-through frame and an intuitive control pad on the handlebar. +Gravel commuter with reflective sidewall tires, fenders, dynamo front hub, and a Bosch-powered front light for visibility on dawn commutes. +Road sprint bike with shallow rimset, aggressive saddle position and responsive steering tuned for crits and bunch sprints. +A touring bike built from Reynolds 531 steel with brazed-on braze-ons, 26-inch wheels, wide handlebars and a deep burgundy lacquer finish. +A nimble single-speed fixed-gear bike with a glossy red frame, flip-flop hub, and minimal riser bars. +Compact BMX freestyle bike with reinforced mid-school top tube and colorful anodized hubs. +Urban commuter with stealth frame, internal lighting, and a quiet belt drivetrain to slip through city traffic. +Vintage road bike converted to modern standards with updated brakes, sealed bearings, and ergonomic bar tape while retaining classic frame lines +A gravel bike with integrated top tube bag, stealth-colored frame, hydraulic disc brakes, and a flared bar profile for enduro gravel racing. +A performance touring bicycle with short chainstays, high-volume tires, vibration-damping seatpost and multiple mounting bosses. +A high-performance gravel race frame with electronic wireless shifting, tubeless-ready wheels, and a short, powerful cockpit for attack-style riding. +Touring tandem with Rohloff-compatible conversion, extra-strong spokes, and double front racks for epic long-distance tours. +A cyclocross-turned-gravel rig with tubeless 40mm tires, disc brakes, and a slightly taller head tube for comfort on long dirt roads. +E-MTB with Bosch-style mid-drive, active geometry, long travel fork and trail-tuned suspension settings for remote singletrack. +A single-speed beach cruiser with wide balloon tires, retro banana seat, and matte cream paint to match summer boardwalk vibes. +A sleek urban fixed-gear with Bakelite-style grips, minimal branding, and a glossy gunmetal finish for understated city style. +A city cargo bike with long platform, low center of gravity, hydraulic brakes, and tie-down loops for every imaginable load. +A folding city bike with secure latch system, easy-fold handlebars, and an elegant matte paint finish to blend into urban settings. +Mountain hardtail with tubeless-ready rims, 29-inch wheels, and progressive slack headtube for steep trails. +Track time-trial pursuit bicycle in bright yellow with disc rear wheel and steep seat tube angle. +A commuter with low-maintenance belt drive, internal gear hub, and a small integrated solar-powered taillight for eco-minded urban riders. +Cargo trike with electric assist, side-loadable box, and foldable canopy for rainy deliveries. +A race-cross mountain bike with 120mm fork, 1x12 groupset, wide handlebars and tubeless-ready 29er wheels for rapid climbs and descents. +Gravel endurance racer with integrated headset storage, 38mm tire clearance and a full carbon cockpit for comfort. +A mountain fat-tyre snow bike with rigid fork, oversized studs in tires, big-volume frame and camo-snowflake paint. +Track sprint machine with stiff carbon frame, narrow handlebars, and polished track hubs. +A down-country hardtail with lightweight frame, low-slung geometry, and climbing-focused 120mm fork travel. +A boutique gravel rig with hand-applied patina finish, wide carbon bars, and 700x45 gravel tires. +Gravel touring bicycle with double-braze-on fork, integrated framebag-friendly top tube, and ample bottle mounts for multi-day adventures. +A modern trail hardtail with 29-inch wheels, low bottom bracket, wide bars and quick-rolling tires for efficient climbing. +A high-traction winter commuter with studded tires, chaincase, and unplated hub motor for consistent performance on salted roads and icy patches. +A dirt-road touring bike with triple mounting points, large tire clearance, and an alloy front rack for lightweight expedition support. +A gravel endurance machine with vibration-damping carbon seatpost, 700x40 tubeless tires, a versatile cassette and a geometry that favors comfort on long days. +Gravel sport frame with carbon layup tuned for compliance, 700c wheels, and a 2x11 Epic-range drivetrain for fast mixed terrain. +Electric cargo bike with longtail deck, modular seats, heavy-duty brakes and robust motor for hill-heavy urban routes. +A retro fixie with riser bars, polished chrome hub, and a colorful frame that stands out in crowded bike lanes. +A gravel-adventure rig with handlebar harness, oversized frame bag, and multi-bolt fork rack mounts for ski-pole or shovel storage. +Retro-styled roadster with swept chrome handlebars, sprung saddle, bell and lamp for leisurely rides around town. +Fixed-gear commuter with deep matte finish, narrow aero seat, and a small taillight tucked under the seatpost. +A steel touring bicycle painted British racing green with full-length racks, leather saddle, wide gear range and durable 36-spoke wheels for loaded trips. +Compact city cargo bike with removable insulated box, rear-wheel hub motor, and sturdy kickstand for market runs. +Vintage military-style bicycle with olive drab paint, heavy-duty rear rack and leather straps. +Cargo trike with three wheels, wooden crate, electric assist and a low center of gravity for heavy loads. +Classic steel road touring frame with lugged joints, triple chainrings, leather saddlebags and a dynamo lighting system for dawn departures +Race BMX park bike with short wheelbase, high clearance, and anodized purple stem and hubs. +Mountain hardtail with lightweight alloy frame, fast-rolling 29" wheels and a shallow offset fork for snappy handling. +Mountain enduro frame with adjustable geometry, serpentine linkage, and burly 27.5-inch wheels for aggressive terrain shredding +A performance gravel racer with aero handlebars, 750g carbon frame, 700x35mm slick gravel tires and SRAM electronic groupset for fast mixed-surface events. +Gravel endurance machine with endurance-focused cockpit and 1x gearing for simplicity on long rides. +Touring tandem with long-chain runs, twin racks, and a paint finish that includes hand-painted route waypoints. +Fixed-gear single-speed track-style commuter with riser bars, matte gray frame, and platform pedals. +Classic British three-speed with hub gear, chrome fenders, low top tube and a leather saddle for relaxed city touring. +Retro British-style roadster with three-speed hub, polished fenders, swept bars and leather saddle for leisurely city rides. +Touring steel frame with painted headbadge, reinforced bottom bracket wrappers, and generous rack mounts for heavy touring loads. +A stripped-down kids’ balance bike in bright lime with rubberized handles and a stable low-slung chassis for toddling confidence. +Electric step-through commuter with low center of gravity, palm throttle, and integrated front basket for groceries. +Gravel all-road frame with generous bottle mounts, stealth rack compatibility and clearance for fast 38mm rubber. +Gravel all-road titanium bike with oversized bottle mounts, excellent ride compliance and modern clearance for 45mm tyres. +A titanium endurance road bicycle with subtly flattened top tube for responsive steering and a brushed finish that endures commute wear. +A fixed-gear commuter with polished spokes, minimal chain guard, and a bright colorway to help visibility in urban traffic. +City commuter with built-in front rack, integrated LED lights, step-through design and sealed hub for easy maintenance. +Recumbent trike with three wheels, comfortable reclined seat, and a low-slung center of gravity for stability on long rides. +Custom-painted downhill sled with coil shocks, 27.5" wheels, wide bars and reinforced chainstay gussets. +Folding commuter with rust-resistant components, deep dish compact wheels and quick-release hinges for easy storage. +A vintage-inspired touring frame built from Reynolds steel with brazed-on fittings and a patina paint job that hides scuffs from road stories. +Touring e-bike with mid-drive motor, reinforced wheels, and large-capacity panniers for extended powered tours. +A compact trail hardtail for kids and small riders with 24-inch wheels, short top tube, front suspension, and durable finishes for rough playground use. +Urban single-speed with tall riser bars, rear coaster brake, and a full chain guard for casual city cruises. +A compact cargo trike for urban kids with safe low bed, electric assist, padded seating and safety-green paint with playful patterns. +A cyclocross gravel hybrid with disc brakes, wide clearance, durable frame mounts and a paint finish designed to hide dust and dirt. +Mountain cross-country full-suspension with lightweight carbon rear triangle, 120mm travel, and fast-rolling 29er wheels for XC laps +Gravel commuter with dynamo front hub, USB charging port, and 38mm puncture-resistant tires for reliable daily use. +City cargo trike with a wide front box, integrated braking system, electric assist and safety flag for visibility. +City commuter with suspended seatpost, puncture-proof tires, internal hub gear and integrated low-beam headlight. +A bespoke steel frame with hand-swept lugs, scalloped chainstays, and a sunburst paint revealing brushwork under clearcoat. +A commuter with carbon-reinforced alloy frame, internal routing, belt drive and a quiet rear hub optimized for low maintenance. +Urban commuter with integrated lock mount, reflective rims, and puncture-resistant casing for winter reliability. +Lightweight cyclocross alloy frame with tapered headtube and mud-shedding stays for winter training. +Lightweight carbon gravel bike with dropper post compatibility and stealth matte finish. +A high-end road frame with integrated seatpost clamp, stealth cabling, 25mm tires and matte metallic graphite finish. +A light gravel bike with aero-shaped tubing, D-shaped seatpost and tubeless-ready 700x38 tires for fast comp rides on rough roads. +A lightweight hardtail cross-country bike with a tuned fork, narrow bar, and an efficient pedaling platform for endurance events. +Track pursuit machine with narrow aero bars, rear disk wheel and a clinical minimalist finish. +A front-load cargo bike with heavy-duty steel box, bench seats, and a robust mid-drive motor to help with hill starts while loaded. +Mountain freeride with slackness, stout alloy frame, long-travel fork, and platform pedals for big-air antics. +A commuter with automatic hub lighting, a shiny integrally mounted bell, and reflective paint on the fork for visibility. +Beach cruiser with retro spring saddle, whitewall tires and beach-themed decals in turquoise. +Gravel e-bike with stealth battery integration, hydraulic disk brakes, and a long-range mode optimized for climbs. +A touring tandem with comfortable legroom, long wheelbase, multiple bottle cages, durable racks and glossy hunter green paint. +Commuter e-bike with pedal-assist modes, oversized tires for comfort, and foldable rear rack for quicker storage. +A handbuilt lugged steel city bicycle with enamel paint, pastry-sweet pinstriping, Brooks leather saddle, and classic swept-back handlebars. +A light-duty cargo bike with short wheelbase, front bucket for groceries, coaster brake option and subtle cream paint with brown accents. +A folding commuter with reinforced hinge, 20-inch wheels, single-handed fold and a quick-release seatpost for packable transport. +Lightweight road frameset with compliance seatpost, tuned layup for a balance of stiffness and comfort, and hidden cables. +A commuter with sealed belt drive, hydraulic coaster brake conversion, and an integrated anti-theft locking mechanism on the frame. +A folding commuter with compact 14-inch wheels, mid-drive motor, belt-drive and brushed aluminum finish for apartment living. +A high-traction fat-tire mountain bike with 26x5-inch tires, wide rims, and an oversized fork crown for snow and sand performance. +Road endurance bicycle with more relaxed geometry, disc brake compatibility, and a two-tone paint split down the seat tube. +Gravel commuter with mudflaps, reflective tape, and comfortable upright geometry for daily rides. +A commuter with LED-integrated rims, low maintenance belt drive, and a 3-speed internal hub for simple shifting. +Lightweight fixed-gear track-style bike with polished headset, tubular tires, and an ultra-clean frame aesthetic for city crits. +A vintage-inspired cruiser restored with modern components for reliability while keeping period details like an old enamel head badge and leather grips. +A touring steel bike with triple-chainring setup, full-fender mounts, wide low gears and dark moss-green paint with glossy highlights. +Mountain trail hardtail with progressive geometry, 130mm fork, 29x2.35 tires and tubeless setup for traction and speed. +Track bike with fixed gear, deep-section aluminum wheels, and a super-stiff carbon fork for the velodrome. +Electric city bike with torque-sensing assist, soft suspension fork, puncture-resistant tires and hydraulic brakes for confident commuting. +A gravel race rig with electronic wireless shifting, 42mm high-volume tires, and tubeless-ready carbon rims built for speed and comfort. +Gravel touring frame with multiple mounting points, wide tyre clearance, and a simple matte paint to hide wear. +Kids' balance bicycle with wooden frame, low step-over height, and bright yellow paint. +A gravel adventure frameset with machined dropout reinforcements, stealth paint, and custom fork braze-ons to carry extra gear. +Retro steel touring frame with stamped fork ends, charming s-bend fork and polished lug edges. +A racing gravel bike with superlight carbon tubing, stealth cable routing, and mounting points for minimalistic aero bottles. +A bicycle built for kids' first off-road experiences with durable frame, 16-inch wheels, and wide knobs for safe traction. +A commuter with integrated lockable top tube, quick-release rear rack, puncture-proof tires, and reflective lacing along the rims for night rides. +A commuting city bike with vertical step-through, integrated phone mount, puncture-resistant tires and a small front basket. +Touring bicycle with triple-butted steel tubing, long chainstays, integrated rack mounts, and a supple ride quality for loaded trips. +An urban cargo e-trike with enclosed weatherproof box, hydraulic brakes, mid-drive motor and step-through access for delivery work. +Cargo-carrying utility bicycle with reinforced rear rack, dual-leg kickstand, reflectors and puncture-proof tires for heavy loads +Touring gravel bike with frame-mounted sleeping bag straps, triple chainring adaptability and extra bottle bosses. +City cargo trike with insulated box, electric assist, hydraulic brakes and heavy-duty mounting points for regular deliveries. +Kids' trail bike with easy-shift 7-speed, front suspension, and protective chain guard. +Retro road bike with fenders, leather grips, polished chrome rack and weathered paint for vintage character. +Gravel endurance aluminum frame with carbon fork, 700x42 tires, flared bars and relaxed geometry for long days on rough roads +A cyclocross bike with tubular tires, chainring protection, and a textured matte finish to hide mud splatter. +Folding travel bike with compact folded size, robust hinge lock and an ergonomic saddle for multi-modal commuting. +Electric-cargo longtail with adjustable kid seats, mid-motor drive, integrated lights, and an extended rear platform for family use. +Classic beach cruiser with pastel pink paint, chrome accents and wide, cushioned saddle for comfort rides. +A cyclocross race bicycle with clearance for mud, short-chainstays for quick handling, cantilever or disc brakes, and aggressive treaded tires. +Mountain bike with internal cable routing, dropper post, 12-speed cassette and wide flat pedals for aggressive trail riding +E-MTB with trail-tuned motor, responsive suspension, dropper post and grippy 2.6" tires for technical singletrack. +Mountain trail bike with rowdy geometry, tubeless setup, and 30mm inner width rims for stability. +A rowdy dirt jumper with 26-inch wheels, single-speed freewheel, and reinforced gusseting on the top tube. +A lightweight steel cross-country race frame with tapered headtube, internal routing, and careful tube profiling to spare grams and improve stiffness. +Downcountry hardtail with 120mm travel fork, wide-range cassette, and fast-rolling 29-inch tires. +A kids' pedal bike with coaster brake, training-wheel mounts, colorful safety reflectors, and cushioned saddle for first big trips. +Urban cargo e-bike with midtail platform, dual batteries, and integrated lighting for heavy-duty daily use. +Youth BMX with sturdy chromoly frame, sealed bearing hubs, and wide platform pedals built for park confidence. +A gravel bike with mixed-mount racks, ultra-wide flared bars, 650b wheels with 47mm tires, and a removable front cargo block. +Folding electric bike with quick-release wheels and integrated carrying handle for last-mile travel. +A gravel endurance machine with roomy wheel clearance, shock-absorbing seatpost, and bar-end shifters for tactile gear changes. +A mountain bike with dedicated dropper lever, 150mm fork, and medium-travel rear suspension for trail versatility and comfort. +Compact folding commuter with lightweight alloy frame, single-handed fold, and compact storage footprint for apartment life. +Gravel endurance bicycle with subtle camo paint, 2-bolt front derailleur clamp option and geometric compliance for long days. +A full-suspension downhill bike with reinforced brakes, long-travel fork, and a slack geometry that inspires confidence at speed. +A drop-bar commuter with upright, endurance geometry, fender mounts, and reflective decals for safe nighttime rides. +A boutique single-speed commuter with polished aluminum frame, leather saddle and hand-painted pinstriping for artisanal flair. +Urban commuter with integrated smartphone mount, low-maintenance hub, and puncture guard tires for daily reliability. +A high-performance carbon race bicycle with electronic shifting, integrated power meter crankset, and ceramic bearings for marginal gains. +Classic Dutch upright utility bike with chainguard, fully enclosed hub, coaster brake, skirt guard, and a tall, comfy saddle for errands. +Gravel plus touring bike with wide clearance, alloy braze-on points, and a muted green paint that ages gracefully in the sun. +A modern commuter with integrated frame-lock, foldable rear rack, and an internal gear hub that shifts when stationary for traffic lights. +Track bike with carbon aero frame, fixed gear drivetrain, deep carbon tri-spoke rear wheel and no brakes +A mountain bike with mullet wheel setup, 170mm front travel, and a geometry aimed at big-hit confidence and technical stability. +Vintage city bicycle with coaster hub, fenders, chain guard and classic step-through styling in pastel yellow +A cruiser bike with a retro vinyl seat, simple coaster brake, and oversized balloon tires for cushioned, easy cruising. +Road criterium bike with quick-steering geometry, shallow rims, and responsive chainset for corner-to-corner acceleration. +Gravel-friendly frameset with clearance for 700x50 tires, robust mounting points, and protective skid plates on the downtube. +A kids' mountain bike with front suspension, easy-to-reach brakes, a low stand-over height, and rugged tires for exploring dirt paths. +Handbuilt titanium frame with custom paint, flared top tube and drilled mounting points for racks. +A touring-ready steel bicycle with triple cargo racks, dynamo hub powering front and rear lights, and wide clearance tires. +Urban folding electric commuter with hidden battery, mid-drive motor, compact folded footprint and sturdy hinge for daily use. +Gravel bike setup for bikepacking with oversized framebag, 2.25-inch tires up front, sleeping pad straps and light mounts +A titanium commuter with subtle bead-blasted finish, silent belt drive, and a concealed rear hub motor for discreet urban assistance. +Commuter single-speed with matte powder coat, sealed bearing hubs, and a minimalist approach for low-fuss city travel. +A gravel-friendly versatile bike with modular rack mounts, quick-release skewer compatibility, and comfortable endurance geometry for long days. +A titanium road frame with subtle bead-blasted finish, smooth welds, and a comfy ride tuned for big day touring. +Mountain freeride with thick-walled tubing, gravity-appropriate geometry, 27.5-inch wheels, and massive braking rotors for heat management +A lightweight steel cyclocross frame with shaved dropout edges, internal cable routing, and seatpost clamp recessed for stealth aesthetics. +Gravel race frameset with stiff bottom bracket, compliant seat area, wireless shifting and generous tire clearance for multi-day events. +All-mountain bike with 160mm front travel, adjustable geometry chip, strong 2.4-inch tires and reinforced rims for big lines +A road aero bicycle with integrated cockpit, rear wheel fairing option, and paintwork designed to reduce visual wind drag. +A full-suspension enduro mountain bicycle with 160mm travel, coil shock, and mud-shedding frame finishes. +Gravel day-to-day commuter with fender mounts, reflective decals and a sturdy rear rack for grocery transport. +A commuter with single-sided rear rack, internal 11-gear hub, and long fenders to keep riders dry on rainy morning commutes. +A commuter with built-in anti-theft system, hub dynamo lights, and a belt drivetrain that needs almost no maintenance for urban life. +Road endurance bike with vibration-absorbing features, comfortable bars, and 32mm tire clearance for rough tarmac. +Electric folding cargo bike with rear box, throttle mode, foldable frame and easy battery removal for apartment storage +Road bike with electronic groupset, hollow carbon cranks, aero handlebars and sculpted seat tube for airflow control. +Mountain trail hardtail with modern slack geometry, 29" wheels, and wide tires to smooth out rough singletrack trails. +A retro-styled road bike with downtube shifters, chrome fork crown, leather saddle, and 32mm clincher tires for weekend cafe rides. +Gravel bike with mixed-mount boss system, titanium bolts, stealth black decals and 650b wheels for rougher tracks +A flat-bar gravel bike with commuter-friendly geometry, 650b wheels with 47mm tires, hydraulic disc brakes, and a relaxed cockpit. +A versatile all-road bike with 40mm tire clearance, comfortable geometry for long days, and rack- and fender-ready mounts for fully loaded touring. +A hardtail mountain bike with 29-inch wheels, 120mm air fork, SRAM NX 12-speed, and a dropper post for efficient cross-country climbs. +Road endurance machine with endurance geometry, vibration-absorbing seatpost, 32mm tires and relaxed headtube height for all-day rides +A single-speed urban commuter with matte gray frame, leather saddle and minimalist decals for understated city style. +All-road e-bike with torque sensor, low-step frame, and integrated lights for commuting and weekend adventure rides. +A commuter with puncture-resistant lining, rear seatpost-mounted battery for extra range, and a kickstand for frequent stops. +Gravel endurance frameset with comfort-focused geometry, generous tyre clearance, and mounting points for everything needed on tour. +Mountain downhill race frame with reinforced lower link and high-volume trunion-mount shock for durability. +A rigid singletrack mountain bike with slack geometry, 29" x 2.6 tires, and burly 35mm inner-width rims to handle rock gardens. +A cargo cargo-trike with electric assist, low flatbed, and modular rails for quick swaps between deliveries. +Compact fixed-gear commuter with custom anodized hubs, narrow tires, and minimalist frame decals for stealth city riding. +Gravel-stage bike with compact cockpit, stealth paint, tubeless-ready 700x40 tires and race-focused clearance for quick transitions. +A 29er plus hardtail for technical singletrack with 2.6" tires, wide rims, single-ring drivetrain, and a stealthy matte finish. +A classic lugged steel road frame with thin wall tubing, artisan paint, and polished chrome fork to accent the retro aesthetic. +A high-pivot enduro bike with progressive geometry, 165mm travel, aggressive chainstay design and a robust linkage to tame rough descents. +A gravel-ready exploration bike with three-bottle capacity, wide tire clearance, and a lower bottom bracket for planted stability on rough descents. +A classic Italian steel racer with thin tubing, downtube shifters, leather saddle, and hand-lugged joints polished to a metallic sheen. +Fixed-gear track conversion with narrow flare bars, polished crankset, and an intentionally stripped aesthetic for city riding. +A custom handbuilt frame with brazed-on headbadge, elegant paint fade, and polished lugwork that captures artisanal bikebuilding. +Touring bicycle with front lowrider racks, brazed-on pump pegs, and an array of mounting points for extra gear. +Gravel race frame with sleek cable routing, aggressive geometry, and lightweight carbon fork for punchy sprints. +Lightweight endurance road frame with elastomer inserts, comfortable geometry, and space for 32mm tires for rough-road comfort. +A commuter with frame-integrated lock, rear lights automatically turning on in low-light, and a weatherproof top-tube pouch for keys. +Long-distance touring bicycle with triple front chainrings, reinforced hubs, heavy-gauge spokes and a commodious handlebar bag for maps +A cyclocross model with flat-mount disc brakes, braid-ready carbon fork, and strategic mud channels to keep drivetrain clean in wet races. +City utility bike with step-through frame, low gearing and heavy-duty tires built for reliability over style. +A hand-painted chopper-style cruiser bicycle with extended rake fork, banana seat, and chrome sissy bar. +Cargo longtail bike with extended rear rack, passenger deck, and reinforced steel frame for heavy loads. +Compact urban folder with 16" wheels, simple folding latch, and a lightweight aluminum frame for daily transit. +A boutique gravel build with handcrafted leather saddle, engraved seatpost, and an intentionally imperfect hammered paint texture. +Road race aero bike with integrated bar/stem, hydraulic disc brakes, tubular wheels and aggressive geometry for club-level competition +A step-through electric urban bike with mid-mount motor, easy-access frame, and integrated rear lights on the rack. +Gravel bike with custom paintwork, leather-wrapped stem, tubeless 700x40 tires, and a practical tool pouch under the saddle +Road endurance build with disc brakes, vibration-dampening materials, and 32mm tires for stable long-distance performance. +A lightweight alloy gravel bike with flared drops, integrated GPS mount, tubeless-ready 700x40 tires and polished welds. +Urban single-speed with bullhorn bars, chrome accents, and a rubberized matte black powder coat. +A classic Dutch city bike with upright posture, chaincase, sturdy rear rack, wicker front basket and powder-blue enamel with chrome trim. +Gravel-adventure frameset with triple-bottle provision, mud-shedding clearances, and thick protective paint for off-grid durability. +A cyclocross alloy frame with stiff head tube, clearance for racing tires, and molded chainstay protector to reduce noise and wear. +Touring tandem with independent suspension seatposts, twin racks, and matching frame color for cohesion. +A cyclocross frame with top-tube pump peg, diagonal seatpost clamp, and mud-shedding seat tube shaping in a bright signal yellow. +Steel frame fixed-gear with horizontal dropouts, polished silver decals, and a minimalist leather saddle. +Single-speed commuter with coaster brake, cream-colored paint and basket on the front for groceries +Folding cargo e-bike with long deck, hydraulic disc brakes, and a removable cover to protect passengers from rain. +A modern gravel-adventure bike with a resilient paint finish, 700c x 42 tires, and a mountable front rack for longer trips. +Lightweight time trial frame with integrated hydration behind the seatpost and a sculpted tail for aero gains. +Performance triathlon bike with integrated computer mount, steep seat tube, fast aero headtube, and stiff BB for sustained power. +A lightweight touring steel frame with extra rack mounts, lugged construction, and a hand-painted map motif on the top tube commemorating favorite routes. +Touring bicycle with reinforced dropouts, wide gear range cassette, and tasteful map-inspired vinyl around the seat tube. +Gravel bike with dropper post, 44mm rubber, and stealth matte black paint for understated looks. +Cyclocross training bike with bar tape worn in, steel frame, and knobby 33mm tires for early-season practice. +Touring tandem with custom leather saddles, reinforced forks, and a matched gearing system for long, harmonious rides. +A fixed-gear commuter with clean top tube welds, matte finish, and hollow forgings for a sleek urban profile. +Mountain enduro with adjustable reach, 170mm front travel, and a durable chain guide for rowdy descents. +A kids' starter bike with low seat height, easy-brake levers, steel rims and cheerful yellow frame with smiley decals. +A gravel frame with integrated routing for lights, extra cable bosses, flared drop bars and a stealthy matte blue finish to match the trails. +A British-style folder with Sturmey-Archer hub and small folding frame, classic leather saddle, and compact wheel size for easy stowage. +A gravel endurance frame with long wheelbase, tire clearance to 45mm, integrated bento box and mount points for extended cycles. +Steel city bike with classic enamel paint, comfortable upright position, and integrated rear carrier for daily tasks. +Cargo electric tricycle with tilting front box, weatherproof canopy, and commercial-grade drivetrain. +A classic British postal-style bicycle with sturdy frame, large rear rack, and a bold red finish engineered to last through heavy deliveries. +A road endurance bike with slightly taller head tube, vibration-damping carbon layup, wider tire clearance, and relaxed geometry for comfort. +A mountain enduro bike with medium travel, adjustable geometry, and beefy triple-clamp fork for heavy downhill segments. +A full-suspension cross-country bike with 120mm rear travel, lightweight linkages, 29-inch wheels and gloss white finish with neon highlights. +A vintage-looking cruiser with leather grips, spring saddle, and a gently curved frame that invites slow scenic rides and conversation. +A gravel bike built for winter riding with stud-compatible tires, mudguards, internal cable routing and matte charcoal paint with white logos. +A touring-ready steel frame with integrated chaincase, high-volume tires, and triple-bolt rack mounts for long self-supported routes. +A sleek silver folding commuter with an internal gear hub, belt drive, and a secure latch that folds flat for apartment storage. +A race-tuned road bicycle with electronic group, ultralight wheelset, and a shaved-down finishing kit for climbing performance. +Compact commuter with step-through frame, full chaincase, heavy-duty kickstand and reflective sidewalls. +Lightweight aluminum gravel bike with carbon fork, thru-axles, and a thin translucent logo exposing brushed metal. +Folding cargo bike with cargo platform, locking hinge, and a quick fold that fits in compact storage. +A city cargo bike with modular children’s seat, integrated footrests, and a stable wide platform for multiple configurations. +Classic Dutch-style city bicycle with enclosed chaincase, upright riding position, three-speed hub, and sturdy rack for daily loads. +Gravel-allroad titanium frame with clearance for 2.0-inch tires, S&S couplers and multiple top-tube accessory mounts +A gravel racer with electronic shifting, tubeless wheelset, 700x32 tires and pearl-gray finish with thin orange stripes. +A single-speed mountain bike with boost spacing, tubeless-compatible rims, chunky 2.6" tires and extra chainstay protection for trail abuse. +A downhill race bike with long wheelbase, heavy-duty chain guide, and protective frame guards from rock strikes. +A commuter with low-maintenance hub gears, integrated lock, full fenders, and a comfortable upright geometry for slow commutes. +Urban commuter with shock-absorbing handlebar stem, puncture-resistant tires, and integrated light mounts for visibility. +A commuter with bamboo fenders, light integrated rack, and a joyful pastel paint job to brighten daily travels. +Touring frame with double eyelets on the fork, brazed-on fender supports, and a restful geometry for loaded freedom. +A full-suspension trail bike tuned for playful responsiveness, with lightweight components and a lively paint scheme to match its personality. +Aggressive cyclocross race rig with mud-shedding frame, SRAM Rival 1 groupset, and light but stiff wheelset. +Steel fixed-gear track-style bike with enamel paint, narrow saddle and toe-clip pedals for city rides +A high-performance gravel frame with a tapered head tube, hidden bottle mounts, and internal routing optimized for tidy cables and hoses. +Touring steel mixte with graceful curves, elegant lugwork, and multiple mounting points for racks and bottles. +Commuter with upright swept bars, integrated bell, retro fenders and a low-maintenance coaster brake setup. +Gravel adventure rig with large tire volume, protective frame gussets, and robust racks for expedition-grade touring gear +Mountain hardtail designed for cross-country racing with stiff carbon frame, 100mm fork, tubeless tires, and lightweight alloy wheels. +Urban single-speed with drop bars, polished steel frame, leather saddle and classic headbadge for style. +High-end road bike with full carbon weave, diagonal aero tubing, and electronic wireless shifting optimized for crits. +A mountain freeride bike with reinforced top tube gusset, durable bearings, and a beefy fork designed for repeated heavy impacts. +Fat bike with big balloon tyres, wide rims, and a low gear range for pedalable stability on dunes and snowy singletrack. +Bicycle built for bike polo with short wheelbase, strong frame, knobby tires and reinforced fork for ball handling +High-pivot downhill mountain bike with coil rear shock, 200mm travel fork, reinforced chainstays, and aggressive geometry. +Vintage road bike with period-correct downtube shifters, tubular rims, leather saddle and faded but dignified paintwork. +A gravel race machine with aggressive geometry, 1x electronic drivetrain, aero-optimized tubing and a stealthy matte black finish with gloss accents. +Electric commuter with belt drive, 7-speed hub and strong integrated frame lock for city security. +A city commuter with solar tail light, reflective paint strips, and a low-maintenance internally geared hub for busy weekday use. +Gravel racing bike with flared bars, 36mm tires, and a comfortable climbing ratio for mixed surfaces and long races. +A road endurance bike with relaxed geometry, clearance for 32mm tires, and a comfortable carbon fork. +A sleek commuter with an integrated lock system, hidden battery, and a comfortable upright position for leisurely urban rides. +Folding commuter with heavy-duty hinge, reinforced rear triangle and integrated carry handle for transit commuting. +Classic touring steel bike with brazed eyelets, genuine leather saddle, heavy-duty spokes and a clockwork-like reliability. +Track fixed-gear with minimalist decals, polished chrome accents and a single-speed drivetrain for pure mechanical simplicity. +Cargo pedelec with enclosed chaincase, electric assist, and large wooden box for deliveries. +Track sprint special with oversized seattube diameter, stiff bottom bracket, and bright team-colored livery. +Folding electric commuter with integrated front basket, quick fold mechanism, and low step-over for ease of use. +Race-ready carbon aero road bike with integrated hydration, deep-section rimset and a 12-speed cassette. +Electric folding commuter with wide rubber tires, compact battery and an easy-to-operate folding mechanism for daily convenience. +Classic BMX with period-correct steel frame, single-speed freewheel, and stout welds for consistent skatepark use. +Racing triathlon frame with integrated hydration bladders, aero handling, and a lightweight monocoque carbon layup for speed seekers. +Adventure gravel bike with stealth paint, top tube bag mount, and compatibility for full-coverage bikepacking luggage. +A touring bike with triply reinforced head tube, long wheelbase, and triple bottle mounts to support lengthy unsupported rides. +Old-school steel road bike with ornate head tube badge, toe-clip crankset, and supple ride for classic Sunday spins. +Minimal commuter with coaster brake, classic saddle and durable steel frame built for low-maintenance ownership. +Urban single-speed with coaster brake, mint green frame, and ivory grips for nostalgic appeal. +Road aero single with blade fork, aero seatpost clamp, disc brakes, and 28mm tire clearance for fast events +A bmx-street bike with reinforced top tube, gyroscope steering, pegs, and a matte black powder coat for gritty urban shredding. +A carbon endurance road bicycle with compliance in the seatstays, 32mm tire clearance, and endurance fit for long gran fondos. +A touring bike with wide gear range triple crank, handmade leather saddle, two full panniers and deep maroon enamel finish. +Gravel bike with camo paintjob, 42mm semi-slick tires and chain-guide for rough mixed terrain. +Mountain downhill frame with reinforced pivots, dual-crown fork compatibility, and extra clearance for large-travel tires. +Urban step-through e-bike with rear hub motor, integrated lights, and a comfy saddle for shopping and Sunday coffee rides. +Gravel endurance bike with titanium tubes, low-gear options, long wheelbase and comfortable contact points for multi-day rides +Dirt jumper with reinforced headtube, short chainstay, thick tires, and a durable frame paint for park abuse. +Electric cargo longtail with quiet mid-drive, swappable batteries, and integrated lights that change color with mode. +A gravel racer with aero elements, integrated stem, and a slightly extended chainstay to maintain traction when pushing power out of corners. +A cyclocross bike with robust cable routing, strong fork crown reinforcement, and plenty of tire clearance for season-long durability. +Lightweight urban folding bike with carbon seatpost, quick-fold frame, and integrated carry strap for daily commuters. +Tandem recumbent trike with two comfortable reclined seats, storage compartment behind the seats, and low rolling resistance wheels. +Performance gravel bike with tapered headtube, integrated GPS mounting, and an efficient 1x12 drivetrain. +A fat-tire bike with 4.0-inch rubber, wide alloy rims, and simplified single-ring drivetrain for snowy and sandy conditions. +A downhill race sled with massive rotors, coil shock tuned for big hits, ADD-ON protection plates, and a low standover for technical sections. +A compact cargo bike with front-loading box, hydraulic disc brakes, e-assist and a robust matte black finish with yellow safety stripes. +A welded aluminum trail hardtail with crisp geometry, 120mm travel fork, and grippy 2.3-inch tires for fast flow trails. +Gravel racer with alloy frame, efficient wheelset and a crisp shifting 2x11 drivetrain optimized for mixed-surface races. +A commuter with full-length integrated mudguards, cargo-ready rear rack, and a lockable frame compartment for valuables. +Vintage touring steel bike with S&S couplers for easy travel, leather handlebar wrap, and a wide double-crank for steep climbs. +A cargo bicycle with front-loading box, foldable seat for passengers, and reflective trim to keep the load visible at night. +A tandem recumbent with two-chain system, low-slung frame, aerodynamic fairing and pannier racks for long-distance comfort. +A mountain hardtail with light alloy frame, 12-speed drivetrain, and durable hubset optimized for consistent weekend trail use. +Compact folding bicycle with 20-inch wheels, simple 7-speed internal hub, quick-release hinge, and integrated carry handle. +Cargo e-bike with robust rear axle, multi-height rear deck, and integrated bell and horn for urban maneuvers. +Gravel bike with painted geometric pattern, 650b compatibility and reinforced brake bridges for heavy use. +A performance mountain bike with mixed-carbon layup, electronic suspension remote, and SRAM Eagle 12-speed drivetrain. +A compact folding bike with 16-inch wheels, locking hinge, and lightweight alloy frame for commuters who combine cycling and public transit. +Urban folding cargo bike with durable hinge, integrated lights, modular storage and powerful brakes for urban logistics. +Road endurance bike with disc-ready frame, comfortable cockpit, and subtle gloss lacquer with micro metallic fleck. +A classic steel commuter with three-speed hub, chaincase, and softly curved fork crown for an approachable and timeless daily ride. +Classic touring double-butted steel frame with triple bottle bosses, low-cadence touring gear and quill stem for comfort +Steel gravel bike with ornate lugwork, custom paint, wide tire clearance, and neatly routed cables through the frame. +Electric mountain bike with powerful battery, robust frame mounts, and motor cutout for technical sections. +A compact folding commuter with variable seatpost, secure latch system, and small 16-inch wheels to tempt city workers who need portability. +A gravel grinder with mixed-wheel setup: 700c front and 650b rear, clearance for 45mm tires, low gearing, and a vibration-damping carbon fork. +Touring e-bike with mid-drive motor, reinforced rack, integrated lights, and long-range battery to support loaded travel. +Commuter with enclosed chaincase, internal hub, integrated rear light, and durable skirt guard. +Electric commuter with low-maintenance belt drive, automatic shifting, integrated lights, and a responsive pedal-assist system. +A gravel-specific alloy frame with triple bolt-on bottle mounts, internal cable guides, chainstay protection and rugged powdercoat for frequent use. +Commuter hybrid with front-month shock, reliable chaincase, LED headlamp, and pannier-ready rack for grocery or work commutes +A race-ready cyclocross bicycle with full carbon fork, fast-recovering geometry, tubeless tubulars and a stealth anodized finish for muddy circuits. +Dual-suspension trail bike with progressive leverage rate, 150mm travel and reliable 12-speed shifting for long descents. +A commuter with integrated USB-charging taillight, low-step frame, and puncture-reducing tire liners for city reliability. +A utilitarian farm bike with steel frame, heavy-duty rack, and thick rubber tires for rough rural roads. +A city utility bike with cargo platform over the front wheel, extra-low standover height, and bold reflective graphics. +Durable city bike with enclosed chaincase, low-maintenance hub gears, reflective piping and wide comfortable saddle for errands. +Steel fixed-gear with straight-piped aesthetics, minimal decals, and polished silver headset for a clean look. +Mountain enduro rig with slacker headtube, long travel fork, dropper post, and broad handlebars for aggressive riding. +A lightweight titanium touring frame designed for loaded travel with rack mounts and large-diameter tubing to resist flex. +A restored steel classic with original decals, chrome fork, and brown leather saddle that ages gracefully with use. +Touring steel frame with careful brazing, triple chainrings, and a practical low-trail geometry for loaded stability. +Classic ladies step-through bike with floral decal, cream saddle, low-step frame and easy single-speed hub for relaxed rides. +A BMX street build with a gyro system, aluminum rims, and platform pedals that withstand daily sessions at the skatepark and public plazas. +Youth mountain bike with sturdy frame, simple twist shifters, front suspension and wide knobby tires for trail confidence. +A city cruiser with swept-back bars, wide leather saddle, and a wicker basket to ferry small purchases in comfort and style. +A performance commuter with integrated rear light, reflective sidewalls, puncture-sealant tires, and a comfortable gel saddle. +A commuter with integrated hidden lights, belt drive, internal hub gearing and satin graphite finish with reflective microdots for safety. +A polished steel cyclocross frame with classic lugwork, thumb shifters and a comfortable wheelbase for mixed-weather training. +Full-suspension enduro mountain bike with 170mm travel, coil shock, 27.5+ tires and burly aluminum frame. +A gravel race bike with shallow-section aerodynamic rims, power meter integrated into the crankset, and a race-ready geometry for fast events. +Cyclocross race frameset with carbon layup tuned for compliance, quick handling and mud-friendly tubing shapes. +Steel framed cross-country bike with classic curves, modern thru-axles, and discreet gusseting for added rigidity. +Gravel endurance frame with integrated mudflaps, stealth rear rack mounts, and generous tire clearance for exploratory rides. +Gravel bike with clearance for 45mm tires, flared drop bars, rack mounts and a subtle camo paint fade. +Fixed-gear single-speed with colorful geometric paint, deep-v rims, platform pedals and rear brake for safety +Cyclocross race machine with tubeless-ready rimset, clearance for sweat-inducing mud and a discreet team decal. +A gravel racing rig with aero-focused tubing, integrated GPS mount, and a geometry that encourages a low, powerful position on long mixed surfaces. +A mountain e-bike with burlier wheels, 170mm rear travel, torque-sensing motor and a long-range battery for all-day backcountry rides. +A kids' balance bike with bright primary colors, safe rounded frame edges, and a seat that adjusts as little legs grow stronger and more confident. +Road aero machine with integrated electronics, a shallow front rim and a carbon cockpit that hides all cables. +A modern gravel racer with adjustable geometry inserts, shallow carbon fork, and an aggressive 45mm tire platform. +Gravel adventure plus with fat 27.5+ rear wheel, 700c front, and flexible tire options for exploratory rides. +Handmade steel touring frame with replaceable derailleur hanger, integrated fender mounts, and traditional brazing. +A retro BMX freestyle bike with chromed steel, short wheelbase, and a soft-backed nylon seat to make tricks more forgiving on the landing. +A restored vintage, period-correct city bicycle with painted fenders, brass bell, and leather saddle renewed with real traditional stitching. +A gravel touring bicycle with welded-on rack mounts, triple bottle bosses, and a protective powdercoat for long exposure to elements. +A stripped-down skinnified road bike with minimal paint, drilled parts, and a concentration on light weight for hill-focused events. +Endurance road bike with long wheelbase, vibration-damping seatpost and a comfortable, endurance-oriented cockpit. +Gravel bike with stealthy matte charcoal finish, wireless shifting, carbon cockpit and wide 700x44 gravel tires +A restored classic track bike with pillowed chrome, tubular wheels, and a narrow pursuit bar for velodrome sprints. +Touring folder with telescoping handlebars, integrated luggage clip, and a matte desert tan finish. +A vintage track frame repurposed for fixed-gear street riding with polished chrome parts, narrow tires and a minimalist saddle. +Performance gravel racer with carbon fork, electronic 12-speed groupset and tuned tubeless pressure for speed and comfort. +Urban cargo bike with foldable benches, secure cargo straps, and an extra-low frame for easy passenger loading. +A titanium gravel bike with understated polish, wide tire clearance, belt-drive compatibility and multiple threaded rack mounts. +Road aero time-trial bike with a triathlon cockpit, integrated hydration, and disc wheel compatibility for speed. +Race-ready road bike with 700c tubulars, integrated stem/handlebar combo, ceramic bearings, and a lightweight carbon fork. +A gravel race frame with asymmetric chainstay, impactful paint fade, 700x38 tubeless tires and an emphasis on stiffness-to-weight balance for aggressive riding. +A commuter with padded saddle, tall lace-up handlebar grips, and an internal wiring harness for a tidy look and easy maintenance. +A commuter with integrated LED taillight in the seatpost, puncture-resistant tires, semihidden rack mounts, and a pearl gray finish. +A race-ready fixed-gear track bicycle with aerodynamic, shallow-profile wheels and a welded aluminum frame designed for velodrome sprints. +A vintage track bicycle restored with modern sealed bearings, period-correct crankset, and tubular tires for velodrome evenings. +Vintage fixed-gear conversion with slim steel tubing, polished lugs, leather saddle and narrow tubular tires for weekend crits +Cyclocross race-ready bike with ovalized chainstays, lightweight seatpost, and gritty matte burgundy paint. +Mountain enduro bike with adjustable idler pulley, robust hardware, and a stealth matte paint that repels scratches. +Track sprint bike with disc chainring, carbon fiber fork, high-flange hubs and a very short wheelbase +Mountain hardtail with tapered headtube, boost spacing, and tubeless-ready rims for rough singletrack. +A compact speed-focused road bike with full carbon fork, narrow 25mm tires, and a crisp responsive aluminum frame for criterium sprints. +City utility bike with integrated LED headlight, rear dyno hub, chaincase and built-in pannier hooks. +Urban cargo electric bike with low-step deck, mid-drive assist and an adjustable cargo tie-down system for varying loads. +A city e-cargo bike with low-step frame, passive suspension on the box, and bright safety-yellow paint. +A commuter with wide tires and disc brakes, integrated rear rack, and built-in security loop for locking to urban fixtures. +Road endurance bike with compliance-focused seatpost and subtle graphics that mimic rolling hills. +A little cruiser for kids with training wheels, chrome fenders, colorful streamers and a small bell for neighborhood rides. +Electric mountain bike with big-battery and durable frame protection, designed for long uphill assists and spirited descents. +A drop-bar gravel bike with flared bars, bikepacking framebag, dynamo hub for lights, and reinforced fork mounts for racks. +City commuter with internal gear hub, enclosed chaincase, front basket, and an elegant brushed metallic paint. +A gravel machine with integrated top-tube bag, double-sided bottle bosses, and a robust fork to mount extra gear for long routes. +A classic cruiser with two-tone paint, manual coaster brake, wide balloon tires and a vintage-style chain guard for a throwback look. +A commuter with low maintenance shaft-drive, internal gear hub, puncture-proof tires and a simple, clean aesthetic. +A classic mixte step-through frame with wicker basket, leather straps, and a simple coaster-braked hub for relaxed urban rides. +Urban commuter with folding pedals, sturdy rack, and a quiet hub gearing for easy door-to-door transit. +Gravel bike with stealthy clear coat over brushed carbon, wide rubber and discreet rack mounts. +Touring tandem with elegant lugged-steel construction, matching paint, and long-chainstay geometry for stable multi-day rides. +Lightweight titanium road bike with racing geometry, 23mm tires, full internal cable routing, and brushed raw finish. +Vintage mixte with chrome accents, wicker basket, leather touchpoints and timeless cream paint for leisurely rides. +A vintage-inspired track bike with steel fork, classic chrome finish, and narrow 23mm tubular tires for smooth velodrome runs. +A BMX race bike with light alloy frame, sealed hubs, narrow knobby tires and a race-ready cockpit for tight tracks. +Mountain freeride hardtail with reinforced headtube, slack geometry, and short stem for trick-friendly handling. +A lightweight titanium commuter with discreet reflective accents, high-flange hubs, and an effortlessly smooth ride that resists corrosion. +A steel frame cyclocross build with hand-filed lugwork, mud-clearance dropouts and a classic gumwall tire profile. +Folding utility bike with cargo platform, quick-release pedals and a foldable handlebar for compact storage. +Cyclocross carbon frameset with asymmetric chainstay reinforcement, ample mud clearance and race-focused cable routing. +A gravel e-bike with range-focused battery, torque-sensing motor, and robust tires for long unsupported rides across varied terrain. +A modern full-suspension trail hunt with 150mm travel, adjustable geometry headset, and integrated chain guide to keep the chain quiet and secure. +A custom framebag-ready bikepacking bicycle with low gearing, S&S couplers, wide tires and multiple framebag anchor points for multi-day trips. +Road time-trial build with long tail profile, internal hydration and a stiff, race-oriented bottom bracket. +A classic city fixer-upper with swept bars, coaster brake hub, wooden basket and pastel turquoise paint reminiscent of mid-century design. +Time trial-oriented aero bike with integrated hydration, disc brake rear wheel, and steep seat angle for aerodynamic posture. +A modern trail hardtail with wide bars, short stem, and a life-long frame warranty, ready for fast singletrack loops and enduro laps. +Urban fixed with bullhorn bars, minimal fenders, and a single-speed conversion for simple city use. +A commuter with wide upright bars, suspension seatpost, puncture-resistant kicks, and a low center of gravity for comfort and stability. +A commuter with quiet hub motor, integrated rear rack, wide puncture-resistant tires and a simple, elegant silhouette for city rides. +Electric mountain enduro with dropper post, robust battery plate and torque-optimized motor for high-altitude climbs. +Electric cargo van-style bicycle with enclosed weatherproof box and integrated child seating options. +A classic mixte frame city bicycle with step-through top tube, wicker basket, and caliper brakes for effortless urban style. +Cyclocross competition bike with carbon frame, refined mud-shedding shaping, and quick-change wheels for race-day agility +All-road adventure with 650b compatibility, long-travel fork, and integrated fender mounts for wet-weather versatility. +A boutique titanium gravel frame with brushed finish, discreet framebag mounts, internal routing and hand-engraved head badge. +Racing mountain bike with light alloy frame, race-focused suspension tune, and narrow cockpit for efficient pedaling. +A BMX race frame with optimized tube diameters, reinforced gussets, and a narrow bottom bracket for tight cornering on the track. +A lightweight carbon gravel frame with flared drop bars, 700c wheels, tan wall tires and metallic forest-green finish. +Cargo tricycle with two-front boxes, heavy-gauge frame, and ergonomically shaped bench to carry produce or pets. +A fixed-gear track-inspired commuter with riser bars, single cog, utility fender mounts, and reflective sidewall tires for city nights. +Light gravel bike with carbon seat tube, alloy chainstays for durability, and a pearlescent paint finish. +Lean track fixed-gear with aero seatpost, polished fluted chainring and custom hand-applied pinstriping. +Urban commuter with integrated phone charger in the head tube, quiet drive system, and full fender set for convenience. +A gravel race machine with ergonomic bar flare, tubeless wheels, and an optimized standover height for technical cornering. +Road bike with classic geometry, steel frame, down-tube shifters, and polished chrome fork crown reminiscent of era aesthetics. +Gravel fatbike hybrid with 27.5+ plus setup, 2.8" tyres and wide rims for extra traction. +Touring bicycle with integrated GPS cradle, dyno hub lighting, and a thick rear rack for multi-day loads. +A randonneur-ready steel frame with long wheelbase, generator lighting, and fiber-glass-enforced forks to smooth out very long rides. +Steel randonneur bicycle with double-butted tubing, dynamo headlamp, triple cages, and long-reach caliper brakes for comfort. +A heritage steel road bicycle with ornate headbadge, polished lugs, and period-correct accessories for classic club group rides. +A gravel explorer with 650b+ wheelset, big knobbed tires, low gearing, and integrated framepacking solutions for remote backcountry runs. +Gravel-focused bike with a sloping top tube, flared drop bars, and CNC-machined bottle bosses for accessories. +Modern city bike with 1x drivetrain, hydraulic disc brakes, internal cable routing, and discreet battery pack for daily reliability. +Lightweight everyday road bike with sensitive compliance, 25mm tires, and a wheelset tuned for quick acceleration. +A touring bike with Reynolds 853 steel tubing, triple chainring for steep climbs, dynamo lighting, and reinforced low-profile racks for heavy luggage. +A classic city crosser with full fenders, upright bars, basket on the front, and a cozy bench-style saddle for casual errand looping. +Cyclocross bicycle with cantilever brakes, 35mm tubeless tires, clearance for mud, and a rugged, race-ready carbon frame. +Vintage-style Dutch city bicycle with upright bars, chainguard, built-in lock and steady kickstand for everyday errands +A city mixte with sweeping frame, vintage-style headlamp, chainguard, and comfortable upright bars for commuting in style. +A lightweight triathlon bike with race-oriented geometry, integrated hydration, and matte black with minimal sponsor decals. +A gravel racer with flared bars, 2.0–2.2-inch tires on 650b rims, and a lower BB for confident cornering on fast tracks. +A gravel racer with asymmetric carbon layup, wide 700x42 tires, electronic wireless shifting, and a purposeful paint pattern indicating speed orientation. +A classic mixte cruiser adapted with a modern hub-drive e-kit, retaining step-through ease and adding pedal assistance. +A commuter with drop bars converted for city use, fender mounts, dynamo-powered headlight and a comfortable upright posture. +A gravel rig with multi-stance stem, flared bars, and tire clearance designed for 2.2" rubber to make rough roads manageable. +A bikepacking-ready hardtail with bag harnesses on the top tube and fork, dropper post, 29+ tires and a flat khaki paint scheme. +Mountain trail full-suspension with progressive kinematics, wide tubeless-ready rims, and an impact-resistant lower tube guard. +Track sprinter with solid disc rear, shallow front rim, and an ultralight frame optimized for velodrome acceleration. +Touring tandem with elegantly brazed steel, matching components, and plenty of toe clearance for long, comfortable rides two-up. +Road endurance build with larger head tube, stable handling, 32mm tires, and a pillowy seatpost for all-day comfort. +A gravel adventure bike with sand-colored paint, multiple frame mounts, long-range gearing and an internal storage bottle under the downtube. +Electric folding cargo bike with dual-mode assist, large-capacity deck and a low-variance fold for easy transport. +A touring bike with reinforced frame, low gear ratio triple, stainless racks, full fenders and deep forest green lacquer. +A mountain trail bicycle with 140mm travel, burly 30mm-width rims, and long 460mm chainstays for stable descending. +Track time trial bike with monocoque carbon shell, integrated seat clamp, and a matte finish with subtle gloss accents. +Gravel adventure plus with flexible tire compatibility, reinforced fork legs for racks, and understated matte finish with a pop of color. +Recumbent trike with low seat, step-through access, three wheels for stability, and a comfortable reclined position for long outings. +A modern cyclocross machine with disc brakes, 1x drivetrain, tubeless gravel tires and asymmetric chainstay for chainline strength. +Gravel race bike with aggressive rolling geometry, 32–36mm tire compatibility, and fast gearing. +A commuter with a single-sided fork, integrated fender, and low-maintenance shaft drive for a clean, futuristic look. +A gravel racer painted moss green with stealth decals, tubeless 700c rims, and a 1x12 drivetrain for simplicity on mixed surfaces. +A performance road frame with carbon layup, aero seatpost, hidden cable routing and a special edition metallic flake finish. +Beach cruiser with two-tone paint, spring saddle, chrome spoke covers and coaster brake for nostalgic Sunday rides +Gravel race frameset with modern aero touches, 700x38mm tyre clearance, and discreet electronic shifting cable ports. +Cargo longtail with dual child harnesses, reinforced frame rails, modular seating and hydraulic disc brakes for secure family shuttling +A high-end track sprinter with aero-optimized tubing, stiff headtube area, and a minimal saddle to help the rider reach top speed. +A gravel-friendly adventure bike with double-fork mounts, 650b tires, epoxy-coated frame for water resistance, and stealthy black bolts. +Mountain bike with mixed-wheel setup, burly tyres, and gravity-assisted geometry for rapid descent stability. +Gravel commuter with puncture-resistant tires, rear rack, integrated light mount, and practical fender systems for daily riding. +A race-ready road bicycle with a 58-tooth big cog option, ultra-narrow tubulars, and an aero-optimized fork for critical race seconds. +A steel cyclocross frame with a classic matte finish, guarded head tube, and a geometry that balances running and riding efficiency. +Race-ready cyclocross bike with tubeless-ready rims, quick-release wheel skewers, and short top tube for nimbleness. +Gravel e-bike with integrated light strips, flared drop bars, and durable alloy rims for mixed-surface touring. +Compact urban commutermate with 20-inch wheels, step-through hinge, and a rear parcel rack for quick errands and transit. +City utility bike with step-through frame, built-in lock, integrated front basket, and puncture-resistant tires. +A downhill race frame with carbon links, long-travel suspension, and integrated protection plates, ready for race-team componentry. +A high-volume fat-bike with 5-inch tires, custom decals, reinforced rims and wide handlebars for floatation over soft surfaces. +Touring bike with disc brakes, heavy-duty double-butted tubing, multiple rack mounts and reflective rim strips for night travel. +A compact urban folder with 16" wheels, folding handlebars, quick-release seatpost and a carry handle in the frame. +A boutique titanium gravel frame with subtle bead-blasted finish, integrated fender mounts, and a lifetime corrosion resistance selling point. +Suspension-corrected gravel bike with flex stays, 650b-compatible clearance and modern disc brakes for rough roads. +A gravel tilting frame with integrated GPS mount, top tube pump mount, and internal battery cavity for overnight expeditions. +A city single-speed cruiser with matte teal paint, white grips, small front basket, and a classic coaster brake for simplicity. +A commuter with integrated smartphone charging, step-through frame, quiet belt drive and puncture-resistant tires for everyday reliability. +A longtail cargo bike with kids' bench seating, integrated three-point harness points, and shock-absorbing rear leaves for smoother family rides. +Kids' balance bicycle with low-step frame, bright reflectors, and a simple, safe build to teach steering and balance. +Lightweight urban fixed-gear with matte finish, deep saddle, and sealed-bearing wheel hubs. +Time trail bike with integrated storage in the downtube, ceramic bearings and an ultra-low drag profile. +Cyclocross frame with welded mounts for a chain catcher, large mud clearance, and painted camo-style colorway for style. +A cyclocross training bike with durable tire clearance, disc brakes, flared hood shape and a comfortable upright stance for repeated practice sessions. +A modern cyclocross bike with thru-axles front and rear, reliable disc rotors and a bold anodized fork crown for stiffness. +Smooth commuting e-cargo with low step-in frame, integrated lights, and weatherproof panniers. +Track fixed-gear with high flange hubs, mirror polish frame, and narrow aero saddle for cleaner rides. +A commuter with integrated smartphone cradle on the stem, dynamo-powered lights, and an easily adjustable kickstand. +Classic leather-seated cruiser with pastel mint paint, wicker basket front and spring-loaded saddle for comfort. +A classic Dutch bicycle with upright posture, full chain guard, rear hub with low-maintenance braking and a comfortable leatherette saddle. +A cyclocross training bike with knobby tires, higher bottom bracket, and durable alloy frame for muddy winter intervals. +A vintage single-speed with classic chrome, narrow tires, polished crankset and a simple, timeless silhouette for city streets. +A low-maintenance commuter with internally geared 8-speed hub, belt drive, and wide urban tires for durable year-round transit. +Adventure cargo bike with low-loading platform, hydraulic brakes, electric assist and weatherproof panniers for dependable deliveries. +A vintage-style fixed-gear with brushed steel frame, leather wrapped bar tape, and polished aluminum wheels for clean, timeless city rides. +A cyclocross racer with tapered steerer, disc brakes, 33mm mud tires and matte gravel-gray paint with high-contrast white logos. +Gravel commuter in stealth matte with reflective sidewalls, integrated light mounts and hidden security features. +E-MTB with dropper post, stage-tuned suspension, motor cutoffs, and reinforced drivetrain for steep climbs +Triathlon specific frameset with integrated hydration, steep head angle, aero tube shaping and race-oriented cockpit. +A minimalist fixed-gear with glossy red paint, narrow 23mm tires and clamp-on handlebars for close-quarters urban speed. +A city utility bike with upright trekking bars, cushioned seat, multiple cargo loops, and puncture-resistant compound tyres for reliability. +A classic steel fixed-gear single-speed with deep-polished track hubs, narrow profile, and minimalist brake setup for urban track style. +Vintage cruiser with tongue-style saddle, chrome accents, balloon tires and a show-quality flake finish for leisurely rides. +Gravel adventure convertible with removable fenders, adjustable stem and frame accessory bosses for adaptability. +Track sprint frame with tall head tube, short wheelbase, and mirror-polished steel paint for heat-sparkle effect. +Gravel endurance bike with cushioned top tube, micro-suspension seatpost, and long-distance comfort geometry. +Gravel frameset with brushed aluminum finish, flared drops, and a generous front triangle for gear integration. +A titanium touring frame with low-maintenance sealed pivots for a modular rear rack system, and internal routing for rear lighting. +Gravel all-mountain rig with burly 2.2-inch tires, dropper post and robust rims for technical singletrack. +Gravel bike with whimsical lime green paint, tubeless 38mm tires, bar-end shifters, and a small frame pump mounted on the downtube. +Urban single-speed with polished chainring, narrow deep-section rims, and a matte powdercoated frame for subtle attitude. +A rugged all-terrain fatbike with front suspension option, 4.6" tires, and reinforced rims for pressure-tuned flotation over soft surfaces. +Touring city bicycle with sturdy rear rack, rainproof panniers, dynamo lighting, and a comfortable upright position for daily miles. +Race-ready road bike with ceramic bearings, electronic wireless shifting, and aero seatpost. +Gravel bike with custom paint, leather bar tape, and a 1x11 gruppo optimized for simplicity and reliability off-road. +A performance track bicycle with stability-focused head angle, stiff rear triangle, and aero-optimized chainstay shapes for track sprints. +Classic steel roadster with chrome fenders, rod brakes, sprung saddle, and a joyful vintage patina for casual rides. +BMX race bike with short 20-inch wheelbase, pegs removed, gyro-free front end and high-pivot rear plate +Gravel endurance carbon with compliance-focused seatpost, wide tire clearance and discreet mounting points for expedition gear. +A rugged cargo bike with longtail platform, passenger pegs, and low-center-of-gravity design to keep kids stable behind the rider. +Gravel touring rig with triple mounting points, sturdy rack mounts, low gearing and a utilitarian matte olive paint. +Road track-style single with flip-flop hub, fixed-ring setup, aero stem and deep-section rims for polished city crits +A city cargo bike with front-loading box, electric assist, heavy-duty tires and reflective safety red paint with large logo space. +Gravel adventure titanium bike with raw finish, discreet brazed bosses, wide tire capability and handcrafted leather touches for long journeys +A vintage-style coastal cruiser with wicker front basket, chrome headlamp, and spring saddle for a leisurely seaside ride. +City commuter with reflective striping on the frame, integrated lock and a simple three-speed internal hub for reliability. +A road bike with endurance geometry, tubeless-ready rims, and a carbon fork with vibration damped legs for smoother miles. +Adventure-ready gravel bike with titanium frame, multiple storage points, 650b plus compatibility, and a neutral raw finish. +Touring tandem bicycle with dual saddles, steel lugged frame, triple chainset, and matching panniers for two-up adventures. +Mountain bike hardtail with aggressive 29er geometry, 2.35" tires, and a tapered head tube for tough technical climbs and descents. +A classic mixte city bicycle with wicker basket, skirt guard, three-speed hub and pastel lavender finish with white pinstriping. +A modern gravel bike with integrated seatpost clamp, generous bottle cage clearance and slate-gray splatter paint. +Gravel all-road with measured compliance, 38mm rubber, and a cockpit set up for all-day comfort. +A trail bike with 29-inch wheels, aggressive geometry, and a stout alloy frame that handles rock gardens and bermed turns with aplomb. +A vintage road bicycle with restored chrome, new tires, and a modern compact crankset sympathetically installed to retain classic looks. +Lightweight track bike with aerodynamic tube shapes, smooth welds, deep rear rim, and minimal graphics for streamlined performance. +Folding electric cargo bike with modular front box, hydraulic brakes, and a reinforced frame for urban deliveries. +Modern commuter bike with internal gear hub, belt drive, dynamo front hub and matte gray finish. +A trail-hopping hardtail with 29" wheels, a reserve of small-bump compliance, and a geometry that balances speed and playful handling. +A mountain trail bike with tuned kinematics, internal chain slap protection, and a wide range cassette to handle technical climbs. +A commuter bike with integrated front and rear lights, full fenders, upright handlebars, chaincase and a practical rear rack for daily errands. +Mountain freeride bike with mid-travel frame, aggressive geometry, and reinforced hubs to withstand big landings and drops. +A racing tandem with integrated hydration for each rider, matched crank lengths, and narrow aero bars for time-trial speed. +Mountain hardtail with fast-rolling 29" wheels, lightweight alloy frame, and tapered steerer for responsive handling. +Lightweight carbon road frame with integrated seatpost and a sleek glossy finish intended for fast climbing and responsive handling. +A refined single-speed roadster with enamel paint, narrow racing saddle, and thin 23mm tires for crisp city pace. +Urban utility bike with integrated cargo platform, low-maintenance internal hub, chaincase and a stable upright ride for short hauls +Road aero race machine with deep carbon rims, stiff oversized bottom bracket, and ultra-fast shifting for sprint finishes +A utility trike with cargo bed, reclining seat, wide pedals and a low step-in frame for ergonomic deliveries. +Urban commuter with three-speed hub, chaincase, and swept-back bars for an easy upright posture. +Adventure tandem with long wheelbase, low-end gearing, and reinforced wheels for remote route carrying. +Track pursuit bike with stiff carbon layup, narrow geometry and a single-minded focus on lap time performance. +Gravel drop-bar bike with low bottom bracket, extra tire clearance, and brass head badge. +Classic step-through Dutch commuter with roller brake hub, chaincase and wicker basket for groceries. +A classic mixte with faded lacquer, wicker basket, and springer-style saddle for nostalgic city cruising. +A cyclocross-style commuter with slicks on a race frame, eyelets for fenders, and a shorter wheelbase for nimble city weaving. +Gravel racer with through-axle front and rear, flared drops, and dyno hub option for night-stage adventures. +Mountain cross-country full-suspension racer with rigid carbon links, 100mm travel optimized pedaling efficiency and race-ready wheelset +A vintage-inspired road bicycle with steel lugs, polished chrome fork, leather grips and a harmonious color palette for classic rides. +Gravel touring frame with multiple mounting points, sturdy dropouts and welded-on fender mounts for expedition readiness. +A commuter bike with a low center of gravity, front cargo basket, step-through frame and burgundy enamel with cream pinstripe. +A touring frameset with reinforced dropouts, triple-bolt chainstay mounts, and plenty of room for touring panniers and spares. +Urban commuter with full fenders, integrated rear rack, belt drive and a simple step-through frame for easy mounting. +Cyclocross-inspired gravel bike with 40mm tires, carbon fork, and flared drops for control on muddy descents. +A cyclocross endurance bike with disc brakes, wide clearance, compact crankset and a tuned compliant seatpost for rough courses. +Gravel bike equipped for self-supported rides with framebag, top-tube GPS mount, extra water capacity and durable tires +A compact BMX street bike with gyro rotor, cassette hub, reinforced 14mm axles, and crunchy grips for ledge tricks and grinds. +A classic Dutch-style upright bicycle with coaster brake, basket on the front, chrome racks and a baby-blue enamel finish. +A long-distance touring machine with oversized pannier bosses, multiple water mounts, and a durable 26" wheelset for remote reliability. +A steel framed track sprinter with minimal graphics, toothy tubular tires, and an old-school red enamel paint job. +A sleek carbon time trial bicycle with deep-section front wheel, full-width fairings, and a race-specific geometry for maximal aero advantage. +A rigid gravel bike with simple lines, steel fork, 38mm all-road tires, and a neutral stone-gray finish that hides dust and road salt. +BMX flatland rig with low top tube, flat handlebars, low gearing, and small 20" wheels for precise balance tricks. +Cyclocross commuter with versatile tire choice, simple shifting, and durable frame finish to withstand frequent washes. +Touring tandem bicycle with three bottle cages, low-slung frame, sturdy racks and matching disc brakes for two riders. +A commuter with step-through bamboo frame, belt drive, and integrated rear hub for a sustainable, quiet daily ride. +Fat-tire electric bike with powerful hub motor, frame-mounted battery, wide handlebar, and aggressive tread for sand and snow. +Gravel race bike with minimal paint, carbon fork, and beefy thru-axles for stiffness under load during competitive events. +Touring-ready bicycle with full fender set, dynamo lighting, three-bolt racks, and low gear ratios for loaded climbs. +Electric longtail cargo bike with twin passenger benches, integrated canopy attachments and a reinforced frame for safety. +A classic steel beach cruiser with sweeping frame lines, whitewall tires, chrome fenders and gleaming sky-blue enamel. +Compact recumbent with under-seat steering, aerodynamic shell and long chainline for smooth pedaling efficiency. +Handbuilt cyclocross frameset with straight-blade fork, thick mud-clearance dropouts, and subtle hammered lugs. +Commuter e-bike with integrated lights, smartphone mount, hydraulic disc brakes, and built-in theft alarm. +All-road titanium racer with clearance for 32mm tires, disc brakes, and a polished headbadge for understated performance. +Urban single-speed with polished chain, trumpet-style bell mount and a small integrated front basket for essentials. +Urban single-speed with narrow tires, comfortable upright stem, polished headset and minimalist branding. +A suburban cruiser with comfortable sprung saddle, large chainring guard, banana seat and vintage pastel pink lacquer. +A BMX flatland bike with shorter top tube, gyro steering system, and narrow tires tuned for spinning and technical moves. +A fat-tire snow cruiser with studded tires, wide aluminum rims, and a low-pressure-sprung saddle for winter trail fun. +Mountain trail bike with 140mm travel, dropper post, stout cranks, and a balanced geometry for long trail days. +Classic step-through Dutch bicycle with thick chaincase, three-speed hub, and cushioned leather saddle. +Gravel endurance bike with micro-adjustable seatpost, wide tyre clearance and a hand-painted logo on the top tube. +Road racing frameset with aerodynamic tube shaping, hidden cables, and a lightweight carbon layup for high-end competition. +A road race bike with power-meter crankset, disc brake aerodynamics, 28mm tires and a climbing-friendly mass optimization. +Fat-tire commuter with oversized tread, insulated grip tape and motor-assist option for snowy sidewalks. +Retro single-speed cruiser with banana seat, high-rise handlebars and bright orange paint for beach vibes +A 29-inch hardtail cross-country race bicycle with lightweight alloy frame, locking remote fork, and race-ready tubeless tires. +A titanium touring bike with smart storage mounts, wide-knife bottle bosses, and a baked-on stone finish that survives rough travel. +Mountain freeride bike with sturdy frame, long-travel fork, chain guide, and reinforced rims for bike park laps. +Urban commuter with step-through alloy frame, internal cabling, integrated lights, and disc brakes for confident stops. +Road race oriented with shallow rims, low stack height, and a stiff bottom bracket area for crits and fast group races. +Beach cruiser with low-slung frame, banana saddle, chrome chain guard and wide handlebars for laid-back shorelines +A cyclocross aluminum frame with large bearing headtube, mechanical disc brake mounts, 1x compatible drivetrain and an efficient, nimble ride. +A commuter with compact step-through, motor-assisted pedal, integrated lights, and a rear child seat mount for family duty. +A gravel race rig with aero seatpost, titanium bolts, and a 1x wide-range cassette for steep tactics and fast flats. +Tandem recumbent with long wheelbase, two recumbent seats, and pinion gearbox for low-maintenance multi-day cruising. +Track bike with polished steel frame, tubular tires and a high-gear ratio for racing the velodrome. +A gravel endurance frame with vibration-absorbing layup, clearance for 700x45 tires, and a relaxed geometry to ride all day in comfort. +A kids' balance bike with colorful frame, non-slip grips and a lightweight design to ease first-ride confidence. +A BMX street bike with chromed frame, sealed bearing hubs, gyro rotor and a reinforced two-piece crank for park and street durability. +A children's balance bicycle painted bright purple with wide handlebars, low footrest area and rubberized wheels for stable early rides. +A drop-bar urban grinder with flared bars, short chainstays, 650b wheels and large-volume tires for mixed surfaces. +A polished steel city bike with a comfortable upright saddle, straightforward three-speed hub, and a charming classic style that ages gracefully. +All-terrain folding bike with 20" fat tires, hinge-reinforced frame, and a sturdy handlebar stem for multi-surface commuting. +Gravel race bike with aero fork, flared drops, 35mm tires and electronic shifting tuned for fast mixed-surface events +Handmade lugged steel road bike with classic geometry, polished chrome accents and a smooth patina finish. +Road time-trial machine with custom skinsuit-matching paint, stealth cockpit and extreme aero tubing. +A custom-painted city single-speed with candy finish, bold white-wall tires, and chrome fenders that make a statement at every stoplight. +Recreational folding city bike with 16-inch wheels, upright handlebars, and a bright red finish for visibility on transit. +A dirt-track racing bike with narrow bars, no suspension, single-speed gearing, and steel frame geometry tuned for oval track handling. +A vintage cruiser restored to period with original badges, polished chrome fenders, and a leather spring saddle to match its era. +A road race machine tuned for sprint finishes with shallow-section wheels, stiff chainstays, compact crankset and crisp shifting. +Adventure expedition bike with reinforced fork, heavy-duty spokes, triple-bottle mounts and paint that hides scratches from long routes. +A minimalist single-speed cruiser with chrome fenders, low-slung frame, and springer fork for classic lowrider appeal. +A performance gravel bike with 700c wheels, 40mm rubber, flared bar drops, and hand-sprayed splatter green finish. +A racing criterium bicycle with ultralight carbon frame, short wheelbase, shallow section wheels, responsive handling, and an SRAM red mechanical groupset. +A downhill beast with 200mm travel, reinforced headtube, wide bars and fluorescent yellow paint for maximum visuals on the trail. +Gravel endurance bike with vibration-absorbing seatpost, wide handlebar sweep, and compatibility with large framebags for supported rides. +A folding electric commuter with mid-mount battery, throttle and pedal-assist modes, compact folded size and puncture-resistant tires. +Performance track bike with aerodynamic tubing, integrated stem, and a narrow saddle for sprinting. +A rugged mountain tourer with low gearing, racks, and wide 27.5+ tires to carry panniers across rough, remote mountain roads. +A carbon aero road bike with integrated hydration and stealth battery for race-prep reliability over long TT stages. +Touring tandem with Rohloff hub, dynamo lighting, and reinforced chainline for reliable cross-country tandem touring. +A lightweight aluminum road bike with shallow-section rims, tuned endurance geometry and micro-suspension seatpost for comfort. +A retro-inspired touring bicycle with polished lugwork, leather saddle, full chrome fenders and a long-life steel frame for multi-day touring. +A purpose-built bikepacking frame with tapered top tube, hidden map pocket, and titanium rack mount points. +Mountain freeride slayer with sturdy bash guard, heavy duty spokes and aggressive knobbed tires. +A lightweight cyclocross race bike with carbon fork, ceramic-infused bottom bracket bearings, and a supple tire selection for muddy races. +Urban cargo longtail with child seating pads, robust deck, and an integrated motor for heavy family errands in comfort. +A cargo e-bike with rear bench seat, adjustable footrests, and modular attachment points for various hauling needs. +A bikepacking-ready gravel bike with low slung frame bag, fork-mounted cages, and secure handlebar harness for shelter and sleeping gear. +Gravel pro-level with wireless shifting, aerodynamic tubing, and full-length internal cable routing for a clean, high-tech aesthetic. +Classic mixte step-through city bicycle with wicker basket, chrome mudguards and a low saddle for easy mounting +Cargo e-bike with front-loading cargo box, bench seating, and weatherproof canopy for family transport. +Fixed-gear urban commuter with subdued matte finish, fender mounts removed, and a stout chromoly fork. +Commuter with integrated rear rack, pannier-friendly clearance, and a soothing matte blue finish. +A trail-hardened mountain hardtail with 130mm front travel, 29-inch wheelset, tubeless setup and shadow-black finish with lime highlights. +Electric step-through town bike with low standover, integrated USB charger, comfy upright bars and basket mounts +A city step-through e-bike with swept bars, pedal-assist, integrated rear carrier, and easy step-in frame for comfortable commuting. +Vintage road bike with chrome-plated fork crown, leather saddle, and original steel-framed water bottle cages. +A retro-inspired road bike with steel Reynolds tubing, down-tube shifters, toe-clip pedals, leather saddle, and traditional decals. +Cyclocross training bike with practice panniers, gravel tires, removable fenders and durable steel frame for winter miles +Urban utility bike with integrated basket, easy-step low top tube, and wide saddle for comfort during quick errands. +Fatbike plus touring rig with reinforced frame mounts, wide rims and pannier eyelets for winter expeditions. +Gravel speed machine with aero-optimized tube profiles, integrated storage, and deep-section 45mm wheels. +A high-performance road bike with SRAM Red eTap wireless groupset, full carbon cockpit, aero seatpost and pearl-black lacquer. +Lightweight carbon gravel frameset with flared drops, stealth cable routing, and comfortable rake for long mixed-surface rides. +Gravel commuter with clearance for 42mm tires, integrated LED on the seatstays and a utilitarian matte finish. +Folding commuter with quick-release clamp, small-wheel suspension, and integrated lock for compact transit storage. +A gravel grinder with flared carbon drops, 650b wheelset, long-range gearing and a subtle stripe paint scheme. +A kids' dirt jumper with strong chromoly frame, short travel fork, 24-inch wheels and vibrant teal paint with bold decals. +A beach cruiser with bold tropical print, oversized saddle, swept bars, and glossy enamel finish that invites slow rolling. +A kids' trail bicycle with front suspension, 20-inch wheels, knobby tires and lower gearing for rapid skill progression. +A sleek road race frame with carbon layup for sprint stiffness, minimal branding, and a geometry focused on aggressive racing posture. +A kids' balance bike with steel frame, easy-grip handles, foam tires, and sunrise-orange paint with smiley-face head tube sticker. +Electric cargo longtail with foldaway passenger seats, rear pantograph rack, bright taillights and a stable ride for school runs +A triathlon-specific bicycle with integrated aerobars, narrow-hourglass tubing and mirror-like paint for minimal visual drag. +Cyclocross race setup with short headtube, mud-optimized frame shapes, and powerful hydraulic discs for consistent stopping. +A hand-painted steel touring bicycle with enamel finish, leather saddlebags, and brazed-on mounting points for cooker and tools. +Lightweight race bike with flush cable routing, aero-optimized fork, and a full carbon cockpit for minimal drag and responsive handling. +Gravel light tourer with full mudguard compatibility, low-slung frame bag area, and a robust headset for rough backroads. +A modern gravel bike painted in metallic moss with stealth cables and a modular fork for racks or lowrider bags. +Cyclocross race frame with maximum tire clearance, tubular-specific rims, minimal weight, and a quick-release oriented cockpit for fast pit stops +Gravel touring frame with integrated storage, wide tire clearance, dropper compatibility, and multiple frame mount points. +Folding commuter with dual suspension design, small wheels and ergonomic saddle for daily rides with little storage. +A gravel touring bike with triple crankset provision, low gearing, three-bolt rack mounts, and clearance for 45mm tires laden with gear. +A bespoke lugged steel frame with hand-enameled headbadge, slender tubing, and polished chrome fork for classic showpieces. +A lightweight singlespeed gravel grinder with tubeless-ready 700c wheels, oversized tires, and a purposeful yet simple drivetrain for freedom on remote tracks. +A mountain downhill bike with carbon-fiber linkage, double-bolt-through axle, and SRAM Code brakes with 200mm rotors for heat management. +Mountain downhill beast with reinforced headtube gussets, long travel suspension and a mud-resistant finish. +Cyclocross training bike in bright livery with grippy tires, mud-clearing frame, and an upright cockpit for mixed-terrain sessions. +A mountain trail full-suspension with adjustable travel, bluetooth-synced suspension, 29-inch wheels and muted forest paint. +Gravel touring with full-length fenders, multiple bottle mounts, and a relaxed geometry that keeps the loaded bike stable. +Electric folding bike with throttle and pedal assist, 20" wheels, and a robust hinge for frequent folding and unfolding on commutes. +Aero time trial bike with integrated stem/bar, smooth cable transitions, and polished fairings for wind tunnel gains. +A sleek carbon road bicycle with rim-brake styling adapted to modern disc system, polished headset, and racing geometry. +A cyclocross race bike with rewired discs, extra chain protection, and an ergonomic saddle to endure muddy, bumpy race surfaces. +A full-suspension trail mountain bike with modern progressive geometry, 150mm rear travel, and a durable finish for rough trails. +Commuter e-bike with large-capacity battery, quiet mid-drive, and integrated canopy for rainy commutes. +A rugged expedition touring frame with extra-wide racks, heavy-duty bearings, reinforced dropouts and matte khaki paint. +Compact commuter folding bike with quick-release hinge, single-speed simplicity, and a bright reflective paint for visibility. +A racy fixed-gear track bike with deep-section carbon front wheel and tubular rear, drop bars and ultra-stiff carbon fork. +Vintage commuter with pressed steel frame, sprung leather saddle, and timeless enamel paint for nostalgic urban comfort. +Urban cruiser with banana seat, sissy bar, coaster brake and flamboyant candy apple paint. +Urban folding commuter with step-through frame, chainless belt drive and built-in lock on the frame. +A classic Dutch style bicycle with full chainguard, skirt guard, and a deep sea-blue finish fit for city commuting in any weather. +Classic cruiser with custom flame paint, chrome springer fork, oversized banana seat and whitewall tires for show rides. +A steel road frame with classic geometry, threaded headset, and chrome-plated fork crown for a warm, vintage ride feeling. +Road time trial rig with minimal frontal area, advanced wheel fairings and integrated hydration behind the aerobars. +Urban single-speed with reflective accents, sealed bearings, and a narrow aerodynamic saddle for quick urban sprints. +Mountain enduro rig with coil shock, steel-through axle, and heavily treaded DH tires. +Gravel bike with dropper post, 1x drivetrain, clearance for 700x50 tires, and an ultralight frame for mixed-surface speed. +Electric cargo trike with heavy-duty low platform, dual batteries and a secure cab for parcel or package transport. +A classic British-style city bicycle with upright posture, brass bell, front wicker basket and hunter green lacquer. +A gravel touring bike with double-bottle fork mounts, welded rack bosses, and a paint inspired by topographic maps of mountain ranges. +Mountain enduro rig with 170mm travel rear shock, coil spring option, 27.5+ wheels and aggressive geometry. +Vintage mixte city bicycle with soft pastel livery, wicker basket, leather grips, and a spring saddle for comfort and charm. +Vintage track classic with cotton-wrapped bars, narrow saddle and an aged chrome finish on the fork crown. +City folding cargo bike with double-deck platform, small-diameter wheels, quiet hub motor and reinforced hinge for secure multi-stop delivery +Electric cargo trike with three-wheel stability, large rear box, hydraulic brakes, and a powerful mid-motor for hills. +Gravel adventure bike with titanium hardware, extra water bottle mounts and a matte olive green paint that hides dirt. +Touring tandem with tall headtube, low bottom bracket, and reinforced chainline for stable two-person touring with luggage. +A gravel-ready hardtail with 29" wheels, low gearing for loaded climbs, and a robust rack on the fork for extra cargo capacity. +Vintage mixte with enamel paint, swept bars, wicker crate and full chainguard for town errands. +A full-suspension downhill bike with 200mm travel, reinforced headtube, gravity-oriented frame and long wheelbase built to withstand rocks and roots. +A modern BMX street build with gyroscopic system, pegs both ends, and mid-school vibe graphics that pop at night under park lights. +Commuter bicycle with front basket, bell, smooth center-pull brakes and a relaxed upright pedaling position. +Electric folding commuter with low-step frame, 250W hub motor, and a toolbox compartment for small essentials. +Gravel tourer built for remote roads with triple water-bottle mounts, integrated GPS mount, and heavy-duty thru-axles. +Folding commuter with small footprint, center-fold hinge, and a quick-release clamp for rapid folding. +High-performance track sprinter bike with oversized down tube, stiff BB shell, large chainring, and track-specific narrow handlebars. +Folding cargo bike with strong hinge, built-in tie-down points, wide deck and compact folded dimensions for multi-use utility. +A fixed-gear urban machine with tracks-style handlebars, polished cranks, and a loud anodized accent color for visual impact. +Mountain cross-country race machine with featherweight build, stiff fork, quick-rolling tires and race-specific geometry for podium ambitions +Modern gravel racer with electronic 1x groupset, 700c wheels, lightweight alloy frame, and race-ready geometry for fast mixed-surface events. +A city cargo bike with longtail deck, child tether points, and a low frame to make loading and unloading kids and cargo simple. +A gravel-ready hardtrail with thought-out luggage mounting points, robust cables, and a sleek matte charcoal paint. +Folding electric bike with compact frame, mid-drive motor, reliable hinge system and safety fold latch for city dwellers +Gravel endurance bike with split top tube, integrated fender eyelets, and subtle speckled paint finish. +A gravel touring frameset with titanium hardware, removable fender mounts, and an elegant sand-cream paint scheme that hides scratches. +Single-speed urban with matte gray finish, deep-section front rim, and narrow saddle for a purposeful minimalist stance. +Classically-styled fixed-gear single-speed with riser bars, matte army green paint and minimalist saddle. +A lightweight criterium bike with stiff alloy frame, shallow deep-section wheels, compact crank and a geometry that favors sprinting. +Cargo e-bike with side-loading panniers, hydraulic disc brakes, and torque sensing motor for heavy deliveries in town. +A robust cargo bicycle with reinforced rear deck, hydraulic disc brakes, reflective paint and secure tie-down points for odd loads. +Urban utility trike with cargo platform, sealed bearings and electric-assist option for last-mile deliveries. +Beach cruiser with wide balloon tires, chromed springer fork, and coasting-friendly gears for leisurely shoreline rides. +A touring-ready steel frame with low-slung geometry, triple water-bottle mounts, sturdy 36-hole wheels and reinforced disc tabs. +Racing BMX with lightweight sealed-bearing hubs, stiff chromoly cranks and foam grips for sprint track starts +A folding cargo bike with long wheelbase when unfolded, integrated cargo clips, electric assist and a low center of gravity to keep loads stable. +A commuter e-bike with integrated front rack, step-through frame, low-maintenance belt drive and pearl-white finish with subtle reflective piping. +A folding e-bike with a mid-drive motor, compact battery, and a one-handed fold mechanism designed for commuter convenience. +Gravel long-hauler with titanium frame, multiple mounting points, 650b wheel compatibility, and a refined brushed finish for durability. +A full-suspension enduro frame with seamless linkage bearings, air-adjustable shock with 4-position damping, and reinforced downtube protection. +A cargo e-bike with a reinforced front deck, long-range battery, and adjustable harnesses to secure irregular loads safely. +A vintage-inspired road racing frame restored with new components, narrow tubular tires, and a polished headset. +Urban folding bike with single-hand fold latch, small wheels, low weight, and a compact footprint for easy stowage. +Classic track-inspired fixie with bullhorn bars, silver polished frame and narrow 23mm tires for brisk urban spins. +A lightweight disc-road bicycle with aerodynamic profile, 28mm tires, and mechanical reliability for fast group rides. +Mountain singletrack bike with 140mm travel fork, 1x11 cassette, and reinforced dropouts for aggressive trail-day abuse. +A gravel bike with full fender setup, dynamo lighting, and a dedicated GPS/phone mount for remote self-supported riding. +A commuter with a front cargo platform, hydraulic brakes, and a low, stable center of gravity for hauling crates and deliveries safely. +Gravel-focused carbon frame with clearance for 45mm tyres, integrated cables, and a polished aero profile for speed and comfort. +A carbon full-suspension enduro machine with 170mm travel, coil shock and stealth black paint with neon accent. +Gravel endurance build with elastomer inserts in the seatpost, wide flared drops and a discreet matte olive finish. +A bespoke painted road bike inspired by classic racing liveries with hand-painted numbers and subtle gradients. +A cyclocross commuter with 38mm semi-slick tires, rack mounts, dynamo lighting and an urban-ready low-maintenance build. +A lightweight gravel frameset with strategic compliance layup to smooth rough gravel sectors and a geometry aimed at speed and comfort. +Gravel plus reconnaissance bike with 27.5+ tires, rugged rims, reinforced dropouts and multiple luggage mounting points for exploration +A kids' BMX race model with lightweight frame, narrow handlebars, and race-ready tires for weekend club meets and track starts. +Retro city cruiser with big balloon tires, chrome accents, and a comfy wide saddle to float over pavement. +A high-volume balloon-tire cruiser with relaxed geometry, wooden platform rack, spring-loaded saddle and turquoise fade paint. +Mountain hardtail race bike with race-tuned fork, narrow bars, and tubeless tires ready for fast singletrack. +A commuter with mid-drive motor, integrated shift sensors, and a discreet headlight that retracts when not in use to maintain aesthetics. +Gravel racer with partial aero tubing, 36-40mm tires, electronic shifting, and a purposeful geometry for endurance speed. +A commuter bike with welded aluminum frame, belt-driven hub, 8-speed internal gearing and integrated lighting for hassle-free everyday riding. +A matte silver commuter e-bike with torque-sensing motor, wide saddle, integrated fenders and a cargo-ready rear rack for groceries. +Old-school road bicycle with downtube shifters, steel forks, cotton-wrapped bar tape and a patina finish. +A restored 1970s road bike with period components restored to original glory, tubular rims and leather toe straps. +Track fixed-gear bike with horizontal dropouts, deep-section rim, steep geometry and polished silver finish. +Track pursuit bike with straight fork, deep-section rims, fixed hub, and very short cranks for aerodynamic posture. +A recumbent two-wheeler with ergonomic seat, adjustable headrest, and long-chain tuning for comfortable long-distance touring. +A city cruiser with a bamboo rear rack, leather straps, and soft jumbo tires for a smooth and stylish Sunday ride. +A compact folding utility bike with 20-inch wheels, strong rear carrier, internal hub gear and glossy crimson color. +A kids' balance bike with neon green frame, foam-filled tires, adjustable saddle height and a safety handlebar pad for soft landings. +Bikepacking titanium frameset with integrated seatpost clamp, extra bottle mounts, and subdued brushed finish for rugged trips. +A mountain trail bike with burly 34mm stanchions up front, 29x2.4 tires, and an adjustable shock with platform damping for pedaling efficiency. +Lightweight carbon aero road bike with integrated cockpit, stealth cable routing, and asymmetric chainstays for power transfer. +A commuter with belt drive and Alfine internal hub, integrated rear rack, and a comfortable upright geometry for zero-maintenance daily riding. +A lightweight steel touring bike with triple-bottle mounts, large-diameter tubing, and chromed fork tips for enduring long miles. +High-end gravel frame with asymmetric chainstay reinforcement, stealth cabling, and clearance for 45mm tires. +A cyclocross race build with 11-speed cassette, compact double crankset, aggressive knobby tires, and a graphic paint scheme that hides mud. +Steel cyclocross racer with classical geometry, drilled brake bosses, and an understated gloss lacquer finish. +A mountain freeride bike with burly frame, long travel fork, and double-barrel shock for technical lines and drops. +A recumbent bicycle with low-slung ergonomic seat, long-wheelbase stability, and aerodynamic full-fairing option for speed records. +Performance criterium bike with nimble geometry, aggressive cockpit, rim brakes, and shallow-section carbon rims for sprinting. +Commuter with puncture proofized tires, fully-enclosed chain and a simple single-lever gearing system for utility. +Fixie with polished steel frame, NJS-styled track hub, narrow handlebars, and a minimalist aesthetic for urban alleyways. +A commuter with integrated child seat mount behind the saddle, step-through frame, practical racks and glossy maroon paint. +A commuter with large-volume tires, extra-thin rack attachments, and a removable battery for practical urban commutes. +A lightweight carbon trail bike with 140mm travel, modern chainstay length, and an integrated chain guide for clean shifts under load. +Urban cargo tricycle with stepped deck, quick-release seat, large-capacity container and reflective safety striping for heavy load visibility +Mountain freeride with long travel, burly rims, wide traction tires and a geometry that invites big drops and jumps. +A time trial bike with full-enclosure fairing, integrated hydration system, and a monocoque carbon frame optimized for long, flat efforts. +A minimal urban fixed-gear with single brake, anodized hubs, and bright paint accents that pop in city streets at dusk. +Cyclocross racer with integrated fleck paint, tubeless-ready rims, and short chainstays for a snappy race response. +A steel gravel frame with hand-applied metallic paint, custom seat tube notch for a dropper, and generous tire clearance for 650b wheels. +A gravel adventure tandem with swapped chainline, robust wheelsets, and luggage rails for two-up exploration into the backcountry. +Gravel all-road commuter with reflective paint, 700x38c tires, mudguard compatibility and discreet rear rack mounts +A touring tandem with reinforced chainset, extra tall head tube for stoker comfort, and rugged 36-hole rims for miles of loaded service. +Commuter with integrated battery lights, rear rack, and puncture-resistant tires for city streets. +A commuter foldable with quick-release front wheel, internal hub gears, and a compact handlebar stem for easy carry on transit. +Downcountry trail bike with light-weight chassis, 130mm suspension and quick-handling geometry for flow trails. +A track-honed single-speed with polished steel frame, pursuit geometry, narrow drop bars and a minimalistic aesthetic for velodrome use. +Mountain trail full-suspension bike with 150mm travel, aggressive geometry, and a coil-compatible shock for fine-tuning traction. +Electric mid-drive mountain bike with adjustable torque modes, long-dropper post, and stiff wheelset for technical climbs +A high-performance track sprinter with stiff bottom bracket, narrow saddle, tubular tires and glossy black paint with red highlights. +Full-suspension mountain bike, aluminum frame, 140mm travel front, 130mm rear, 29-inch wheels, aggressive slopestyle geometry and dropper post. +A classic steel track sprint bike with short wheelbase, muscular seat tube transition, tubular tires and track pump attachment. +Gravel race frameset with aggressive geometry, carbon layup tuned for sprints, and electronic wireless shifting for instant response. +Gravel endurance machine with shock-absorbing seatpost, 38mm tires, and flared drops for high-speed mixed-surface control. +A fat-bike commuter with rounded fenders, 4.5-inch tires, and a sturdy lock integrated into the frame for secure stops on frosty mornings. +A lightweight trail bike with modern geometry, efficient pedaling platform, and a long, narrow paint fade along the down tube. +Cargo box bicycle with front-loading wooden box, reinforced headtube and heavy-duty hub to tote children or deliveries. +A classic Dutch transporter with stable frame, enclosed chaincase, and upright comfort geometry for frequent short trips. +A long-travel enduro bike with progressive leverage curve, 170mm front travel, and burly 2.5-inch tires for high-speed technical sections. +Dirt jumper with short chainstays, 26-inch wheels, single-speed drivetrain and hard-anodized rims. +Folding electric cargo bike with three-wheeled variant, easy folding, protective canopy and stable low center of gravity for family use +High-end road bike with electronical groupset, ceramic bearings, aerodynamic fork, and a stiff chainstay for sprinting. +Gravel gravel-specified bike with stealth color-matched mudguards, GPS mount and quick-release dropper lever for on-the-go adjustments. +Gravel racer with integrated hydration, asymmetric chainstays, and a glossy carbon weave finish. +Vintage steel road bike with chrome lugs, downtube shifters, narrow 23mm tires, and a classic leather saddle. +Fixed-gear urban commuter with powder-coated frame, deep profile rims, and a micro-suspension seatpost for comfort on rough streets. +Folding cargo bike with detachable rear deck, small wheels for maneuverability, and a foldable handlebar stem for compactness. +A vintage road bike with polished chrome rear rack, narrow tubular tires, friction shifters and a leather saddle aged to perfection. +Adventure touring steel frame with triple water bottle mounts, stable geometry, and multiple racks to accommodate long trips. +Touring steel frame with low-trail geometry for stability with loaded panniers and brazed-on rack mounts. +Electric cargo e-bike with mid-drive motor, hydraulic disc brakes, large rear platform and reflective decals. +A gravel touring build with triple-chainset option, chainstay protector, and robust spokes for repeated rough-road service. +A steel randonneur bicycle with front low-rider rack, large 32mm tires, Brooks leather saddles and braze-ons for long-distance comfort. +Classic Dutch cargo bike with wooden box, high sides, and hand-painted nameplate with playful font. +Mountain all-mountain with modern slack head angle, dropper-equipped seatpost, and knobby tires for technical terrain. +Urban fixed-gear with single narrow saddle, short stem, and loud neon accents for night visibility. +Mountain hardtail trail bike with slack geometry, wide handlebars and tubeless setup for rocky descents. +Cyclocross race-ready frame with chainstay protection, fine-tuned tube shaping for mud-shedding, and large-volume tubular tires for race day traction +Folding commuter with sturdy hinge, sealed bearings in headset and an integrated chain guard for urban durability. +A narrow-tubed classic road racer with leather saddle, shallow-profile wheels, and an elegant head badge for vintage club rides. +A long-wheelbase tandem with adjustable stem for rear stoker, heavy-duty wheels, and a two-bolt seatpost clamp to keep riders comfortable for long distances. +Cargo e-bike with rear box and child harness, low-center-of-gravity design, and multi-speed drive. +A cargo e-bike with long rear deck, powered hub motor, and electric throttle assist for moving bulky loads in the city. +A gravel endurance bike with comfortable bar drop, long wheelbase, and small compliance inserts to reduce fatigue on rough roads. +Urban folding electric with 16" wheels, lightweight frame, and a discreet battery hidden in the downtube for commuting convenience. +Commuter foldable with easy-carry strap, internal cable routing, and a high-mounted battery for theft prevention. +A cyclocross racer with carbon monocoque frame, 35mm mud tyres, disc brakes and bone-white paint with subtle sponsor logos. +Tandem recumbent bicycle with dual steering, long wheelbase, and suspension seat for long-distance comfort +A classic road racing build with 23mm tubular tires, thin but strong steel tubing, and tasteful decals reminiscent of old pro teams. +A drop-bar randonneur bicycle with generator light, 28mm tires, and leather mudflaps for long nights on the road. +A cyclocross bike with custom textured paint intended to add grip when carrying on shoulder, integrated guard plate, and 700x33 tubeless setup. +A kids' balance cycle with bright lime paint, soft EVA tires, ergonomically shaped handlebar grips and a lowered frame for safety. +Cargo trike with electric assist and locking cargo box that converts to seating for passengers. +A mountain dirt jumper with reinforced dropouts, short chainstays, and grippy knobby tires for park laps. +Gravel endurance bike with a subtle pearlescent paint, 38mm tires, and a comfortable swept-back cockpit for long days. +A boutique steel commuter with custom enamel paint, leather grips, low-maintenance hub drive and subtle reflective pinstripes. +Mountain enduro carbon frame with adjustable geometry inserts, 170mm front travel, and a burly alloy wheelset for big hits. +Lightweight cross-country race bike with threaded bottom bracket, 100mm fork, and featherweight wheels for efficiency on climbs. +Electric cargo trike with long-range battery, anti-lock braking, and a waterproof cargo box for dependable courier service +Road endurance frame with vibration-damping inserts, higher stack and optimized for 32–35mm tyres for rough roads. +A trail hardtail built for enduro practice with wide bars, 32mm stanchion fork, dropper seatpost, and an aggressive 12-speed cassette. +Mountain freeride bike with stout chainstays, burly linkages, coil shock and graphics for aggressive trail rippers. +Gravel endurance model with vibration-damping seatpost, 700x40 tires, flared bars and secure handlebar wrap for long mixed-surface routes +Folding commuter with 24" wheels, quick-fold hinge, and compact folded height to fit small car boots or tiny apartments. +A mountain hardtail optimized for riders who value efficiency, with a stiff bottom bracket, light wheels, and progressive geo for modern trails. +Mountain enduro with adjustable travel, burly brakes, chainstay protection and progressive geometry for confidence on steep descents. +A touring tandem with robust steel frame, dual racks, comfortable saddles and deep navy paint with cream pinstriping. +A rugged utility bike with reinforced wheels, cargo basket, and a steel frame engineered to handle heavy daily hauling tasks. +Heritage-styled town bike with brass accents, leather grips, chrome fenders and ornamented head badge for nostalgic appeal +A bright red fixed-gear track-style bicycle with riser bars, minimal brake setup, slim saddle and a sleek single-speed drivetrain for city cruising. +Compact folding electric bike with hub motor, quick-release handlebars, battery under the seat, and puncture-resistant tires. +A high-volume downhill machine with 180mm travel, reinforced lower link, and heavy-gauge spokes for aggressive rock gardens. +Touring frame with built-in rear rack mounts, diamond-cut lugs, and a subtle world map graphic along the chainstay. +Adventure touring bike with titanium frame, multiple water-bottle bosses, and a relaxed head angle to carry heavy loads comfortably. +A gravel bike with dual-bottle mounts on the down tube, stealth matte navy finish, clearance for 2.1 tires on 650b wheels, and modular rack mounts. +A practical folding commuter with cleverly folded handlebars, compact chain guards, and a durable aluminum frame for active multi-modal users. +Mountain trail 29er with efficient pedaling platform, dropper post, and balanced suspension tune for all-day rides. +A pearlescent commuter with integrated locks, internal hubs, small rear rack and pearly white finish with iridescent flakes. +A mountain fat bike with 26x4.8 fat tires, rigid fork or short-travel suspension, and wide handlebars for winter trail traction. +A carbon all-road machine with clearance for 42mm tires, discreet luggage mounts, and a discreet two-tone logo placement for understated style. +A 29er cross-country race bike with lightweight carbon frame, 100mm travel fork, and subtle metallic forest green paint. +A lightweight aluminum commuter with wide platform pedals, integrated rear rack, and reflective logos on the frame. +Touring bicycle with triple crankset, reinforced rims, leather pannier straps and a rainproof handlebar bag for long-haul comfort +Gravel stealth build with matte black frame, flared drops, 1x drivetrain, and a discreet frame bag for minimalist adventuring. +Gravel all-road machine with disc brakes, clearance for 50mm tires, and eyelets for mudguards and racks. +A restored classic road bicycle with steel lugs, leather saddle, and period brake levers lovingly matched to the era. +A folding city bike with small footprint, mudguard-equipped wheels, comfortable saddle and 7-speed internal hub for flexible commuting. +Kids' mountain bike with aluminum frame, suspension fork, disc brakes and bold dinosaur-themed graphics. +A gravel race bike with close-ratio cassette, aero-infused dropped seat tube, and a sharp responsive steering for fast technical sections. +A kids' BMX with bright stickers, sealed headset, short 165mm cranks and a wide saddle for park stability. +Gravel bike set up for cyclocross with 35mm tires, tubular rims and a low-profile saddle for sprinting. +A track-inspired urban bike with single-speed freewheel option, aero bars removed, polished aluminum frame and thin black decals. +Lightweight triathlon bike with integrated hydration, deep-section front and rear wheels and aggressive aerobars for time trials. +Gravel plus adventure frame with reinforced dropouts, 2.2" tyres and bag-friendly top tube for long days. +A beach cruiser with oversized balloon tires, sweeping chrome fenders, and a comfortable sprung saddle for casual rides. +Endurance road bike with vibration-damping seatpost, 28mm high-volume tires and relaxed head tube angle. +A performance road bike with asymmetric chainstays, stiff bottom bracket, and dark matte finish with micro-logoing. +A mountain hardtail with plush compliance-focused chainstays, 120mm travel fork, and wide 29x2.35 tires for confidence on loose climbs. +A beach cruiser with coaster brake, oversized balloon tires, chrome fenders, and a retro springer front fork for seaside promenades. +A cargo longtail bicycle with reinforced rear platform, kid seats, and powder-coated frame in sunflower yellow. +Mountain cross-country race frame with lightweight carbon layup, 110mm travel, stiff bottom bracket and race-ready 29er wheels +A classic mixte step-through frame city bike with wicker basket, chrome fenders, three-speed Sturmey-Archer hub and pastel mint finish. +A lightweight carbon gravel frame with stout bottom bracket, tapered head tube, and integrated 1x-specific chainline to avoid chain rub. +Time trial aero bike with integrated hydration, monocoque carbon frame, deep-profile wheels, and aero handlebars. +A trail-slash hardtail with 29-inch wheels, 130mm fork, and modern geometry that crosses technical climbs and quick descents smoothly. +A mountain bike with coil-sprung rear shock, extra-thick downtube protection, and tough rims to resist rock strikes and rim dents. +A commuter with internal cable routing, reflective paint accents, and puncture-resistant 32mm tires designed for wet-season reliability. +Electric mountain slayer with powerful battery, lower link shock, and reinforced head tube for aggressive lines. +Full-suspension cross-country race bike with lightweight carbon frameset and 100mm travel optimized for efficiency. +Folding commuter with magnesium frame, instant-lock hinge and lightweight wheels for daily portability. +A modern cyclocross race machine with carbon dropouts, diagonal tube shapes to shed mud, and a race-oriented weight target. +A rugged expedition touring bike with dynamo hub, extra-durable brake mounts, and a rust-resistant finish for long-distance reliability. +Commuter with switchable hub map for days off the bike and quiet, weatherproof drivetrain. +Touring commuter with internal hub gearbox, triple chainring, and heavy-duty racks for regular cargo duty. +Vintage lugged steel road bike with classic shifters, down tube cables, and faded champagne paint. +A stealth grey gravel bike with hydraulic disc brakes, fully sealed bearings, and a lightweight alloy frame for endurance rides. +A long-distance touring bicycle with wide-gear triple, reinforced fork, pannier-friendly rear triangle and deep forest-green enamel. +A vintage Japanese road bike restored with polished chrome lugs, 27" wheels, downtube shifters and classic leather saddle. +Cyclocross specialist with carbon layup designed for stiffness and mud clearance and a vivid orange livery. +Fixed-gear city-cruiser with enamel flake paint, comfortable swept bars, white-wall tires, and a coaster brake alternative. +Gravel commuter with mudguard compatibility, reflective rim tape, and a lightweight alloy frame for quick urban escapes. +A gravel bike in matte desert sand with flared drops, tubeless tires, and hidden tire inserts for puncture protection. +Mountain cross-country bike with efficient suspension design, lightweight components, and a geometry that favors pedaling efficiency. +A cyclocross frame with steel tubes and modern specs, fitted with wide tires and a minimalist clearcoat to show off patina. +Compact 20-inch wheeled folding bike with 8-speed hub, hydraulic disc brakes, and commuter ergonomics. +Mountain trail bike with adjustable seat tube angle, dropper post, and fast-rolling 29-inch wheels for flow trails. +A tandem touring bicycle with reinforced fork, center stand, dual water mounts, and matching panniers for coordinated long-distance travel. +A mountain hardtail with a burly alloy frame, 29x2.35 tires, reinforced axles, and a short chainstay for playful trail popping. +Electric cargo cargo bike with front-loading box and hydraulic steering assist for heavy deliveries and safe handling +A classic touring bicycle with brooks saddle, alloy racks, dynamo lights and a timeless navy paint job ready for multi-day trips. +Vintage racing frame built for classic criterium style with slender steel tubes, quill stem, downtube shifters, and toe-clip pedals. +Kids' mountain bike with front suspension, easy-to-shift 7-speed trigger shifters, knobby tires, and training-friendly geometry for first trail experiences. +Sleek single-speed city bike with polished aluminum frame and minimalist cable routing. +Gravel touring bike with integrated frametap ports, 3-bolt stem, and high-volume 2.0 tires. +A low-maintenance commuter with belt drive and continuous-variable hub, mudguards, and a small low-load rack for grocery trips. +Gravel touring frame with concealed bolt racks, stainless hardware, and a pebble-gray finish for understated touring. +Vintage-inspired fixed-gear with leather accents, polished crankset, silver flake paint and minimalist chainline for stylish commuting. +A classic road racer with polished lugs, narrow tires, and a historically accurate paint scheme restored to period exactness for club runs. +Gravel all-day explorer with flared bars, wide tires and reinforced fork mounts for bikepacking adventures. +A classic single-speed with artful metallic flake paint, leather grips, narrow tires and a polished aluminum chainring for street style. +A rugged fat-bike with 4.8-inch tires, rigid fork, low gearing for beach runs and sand-beige powdercoat with salt-resistant finish. +A blacked-out time-trial bicycle with integrated hydration, triathlon cockpit, steep seat tube angle and deep aero front and rear wheels. +A commuter with built-in mudguards, a robust kickstand, and a rear rack with bungee system for daily hauling needs. +Tandem with step-through front frame, child-friendly cockpit, and extra stability for casual two-up rides. +A performance gravel frameset with hidden downtube routing, seat tube clamp integrated into the layup, and asymmetric chainstays to resist torque. +A mountain enduro rig with adjustable chainstay length, 170mm of travel, and aggressive tire tread for steep technical trails. +A modern mountain bike with 150mm travel, 29-inch wheels, tubeless setup and stealth matte black finish with neon-blue accents. +Utility folding bike with luggage-style rear rack, sturdy hinge, puncture-resistant tires and simple five-speed hub. +Classic steel road bike with discreet chrome lugs, 23mm tubular tires, and a vintage leather saddle for club rides. +A mini-velo city bike with 451 wheels, upright bars, internal gears, full chaincase, and compact frame for tight urban storage. +Commuter upright hybrid with swept-back bars, wide saddle, puncture-resistant tyres, and a small front basket for essentials. +Touring bike with dynamo lighting, wide-range cassette, and multiple water mounts for remote expeditions. +Cyclocross bike set up with tubeless 32mm tires, mudguards for training and tapered carbon fork. +Randonneur-style touring bike with dynamo hub, leather saddle, steel fenders, and a low-gear triple chainring setup. +Mountain hardtail with 29-inch wheels, rigid chromoly fork, and a 1x drivetrain for simple reliable trail performance. +A beach cruiser with banana-style seat, springer fork, chrome fenders and a bright sunshine yellow paint job for summer joyrides. +Downhill-inspired enduro bike with slack head angle, long reach, burly linkages, and plush suspension for high-speed descents. +Track training bike with stiff carbon forks, minimal graphics and a deep rear rim for aerodynamic focus. +A vintage-styled roadster with chrome fenders, swept-back bars, and a period-correct tan leather saddle for classic neighborhood charm. +Gravel racer with disc brakes, flared drops, and a wide cassette for steep climbs and rough descents in mixed events. +Gravel endurance rig with subtle flex zones, micro-suspension seatpost and wide tyre compatibility for long comfy miles. +Gravel-plus adventure bike with 2.2" tyres, wide handlebars, and lower gearing for extended off-road rides. +Trail-ready full suspension with adjustable rebound, 140mm travel and a sturdy alloy frame for technical lines. +A BMX park cruiser with chromed frame, pegs, and removable brake to fit a variety of street lines and skatepark sessions. +Urban electric step-through with torque sensor and comfortable low-step design, fendered wheels, and rear rack for parcels. +Mountain e-bike with dual-battery option, tuned suspension, and a burly crankset for heavy use. +Mountain cross-country race bike with stiff carbon frame, tapered headtube, and efficient suspension kinematics for climbing. +A gravel endurance bike with steel frame, comfortable micro-suspension seatpost, and a wide-range cassette for mixed terrain. +A BMX race-oriented frame with optimized chainstay length, sealed bearings, and professional-grade cranks for tight oval tracks. +Gravel cyclocross hybrid with 700 x 35 tyres, clearance for mudguards, and a comfortable long-distance geometry. +Youth cruiser with low bun-style seat, wide swept bars, and coaster brake for safe first-teen rides around town. +A gravel racing frame with asymmetric stays, internal cable routing, and a hidden bottle mount for minimalist weight savings. +Performance mountain bike with short chainstays, steep seat angle, and a balanced 140mm travel for mixed-terrain speed. +A commuter with minimalist stainless-steel frame, belt drive, internal hub, reflective paint flecks and a single front basket for errands. +Gravel bike with chromed fork, tan sidewall tires, and leather saddle for classic styling. +Vintage-inspired city bicycle with cream-wall tires, brass fender trim, leather grips and an understated pastel paint scheme. +Mountain enduro bike with adjustable geometry via flip-chip and beefy 2.5-inch rubber. +Gravel adventure bike with stainless steel frame, brazed-on eyelets, fender compatibility, and a rugged powder-coat finish. +Classic Italian track frame converted to single-speed commuting with leather grips and a small tote rack. +A restored steel roadster with leather grips, polished lugs, and historically accurate decals paired with modern brakes for safety. +A kids' small-frame mountain bike with low-gearing, front suspension fork, safety chain guard and bright lime-green paint with fun stickers. +A cyclocross bike with a hand-polished steel fork, wide knobby tires, and a seat tube that accepts dropper posts for modern CX use. +A polished chromoly BMX built for competition with a low-profile frame, lightweight cranks, and race-ready tires to shave seconds at the track. +Road sprint-oriented bike with short headtube, responsive fork, and low stack for aggressive sprinting positions. +A full-suspension downhill bike with reinforced chainstays, massive tires, and a paint scheme that hides the scars of hard runs. +A leisure cruiser with banana-style saddle, sweptback bars, small wicker basket and pastel mint paint with cream accents. +Lightweight aluminum criterium bike with carbon fork, short wheelbase, SRAM Force groupset, and a fluorescent yellow accent stripe. +A belt-drive commuter with internal 8-speed hub, hydraulic brakes, and a removable battery tucked under the downtube for a neat exterior. +A robust cargo e-trike with dual batteries, hydraulic brakes, and a roomy enclosed cargo bay for commercial deliveries. +A retro cruiser with polished chrome, wicker basket, low-slung frame and creamwall tires for picturesque slow rides. +Trail mountain bike with balanced geometry, 140mm rear travel and responsive suspension for technical trails. +Utility bike with heavy-gauge steel frame, bolt-on frame rack, and heavy-duty kickstand for market runs. +A stripped-back gravel singlespeed with wide knobby tires, rigid fork, steel frame and raw metallic silver paint for simplicity. +Classic road bike with steel lugs, 9-speed indexed shifters, cotton handlebar tape, and a pristine metallic navy paint job. +Gravel enduro bike with 1x12 drivetrain, 2.2-inch semi-slick tires, and hover-friendly geometry. +A classic track bike with polished steel frame, small chainring, narrow drop bars and tight geometry for velodrome sprints. +Electric folding commuter with quick fold hinge, 250W motor, integrated lights and compact bag for storage on trains +A touring gravel bicycle with removable mudguards, large-capacity frame bag, and a tough front rack for overlanding with confidence. +Vintage-inspired cruiser with surfboard racks, teak wood accents on the cargo area and cream-white tires for the beach. +Retro-inspired city bike with cream enamel fenders, leather grips, and old-school bell. +City cargo longtail bike with passenger footpegs, dual child seats, and puncture-resistant chaincase. +A cross-country race hardtail with featherweight frame, minimal paint for weight savings, and fast rolling 29x2.1 tires. +Performance cyclocross bike with asymmetric chainstays, hydraulic discs and clearance for thick mud tires. +A gravel adventure bike with 2.2-inch tires on 650b wheels, frame-mounted top tube bag, and stealthy midnight-blue paint with matte decals. +Urban single-speed with drop bars, fixed gear buildup, and a track-style saddle for aggressive cadence-focused riding. +A carbon time-trial frame with integrated hydration, aero seatpost clamp, and narrow cockpit for minimal drag. +A commuter outfitted with cargo panniers, kickstand, reflective tape on fenders, and a low-maintenance internal gear hub for daily reliability. +Gravel adventure titanium with discreet welded eyelets, lightweight feel and reassuring compliance for long backcountry routes. +A beach cruiser with flamboyant pineapple-themed paint, cruiser bars, coaster brake and a wide cushioned saddle for sunny rides. +A single-speed commuter with a coaster brake, pastel mint paint, basket rack, and puncture-resistant tires for a care-free ride to the market. +Electric mid-drive mountain bike with dropper post, 160mm travel fork and integrated torque sensor for assist. +Vintage roadster with chrome fenders, classic leather saddle, brass fittings, and an old-world polish for relaxed city riding. +A graceful step-through town bicycle with woven wicker basket, chain guard, and soft upright saddle for easy errands. +A compact folding bicycle with 20" wheels, a quick-release hinge, internal cables, and a durable aluminum frame for easy train commuting. +Retro-inspired single-speed with polished aluminum, leather saddle, and a bright lime accent to stand out in town. +A commuter with solid puncture-resistant tires, reflective sidewall striping, and a simple three-speed hub for dependable year-round use. +A kids' balance bike with wood frame option, cushioned saddle, and a friendly low-V stand-over to build confidence in toddlers. +A bicycle built for bike polo with a short wheelbase, flat handlebars, fixed gear or single-speed freewheel, and reinforced wheels for quick turns and hits. +Beach cruiser with wide balloon tires, coaster brake, and surfboard rack integrated into the frame. +A fast gravel pro bike with carbon layup optimized for rough roads, tubeless tires and a tapered aero cockpit for low drag. +A classic British roadster with Sturmey-Archer 3-speed hub, full chaincase, and a high-polish black lacquer finish exeptional for wet climates. +Electric commuter in pearl white with clean integrated cables, rear hub motor and clutch-style kickstand. +Classic Swedish city bike with enclosed chaincase, upright bars, and muted gray paint for Scandinavian simplicity. +A polished steel touring bicycle with a long wheelbase, braze-on rack mounts, wide 32mm tires, full fenders, and stainless-steel racks for loaded bikepacking. +Folding city bike with integrated carry handle, 7-speed internal hub and reliable roller brakes. +A racing tandem with aero tubes, time-trial saddles for both positions, and a stiff bottom bracket assembly for power transfer. +Gravel touring rig with steel frame, leather saddlebags, low gearing, and a bar-mounted top tube bag for snacks and tools. +All-mountain rig with 29" wheels, 140mm travel fork and aggressive dropper post for steep singletrack. +A cargo e-bike with front-loading basket and extra-strong spokes to handle heavy grocery loads and child seats securely. +A powdercoated steel commuter with integrated chaincase, full fenders, comfortable upright geometry and rich chocolate brown color. +An all-mountain 29er with 140mm rear travel, split-pivot suspension, and burly tires for steep, rough terrain and technical climbs. +A cyclocross-specific rig with stiff carbon frame, subtle mud protection, flared drops and a toolkit holster discreetly built into the frame. +Track sprinter with stiff monocoque carbon, aero seat tube, and narrow aerodynamic bars for tuck positions. +A gravel-adventure frameset with humbly finished paint, multiple bottle mounts, and the option to mount low riders for long bikepacking runs. +A burly BMX race machine with reinforced gussets, tapered head tube, and narrow race gearing for short track bursts. +Compact folding e-bike with quick-fold hinge, small wheels, long-range battery, and a low-slung deck for convenient transport. +Touring bike with chromed fork crown, brazed-on mudguard eyelets, and a test-proven geometry for loaded stability. +A stealth commuter with matte black fenders, belt drive, internal gearing and low-profile frame-integrated lights for a discreet look. +Race-ready tri bike with steep seat angle, integrated hydration, and carbon bars designed for the aero tuck and all-day comfort. +A minimalist fixed-gear track-style bike with a glossy navy frame, flip-flop hub, narrow riser bars, and 700c wheels for tight urban criteriums. +Family cargo bike with rear bench seating, seatbelts, and weatherproof canopy for rainy days. +A gravel bike with flared drop bars, 700x40c tubeless tires, disc brakes, and steel frame bags mounted for multi-day mixed-surface rides. +A vintage touring bicycle restored with polished chrome, leather saddle, full fenders and deep maroon paint with gold pinstriping. +All-mountain dual-suspension with adjustable geometry, 160mm travel, wide bars and reinforced dropouts for aggressive trails. +Utility cargo bike with long platform, tie-down points, and an integrated bell and light system for urban logistics. +Racing road frameset with integrated cockpit, eye-catching gloss finish, ceramic bearings and aerodynamic seatpost for speed gains. +Commuter with lightweight alloy frame, ergonomic grips, and fenders, built for quick stops and comfortable shoulders. +Gravel minimal build with steel frame, 650b wheels, wide tire clearance, and classic braze-ons for flexible adventure setups. +BMX flatland with tight geometry, narrow handlebar clamping, and an ultra-grippy paint that hides scuffs from tricks. +A stripped-down BMX built for street cred with reinforced head tube, short stem, and a matte-finish frame for a stealthy look at the park. +Mountain 29er with nimble geometry, wide handlebar, and reliable tubeless setup for rough singletrack. +Folding city bike with magnetic latch, compact folded size, small basket and BMX-style handlebars for nimbleness. +High-performance road frame with OCLV-style carbon, integrated seatpost, power-sensing crankset and aerodynamic seat tube for speed hunts +A performance cyclocross frameset with carbon construction, 33mm clearance, mud-flushing geometry and matte sand finish with dark logos. +Lightweight trail hardtail with 29-inch wheels, boosted hubs, and subtle color-matched decals. +Gravel-specific aluminum machine with durable wheelset, wide tires, and a confident, stable handling package for remote rides. +A commuter with integrated pannier rack, secure frame lock, and a low-step alloy frame that combines convenience with modern aesthetics. +A high-pivot downhill bike with coil shock, 29x2.6 tires and reinforced dropouts for extreme gravity runs. +A classic British-style roadster with full chainchase, brass bell, and a sturdy frame meant for daily, dependable riding around town. +A gravel racer with flared bars, 700x36 tires, and integrated cable ports to keep the cockpit tidy for endurance events. +A folding cargo utility bike with reinforced hinge, front bucket, easy-to-fold handlebars and matte black finish for urban logistics. +Commuter folding bike with magnetic latch, 16-inch wheels, small-battery e-assist and quick-release folding pedals +A downhill race bike with large-volume tires, rotor cooling fins on the brakes, and extra protective guards around the chainstay. +A gravel explorer with integrated top tube harness points, multi-mount fork, and a double-bottle downtube for remote touring. +A high-volume e-gravel bike with 700x45mm tires, integrated smartphone mount, and hydraulic disc brakes to handle heavy gear and steep descents. +Lightweight gravel frame with asymmetric chainstays, internal dropper cable, and stealthy matte black paint for understated speed. +Urban commuter with low-key matte finish, hidden chain lubricant reservoir, and anti-theft components for peace of mind. +Lightweight commuter with 8-speed hub, slim tires, and a compact steel frame for nimble weaving through traffic. +Electric city bike with step-through frame, integrated battery in downtube, and rear cargo rack. +A classic steel frame touring rig with ornate head badge, a leather tool roll under the saddle, and stout spokes ready for heavy loads. +Touring gravel machine with long-range battery for an e-assist, wide tire options, and plentiful mounting points for gear. +A gravel all-rounder with 700x45c tires, gravel-specific bars, and a relaxed head tube angle to inspire confidence off-pavement. +A retro single-speed cruiser with mint-green paint, wicker basket, tan leather grips and a comfortable cushioned saddle. +A mountain trail 29er with modern slack head angle, long reach, and short stem for confidence-inspiring cornering at speed. +Gravel explorer with surfboard rack on rear, 700x40 tires, and a stable climbing geometry for coastal adventures. +A mountain enduro bike with a short offset fork option, 29er wheels, and an adjustable flip chip to steepen or slacken geometry. +Fixed-gear downtown rocket with high-contrast wheels, minimalistic seatpost, and a stiff bottom bracket for sprint power. +Lightweight cyclocross race bike with short wheelbase, flicky handling, and 33mm tubeless tires for quick cornering. +A classic commuter with chaincase, internal hub, brass bell, full-length fenders and a deep navy enamel finish for timeless commutes. +A gravel bike with stealth matte paint, integrated computer mount, and wide-tyre compatibility up to 47mm for plus-level comfort. +A lightweight carbon time trial bicycle with integrated bar extensions and a wedge-shaped frame for slicing through the wind. +Touring city bike with full-length chainguard, cargo rack, and puncture-resistant tires for dependable day-to-day transport. +Electric cargo trike with lockable compartment, hydraulic brakes, and a cushioned passenger bench for family runs. +A classic Dutch-style city bike with upright bars, full chain guard, comfortable saddle and a rear hub with weatherproof coaster braking. +Urban single-speed cruiser with glossy pearl paint, leather grips, chrome chainring and minimalist fender kit. +Urban single-speed with anodized hubs, narrow handlebars, minimal decals and clean paint for understated city style. +Full-suspension enduro bike with 170mm travel, coil shock, burly tires and a reinforced headtube area. +A British café racer-style city bicycle with leather-wrapped saddle, polished paint, and small 24-inch wheels. +A utility folding bike with 16-inch wheels that folds small enough to fit under desks, a quiet hub, and simple mechanical disc brakes. +A commuter with automatic light sensors, sealed-belt drivetrain, and a hidden frame lock to secure the bike in urban centers. +A high-modulus carbon road bike with aero shaping, integrated harness cables, 35mm tires and a race-focused fit for speed-hungry riders. +Downhill mountain rig painted electric blue with coil shock, long-travel fork and reinforced dropouts. +Commuter steel frame with comfortable upright posture, integrated fender mounts, rear rack and reflective decals for night visibility +Track sprint frame with short chainstay, low stack, and a very glossy enamel that shines under stadium lights. +A city single-speed with minimalist matte finish, leather saddle, and compact geometry for zipping through traffic. +Kids' BMX with short cranks, reinforced hubs and teen-friendly graphics for beginning tricks at the skatepark. +A classic 1970s steel ten-speed road bicycle restored with period-correct components, downtube shifters, narrow 23mm tires, and shiny chrome. +Mountain trail hardtail with tough alloy wheels, 2.35 tires, and a short stem for precise low-speed steering. +A compact BMX with reinforced top tube, strong crank, and extra-durable pedals for heavy street use and trick abuse. +Mountain trail hardtail with modern geometry, light yet strong alloy frame and wide handlebars for better control in corners. +All-weather urban bike with full fenders, sealed-bearing hubs, hub dynamo lights and anti-corrosion paint for rainy commutes. +A road endurance bike with carbon compliance inserts, low stack for aerodynamics, and endurance-specific geometry to limit neck strain. +A performance road frame with integrated cockpit, disc brake mounts, aerodynamic tube shaping and a satin black finish with subtle logos. +A commuter with integrated cargo rack, dynamo-powered lights, puncture-resistant tires, and a clean colorway for understated utility. +Mountain bike with dual crown fork, massive stanchions, and reinforced headtube for bike park abuse. +Classic cruiser with extra-wide handlebars, two-tone paint, wide saddle, and deep balloon tires for comfortable rides. +Road endurance bike with wider clearance, comfort-focused seatpost and low-stack cockpit for all-day performance. +A polished aluminum cyclocross racer with tubular clinchers, cantilever brakes, and a vibrant gradient paint job to stand out in muddy races. +A classic British-style three-speed hub bicycle with chaincase, rod-actuated coaster, chrome fenders, and leather-handled grips for daily errands. +Vintage beach cruiser with wicker basket, candy-lacquered paint, wide balloon tires, and a padded saddle for easy coast riding. +A carefree cruiser with low-slung frame, wide balloon tires, oversized grips, and pastel turquoise with contrasting cream fenders. +Gravel race frame with bold colorway, stiff bottom bracket, and optimized tire clearance for racing through varied terrain. +A commuter hybrid with flat bars, suspension fork, reflective sidewalls, integrated kickstand, and puncture-resistant touring tires. +A compact children’s balance tricycle made from lightweight aluminum, broad base for stability, and playful lime-and-sky graphics. +A lightweight road endurance bike with slightly relaxed geometry, comfortable handlebar tape, and tires sized to tame rough pavement miles +A retro-modded mountain bike with swept-back bars, leather grips, and a colorful hand-painted frame over modern suspension. +A smooth-rolling commuter with internal geared hub, full chaincase, and a comfortable forward-swept handlebar for ease of use. +Cross-country race carbon bike with minimal weight, 100mm fork, race-optimized geometry, and narrow aero bars for speed. +A luxurious handmade steel road bicycle with bespoke paint, leather-wrapped bars, titanium brake bolts, and engraved headbadge. +Modern cargo bike with child bench, roll-over bumper, and low center of gravity for safe family transportation. +Performance gravel bike with multiple bottle mounts inside the front triangle and a removable fender system for muddy rides. +Gravel-plus adventure rig with wide tyres, strong rims, and a relaxed geometry for loaded off-road touring and exploration. +Enduro mountain bike with variable geometry, coil-adjustable shock, wide rims and protective frame guards for aggressive descents +Mountain enduro with mixed-wheel compatibility, burly pivots, long-travel fork, and a downhill-capable chassis for big hit days. +A gravel race frameset with integrated aero shaping, anti-mud features, and a geometry tuned for long, fast days on mixed surfaces. +A sleek carbon commuter with internal hub gear, shaft drive, slim pannier rack, and a matte graphite finish that repels fingerprints. +A comfortable city cruiser with swept-back bars, wide saddle, sprung fork, and creamy paint for leisurely park loops. +A purposeful gravel bike with low-slung top tube, dropper post compatibility, and rough-road tire clearance. +Lightweight aluminum track bike with steep geometry, high gear ratio and polished silver lugs for velodrome racing. +A compact cargo bike with long rear cargo bay, electric assist, reinforced frame and high-visibility orange paint for delivery runs. +Beach cruiser with big balloon tyres, chrome fenders, pastel paint and a plush, wide saddle for relaxed coastal cruising. +Coastal cruiser with surfboard rack, rust-resistant frame, balloon tires, and a low, comfortable seating position. +Utility bike with built-in cargo platform, hydraulic disc brakes, and reinforced steel tubing for delivery. +A mountain enduro frameset with chainstay guards, link media mount, and a confident planting geometry for aggressive descents. +A retro freestyler BMX with chrome frame, mid-school graphics, 20-inch wheels and white grips for a classic look. +Classic British road bicycle with steel Columbus tubing, downtube shifters, and toe-clip pedals. +Touring bike with leather saddle, classic Brooks rails, and rack-mounted panniers strapped with waxed straps. +A gravel endurance rig with aerodynamic seatpost, fender mounts removed for racing, and a highly tunable headset for varied handling preferences. +Classic road frame with downtube-mounted shifters, waxed cables, and polished quick-release skewers. +A dedicated cyclocross race machine with tubular tires, race geometry, and mud-repellent paint finish for muddy cross seasons. +A race-ready cyclocross rig with aluminum frame, shallow rim brakes, and race-specific tires for fast, muddy circuit races. +A commuter with internal battery, integrated LED strip on the downtube, and hydraulic disc brakes for confident all-weather stops. +Cargo e-bike with refrigeration box, insulated lining, powerful motor and long-range battery for transporting perishables +A gravel racer with electronic groupset, carbon wheelset, 700x32 tires and moss-gray pearl paint with thin orange highlights. +A gravel endurance bike with composite seatpost, oversized downtube, and a cushioned bar wrap for day-long comfort. +A gravel-friendly commuter with 35mm tubeless tires, rack and fender mounts, low-step frame and quiet belt-drive for daily mixed-surface commutes. +Endurance road bike with relaxed geometry, 32mm tires, vibration-damping seatpost, and discreet mudguard mounts for long rides. +Urban cargo longtail with child safety rails, a sturdy kickstand, and a natural wood deck to carry family gear in style. +A city stealth commuter with pinstriped reflective decals, internal gear hub, and a charcoal satin finish that’s both low-key and visible at night. +A kids' BMX with sturdy chromoly frame, pegs for tricks, gyro brake system and bold hi-vis graphics for confident park sessions. +Electric utility bike with integrated rack, low-step frame, torque-sensing motor, and long-range battery for daily errands. +A classic mixte step-through frame with swept-back bars, wicker basket, and pastel paint for relaxed park rides. +A cyclocross race geometry build with modern disc brakes, wide clearance for mud, and a paint job that becomes a racing flag at finish lines. +Performance city bike with drop bars, flat-pace gearing, disc brakes and reflective paint for early-morning commutes. +A cold-weather commuter with chaincase, fully enclosed drivetrain, and stud-proof tires to resist icy conditions. +All-weather winter commuter with studded tyres, robust fenders and corrosion-resistant hardware throughout the bike. +A compact kids' training BMX with low top tube, standing-platform pegs, and durable seat clamp to withstand beginner falls and tricks. +Mountain fat-bike with studded tires, long-travel fork and weatherproof controls for winter shredding. +Classic cruiser bicycle in sky blue with swept handlebars, wide cushioned saddle, coaster brake and a wicker basket. +City electric cargo bike with rear box, hydraulic disc brakes, low center of gravity and easy-assist throttle for grocery runs. +Kids' BMX with lightweight frame, padded top tube cover, and crash-proof components for first trials in skateparks. +Cyclocross race build with wrap-around clearance, sealed bearings and rapid engagement hub for sprint starts. +Tandem recumbent bicycle with adjustable seat angles, dual steering, and long-wheelbase stability for comfortable two-up touring. +Gravel adventure bike with sand-colored paint, internal cable routing, and hidden rack mounts for low-visibility trips. +Time-trial aero frame with steep seat angle, integrated rear wheel cover option and clean cable outlets for smooth airflow. +A sophisticated titanium road commuter with discreet rack mounts, integrated headset, bead-blasted finish and minimalist logos. +Gravel bike with extra framebag mounts, stealthy matte black finish and symmetric clearance for wide tires. +A gravel-day racing bike with aero cantilevered seatpost, narrow 32mm rubber, and an aggressive headtube transition for tight handling. +A commuter e-bike with step-through aluminum frame, wide platform pedals, 50km range battery and discreet integrated rear lights. +A gravel racing machine with electronic shifting, precise cockpit, and a race-geometry that balances aggressive power with sustained comfort. +A light gravel bike with 700c wheels, 28mm rubber for fast mixed-surface rides, carbon fork and lemon-yellow accents. +A dutch-style town bike with enclosed chaincase, swept-back bars, and a comfortable sprung saddle for daily commutes. +Cargo trike with large volume box, stable three-wheel platform, hydraulic brakes and weatherproof canopy for market deliveries. +A lightweight 29er cross-country bike with slender geometry, fast-rolling tires, and a paint job accented with thin metallic flakes. +Adventure touring disc bike with wide gear range, triple cage mounts, heavy-duty tires, and a reinforced fork for loaded travel. +An old-school club racer with 6-speed freewheel, steel shifters, classic drop bars and cream-and-red period-correct livery. +A cyclocross racing frame with low-slung chainstay design, mud-shedding top tube, and a tight chainline to avoid slapping and chain drop. +A fixed-gear track bike with polished crankset, deep-section alloy rims, and a single slick tire setup for velodrome use. +All-mountain hardtail with modern slack geometry, 130mm fork, and a stout wheelset for hitting rough technical lines. +A full-carbon downcountry bike with efficient 120mm travel, lightweight wheelset, and a lively pedaling platform for long singletrack days. +A commuter with modular battery placement, keyed lockouts, and a small integrated front rack for daily essentials. +Road aero bike with integrated cockpit, narrow fork, and a gloss finish that subtly changes color under different angles. +Lightweight hill-climber with 34T chainring, narrow rims, and aggressive climbing geometry. +Mountain enduro assault bike with premium suspension, long-travel fork, aggressive rubber and stiff wheels for steep technical lines +A polished-raw titanium gravel rig with fillet-brazed bottle boss mounts, internal cable routing, and discreet paint logos. +City single-speed with flat silver finish, disc brake conversion, reflective sidewalls and compact frame lock for theft deterrence +A folding cargo bike with long-range battery, reinforced hinge, and a weatherproof cargo box for urban deliveries. +A full-suspension enduro mountain bike with 170mm rear travel, coil shock, 180mm fork, 29-inch wheels, aggressive slack head angle, and wide 2.5-inch tires. +Downcountry mountain bike with 120mm travel, modern slack geometry, lightweight wheels, and efficient pedaling design. +Folding electric commuter with compact battery, torque-sensing motor, 16-inch wheels and quick-folding handlebars for tight storage. +A lightweight cyclocross frame with disc-specific dropouts, hidden fender mounts, and a sleek tapered headtube for precision steering. +Classic road bike with polished steel lugs, leather saddle, three-speed hub option and gentle long-distance geometry. +A commuter with matte finish, integrated lights, internal hub gears and a comfort saddle for steady daily commuting. +Fat-bike with custom camo paint, massive 5" tyres and seatpost shim for a comfortable ride in deep snow. +Electric cargo longtail with child harness rails, integrated turn signals, horn and sturdy frame construction for family transportation needs +Downcountry mountain bike with 120mm travel, light chassis, fast-rolling tires and trail-ready components for cross-country races +A mountain trail bike with progressive geometry, 140mm travel, dropper post and grippy 2.4-inch tires for confident cornering. +A commuter with integrated GPS cradle, built-in rearview camera, and an electric horn to warn pedestrians on busy paths. +Commuter with belt drive and hub gear, puncture-resistant tires, and integrated phone mount for modern urban needs. +Vintage-inspired dutch city bike with upright tubing, heavy-duty chain, wide leather saddle, and chrome mudguards. +Mountain bike with dropper seatpost, 29" plus tires and reinforced hubs for rock gardens. +Cargo longtail bike in industrial black with extended rear deck, reinforced frame, child seats and hydraulic brakes. +Lightweight urban single-speed with tapered headtube, carbon fork, alloy frame, and sleek integrated headset for clean aesthetics. +Touring tandem with integrated tools, dual lights, and spacious panniers for long-haul expeditions. +A mountain trail bike with 140mm front travel, 130mm rear, wide handlebars, 29-inch wheels and confident geometry for all-around trail performance. +A classic steel cyclocross frame with short chainstays, neat paintwork, and a slim seat tube for efficient power transfer. +Touring bike with low gearing triple, 26-inch wheels, and framebags for remote adventure self-sufficiency. +Gravel commuter with quiet belt drive, hub dynamo, integrated fenders and comfortable upright geometry. +Urban commuter with hub gears, belt drive and integrated rear light, finished in matte charcoal for understated style. +Road aero bike with deep-section rim front, disc rear, and integrated wheel/tire pairing for speed days. +Gravel race machine with SRAM mechanical shifting, 35mm tires, and a weight-conscious carbon build for speed on dirt. +A gravel cyclist’s race rig with electronic shifting, 40mm tires tubeless, and an aerodynamic yet comfortable cockpit for all-day speed. +Touring bike with triple chainrings, wide-range cassette, steel fork and a low-maintenance hub for epic routes. +BMX street setup with reinforced fork, pegs, gyro brake and powder-coated frame in a bold neon hue. +A cargo e-bike with built-in refrigeration box ideal for perishables, mid-motor control, and long wheelbase stability for safe urban deliveries. +Lightweight gravel-specific steel frame with specially tapered stays, angled head tube and 650b compatibility. +Vintage steel touring bicycle with lugged chromoly frame, 36-spoke steel wheels, front and rear racks, leather saddle and classic steel fenders. +Modern urban fixie with powder-coated frame, sealed bearing hubs, and synthetic leather saddle for low-maintenance commuting. +Fat-tire bike with reinforced rims, wide alloy frame, and a practical chainguard for winter touring. +Mountain enduro with single-pivot linkage, defensive frame armour and a stealth paint finish to hide trail scuffs. +A modern all-road carbon frame with internal storage in the downtube, oversized headset bearings for sharp steering, and 40mm tires for mixed conditions. +A tasteful hand-built steel road bicycle with classic lug details, understated enamel paint, and modern 11-speed compatibility. +A gravel-adventure frameset with multiple storage solutions, 1x gearing recommended, and a comfortable long-distance geometry. +A gravel expedition bike with heavy-duty racks, water-resistant frame bag compatibility, and 650b wheels for added durability on technical off-road. +A trail-ready hardtail with modern geometry, big-volume 29-inch rubber, 120mm fork and matte graphite paint with neon logos. +A carbon road bike with stealthy integrated cabling, slightly relaxed endurance geometry, and a pearl-black finish that shows faint metallic flecks. +Urban compact with chaincase, internal gearbox, and puncture-defense tires for a no-fuss daily bicycle. +Mountain gravity bike with longer travel suspension, slack geometry, and beefy tires for shredding steep DH trails. +Touring-ready steel frameset with triple-bottle capability, robust spokes, sealed cable routing and friendly long-distance geometry. +A retro-styled road bike with saffron orange paint, downtube friction shifters, and steel fork crown for a period-correct aesthetic. +Gravel bike with integrated chain guide, internal cable routing and a sculpted downtube for stiffness and storage. +A classic town bicycle with rigid fork, 3-speed hub, chaincase, and a durable navy enamel that resists the scratches of daily use. +Folding commuter with full chromed hinge, small 16-inch wheels, and quick-release forged clamp for folding in seconds +Urban cargo bike with low-step frame, built-in lock, child bench and puncture-resistant tires for daily use +Utility bike with low step-over frame, robust kickstand, and front basket for grocery runs. +A full-carbon mountain bike with 140mm travel, electronic dropper, and stealth paint with reflective flakes. +Kids' small-wheeled balance bike with lowered frame for easier footing and soft grips for tiny hands. +A commuter folding bike with small but feature-rich design, puncture-resistant tires, internal hub gears and a lightweight aluminum frame. +A bone-simple city bike with step-through frame, coaster brakes, and an oversized basket for easy market runs and grocery pickups. +Modern urban single-speed with deep dish rims, reflective pinstriping, carbon fork and anodized seatpost clamp +A cruiser with custom airbrushed mural, low-slung frame, and brass-accented fenders for show parking at car meets. +Gravel adventure machine with stout alloy tubing, 700x45mm clearance and reinforced head tube for durable steering. +Urban step-through commuter with enclosed chaincase, dyno hub, and comfy saddle for easy morning commutes. +A vintage steel track frame with elegant tubing, polished lugs, and a geometry that is faithful to classic velodrome handling. +A sporty electric mountain bicycle with torque-sensing mid-drive motor, 150mm rear travel, and a long-travel fork for rough alpine rides. +A city single-speed with coaster and handbrake combo, classic curved top tube, leather grips and soft gray paint with matte accents. +A touring tandem with independent shifting for captain and stoker, reinforced hubs, and an extended wheelbase for load stability. +A high-speed time trial bike with an integrated cockpit, minimal frontal area, and a molded hydration pouch in the frame to maintain aero advantage. +A kids' mountain starter bike with simple shifting, front suspension, protective chain guard and electric blue paint with lightning decal. +A modern carbon trail bike with 140mm travel, short chainstays for snappy handling, and a neat storage compartment in the top tube. +Lightweight commuter with belt drive, internally routed cables and stealthy matte finish for minimal upkeep. +Cargo trike with reinforced frame, wide wooden deck, and adjustable side rails for varied load shapes. +Handbuilt steel racing frame with custom geometry, ornate lugwork, classic components, and a hand-polished finish. +City cargo trike with a wide front box, electric assist, reflective safety striping and a weatherproof cover for loads. +Adventure e-gravel bike with mid-motor assist, extra water mounts and durable alloy frame. +Mountain enduro bike with adjustable shock tune, reinforced downtube guard, and optimized linkage for traction. +A retro-styled road bike with lugged steel frame, classic leather bar tape, and polished chrome forks for a timeless weekend cafe aesthetic. +Road bike with retro campagnolo components, tubular rims set up for classic criterium vibe. +Lightweight aluminium criterium bike in neon orange, shallow-section rims for quick acceleration, race geometry, and an aggressive drop bar setup. +Adventure e-bike with fat tires, torque-sensing motor, integrated GPS, and long-range battery for weeklong off-grid tours. +A fat-tyred commuter with insulation-ready frame, 4.6-inch tires, and a powdercoat that hides salt from winter commuting duties. +Classic French road race bike with ornate lugs, steel fork crown, 650c rims, and vintage decals for nostalgic rides. +A commuter with internal lighting powered by a hub dynamo, full-metal rack, and puncture-resistant 35mm tires for city miles. +Racing gravel bike with micro-suspension seatpost, carbon wheels, and a 1x12 wide-range cassette for steep climbs. +Touring gravel setup with welded steel racks, axle-mounted panniers and triple chainring adaptability. +Lightweight gravel racer with ceramic bearings, tubeless sealant installed and a thin-walled seatpost for comfort. +Electric mountain fatbike with low gearing, torque sensor, and super-wide 5-inch tires for floating over sand dunes. +Mountain enduro bike with progressive leverage curve, adjustable geometry and a burly wheel build for bike parks. +Classic mixte city bicycle with feminine curves, step-through ease, and subtle pinstriping for elegant neighborhood spins. +A gravel endurance bike with endurance fit, 700x40 tires, and internal cockpit cable routing to reduce snagging on rough sections. +A gravel touring bicycle with custom framebags, integrated rear light mount, and thick gauge spokes to handle long loaded miles. +A compact folding utility with tiny 12-inch wheels, quick-fold seatpost, low cockpit and bright safety orange finish for last-mile schlep. +A compact city bike with integrated locking system, battery-assisted kickstand, and inner-gear hub for urban convenience. +Urban step-through hybrid with internal hub, chainguard, rear rack and reflective sidewalls for safety. +Road racer with polished alloy finish, rim brakes, and sprint-tuned gearing for crits and accelerations. +A gravel carbon frameset with thick downtube, integrated mount points, and a two-bottle capacity for endurance rides. +A time-trial bike with fully integrated hydration, steep seat angle, 54-42 chainrings, and a glossy pearl-white paint job ready for QOMs. +A versatile mountain bike with 150mm front travel, 140mm rear, dropper post, wide carbon rims and modern geometry for aggressive trail riding. +Single-speed fixed-gear with anodized purple frame, narrow handlebars, and track chainring. +Vintage-styled steel city bike with leather grips, brass bell, and a woven basket for charming urban runs. +Urban cargo longtail with horse-saddle-style passenger seat, integrated parking stand, and rugged tires for loads. +A gravel e-bike with torque-sensing motor, big-range cassette, reinforced fork and desert-oxide finish with matte texture. +Cargo long-john bike with low front deck, padded passenger seat, and adjustable footrests for families. +Lightweight aero time-trial frameset with integrated hydration bladders, aero-optimized tubing and clean hidden cable routing. +Track time-trial frame with built-in aerobars, integrated hydration, and an ultra-stiff bottom bracket for sprint power. +A performance-oriented cyclocross bike with asymmetrical chainstays for stiffness, flared drops, and a functional, no-nonsense paint job. +Urban commuter with integrated bell, low-maintenance belt drive, and puncture-proof tires for daily reliability. +A rugged mountain hardtail made for bikepacking with rack mounts, extra water bottle mounts, slacker head angle and 29+ compatibility. +Kids' balance bike with alloy handlebars, adjustable saddle, and colorful rubber tires to learn balancing before pedaling. +Retro mixte with pastel decals, brown leather accents, and a spring saddle for gentle neighborhood rides. +A downhill bike with 200mm of travel, dual-crown fork, coil shock tuned for big hits, and super-burly aluminum frame for bike park abuse. +Urban folding bike with comfortable saddle, high-rise bars and a small footprint to stow under office desks. +A mountain trail bike with 150mm travel, plenty of stack for comfort, and a burly 35mm fork for rock gardens and rough descents. +A mountain hardtail with 120mm fork, nimble geometry, and a dropper-compatible frame that makes it easy to learn technical riding skills. +A cargo longtail bicycle designed to haul kids and cargo with reinforced rear rack, passenger footpegs, and hydraulic disk brakes for controlled stops. +A vintage-inspired town bicycle with cream-colored tires, chrome fenders, and brass accents that evoke early 20th-century charm. +A carbon gravel bike with a stepped seat tube to improve compliance, an integrated top cap tool, and deep-section alloy rims for mixed terrain. +Touring mixte with reinforced lugged steel, triple cage mounts, leather handlebar wrap, and a practical kickstand. +A practical family cargo bike with convertible seat arrangements, weatherproof canopy, and a parking brake that keeps it stable during loading. +A classic urban utility bicycle with rack, fenders, internal hub gearing, chain guard and a durable powdercoat finish for daily practical use. +A handmade chromoly dirt jumper with 26-inch wheels, stiff geometry, one-piece cranks, and a vivid candy-red powdercoat to stand out in the park. +A beach cruiser with glossy sea-glass finish, teak rear rack, wide saddle and whitewall tires with chrome fenders. +Belt-drive touring bicycle with steel frame, Rohloff internal hub, triple water-bottle mounts, and reinforced rear triangle for loaded miles. +A touring-ready steel frame with reinforced stays, triple-chainring front, heavy-duty wheelset and classic brass fittings for traditional touring. +A full-suspension cross-country rig with 120mm travel, snappy geometry, and lightweight carbon linkages for climbing performance. +Mountain park bike with short chainstays, strong wheels, and BMX-style handlebars for aerial tricks. +A boutique painted gravel frame with hand-sprayed metallic flakes, matching color-matched components, and subtly contrasting hoods. +Gravel adventure titanium frame with brushed finish, integrated bottle mounts, 650b tire capability and a discreet serial plate for long-term use +A downhill sled with full 200mm travel, triple-clamp fork, coil shock, and a geometry that prioritizes big hits and high speeds. +A factory-built alloy cyclocross race bike with wide flared handlebars, disc brakes, and a sealed-bearing headset for reliability. +Cross-country full-suspension bike with efficient suspension platform, 120mm rear travel, and lightweight wheelset for XC marathons. +Touring tandem with matching saddles, double racks, and classic lacquer finish with delicate floral motif. +A high-end road bike with integrated cockpit, hidden cables, shallow 35mm wheelset, and a 24mm tire profile for comfort-speed balance. +A cyclocross-inspired commuter with clearance for wide tires, disc brakes, and mounting eyelets for mudguards and racks. +Folding travel bike with hinged handlepost, locking clamps, 16-inch wheels and a hard case-friendly fold. +Classic steel racer with braze-ons for a pump, downtube cable stops and a classic two-tone enamel. +A kid’s balance bike made of lightweight alloy, rubber grips, a low-placed saddle, and a fun sun-yellow paint for early balance learning. +Gravel bike with clearance for 700x45mm tires, flared drop bars, disc brakes and integrated frame bag mounts for bikepacking. +Kids' pedal bike with stabilizer-friendly frame, colorful decals, coaster brake and low-step frame for easy mounting. +A classic porteur-style city bicycle with large front rack, leather straps, polished headlamp mount and a robust steel frame for deliveries or displays. +Chocolate brown city bike with integrated lights, low-maintenance hub gear, and a coaster-style step-through frame. +A mountain trail bike with 150mm fork, larger clearance, boost hub spacing and a dropper post that prioritizes aggressive trail riding. +City step-through with front rack, integrated light, and puncture-resistant tires for hassle-free errands. +A gravel expedition bike with titanium hardware, a steel replacement-friendly fork, and cozy geometry to make multi-week trips less punishing. +A kids' balance bike with wooden frame, rubber handle grips, varnished finish and rounded edges for safe play. +A commuter with symmetrical reflective stripes, integrated frame lock, and hydraulic disc brakes to ensure stopping power in all conditions. +A touring tandem with relaxed geometry, sturdy racks, dual water bottle mounts and classic British green paint with gold pinstriping. +Kids' beginner bike with coaster brake, stabilizer-ready mounts and cheerful cloud-patterned paint for learning confidence. +Folding city bicycle with magnetic latch, padded carry strap, and easy-to-adjust seatpost for multi-user households. +Gravel bike with stealth matte olive finish, low-profile tubing, mixed-width handlebars and a rugged 1x drivetrain for remote adventuring +A commuter with integrated rearview mirrors, internal cable runs, robust 32mm puncture-resistant tires, and a low-rise swept handlebar. +Retro city cruiser with cream-colored frame, chrome fenders, whitewall tires, and a spring saddle for easygoing rides. +A mountain trail full-suspension with long travel tuned for big lines, 29-inch wheels, dropper post and steely blue paint with streaked highlights. +Commuter e-bike with quiet belt drive, hydraulic disc brakes, and a single-button power assist for ease-of-use. +A commuter with integrated cargo deck, detachable panniers, and a smooth shifting internal hub to handle heavy daytime loads. +A single-speed dirt jumper with chromoly frame, reinforced fork, gyro-free brake setup, and chunky 2.4-inch tires for traction in skateparks. +A custom steel frame with hand-finished lugwork, bespoke paint gradient, and a unique headbadge depicting a hometown skyline. +Mountain hardtail trail bike with modern geometry, 120mm fork, and aggressive tread tires for uphill and downhill balance. +A young-adult hybrid with bright purple frame, front suspension, multiple speeds and fun decals for weekend adventures. +Mountain trail hardtail with modern slackness, 29-inch wheels, and internal dropper routing for tidy aesthetics. +Steel mountain hardtail with classic blue paint, grease-shedding cable routing, and rock-solid dropouts. +A road endurance frame with slightly taller head tube, vibration-damping seatpost, 30mm tires, and comfortable geometry for all-day rides. +A retro-styled single-speed cruiser bicycle with swept-back handlebars, wide saddle, balloon tires, and cream-colored paint for beach promenades. +Touring tandem with long-chainstay, comfortable saddles, three-bolt racks, and matched gearing for endurance travel together. +Performance e-road bike with discrete battery, torque-sensing assist, aero fork and gravel-capable 30mm tires. +Mountain freeride rig with strong chainstays, reinforced headtube, long travel fork, and heavy-duty brakes for park sessions. +Urban cargo e-bike with long wheelbase, twin-battery option, reinforced frame, and child seat attachments for family transport. +A polished stainless commuter with sealed-bearings hub, slim fenders, and a modern cable-routing system that keeps the silhouette clean. +A kids' balance bike made from molded composite, low weight, wide-back tires, and an ergonomically shaped seat to support early balance skills. +Gravel commuting hybrid with mudguard attachments, internal routing, and a step-through option for easy mounting in tight clothes. +Fat-tire snow bike with reinforced rims, studded tires and a generous bottom bracket clearance for deep snow. +Mountain trail bike with 140mm travel, lightweight alloy frame, and progressive geometry for jump lines. +Kids' single-speed with coaster brake and colorful flame decals to encourage city cruising safely. +Dirt-oriented mountain bike with alloy frame, 27.5 wheels, and nimble, playful geometry. +Track sprinter with streamlined profile, purpose-built steel tube set, and a narrow saddle for tactical races. +Buoyant beach cruiser with polished chrome and pearl white paint, wide balloon tires, and extra cushioned saddle for relaxed riding. +Touring tandem with Rohloff hubs front and rear, triple chainset, and stable geometry for island-hopping excursions. +A gravel bajan with solid steel frame, wide tire clearance, and classic brazed-on bosses for bottles and racks on long island loops. +A lightweight aluminum cyclocross frame with reinforced bottom bracket, disc brakes and a geometry that balances stability with nimble handling. +A commuter with multi-mode electric assist, reflective frame accents, and an oversized saddle for comfort during long ride commutes. +Mountain downhill competition bike with reinforced chainstay armor, heavy-duty headset, and large rotors for uncompromising stopping power +A modern cyclocross bike with updated tyre clearance, flared bars, and a compact chainset tailored to steep off-camber climbs. +Gravel plus bike optimized for loosened terrain with plus-sized tires, slack geometry and a bombproof alloy frame. +Electric fat bike with 4.5-inch tires, pedal-assist motor, reinforced frame, and cushioned saddle for winter commuting. +A gravel bike with fluorescent accents, reinforced seatpost clamp, large-volume tires, and stealthy internal storage compartment in the frame triangle. +Mountain trail all-rounder with 140mm travel, dropper seatpost, 29-inch wheels, and a reliable 12-speed drivetrain. +A lightweight aluminum city bike with sporty flat bars, sporty geometry, 28mm tires, and a compact rear rack for nimble commuting. +Touring bike with Brooks saddle, leather handlebar tape and reinforced frame for transcontinental trips. +A road race bike with full carbon monocoque, aero seatpost, ceramic bearings, and a race-geometry cockpit tuned for aggressive posture. +A commuter with multifunctional front rack, modular pannier hooks, and a low standover height for quick parking and access. +Endurance road bike with relaxed geometry, disc brakes, and clearance for 32mm tires. +All-road titanium bike with mixed wheel sizes, steel-built racks and discreet mudguard mounts. +Gravel bike with off-road aero features, integrated storage, and 650b compatibility for extra comfort. +Disc-brake commuter with full chaincase, dynamo lights, mudguards and upright posture for city streets. +A high-performance cyclocross bike with steep seat tube, optimized chainstay length, and a paint finish that camouflages dried mud. +Commuter with smart lock integration, onboard diagnostics, and easy-to-read LED assist level indicator. +Long-travel mountain bike with 150mm front and 140mm rear suspension, 29-inch wheels, wide 2.5-inch tires, and a dropper post. +Dirt jump rig with reinforced hubs, short chainstays, hydraulic brakes, and a compact geometry for technical tricks. +A kids' balance bicycle in canary yellow with low saddle, rubber-grip handlebars and wide plastic wheels for safe learning. +A vintage track bike restored with new tubular tires, straight-pull spokes, and a freshly polished chainset. +A lightweight road bike with aerodynamic wheelset, deep but compliant tube profiles, and a gloss white finish with minimalist logos. +A modern gravel e-bike with torque-sensing motor, dropper post, internal battery, and mudguard mounts for mixed-terrain commuting. +Touring tandem bicycle in navy with double water bottle mounts, stronger spokes and wide touring tires. +A high-volume trail ebike with 29x2.6” tires, integrated battery, and long-reach brakes for consistent stopping on technical descents. +A cyclocross-style commuter with practical fender mounts, dynamo hub, and wide 28mm tires for mixed pavement surfaces. +A mountain enduro bike with adjustable shock tune, 160mm fork, reinforced swingarm and a long wheelbase for confidence at speed. +Classic touring steel frame with lugged crown, long chainstay for stability, wide gearing, and multiple braze-ons for camping racks. +Touring steel bike with custom brazed-on mounts, thick-wall tubing and a long wheelbase for coping with luggage. +A gravel-day racer with deep-section front rim, lightweight carbon rear, and a high-cadence gearing setup for peaky efforts. +Gravel race bike with wide 700x40 tubeless tires, SRAM Rival new groupset, and flared bars for fast dusty stages. +A BMX cruiser with retro styling, fat tires, chrome fenders, banana seat and pastel sea-green finish with whitewall tires. +A gravel adventure bike with large tire clearance, reinforced fork, multiple bottle mounts, and a paint finish designed to resist chips from rocks. +A classic road racer with tubular tires, high-flange hubs, and downtube shifters offering a pure, traditional cycling feel. +A handmade steel road frame with ornate lugs, gloss candy-red finish, 25mm tubular tires, and period-correct campagnolo parts for vintage racing charm. +A commuter with integrated dynamo lights, belt drive, internal hub and satin stone finish with reflective microbeads for low-light safety. +Lightweight alloy touring bike with disc brakes, 700c wheels, and stable geometry for loaded cornering. +Utility cargo trike with large wooden platform, low gear range, hydraulic disc brakes and tie-down points. +A commuter with hydraulic rim-styled brakes, reflective paint, and a hidden frame locker to secure the bike quickly. +A cyclocross bike with full disc brakes, quick-release removal axle, and a durable titanium frame for reduced fatigue over long winter races. +A commuter with a built-in battery rear rack e-assist, puncture-resistant tires, LED integrated lights, and a comfortable upright geometry for city cruising. +Classic BMX park bike with top-tube pegs, reinforced forks and 2.3" tires for skatepark sessions. +A gravel ebike with mid-drive motor, long-range battery, wide clearance for 2" tires and modular bag mounts for extended trips. +Electric folding e-bike with long-range battery, compact fold, mid-drive motor, and a low-step frame for urban convenience. +A vintage-inspired commuter with leather-sprung saddle, brass bell, and subtle pastel paintwork that evokes leisurely rides through town squares. +Track sprint special with narrow tubular rims, a polished alloy frame and a classic single-color livery for the velodrome. +A handbuilt steel road frame with fillet-brazed aesthetics, slender seat stays, and a tasteful enamel finish for subtle, timeless utility. +A restored vintage road bicycle converted to modern braking standards while retaining period-correct shifters and leather grips for charm and safety. +A commuter cargo trike with three wheels, rear storage box, and robust tubular steel frame for heavy loads. +A track-first sprinter with a restrictive geometry, very stiff seatpost cluster, and a single chainring optimized for power transfers. +Gravel endurance aluminum frame with 38mm clearance, sturdy brazed-on bosses and a geometry tuned for long rides. +A utility cargo bike with long wheelbase, weatherproof box, heavy-duty kickstand and reflective lime paint for visibility. +Classic step-through city bike with enamel finish, smooth-rolling tires, polished fenders and simple internal hub gearing. +A gravel adventurer with multiple cage options, wide 700x45 tires, and discreet reflective piping for late finishes in the countryside. +Foldable commuter with smartphone integration, small turning radius and a luggage deck for city errands. +Gravel plus bike with compliant frame, big-volume tires and a modular rack system for carrying extra gear. +A cargo trike with low cargo bed, reinforced frame, and upright steering for predictable handling under load. +A road bike with endurance geometry, disc brakes, and vibration-damping seatpost to smooth out rough pavement miles. +Trail hardtail with 120mm fork, dropper post, and durable 2.35-inch tires for mixed terrain. +A folding neighborhood e-bike with basket, torque-sensing hub motor, compact 20-inch wheels and olive-matte paint for understated style. +Folding urban bicycle with magnetic latch, compact folded size, and tasteful leather saddle for comfort. +A mountain downhill bike with integrated chain guide, reinforced swingarm, and a piggyback-charged shock for heat resilience on long runs. +Retro road bicycle with classic lugwork, cotton bar tape, narrow tubulars, and a hand-painted headbadge for authentic period style. +A single-speed cyclocross conversion with durable steel frame, knobby tires, flared drops and a no-nonsense track pedigree for simplicity. +Mountain downhill rig with reinforced swingarm, massive fork travel, and coil shocks built for gravity-fed tracks. +An urban cargo bicycle with long-tail platform, dual child seats, reinforced frame, steering stabilizer, and puncture-proof tires for heavy loads. +Mountain downhill legend with double crown fork, reinforced gussets, large-volume tires and reliable suspension for race-winning confidence +A city folding bike with integral lock, pop-out pedals, internal hub gears and an ergonomic saddle for multi-hour commuting comfort. +Compact folding bike with 16" wheels, silver aluminum frame, single-speed hub and quick-release clamp for urban commuting. +Road aero frameset with internal cable routing, neat seam finish, and painted-on aero logos that fade into the background. +Gravel race frame with short chainstays, flared handlebar drops, and a stealthy matte black coat with contrast decals. +Mountain downhill race sled with double-crown forks, heavy-duty pivots, and engineered impact-absorbing features for speed. +A retro chromed road bike restored with period decals, toothy tubulars, and a leather bar wrap to mirror classic European racers. +High-performance road bicycle with integrated bar/stem, Dura-Ace disc groupset, and ultra-light carbon wheels for competitive climbs. +Minimalist commuter bike with belt drive, internal hub, fenders, and matte gray paint. +Touring expedition bike with bolt-on frame bag mounts, Rohloff drivetrain compatibility and reinforced head tube. +A stripped aero road bike with hidden cables, integrated seatpost, narrow 25mm tires and a matte graphite finish. +Mountain full-suspension bike with 160mm travel, long-travel fork, and burly plateards to absorb big hits and drops. +A lightweight aluminum road bike with race-proven geometry, aerodynamic tube shaping, and a glossy fluorine paint job. +A long-haul touring bike with triple-rack boss sets, replaceable sliding dropout, and 26" wheel compatibility for global resilience. +A commuter with integrated solar-charged taillight, puncture-resistant tires, built-in frame lock and a compact rear rack for daily use. +Mountain long-travel bike with adjustable geometry, coil-sprung shock and massive rotor brakes for alpine descents. +Road time-trial machine with integrated hydration system, steep seat tube angle, and aerodynamic mock tail for triathlon tempos +Adventure-ready gravel frame with transverse bottle mounts on the top tube and integrated GPS mount. +Mountain cross-country bike with light-weight carbon frame, fast-rolling 29er wheels, race geometry and narrow 2.2" tires for speed. +A single-speed steel urban bike with a midnight-blue paint job, riser bars, coaster brake, and minimalist fenderless styling for city cruising. +A touring-ready gravel bike with Rohloff hub, belt drive, and stainless fender set for near-indestructible long-distance touring. +A BMX park build with chromoly top tube, reinforced dropouts, and a stylish decal kit that looks sharp under skatepark lights. +Vintage mixte with glossy cream paint, wicker basket, chrome mudguards and gentle upright geometry for easy town riding. +Classic road bike with hallowed-out steel frame, polished chrome details, leather saddle and a vintage toe-clip setup. +A vintage mountain bike restored with chrome fenders, classic leather grips, and modern sealed-bearing hubs for reliable rolling. +A rugged mountain enduro bike with 170mm travel, reinforced downtube, and steerers ready for heavy-duty trail abuse and bikepark sessions. +Stylish urban mixte with internal hub gears, coaster brake backup, puncture-resistant tires, and leather grips. +A lightweight aluminum commuter bike with internal hub gears, belt drive, full fenders, integrated rear rack, and reflective paint for night visibility. +A commuter with integrated theft-deterrent lock in the frame, full fenders, and an internal 11-speed hub for low-maintenance city miles. +City step-through with built-in chaincase, rear child seat mounts and reflective decals for family runs. +A classic road touring bicycle with triple chainset, steel racks, and hand-stitched leather saddle for multi-day trips. +A sprint-ready track bike with aerodynamic tubing, shallow rims, and steep seat tube for optimizing power transfer during sprints. +A gravel-oriented titanium frame with front rack mounts, stealthy paint, and doubled water-bottle capacity for long remote rides. +A vintage British-style utility bike with wicker basket, coaster hub, and hand-polished fenders for dignified daily use in town. +All-road carbon frame with balanced compliance, disc brakes, and 35mm tyre capacity for versatility on tarmac and gravel. +Track keirin bike with a glossy metallic finish, oversized chainring and a minimal seatpost for stability. +All-road carbon frame with integrated storage holster behind the down tube and stealth paintwork. +A gravel adventure frame with triple rack mounts on the fork, P-Clamps for extra luggage, and a geometry set for heavy loads and long days. +Classic British-style city bicycle with chainguard, coaster brake hub, wicker basket, and a two-toned paint scheme. +Road endurance bike with vibration-damping fork, comfort geometry and puncture-resistant 28mm tires. +Fixed-gear track-style commuter with anodized components, slick black frame, and an attitude-ready single-speed drivetrain. +A titanium commuter with internal battery cavity, discreet mounting points for racks, and brushed metal aesthetics that look professional and durable. +Mountain enduro carbon build with twin-pivot link, plush mid-stroke feel and strong 29-inch wheels for technical descents. +Touring bike with mixte frame, dual kickstands, and high-volume tires for rough roads. +Performance hardtail with race geometry, lightweight carbon fork and fast-rolling 2.2" tires. +A compact folding e-bike with long-range battery, secure hinge, and bright integrated lights that boost safety for late shifts in the city. +A clean urban single-speed with turquoise paint, leather grips, sealed bearings, and a narrow saddle for efficient commuting. +All-mountain bike in lava red with 160mm travel, burly chassis, and coil shock for big-line confidence. +A high-traction fatbike with paddle-like tread, 4.5" tires, and lowered tire pressure for maximum float on soft sand and snow. +Fatbike with 4.8-inch tires, rigid fork, and powder-coated forest green frame for snow riding. +A single-speed mountain bike with rigid fork, chunky 2.4-inch tires, and an anodized blue chainring that contrasts with a raw alloy frame. +Lightweight aero track bike with straight-pull spokes, integrated stem and glossy black paint with subtle pinstriping. +High-performance mountain bike with full carbon frame, 160mm travel, 1x drivetrain and shy geometry tuned for speed. +Performance gravel machine with aero tubing, tubeless-ready rims, and a lightweight 1x groupset for race-winning speed. +Lightweight aero road frameset with integrated seatpost clamp, hidden rear brake and clean internal routing for aerodynamic efficiency. +Performance road bike with integrated di2 junction, aero tube shapes, and wide 28mm tire clearance for speed with comfort. +Classic city bicycle with step-through frame, enamel paint, wicker basket, rear rack, and an upright relaxed geometry. +City utility bicycle with center stand, integrated basket, chain guard, low gearing, and wide tires for stable urban travel. +A vintage lugged-steel touring bicycle with leather Brooks saddle, chromed rack and fenders, downtube shifters, and triple chainrings for loaded trips. +A gravel touring machine with stout steel frame, 650b wheels, low gearing for loaded climbs and forest-camo painted frame. +Fatbike with studded tires in Arctic white, rigid carbon fork, and wide rim for snow and ice riding stability. +Gravel ultralight with carbon tubes, minimal paint, titanium derailleur hanger and ultra-tubular wheels for weight-conscious riders. +Gravel bike with asymmetric carbon layup, 700x45mm compatibility and a stealth black ceramic-coated fork. +A kids' folding bike with quick-release seatpost, small wheelbase, and a handlebar lock for secure transport and compact storage. +A touring bike with triple rack mount options, comfortable swept handlebars, and a battery-assisted auxiliary system for heavy cargas. +Touring-friendly hybrid with reinforced fork, dynamo lighting and wide 32mm all-weather tires for comfort. +A road endurance frameset with sloping top tube, comfortable stack height, and an emphasis on long-ride comfort without sacrificing speed. +A commuter foldable with quick-release folding mechanism, small 18-inch wheels, integrated lights and steel-blue matte finish. +A cyclocross training rig with welded steel frame, mud-shedding shapes, and a race-ready build that prioritizes durability over weight. +A modern steel gravel bike with short chainstays, 1x drivetrain, and subtle cutouts behind the headtube to reduce weight. +Carbon fiber cyclocross race bike with tubular tires, cantilever-style disc mounts, and mud clearance. +Lightweight endurance road bike with forged aluminum dropouts, comfortable seatpost, and micro-suspension compliance. +A bombproof student bike with heavy-gauge steel frame, steel fenders, coaster brake and welded chain guard for campus use. +Lightweight steel road bike with fillet-brazed joints, modern geometry, and subtle lacquer finish. +A classic road bike restored to original specs with downtube shifters, steel rims, and period-correct decals celebrating vintage racing. +A carbon cross-country race frame with internal dropper compatibility, 120mm fork, and asymmetrical chainstay for increased stiffness. +Urban cargo trike with tilting cargo bed, adjustable handlebars, and a lockable cargo lid for safe multi-stop runs +A gravel commuter with fenders, low-maintenance internal hub, belt drive and matte charcoal finish with reflective side panels. +A gravelbike with alternating matte and gloss paint, tubeless 700x40c, and a clutch derailleur to keep the chain glued in rough sections. +A cyclocross racer with mud-shredding fork crown, 700x33 tires, and a subtle anodized decal for understated speed on the course. +A commuter with internal storage compartment in the downtube, integrated lights, and a sealed hub to minimize service time. +Gravel adventure rig with framebag, dual-bolt seatpost harness, and stainless braze-ons for multi-day exploration. +A retro step-through city bike with fenders, chaincase, basket and a comfortable swept-back handlebar for errands. +A mountain trail frame with modern geometry, generous tire clearance, internal battery routing for lights, and a stealth cable port. +Gravel-focused frameset with flared cockpit, reinforced downtube, and lugged steel features for classic handling with modern clearance. +A winter-ready commuter with studded tires, full-coverage fenders, heated grips, and a dynamo-powered headlamp to fight low light. +Adaptive trike bicycle with low step-in frame, comfortable reclined seat, and three wheels for additional stability. +Mountain downhill frame with adjustable geometry chip, reinforced gussets, and long-travel compatible shock for gravity shows +Electric commuter with quiet hub motor, long-travel suspension seatpost, integrated lights and a simple thumb throttle. +A sleek commuter with full carbon frame, integrated fender mounts, tubeless-ready 28mm tires, and a low-maintenance hub gear. +Touring bicycle with removable lowrider rack, reinforced dropout, and an extra-durable paint system to withstand luggage rub. +A titanium time trial frame with integrated hydration systems, custom-fitted aerobars, and an optimized seat tube for aerodynamic efficiency. +Minimalist commuter with belt drive, internal 3-speed hub, integrated rear light, and low-maintenance components. +Trail-capable gravel bike with front suspension fork, robust tires, and 1x12 drivetrain for technical routes. +Gravel bike with integrated computer mount, stealthy internal routing, and flared drops for control off-road. +Vintage track bicycle with lugged steel frame, tubular tires, polished steel dropouts, and a clean, minimalist paint job. +Retro-styled city e-bike with mid-drive assist, leather-look grips and flowing curvy frame reminiscent of the 1950s. +A sleek carbon aero road bike with integrated hydration, deep 60mm wheels, rim brakes for lightness, and a pearlescent white finish. +Mountain all-terrain hardtail with modern geometry, 29" wheels, and a robust fork for varied singletrack conditions. +A performance road frame with smooth seat tube junction, aero downtube, and a paint scheme that visually shortens the frame for speed cues. +Electric cargo bike with modular side panniers, integrated child harness and large diameter wheels for smooth rolling. +A kids' BMX with striking neon rims, padded top tube, and compact cranks designed for short bursts of speed and tricks. +A commuter with integrated smart lock, automatic brake lights, and a small solar strip on the top tube for intermittent charging. +Cross-country race hardtail with aggressive climbing geometry, lightweight build, and 29x2.2" tires for rolling terrain. +A polished chrome moustache-handled touring bicycle with dynamo-powered headlamp, stitch-leather saddle, and triple-bottle mount. +A gravel endurance bike with 700x45 clearance, flared bars, and a micro-suspension saddle for long off-road days without excess fatigue. +A compact folding cargo with extended platform, reinforced hinges, and quick-release clamp that folds flat for easy apartment storage. +Urban cargo longtail with built-in passenger footrests, padded bench seat and reflective safety tape. +Downhill bike with massive 203mm rotor brakes, coil rear spring and adjustable geometry for steep runs. +A carbon gravel bike equipped with 1x drivetrain, clutch derailleur, wide-range cassette and stealth matte charcoal finish. +A kids' tricycle with wide base, low center-of-gravity, and bright decals making it easy to spot in a playground. +A titanium commuter with bead-blasted finish, stealth cable routing, and a geometry that favors relaxed posture for daily rides in any weather. +Adventure-oriented gravel bike with rugged fork, frame bag, bar-mounted GPS, and 700x42 tubeless tires. +Gravel racer with polished steel accents, flared drops, and 38mm tubeless tires for fast mixed-surface rides. +Touring steel frame with triple bottle mounts, long chainstay for stability and classic powder-coated finish. +City e-bike with integrated dashboard, torque-assist motor, long-range battery and comfortable upright posture for daily commuting. +A cyclocross rig with carbon crankset, lugged steel frame, and ample tire clearance that combines classic soul with modern performance. +Lightweight touring bike with titanium racks, small-batch hand-painted logo, and titanium mudguard mounts. +A commuter with integrated lighting, low-maintenance belt drive, internal hub and slate-gray powdercoat with reflective micro-dots. +Road commuter with flat bar conversion, disc brakes, puncture-resistant tyres and a dynamo-powered headlight. +A kids' trail bike with 20-inch wheels, low standover, front suspension and soft grips for first-time trail exploration. +A gravel rig with integrated phone mount, generous tire clearance, and a modular rear rack that supports bikepacking or commuting loads. +A compact folding cargo bike with a foldable deck, large load capacity, and a motor-assisted option to make urban deliveries efficient and painless. +Touring steel bicycle with smooth brazing, rack-compatible stays, full fenders and a durable powder-coat finish for endless miles. +Single-speed fixed gear with polished steel frame, deep V rims, narrow saddle, and minimalist riser bars for track-inspired rides. +Touring steel frame with beefy rack mounts, low-tech but rugged components and classic metal flake paint. +A foldable electric bike with center-fold hinge, small wheels, integrated battery pack, and a torque-sensing motor for hills. +Gravel race rig with tubeless setup, 1x drivetrain, wide cassette range, and a race-focused cockpit for long mixed-terrain events. +A gravel endurance bike with subtle geometry changes for a stable feel on descents and long comfortable climbs. +Gravel puncheur's bike with short wheelbase, low-profile 700x36 tires, powerful brakes and a compact chainset for punchy climbs +A vintage-style mixte frame city bike with step-through top tube, wicker basket, and soft-sprung saddle for leisurely promenades. +A cyclocross practice bike with steel frame, mechanical disc brakes, easily replaceable wheels and robust mud clearance for regular training. +A modern carbon gravel frame with sleek integration, 700x45 tire clearance, and a dedicated top-tube phone pocket under the bar. +A classic mixte touring bike with leather-wrapped handlebars, durable frame, and a large-capacity wicker basket for grocery runs. +Race-ready cyclocross with rapid-release wheels, mud-specific tread and ultralight tubular setup. +Fat-bike commuter with studded tires, low gearing, and a capacity for handling deep snow and soft sand with ease. +Practical commuter with step-over frame, integrated cargo rack and reflective striping for safe dawn departures. +Road endurance frame with subtle carbon weave, generous tire clearance and comfort-focused geometry for long centuries. +Touring adventure bike with welded steel racks, oversized panniers and dynamo-recharging USB socket. +Gravel cyclocross hybrid with 700x40 tire option, flared drop bars, and a low bottom bracket for confident cornering. +City e-bike with pedal assist, integrated rear rack, step-through frame and a matte pastel finish. +A classic British three-speed mixte with chain guard, chrome carrier, and a comfortable upright geometry designed for neighborhood rides. +A gravel-adventure rig with wide 650b wheels, robust wheelset, extra bottle mounts and a flared handlebar for control. +A classic step-through city bike in pastel mint, upright swept handlebars, chaincase, and integrated front basket for errands. +Commuter with comfortable upright bars, plush saddle, and foot-powered bell for safe city riding. +A cargo e-trike designed for small businesses with long flatbed, powerful mid-drive motor, and durable steel tube construction for daily heavy use. +A raw steel single-speed with hand-brushed finish, brown leather grips, narrow tires and classic stamped head badge. +High-end mountain bike with carbon front triangle, SRAM XX1 Eagle, 12-speed cassette and 29" wheels. +Touring bike with heavy-duty steel forks, low-trail geometry, triple chainrings and a sand-colored finish that hides chips. +A folding cargo trike with stable three-wheel layout, low center of gravity, and weatherproof wooden box in the front. +Folding city e-bike with mid-line battery, small-wheeled stability and a lightweight magnesium hinge for commuting. +Road race bike with 12-speed drivetrain, shallow rim profile wheels and ultralight saddle for criteriums. +Off-road adventure bicycle with extra toe clearance, cargo bosses, and a simple, durable paint that conceals scratches. +A gravel bike with wood veneer top tube and custom leather accents, wide gearing range, and tubeless-ready rims for off-road comfort. +Old-school road bike with downtube shifters, leather bar tape, and a chromed steel fork crown that shows classic craftsmanship. +Cyclocross pro alloy frame with oversized downtube, camo decal, and geometry optimized for quick shoulder carries. +Road endurance build with carbon layup for compliance, 30mm tires and slightly higher head tube for comfort-oriented posture. +Fat-tire trail bike with plus-sized rims, burly spoke counts, and matte tan wall tires for retro appeal. +Cargo van-style longtail bike with welded aluminum tray, child harness options, and high-visibility paint. +A utility cargo bike with front-loading bin, low center of gravity, and high-volume tires to ferry weekly groceries without fuss. +Touring steel frame with welded braze-ons, waterproof framepack mounts, triple chainset and durable disc brakes for world travel +Gravel plus-ready frameset with 2.2" tyre clearance, multiple rack mounts, and a progressive geometry for spirited exploration. +A cyclocross race build with mud-optimized frame shapes, wide-mouthed tires, and a race-proven mix of lightness and durability for season-long use. +Compact kids' BMX with bold graffiti graphics, 18-inch wheels, pegs and studded tires for park adventures. +A commuter with integrated solar charging panel on the rack, small USB port, and efficient hub dynamo for reliable lighting. +Gravel bike optimized for mixed surfaces with 40mm tires, multiple bottle mounts, integrated frame pumps and tubeless setup for flats prevention +Gravel plus bike with 650b wheels, 47mm tires, low gearing and internal cable routing. +A cyclocross faithful build with steel fork, 33mm mud tires, upright geometry and reinforced brake mounts to withstand sezon-long training. +Mountain trail all-rounder with 140mm travel, responsive geometry and supportive saddle for long downhill sections. +Trail-focused hardtail with progressive geometry, dropper post, and modern 29" wheels for fast technical lines. +Recumbent trike with comfortable mesh seat, low center of gravity, chain guards and wide reclining frame for long rides. +Urban single-speed with stonewashed paint, leather saddle, platform pedals and a clean minimalist chainline. +A gravel bike with custom mudguard clears, painted fork, and 700x42 tires for confident rides across seasonal farm tracks. +Aero time-trial bike with TT bars, steep seat tube angle, and disc wheels optimized for speed. +A heritage-inspired city bicycle with brooks saddle, brass headbadge, fenders, and enamel paint with subtle metallic flecks. +Road time trial bicycle with integrated hydration and nutrition mounting, shallow wheelbase and aero-optimised tubing. +Racing time trial bike with full fairing, integrated hydration, and exacting aero tube shapes. +A racy aluminum frame with aero-shaped tubing, integrated seat mast, and paint that fades from red to orange in the sun. +A BMX freeride with chromoly frame, removable brake caliper, and a short 10mm axle to endure hard landings and street features. +Fat-bike utility rig with cargo deck, motor assist option, and 4" tires for stable winter commuting with parcels. +Commuter e-bike with step-over frame, cargo-ready rear rack, and integrated rear light cluster. +Gravel plus bike with 2.8-inch tires, slack head angle, dropper post, and a burly frame to soak up rough terrain. +Gravel racer with carbon layup, compact cockpit, and 35mm tires for a race-day balance of speed and comfort. +A lightweight classic road racer with polished lugs, Campagnolo group, and shallow box-section rims for vintage racing fidelity. +A commuter with full-suspension fork for comfort, stepped frame for easy mounting, and a chain guard to protect clothing. +A rugged cargo bike for tradespeople with flatbed, heavy-duty mounts, and a powdercoated steel frame designed to handle tools and loads daily. +A road time-trial bike with smooth integrated cables, hidden hydration, and an aggressive forward-leaning cockpit for aerodynamic position. +Cyclocross bike with mud clearance, 33mm racing tires, and tubular-ready rims for winter races. +A city commuter with enclosed chaincase, step-through geometry, integrated locks and warm chestnut brown paint with cream emblems. +A vintage club racer bicycle with thin steel tubing, classic decals, and a small saddle for authentic period handling. +Pink downhill bike with coil shock, massive 200mm travel, burly frame and SRAM XO derailleur. +A cyclocross racer with fast-rolling treaded tires, compact crank, and a rugged paint job that hides mud splatters. +Classic mixte step-through bicycle in cream enamel with wicker basket and retro chrome bell. +City cargo bike with modular attachments, chain-guard, and a practical low-step frame for quick loading and unloading. +A kids' BMX with hydraulic disc brake, reinforced pegs, and a powder-coated frame that resists scratches from tricks. +A gravel packer with heavy-duty racks front and rear, welded extra mounts for Jerry cans, and oversized tubeless tires for remote places. +Adventure mountain hardtail with frame mounts for bottles and tools, 29er wheels, and durable tires. +A cyclocross training frame with alloy tubing, sealed bottom bracket, and a forgiving geometry to handle long muddy winter sessions. +A track pursuit machine with deep-section training wheels, oversized spindle, and stiff frame to resist torsion under hard sprints. +A titanium gravel bike with shapely slender tubes, integrated front light wiring, and an understated badge etched into the headtube. +A high-performance gravel bike with carbon fork, 2x drivetrain with wide cassette, 700x45mm gravel tires and a carbon cockpit. +A kids' BMX park bike with 20-inch chromoly frame, pegs on both axles, and neon-green grips. +A tiny-wheel city bike with 16-inch tires, upright geometry, and a folding stem for commuters with small storage spaces. +Handbuilt fillet-brazed steel frame with custom geometry, subtle pinstriping, and Brooks saddle. +Mountain freeride bike with stout downhill-ready frame, long-travel fork, strong hubs and extra rotor clearance for heat dissipation +A track-ready sprinter with an aerodynamic monocoque frame, shallow handlebars, and a very stiff rear triangle for maximal power transfer. +A mountain enduro machine with 165mm travel, wide rims, coil-compatible shock and sturdy chainstays to withstand aggressive riding. +Vintage mixte with floral decal paint job, wicker basket, roller brakes, and a comfortable upright geometry for scenic rides. +Road performance bike with lightweight carbon rims, integrated seatpost clamp, and narrow-profile tubing for sprint responsiveness. +Folding electric cargo bike with fold-forward frame, long-tail platform, and dual-battery configuration for long urban routes. +Touring tandem with three bottle mounts per rider, sturdy front and rear racks, and classic enamel varnish. +A touring bicycle with custom brazed-on generator hub, extra water-bottle mounts, and heavy-duty stainless spokes. +Lightweight carbon time trial bike with narrow aero tubing and integrated hydration pockets behind the seatpost. +Mountain freeride frame with reinforced welds, extra thick downtube, slack geometry, and compatibility for big-travel forks. +Kids' balance trike with low center of gravity, plastic shell frame and vibrant stickers for visibility. +Folding city bike with quick-fold latch, compact frame geometry, and a built-in rear reflector for visibility on transit. +Cargo tandem bicycle with two seats, extended cargo platform between riders, heavy-gauge tubing and robust wheelset +Kids' BMX with short wheelbase, light alloy cranks, and bright rainbow spoke beads for flash. +Adventure tandem with reinforced mid-section, low gearing, durable racks, and matching paint for long two-up tours. +Vintage-styled steel road bike with chrome accents, period-perfect components, and a warm oxblood paint reminiscent of classic marques. +Road endurance bike with shock-absorbing inserts, comfortable saddle, and a tire clearance up to 32mm. +Mountain trail hardtail with boosted hubs, aggressive tire profile, and a durable anodized chainstay protector. +A practical city bicycle with upright geometry, large-capacity front basket, and a stable rear rack for grocery runs and weekend errands. +A race-ready mountain bike with carbon frame, 120mm travel optimized for cross-country races, and light but durable wheelset. +A lightweight carbon climbing bike with stiff chainstays, shallow rim depth wheels, compact crankset and an emphasis on low overall mass. +Road racing frame with race-tuned geometry, lightweight carbon layup, and integrated seat clamp for clean lines. +A tandem touring bicycle with dual racks, two saddles, long wheelbase, steering damper, and integrated pannier mounts for long-distance couples' travel. +A minimalist city single-speed with polished steel frame, leather saddled seat, and narrow handlebars for zippy maneuvering. +Gravel bike with custom framebag, dropper post, wide handlebars, and a stealth matte navy finish for bikepacking stealth. +A modern aero road bike with integrated cockpit, hidden cables, 35mm tires fitted for comfort and aerodynamic efficiency on flats. +Folding commuter with 8-speed hub, rugged hinge, and step-through access for easy city portability. +Track pursuit bike with aerodynamic profile, deep-section front wheel, and a narrow aero stem for tucked position. +Carbon gravel bike with full carbon fork, thru-axles, and clearance for 45mm gravel tires to tackle rough trails. +Urban cruiser with retro headlamp, chrome accents and wide saddle for casual neighborhood cruising. +Gravel race frameset with light layup, modern clearance, and minimalistic graphics for understated speed on rough roads. +A steel fixed-gear city track bike with polished-flange hubs, narrow saddle, and toe-clip pedals for old-school pedaling feel. +A high-volume downhill bike with adjustable geometry, dual crown fork, carbon bars, and reinforced rims for heavy impacts. +Cyclocross race build with tubular clinchers, 36mm cyclocross tires and mud-shedding frame design. +Gravel all-terrain bike with mixed clearance for 700c or 650b wheels, bolt-on fender mounts and frame pump clip. +Urban step-through single-speed with chain guard, rear coaster hub, and small front rack for city convenience. +A low-slung recumbent with long-wheelbase, aerodynamic fairing options, and a comfortable mesh seat for long-distance low-drag touring. +Urban single-speed fixed with minimal brake setup, polished steel finish and a symbolic headtube badge. +Folding electric cargo bike with reinforced hinge and large rear deck for groceries, kids, or deliveries in the city. +A cyclocross race machine with heat-treated alloy frame, modern clearance, and a raw finish that hides scratches during the season. +A titanium road racing bicycle with brushed finish, internal cable routing, and lightweight tubular wheels. +A lightweight steel frame gravel grinder with 34mm clearance, low-mass tubes, and a subtle matte lacquer finish. +Lightweight urban fixie with anodized headset spacer stack, narrow-profile cranks, and a slick single cog. +Road race bike with aerodynamic tubing, 25mm racing tires, and a stiff carbon layup for sprint finishes. +Urban fixed-gear with lemon yellow frame, flip-flop hub for single-speed or fixed, and platform pedals for casual rides. +Racing road frame with full carbon layup, aggressive aero shaping, hidden cable routing, and a deep V rimset. +A gravel racing frame with monocoque carbon fork, hidden fender mounts, and a supple compliance profile for rough racing conditions. +Touring tandem with reinforced frameset, dual racks, extra bottle mounts, and comfortable matching saddles for long journeys together. +Comfortable cruiser with plush saddle, upright bars, and pastel shaded paint designed for seaside promenades. +Gravel pedaler with dropper post, multiple framebag-friendly attachment points, and a matte olive finish for stealthy touring. +Modern gravel grinder with two-bolt direct-mount front derailleur, thru-axles, and stealth dropper compatibility. +A gravel e-bike with mid-mounted motor, long-range battery, and a rear rack prepped for panniers and camping gear. +Adventure touring bike with hi-viz reflective decals, three bottle mounts and long-range gearing. +Mountain enduro bike with adjustable geometry headset, long travel fork and robust chainstay protection. +Dirt jumper with short travel fork, single-speed drivetrain, and reinforced top tube for tricks. +Urban single-speed with polished crank arm, minimal decals, and reflective sidewall tires for safer night commutes. +Lightweight city fixie with polished aluminum frame, deep-section rims, and a classic leather saddle for style and speed. +A folding commuter with robust folding latch, 8-speed internal hub, puncture-resistant tires and a carry harness for easy multi-modal transit. +Classic cruiser with chrome fenders, scalloped paintwork, and a plush wide saddle for relaxed rides. +A commuter with built-in smartphone mount, integrated lights, anti-theft wheel skewers and a quiet belt drive for nightly city runs. +Electric folding commuter with hydraulic disc brakes, USB charger built into the battery, and a quick-fold latch system. +A beach cruiser with surfboard-style frame decoration, wide handlebars, and a comfortable saddle for ocean-front Sunday jaunts. +Touring mountain bike with 26-inch wheels, wide gear range, frame-mounted water bottle cages and heavy-duty rims +A custom-painted touring bicycle with matching panniers, reinforced dropouts, and braided cable housing for long-life reliability. +Commuter e-bike with auto-sensing lights, internal gears, rustproof chaincase and comfortable saddle for everyday trips. +Classic steel mountain bike with rigid fork, wide-range cassette, and vintage paint but modern components for reliability +A classic track bike with tubular rims, minimal branding, high-flange hubs and deep navy gloss paint with thin pinstripes. +Gravel paceline bike with shallow rims, fast-handling geometry and reliable mechanical shifting for long group rides. +A touring tandem with independent chain systems, heavily reinforced rims, multiple bottle mounts and durable pannier rails for long-haul riding. +A gravel race machine with tubeless 700x38 tires, flared drops, electronic shifting and an ultra-clean paint finish for competitive gravel events. +A stripped-down track bike with polished aluminium frame, narrow deep-V rims, and a taut single-speed chain for raw track experiences. +Cyclocross spare bike with mud-specific geometry, quick-release wheels, and a buildup optimized for mid-race swaps. +Performance road frameset with disc brake mounts, hidden seatpost clamp and tire clearance for modern 30mm rubber. +Classic steel touring machine with lugged construction, leather bar wrap, Brooks saddle and proven reliability for world travel. +A handbuilt steel road frame with semi-sloping geometry, leather saddle, hand-stitched bar tape and classic thin tubing for refined handling. +Classic chrome steel frame fixed-gear track bike with horizontal dropouts, deep dish rims, and a slim saddle for velodrome work. +Lightweight cyclocross bike with full-carbon frame, knobby 33mm tires, and a narrow chainline for efficient power transfer. +City commuter with low-maintenance hub gear, built-in lock mount, and padded ergonomic grips for comfort. +A mountain hardtail optimized for local trails with 120mm fork, 2.35-inch tires, and a short offset for sharp cornering feel. +A vintage-inspired single-speed cruiser with minted chrome details, cream fenders and a memory lane aesthetic for slow weekend rides. +A commuter folding e-bike with hub motor in the rear wheel and integrated headlight on the stem. +Urban utility bike with integrative handlebar stem storage, spring-loaded rear rack, and puncture-elastic tires for city errands. +Touring steel mixte with accent trim, durable rack, and comfortable upright posture for relaxed long-distance travel. +Electric assist city bike with mid-motor, large-diameter tires for stability, integrated lights, and a low-step design for everyday use. +Comfortable hybrid with suspension fork, upright handlebars, 700x38 tires and integrated rear light for casual fitness rides. +Gravel race cross-frame with compact geometry, hydraulic shifting, wide rims, and a frantic, race-ready stance. +Lightweight climbing road bike with slender tubing, rim brakes, compact crankset and a pearl white paintjob. +A gravel race bike with slightly more upright position than an all-out racer, 700x35 tires, and a cunningly integrated frame pump mount. +Classic Dutch city bicycle with upright posture, enclosed chaincase, step-through frame and a lock integrated into the frame. +Touring steel bicycle with extra mount points, sealed bearings in hubs, and a durable enamel paint chosen for resilience. +All-weather steel commuter with full fenders, hub dynamo, chaincase and large comfortable saddle for rainy days. +Custom handbuilt steel road bike with brushed-polish lugs, leather accents, and period-correct components for a classic ride feel. +Electric mountain fatbike with center-mounted battery, torque-limited motor, and grippy tread for icy conditions. +A BMX street-edit build with shortened top tube, gyro, pegs and matte black frame with neon green forks. +A kids' coaster-brake bicycle with low step-over, training-wheel-ready axle slots, and colorful streamers for an engaging first ride. +A fixed-gear street bike with flip-flop rear hub, polished chrome spokes, low-profile tires and a minimalist single-color paint scheme. +A performance MTB with mixed wheel sizes (mullet), 170mm rear travel, and a low-slung frame for maximum descending control. +Fixed-gear cruiser with bright pink frame, white-wall tires, coaster brake, and a chrome bell for cheerful neighborhood rides. +Road race machine with full electronic shifting, deep carbon rims, and an aerodynamic tube profile tuned for sprint finishes. +Adventure bike with reinforced fork mounts, cargo-cage mounts, and clearance for 2.2-inch tires. +A steel cyclocross frame with comfortable geometry for long seasons, tubeless-ready rims, and simplified cable routing for mud clearance. +A cyclocross practice bike with steel frame, reinforced fork, 35mm mud tires and user-friendly geometry for heavy training schedules. +A lightweight kids' trail bike with 24" wheels, grippy tyres, disc brakes, and a short crank length to teach proper pedaling technique safely. +A gritty urban BMX with pegs, reinforced head tube, and a full chromed finish ready to take hits at local skateparks. +Mountain trail bike with modern alloy frame, 29-inch wheels, tubeless tires, and a dropper post for technical trail confidence +A gravel frame with modern clearance, additional mudguard mounts, and frame reinforcement for robust luggage carrying on remote trails. +A hand-built titanium gravel frame with custom flare powder, integrated bottle mounts, and careful attention to tube thickness for resilience on long rides. +A gravel adventure tandem bicycle with wide tires, integrated framebag options, and low gearing for climbs with gear. +Cyclocross machine with aggressive knobbly tires, disc brakes, short chainstays, and mud-shedding geometry for winter races. +Lightweight cyclocross bike with tubular tires, narrow chainstays, and a subdued gray livery. +Cyclist's training road bike with disc brakes, 28mm tires, compact crankset, and GPS mount on the stem for interval sessions. +Road time trial bike with integrated front fairing, shallow rear rim, and narrow cockpit for minimal drag. +Dirt jumper with overbuilt head tube, single-speed setup, and reinforced rims for repeated launches and landings in the park +Vintage track bicycle with steel lugged frame, high-flange hubs, tubular tires, and a refresh of patina for classic velodrome style. +Electric commuter with ergonomically shaped grips, built-in alarm, and auto-off lights for safe city parking and night rides. +Mountain trail full-suspension with 150mm travel, adjustable geometry, and a short stem to keep handling quick on tight trails. +Gravel explorer with natural brushed metal finish, 42mm tires, wide handlebars, and a comfortable endurance fit. +Cyclocross rig with painted camo splatter, cantilever brake option, and a slightly higher bottom bracket to handle muddy conditions. +Urban folding e-bike with throttle-free assist, led display, 20-inch folding wheels and secure locking hinge for commuting. +A gravel endurance bicycle with vibration-tuned carbon layup, 38mm tires, and flexible geometry for rough-road comfort over long segments. +A vintage-style city bicycle with brass bell, leather saddle, ornamental headbadge and classic cream paint for picturesque rides. +Gravel race bike with electronic shifting, deep-section wheels for aerodynamics, and a race-oriented cockpit setup. +A handbuilt titanium gravel tandem with smooth welds, brushed finish, and discreet rack mounts for lightweight touring. +A retro cruiser with glossy candy paint, oversized saddle, swept bars and an elegant chain guard adorned with pinstriping. +Touring tandem with two independent contact points, tandem-specific crankset and extra-strong wheelsets for loaded miles. +Light electric commuter with front hub motor, integrated soft LED lights and large comfy saddle. +Fixed-gear minimalist bike with invisible welds, tiny decals, and a near-monochrome component choice for a disciplined look. +Tandem recumbent with a long wheel base, comfortable head rests, and aerodynamic bodywork to cruise efficiently across long distances. +Gravel race bike with semi-aero tubing, tubeless-ready wheels, and a gravel-specific saddle for long single-track stretches +City step-through e-bike with torque sensor, comfortable step-in height, and integrated rear light for safety. +Retro track-style fixie with polished frame, narrow saddle, and flip-flop hub to alternate between fixed and free riding. +Urban cargo longtail with foldable rear seats, reflective panels, and a stable frame geometry for carrying both passengers and groceries +Adventure touring bike with dynamo-powered GPS charger, heavy-gauge spokes, and reinforced fork crowns for expedition reliability. +Urban utility folding bike with quick-release seat post, compact folded length and integrated reflectors for safe storage. +A beach cruiser with teal paint, chrome trim, wide-sprung leather saddle and whitewall tires for classic seaside summer rides. +Commuter folding bike with upright handlebar, 8-speed cassette, and reflective sidewalls for early morning rides. +A commuter with internal cable routing, stepped-over frame, dynamo hub and wide fenders for low-maintenance daily riding. +Gravel race machine with carbon forks, lightweight alloy rims, race-ready geometry and 700x35 tires for fast gravel days +A modern trail bike with burly linkages, 170mm rear travel, coil-tuned suspension, and orange-to-black gradient paint. +Cyclocross race-specific bike with tubeless-ready rims, disc brakes, and a mud-repellent paint that keeps the frame lighter. +Surf-themed cruiser bicycle with beach graphics, banana seat, rear rack, and hub brake for effortless coastside rides. +A children’s BMX with low-profile geometry, sealed bearings, pu grips, and a bold neon graphic set for playful trick sessions. +Gravel adventure frame with clearance for 2.2" tires, welded stainless eyelets for racks, and a stout head tube for stability. +A gravel race frame with aero shaping, 700x35 tires, and a stiffness-to-weight ratio tuned for sustained speed on rough surfaces. +Classic BMX race bike with light chrome finish, tubular rims, and a race-optimized single-speed drivetrain for sprints. +A long-travel freeride mountain bike with burly linkages, 180mm fork, and a chain guide to prevent chain drops during massive hits. +A rugged steel gravel bike with 40mm knobby tires, bar-top bag mounts, and a matte olive finish. +A commuter with reinforced chainstay protector, deep mudguards, and an integrated theft-deterrent alarm in the rear rack for peace of mind. +Electric folding bike in graphite with integrated lights, LCD battery display, and hinge that folds into a compact suitcase-like shape. +A utility folding cargo bike with longtail platform, sturdy hinge, and extra safety reflectors for urban deliveries. +Gravel endurance alloy with generous rake, 38mm tires, and internal cable routing for a tidy cockpit. +Commuter with compact chain guard, 5-speed hub, and a bright integrated headlight in the fork crown. +A drop-bar commuter with internal hub gear, belt drive, full fenders, and a matte baby-blue paint scheme for tidy urban runs. +High-end gravel adventure bike with electronic shifting, carbon fork, under-the-top-tube framebag clearance, and a 1x drivetrain for reliability. +Cyclocross bike with lower bottom bracket, mud-shedding frame, cantilever or disc brakes and knobby 33mm tires ready for fall races. +Track pursuit bike with fixed gear, deep-section carbon disc front, and aerodynamic swept aero bars. +Cyclocross race bike with cantilever brakes, mud-shedding frame, 33mm cyclocross tires, and a short wheelbase for tight turns. +Gravel adventure bike with long-travel fork, reinforced headtube, and multiple low-mounted braze-ons. +Touring steel frame with triple bottle cages, lugged construction, and a sprung leather saddle for epic rides. +Commuter with integrated front light, reflective sidewall tires and an easy-to-use hub brake for city reliability. +A minimalist commuter with hub dynamo, internal hub gearing, belt drive and a soft dove-gray powdercoat. +Vintage steel city bicycle with chrome lamp, sprung saddle and heavy-duty hub for dependable town transport. +A city cargo e-bike with dual batteries, rear platform, and safety harness mounting points for commercial errands. +A commuter with internal lights, mudguards, low-step frame, and a hub dynamo that powers a front LED array for safer night rides. +Mountain all-mountain rig with balanced travel, adjustable headset, wide rims, and durable frame finish for heavy trail use. +Cyclocross commuter with clearance for large knobby tires, disc brakes, and an upright build for winter reliability. +Gravel all-terrain with asymmetric chainstays, through-axles, durable bar tape, and a tapered head tube for stable steering at speed +Steel hardtail mountain bike with 29-inch wheels, 100mm fork travel, wide range cassette, and tubeless setup for trail versatility. +A fixed-gear single-speed with polished silver frame, minimal head badge, ultra-slim saddle and clipless pedals for precise urban commuting. +A winter commuter with wide 38mm tires, spray guards, and insulated cables to keep things functioning in cold wet conditions. +Carbon cross-country race bike with superlight frame, 100mm suspension, narrow rims, and race-oriented geometry. +Steel city bike with chrome-plated fenders, dynamo front light, and comfortable upright stem for casual rides. +Mountain downhill frame with heavy-duty linkages, reinforced bearings, and a geometry set for maximum stability at speed. +E-mountain hardtail with mid-drive motor, durable alloy crank, and color-coded shock adjusters for on-trail tuning. +Touring tandem with heavy-duty wheels, matching saddles, and long-range gearing for remote two-up journeys. +A traditional Dutch porteur bike with a wooden front platform, upright bars, and a low, springy saddle for comfortable errands and deliveries. +A kids' trail bike with easy-reach brakes, padded handlebar stem, and a tidy protective chain guard to prevent snags. +Gravel ebike with torque-sensing motor, low-slung battery, and mud-shedding geometry for long days. +Road sprint-oriented frame with short chainstays, high bottom bracket and shallow deep-section rims for quick accelerations. +Electric cargo longtail with removable passenger bench, weatherproof seat covers, and a low center of gravity for comfortable hauling +Cyclocross commuter with rugged knobby tires, fender-compatible fork, and cantilever-style caliper brakes for seasonal versatility. +A full-suspension trail bike with progressive linkage, smooth pedaling characteristics, and 150mm travel front and rear. +Modern track bike with integrated headset, deep tubular rims, and single-purpose gearing for velodrome speed sessions. +Lightweight urban folding bike with quick-folding stem, small chainring, and slick commuter tires. +A small-wheeled folding bike with long-range battery, belt drive, quick-release wheels and understated pewter finish. +Touring frame with monostay rack mount, extra-strong dropout, and triple-bolt water cage mounts for loaded trips. +A trail-oriented hardtail with responsive 120mm suspension and a geometry designed to balance climbing efficiency and descending agility. +Road aero frameset with lightweight layup, drag-reducing features, and a focus on sustained high-speed efficiency. +A restored classic touring frame with original decals, chromed racks, long chainstays and a saddle that has softened with decades of use. +Kids' balance bike with anodized aluminum frame, height-adjustable seat, and no pedals to teach kids balance and coordination. +A light enduro bike with a supple suspension platform, upgraded seals, and a geometry that balances climbing efficiency with descending control. +Urban single-speed with fenders, chain tensioner, and discreet rack for lightweight loads. +A restored vintage road racer with steel frame, modern sealed hubs, and a sensitive, responsive ride that keeps the classic aesthetic. +A gravel adventure bike with 2x drivetrain for extended ranges, 650b wheelset compatibility, and modular mounting points for a wide array of accessories. +A rugged steel chopper-style bicycle with elongated frame, springer fork, low seat height and custom paint for low-riding street flair. +MTB hardtail with tapered headtube, 1x drivetrain, and color-coordinated anodized components. +Dirt-jump bike with short chainstays, rigid fork, single-speed drivetrain, and reinforced frame for aerial tricks. +Electric trekking bike with upright bars, integrated pannier racks, comfortable saddle and pedal-assist for extended commutes. +A modern all-road bike in a subdued slate-gray with tubeless-ready rims, flared drop bars, and a slightly tuned compliance seatpost. +A cyclocross bike converted to single-speed with vertical dropouts, mud-shedding fenders removed, and a rugged 700x35 setup. +A folding commuter bicycle with 16-inch wheels, quick-fold hinge, adjustable stem, commuter rack and candy-apple red paint. +A practical urban utility bike with integrated lock housing in the frame, puncture-resistant tires, and a painted-on reflective stripe. +A touring bike with Reynolds-style tubing, brazed-on racks, leather-wrapped handlebars and hand-applied detailing for long-distance elegance. +Tandem recumbent with aerodynamic fairing option, comfortable reclined seating, and efficient belt drive for speed. +Gravel adventure rig with reinforced fork, flared bars, 650b compatibility and heavy-duty frame protectors for long hauls. +Light commuter with integrated lights, reflective paint, narrow fenders and internal cable routing. +A commuter folding e-bike with compact frame, quick-fold pedals, and a keyed battery lock for secure, portable power assistance. +A gravel bike set up for racing with sub-30mm tires, light wheels, and a slightly more aggressive position than typical adventure rigs. +A rigid-fatbike with fat 5.0-inch tires, powdercoated steel frame, and conservative geometry for winter commuting on packed snow trails. +Gravel race bike with high-volume tyre clearance, race-geometry and an understated matte black paint with tasteful logos. +A classic touring bicycle with triple triangle frame configuration, pannier rails, and a carb-resistant paint job for salty coastal touring. +A gravel mutt with mixed wheel sizes, soft geometry, and a diy aesthetic with stickered frame and patched fork. +Electric cargo trike with hydraulic steering assist, foldable passenger seat and robust powertrain to handle heavy loads. +Gravel race machine with flared drops, lightweight carbon cockpit, and SRAM Red eTap for crisp shifts. +Lightweight cyclocross frame with carbon fork, discreet decals, and ready-for-season tubular rims. +A road race frame with tapered headtube, integrated seatpost clamp, shallow aero seatpost and high-end ceramic bearing headset for precision. +Gravel long-hauler with integrated frame packs, triple-bottle compatibility, and a chill geometry for all-day riding comfort. +A commuter with chrome fenders, wooden pannier rack, integrated rear reflector, and a comfortable town saddle for relaxed urban commuting. +A track sprinter with short wheelbase, ultra-tight geometry, single fixed gear and glossy crimson paint with tiny gold accents. +A mountain trail bike with responsive rear suspension linkage, 130mm travel, and a balanced feel between playful and stable. +Gravel-specific frame with multiple bottle bosses, frame-mounted pump clips and discreet mudguard mounts. +Gravel grinder with custom paint splatter, 700x42 tires and a reliable mechanical disc setup. +Gravel gravel grinder with flared drops, comfortable geometry for long days, and semi-matte paint with tone-on-tone logos. +Touring-friendly steel frame with wide-range gearing, robust spokes, full fenders and comfortable touring saddle for multi-day trips. +A high-performance road bike with aero-optimized downtube, integrated seatpost clamp, and a paint job with subtle tonal stripes. +A stripped-down fixed-gear track bike with narrow tires, aerodynamic tuck geometry, and a slick single ring for track days. +A randonneur touring bicycle with dynamo lighting, large fenders, low gearing, and long wheelbase for steady unloaded miles. +Cargo box bike with weatherproof cover, electric assist, and molded child seating for safe transport. +A commuter with minimalist frameset, integrated rear light, puncture-resistant tires and a subtle paint finish that hides scuffs. +Lightweight sprint road bike with minimal paint, tubeless tires, and an aggressive stack-to-reach ratio for sprint dominance. +Gravel hybrid with flared bars, 40mm tires, and discreet luggage mounts for weekend exploration. +A minimalist fixed single-speed city bike with toothy tread tires, deep-section rims, polished steel fork and satin black finish. +Lightweight cyclocross practice bike with aluminum frame, sealed bearings and wide tyre clearance for muddy conditions. +A carbon monocoque mountain bike with inline linkage, 160mm travel, and signature neon graphics for aggressive trail performance. +Cyclocross training bike with durable components, mud clearance, reinforced fork and simple mechanical disc brakes for reliability. +A touring frameset with tapered headtube, rack mounts, longer chainstays and eyelets for cordless fenders and heavy loads. +Classic cruiser with long chrome fenders, plush saddle, and elegant hand-painted pinstriping for style-conscious rides. +A commuter with rear child seat mount, adjustable stem for quick reach changes, and puncture-resistant tires for family trips to school. +A belt-driven commuter with Gates transmission, internally geared hub, low-maintenance platform, and integrated frame lock for security. +A carbon fiber gravel adventure bike with integrated fender mounts, stealth bolt-ons, and an endurance geometry that soaks up rough backroads. +Cyclocross practice bike with reinforced fork, 36mm tires and disc brakes for training in mud. +A city single-speed with coaster hub converted to fixed drive, polished chrome fenders, and matching bell for an iconic silhouette. +A vintage touring bicycle restored with waxed cables, leather brakes hoods replaced with period-correct stitched tape, and modern wheelset. +A commuter cargo trike with rear box, low-step frame, electric assist, and sturdy disc brakes for local deliveries. +A long-travel freeride machine with robust linkages, high-volume tires, and an intentionally burly chassis to stand up to big landings. +A commuter with a fully enclosed chaincase, integrated reflective striping, and a modular pannier rack for adaptable daily hauling. +Mountain cross-country full-suspension bike with 120mm rear travel, lightweight linkage, and efficient pedaling platform for long climbs. +Urban cruiser with banana seat, sissy bar, chopper-style long forks, and decorative chrome for a showy beachside bike. +A mountain bike with 29+ wheels, progressive head angle, and dropper post with long travel to improve descending and control. +Urban fixed-gear with minimalist stem, polished chainset, and a slim profile for fast, predictable commuting. +Retro fixed-gear with deep polished rims, leather straps and a classic steel frame for low-key urban style. +A cyclocross race machine with aggressive tires, stiff bottom bracket, and cleverly routed cables that resist mud and grit intrusion. +Road endurance bike with compliance-focused seatstays, flared headtube, and neutral gray paint. +A slim-profile road racing frame with minimal paint, exposed carbon weave, integrated seatpost clamp, and a tapered steerer for precise handling. +Lightweight alloy commuter with modern paintwork, internal routing, and wide 32mm tires for smooth city pavement rides. +Kid's balance bike with bright safety colors, rounded edges, foam-filled tires and low seat for easy practice. +Folding city cargo bike with a convertible box, electric assist, and fold-flat handlebars for compact storage. +Gravel plus hardtail with 27.5+ tires, slack head angle, wide bars and a satin midnight blue paint. +Urban single-speed with polished chrome finish, chain tensioner, narrow handlebars and serious minimalism for the style-conscious rider. +Gravel adventure bike with stealthy matte graphite paint, 650b wheels, large-volume tires and integrated GPS mount for navigation +A marathon road bicycle with oversized downtube, endurance geometry, and vibration-sensitive seatpost for long-day comfort. +A classic mountain bike restoration with 26-inch wheels, vintage decals, and crisp fresh paint that takes riders back to old-school singletrack. +A cyclocross race bike with a light alloy frame, strong disc brakes, and a stiff bottom bracket designed to handle repeated bursts of power. +Gravel bike with bamboo frame panels, natural varnish finish, wide clearance tires and handcrafted leather accents +A commuter with rear panniers, integrated lights, and internal gear hub that keeps maintenance low and reliability high for daily commuting. +A commuter with hydraulic rim-style brakes mimic disc feel, aluminum rack, and a puncture-resistant tire set for daily use. +Touring recumbent bicycle with long wheelbase, comfortable seating and fairing-ready mounting points. +High-traction downhill bike with coil shock tune, burly 4-piston brakes and reinforced twin-plate chainstay protector. +A commuter with integrated smartphone charger in the stem, front rack, dyno hub and brushed aluminum finish with light-scattering flecks. +A handbuilt titanium touring bike with brazed mounts for racks, 36-spoke wheels, and a subdued bead-blasted finish for understated durability. +Lightweight track frame with tapered headtube, aggressive chainline, and minimalist paint for pure velocity. +A must-have cyclocross sensibility gravel bike with cantilever mounts replaced by hydraulic calipers, wider droputs, and flared drop bars. +A commuter with built-in phone holder, small front rack, low standover frame and puncture-resistant sidewalls for daily utility. +A high-volume fat-tire touring bike with reinforced racks, wide clearance, and a sturdy frame to cross sand and snow on remote expeditions. +Gravel rig with dropper post, flared handlebars, 2.2-inch semi-slick tires and a comfortable saddle for long gravel days +Mountain enduro alloy frame with adjustable geometry, widely spaced bearings and a linkage designed to soak up big hits. +Street cruiser with chopper-style extended fork, sissy bar, and big ape-hanger handlebars in glossy black. +Urban single-speed with BMX-style riser bars, short stem, and reinforced chromoly fork. +Vintage step-through bicycle with pastel detailing, chrome trim, sprung saddle, and a child-friendly step height for easy mounting. +Cyclocross training bike with steel frame, shallow mudguards, and a wide-range cassette for mixed conditions. +Mountain freeride bike in camo livery with downhill geometry, coil shock and reinforced wheelset for big air days. +Minimal single-speed city bike in candy blue with flip-flop hub, narrow riser bars and smooth commuter saddle. +A cyclocross bike with lightweight steel frame, modern disc brakes, and custom mudguards for winter training. +A high-end titanium road bike with tapered head tube, internal routing for electronic groupsets, and 28mm tires for a smooth race-ready ride. +High-performance TT bike with disc wheel, super-stiff bottom bracket and triathlon-specific cockpit. +Kids' balance bicycle with wooden frame, velvety seat and non-slip foot areas for toddlers starting out. +A compact folding commuter with single-speed simplicity, fast-fold geometry, and bright color choices to match city style. +Mountain hardtail with modern slack geometry and wider rims for better tire support in rough terrain. +A gravel machine with a relaxed headtube angle, wide handlebars, and a focused geometry to inspire confidence over rough, steep descents. +Road semi-aero frame with integrated headset, shallow carbon rims, and a comfortable position for all-day efforts. +Folding mini velo with reinforced hinge, adjustable stem, and a fold locking system for secure transport on trains. +A commuter with integrated handlebar phone mount, mid-motor assist, and a small lockbox beneath the top tube to stash keys and a wallet. +City bike with built-in frame lock, coaster brake, front basket and canvas pannier for errands and market trips. +A commuter with magnetic quick-release rack, integrated lock, and puncture-resistant tires for worry-free daily transportation in urban cores. +A commuter with a belt drive, internal hub, and matching fenders for a quiet, low-maintenance urban ride. +A cyclocross bike with tubular tires, CX-specific gearing, 34mm tire clearance, and a mud-shedding frame design in high-visibility orange. +A mountain hardtail built with steel tubing, classic finishes, and modern angles for a retro feel with contemporary performance. +A gravel-specific frameset with titanium-welded cable guides, through-axles, and a shiny champagne enamel that shows fewer scratches. +A lightweight track bike optimized for velodrome racing with high-stiffness BB, narrow tires, and minimal frontal area for low drag. +A mountain cross-country race bike with featherweight build, narrow 2.0 tires and quick-response geometry for fast lap times. +A commuter with hub dynamo lighting, integrated lock loop, belt drive and matte slate finish with subtle metallic flecks. +Lightweight commuter with step-through aluminum frame, integrated lights, rear rack, and comfortable upright posture. +Gravel commuter with bolt-on fender compatibility, internal routing and durable alloy rims to handle potholes and rough lanes. +Urban commuter with belt drive and internally geared hub, step-through frame, mudguards and integrated rear light for safe trips +A family load bike with reinforced bench seating, child harness points, and an easy-step frame so little ones can hop on quickly at stops. +Road endurance frame with seatpost micro-adjust, generous tire clearance, and a sand-texture paint to hide road grit. +Single-speed mountain commuter with robust frame, wide tires, and a simple coaster or disc brake for maintenance ease. +A mountain fat-tire e-bike with mid-drive motor, suspension seatpost, 26x4.8 tires and camouflage decals for winter trails. +A robust winter commuter with fenders, mudflaps, and stamped steel racks designed to shrug off salt and slush while keeping cargo dry. +Folding bike with 16-inch wheels, compact hinge mechanism, and matte silver paint for urban commutes. +A full-suspension mountain bike optimized for enduro racing with adjustable anti-squat, 170mm rear travel and a stout alloy frame. +Single-speed urban cruiser with powder-coat finish, coaster brake, wide cruiser bars and a spring-loaded seatpost for comfort +A folding commuter bike with small 20-inch wheels, quick-release folding hinge, and compact carry strap. +Trail-ready hardtail with modern geometry, tubeless tires, stout rimset, and a dropper post for confident line choices. +A downhill machine with twin-crown fork, heavy-duty linkage, and an articulated rocker for consistent big-hit control. +Gravel race bike with disc brakes, eTap wireless shifting, and a tasteful gloss finish with UV-reactive flakes. +A small-wheel folding bike with oversized saddle, upright posture, and a compact package designed for last-mile transit. +A gravel bike with bombproof wheelset, thick 42mm tubeless tyres, frame-mounted pump holster, and anodized headtube reducer. +Cargo trike with foldable bench, electric assist, and weatherproof canopy for family-friendly transportation and errands. +Classic steel fixed gear with narrow tires, subtle metallic flake in paint and a short cockpit for nimble handling. +Mountain cross-country carbon frame with featherweight build, optimized for quick climbs and snappy handling on singletrack. +A mountain trail hardtail converted to single-speed with wide-rim 29er wheels, dropper post and rust-colored matte paint. +A touring tandem with double racks, three-bottle mounts per rider, double-chain system and reinforced hubs for long-distance two-up travel. +A classic steel touring bicycle painted cream with triple crankset, fender mounts, front and rear racks and cushioning leather saddle for long-distance travel. +Vintage-style town bicycle with full chainguard, leather saddle, swept-back bars and a hand-painted crest on the head tube. +A dual-suspension XC race bike with 110mm rear travel, efficient pedaling kinematics, and a featherweight carbon layup for cross-country speed. +A beach cruiser with wide saddle, swept chrome handlebars, wicker basket and soft mint-green enamel with whitewall tires. +BMX park specialist with short wheelbase, gyro setup, small frame, and a stiff chromoly fork for technical tricks. +Kids' BMX with low-slung top tube, reinforced frame gussets, pegs and knobby tires for park and street riding. +Cyclocross aluminum frame with disc brakes, 35mm mud tires, and reinforced fork for cyclocross seasons. +A commuter with built-in USB port powered by dynamo, internal gearing for low maintenance, and a low-step diamond frame for easy mounting. +A custom wooden cargo bike with varnished timber box, sculpted handlebars, and handcrafted joinery for artisanal deliveries. +Classic steel road frameset with seatpost clamp binder, polished lugs, copper lacquering and narrow 25mm tires. +Urban minimal commuter with stealth black paint, hidden rack mounts, and a comfortable seat for everyday errands. +Modern trail full-suspension mountain bike with adjustable geometry and 150mm rear travel for aggressive descents. +A downhill race sled with reinforced swingarm, dual-bolt linkage, and heat-treated wheel axles for repeated high G landings. +A cyclocross-inspired gravel all-rounder with drop bars, 38mm tires, mudflaps, and a stealth matte black finish with contrasting white logos. +Cargo tricycle with fold-down rear tailgate, reinforced decking, and electric assist for practical marketplace deliveries and weekend projects +A titanium custom frame with lugless construction, brushed finish, integrated seatmast, and triple-bottle cage mounts for long hauls. +A BMX park build with short crank arms, durable alloy rims, and a reinforced headtube to survive daily skatepark abuse. +Gravel utility with hidden mudguard mounts, reinforced chainstays and a practical geometry for day-long mixed-surface rides. +City folding commuter with lightweight alloy frame, adjustable stem and compact carry strap. +Classic cruiser with retro pinstriping, chrome headlamp, and wide swept handlebars for easy going rides. +Commuter with sleek aluminum frame, internal wiring, and an elegantly integrated kickstand for tidy storage. +A commuter with heavy-gauge fenders, chaincase, and a built-in carrier for grocery trips and school runs. +A custom hand-painted anodized aluminum road bike with flared headtube, carbon fork, and tubular rims for lightness and class. +A trail-ready 27.5+ mountain bike with long travel fork, aggressive tread tires, and frame protectors in key impact zones. +A folding utility bike with robust frame, quick-release folding hinge, 20-inch wheels and a carry hook for easy transit storage. +A commuter with belt drive, internally routed cables, and an integrated rear carrier system for sleek, low-maintenance weekday use. +Gravel bike with titanium frame, double-butted tubes, and a stripped-back aesthetic for long-distance comfort. +A commuter with upright bar, integrated display for speed and battery, step-thru frame and bright LED strip lighting for safety at night. +Folding compact commuter with micro-suspension, 20-inch wheels and a light alloy frame for train-friendly portability. +Touring gravel bike with full mudguard set, long wheelbase stability, and triple bottle mounts for extended off-grid trips +Lightweight aluminum commuter with quick-release wheels, integrated locking, and a dynamic sporty look. +Cargo e-bike with extended rear platform, heavy-gauge tubing, and a low-slung center of gravity for safe loading of bulky items. +A downhill-focused aluminum frame with broad tire clearance, reinforced headtube, and geometry primed for heavy landings. +A modern trail mountain bike with 140mm travel, internally routed dropper post, wide rims and tubeless-ready tires for technical trails. +Kids' stunt BMX with short chainstay, long top tube painting and durable paint to withstand skatepark scuffs. +Road aero bike with deep-section wheels, integrated seatpost clamp and a smooth fast-rolling setup for crits. +A low-slung cruiser with banana saddle, chrome accents, wide whitewall tires, and a paint job inspired by classic 1950s styling. +Mountain all-mountain with mixed wheel sizes for playful handling, burly tires, and a traction-focused suspension tune for steep trails +A mountain bike with coil-sprung rear shock, long travel fork, and progressive geometry for confidence on steep, technical terrain. +Gravel racer with lightweight wheelset, SRAM wireless drivetrain, 700x38 tubeless tires and a crisp race-cut paint job. +A bespoke lugless titanium gravel bike with custom paint accents, wide-range cassette, and oversized tubing for stiffness and comfort. +Touring mountain bike with 29-inch wheels, wide-range gearing, framebag-compatible top tube and robust spokes for loaded miles +A family cargo trike with a lockable cargo box, reinforced steering assembly, and comfortable bench for two kids and their gear. +A mountain hardtail optimized for climbing with a light alloy frame, narrow rims, and an efficient 1x11 drivetrain for fast ascents. +Cargo front-load bicycle with long wheelbase, reinforced head tube, hydraulic brakes, and child safety harness mounting. +Office commuter with built-in phone cradle, quiet belt drive, reflective tape and comfortable upright posture. +Commuter gravel bike with mud flaps, integrated lock mount, reflective tape and a comfortable upright cockpit +A gravel bike with anodized headset, threaded bottom bracket for reliability, and flared drops for better control on steep descents. +A compact road bike with endurance geometry, short reach handlebars, 28mm tires and a pillowy saddle to minimize fatigue on long rides. +Lightweight climbing road bike with oversized BB, tapered head tube, and 700x25c tubular wheels. +Performance gravel bike with SWAT-style storage integration, aero tubing and responsive handling. +A commuter with built-in GPS tracker, internally routed locking cable, and a studded tire option for icy city commutes. +A full-suspension trail bike with 140mm travel, supportive mid-stroke tuning, and a comfy saddle for all-day singletrack laps. +A track sprint bicycle with aerodynamic chainring, deep carbon cockpit, and a narrow saddle placed for max sprint power. +A lightweight aluminum cyclocross frame tuned for damping, wide clearance, and a solid axle system to maintain wheel alignment through rough courses. +A full-suspension mountain bicycle in neon orange with 140mm rear travel, 150mm fork, 29-inch wheels and burly tires for aggressive trail shredding. +Lightweight titanium gravel racer with 700x35 tires, integrated cable routing, and a semi-compact crank for mixed-terrain performance. +A fast cyclocross race rig with stiff carbon frame, sealed hubs, and tapered steerer for precise handling and quick accelerations. +Classic cruiser bicycle in seafoam green with swept-back bars, balloon tires and a comfortable wide saddle +A kids' balance bike with composite frame, foam tires, low seat height and cheerful sunshine yellow paint with cute animal decal. +Classic road bike with polished steel, 9-speed indexed gearing, and vintage-style long-reach brakes for period-correct restoration. +A compact e-bike with hub-mounted motor, simple single-gear drivetrain, and a removable battery for quick charging at work. +Urban utility with low center of gravity, wide platform pedals, and a large front basket welded to the frame. +A commuter with integrated fenders, full-chain enclosure, and a comfortable seat for daily door-to-door riding regardless of weather. +Single-speed beach cruiser with banana seat, coaster brake and flamboyant surfboard-themed paintwork. +Kids' mountain bike with easy-to-use coaster brakes, low gears, colorful frame and reinforced fork for safe trail learning. +A gravel race build with ovalized chainstays, tubeless 700x35 tires, and precise 1x shifting for quick transitions between surfaces. +Full-suspension mountain bike with 150mm travel, coil shock, 29-inch wheels and aggressive 2.4-inch knobby tires +A lightweight triathlon time-trial bike with integrated storage, torqued aero bars, and dual bottle mounts housed in the frame. +Lightweight racing frame with aero-optimized profile, ceramic bearings, multi-plate chainrings, and race-proven tubeless compatibility +Gravel race machine with slim aero finishing kit, 38mm race rubber and precise shifting for competitive events. +A mountain e-bike with specialized high-torque motor, long-life battery pack, and aggressive trail geometry for steep mountain climbs. +Touring steel frameset with elegant lugwork, full braze-ons, wide gear range and mounts for heavy-duty racks and fenders. +A commuter with step-through frame, integrated reflective piping, and an easy-to-use rear-rack system for work commuting. +A robust cargo e-bike with longtail frame, powered assist tuned for heavy loads, and a low center of gravity to keep passenger rides stable. +A drop-bar gravel all-road bike with titanium beads on the paint, 700x42 tires, and cleverly integrated framebag mounts. +Vintage chrome-plated road bicycle with downtube shifters, tubular tires and etched lugwork on the head tube +A gravel drop-bar bike with a modular mount system for cages, 650b compatibility, and a discreet storage compartment in the top tube. +Lightweight track sprinter with ultra-stiff BB, carbon crankset, and narrow aero bars for explosive acceleration. +Track sprint bike with short wheelbase, huge chainring, and deep stiff aero rims for explosive track power. +Bicycle camper conversion with rear rack tent mount, extra water bottle cages, and reinforced frame for long off-grid trips. +A utility cargo tricycle with front-loading box, hydraulic disc brakes, low center of gravity and a stable three-wheel platform for deliveries. +Vintage city cruiser with restored chrome, leather grips, basket, and classic bell for nostalgic daily rides +Commuter with internal hub gears, sealed lantern-style headlamp, and full chain guard to keep clothes clean. +Gravel plus bike with large-volume tyres, reinforced fork, and clear braze-on points for expedition racks and lights. +A gravel touring bicycle with welded stainless-steel eyelets, heavy-duty spokes, and frame color modeled after old maps. +Gravel adventure frame with built-in shock mount, integrated framebag shape and discreet paint to avoid reflecting sunlight. +A mountain freeride sled with massive 180mm travel, coil-sprung rear shock option, and an extremely confidence-inspiring rear end. +Gravel e-bike with mid-drive motor, stout carbon frame, integrated battery, and wide tyre clearance for mixed-terrain support. +Folding electric cargo bike with battery under the platform, sturdy frame, and an integrated rear light cluster. +Children's balance bike in bright orange with foam tires and lightweight alloy frame for learning to ride. +City step-through e-bike with pedal-assist, skirt guard, and easy-to-use twist throttle. +Mountain downhill rig with 200mm front travel, twin-crown fork and heavy-duty rims for rock-solid descending. +A gravel e-cargo bike with longtail frame, adjustable bench seating, electric-assist mid-motor, and a weatherproof cargo box for kids. +Touring mixte with step-through twin top tubes, rack-friendly stays, and integrated handlebar basket mounts for convenience. +A foldable electric bike with compact battery pack, 16-inch wheels, mid-hinge latch and urban gloss black color. +Retro-styled cruiser with classic chrome accents, leather saddle, swept handlebars and a soft pastel candy coat for Sunday rides. +Gravel touring tandem with extended wheelbase, matching gear range, and heavy-duty racks for long adventure rides together. +Touring steel frame with weatherproof pannier mounts, long wheelbase and high-stack headtube for comfort. +Mountain trail rig with burly 34mm fork stanchions and stiff 1x crankset for precise pedaling. +Urban step-through with low-maintenance hub gears, full fenders, and a comfortable sprung saddle. +A wet-weather commuter with sealed bearing hubs, full-coverage fenders, and reflective piping on the frame for enhanced visibility. +Mountain trail full-suspension with stealth dropper, wide 800mm bars, and grippy sidewall traction. +A performance trail bike with 130mm travel, steep seat tube angle, 29" wheels, and responsive pedaling characteristics. +Gravel adventure with dropper post, wide tires, and multiple frame bottle mounts for full-day exploring. +Full-suspension mountain bike in electric blue, 150mm travel, coil shock, wide handlebars, and a dropper seatpost for technical trails. +Cyclocross frameset with modern clearance, precise welds, and a deep forest green matte finish accented by small logos. +A high-modulus carbon gravel frame with integrated top-tube bag mount, flared drops, and a compliance-tuned rear triangle for comfort. +Gravel bike with mixed wheel sizes—29" front, 27.5" rear—plus clearance for chunky tires and a balanced geometry. +Hardtail trail bike with 130mm fork travel, 29" wheels, and a tapered headtube for precise steering in tight singletrack. +City folding electric bike with step-through hinge, 250W motor, small 14" wheels and basket-ready front rack. +Cyclocross race frame with shallow headtube, semi-integrated cables and a paint scheme that hides mud between laps. +Gravel plus bicycle with ample clearance, stout axle mounts, and a powdered matte finish that resists salt spray. +Gravel bike with industrial matte paint, wide alloy rims, thru-axles, and leather saddle for mixed-surface reliability and comfort +Compact commuter with folding handlebars, internal hub, low-maintenance belt drive and vivid urban colorway for visibility. +A single-speed beach cruiser with wide balloon tires, retro chrome accents, and a saddle with springs for cushioned seaside cruising. +A commuter with electric-assist in the rear hub, hydraulic brakes, and a low-step alloy frame for easy mounting and dismounting. +Cargo electric bike with twin batteries, programmable assist modes, and integrated GPS tracker. +A cyclocross race-ready bike with 38mm tubular tires, quick-release wheels, lightweight cantilever calipers and flared drop bars for control. +A recumbent tandem with aerodynamic fairing, comfortable seating, and efficient chain routing for long-distance rides. +Mountain trail full-suspension with active anti-squat and tuneable rebound for precise handling on technical lines. +Mountain freeride bike with long-travel suspension, coil shock compatibility and chain guide for aggressive lines. +Beach cruiser with custom surfboard rack, whitewall tires and wicker basket for seaside errands. +A trail-capable 29er with 130mm travel fork, wide handlebar, and grippy tubeless tires for cross-country adventures. +Fat-tire cargo bike with integrated side panniers, heavy-duty aluminum frame and low center of gravity for stability with loads. +A compact urban folding bike with 16-inch wheels, single-speed drivetrain, quick-release seatpost and a built-in clip for compact storage. +A durable mail-delivery utility bicycle with low gearing, heavy-duty cargo front box, puncture-resistant tires, and a step-through frame for quick stops. +Steel mountain hardtail with 120mm travel fork, durable paint, and welded rack mounts. +A gravel adventure bicycle with removable fenders, quick-release racks, and a comfortable endurance geometry for long singletrack approaches. +A lightweight marathon road bike with elliptical chainstays, comfort-tuned seatpost, and tubeless 32mm tire setup for endurance events. +Gravel race carbon frame with integrated seatpost clamp, light mounts and aerodynamic tube shapes to cut wind on mixed surfaces. +A lightweight time trial bike with integrated hydration, aero seat tube, and a glossy carbon weave that reflects sunlight. +A gravel race bike with wireless shifting, lightweight carbon wheels, and an inverted top tube to accommodate a framebag for long races. +Mountain full-suspension with modern linkage, tapered headtube, and a protective frame guard where chain slap occurs. +Kids' balance bicycle with foam tires, lightweight frame and bright safety colors to encourage confident first rides. +A practical cargo e-bike with folding deck, throttle assist, and a sturdy rear rack that supports heavy crates for urban merchants. +City commuter with quiet belt drive, internal hub, and minimalist integrated taillight in the seatpost clamp. +Road race bike with sub-7kg build, stiff carbon layup, and silk-gloss paint for race-winning aesthetics. +Cargo bike with front-loading box (bakfiets), long low chassis, wooden panels and sturdy child harnesses +A kids' mini MTB with 20-inch wheels, front suspension fork, coaster brake and tough steel frame for playground rides. +Road endurance bike with flexible seatstays, slightly taller headtube, and vibration-absorbing bar tape for long days in the saddle +A gravel bike with engraved titanium bits, discrete frame serial number, and hand-buffed welds for a custom look. +A lightweight titanium road touring bicycle with extra braze-ons, polished finishes, and a geometry tuned for all-day comfort and durability. +A cyclocross bike with rugged disc-brake calipers, shallow bar flare, and a coordinate paint that hides water marks from winter training. +A painted steel road frame with hand-applied pinstripes, braze-on accessories, and a traditional quill stem to complete a period-correct build. +Fatbike commuter with wide platform pedals, low-pressure tires, and mud-shedding fenders for winter city commutes +Mountain downhill bike with adjustable geometry, strong alloy rims, massive tires and coil shock tuned for heavy impacts. +Commuter bike with hub dynamo, LED front light, chaincase, and low-maintenance Shimano Nexus gears. +Adventure gravel bike with stealth black, splatter paint, and multiple mount points for gear and tools. +Urban single-speed with powder-coated frame, deep drop bars and minimal saddle for efficient short hops. +A classic cruiser with deep saddle, swept handlebars, full chaincase, and glossy two-tone paint for Sunday rides along the boardwalk. +Mountain trail hardtail with progressive geometry, 140mm fork, and carbon cockpit for reduced vibration. +Performance road bike with integrated power meter, rim-brake conversion kit and 25mm tubeless-compatible tires +Electric fat bike with 4.8" tires, mid-drive motor, integrated display, and extra torque for snowy beach rides. +A cyclocross pro build with carbon seat tube, fast-rolling tubulars, electronic shifters and a stiff, lightweight frame set for race aggressiveness. +Custom-painted cafe racer-style bike with clip-on aero bars, leather-wrapped top tube and retro head badge. +Classic randonneur with long wheelbase, big fenders, dynamo light, and a leather saddle for long comfortable nights. +A stripped-down city fixie with minimal branding, deep polished rims, leather bar tape and a cherry-red lacquered frame. +A commuter with belt-drive single-speed, integrated rear rack, hub dynamo and matte basalt-gray finish with reflective logos. +A gravel bike built for rough-and-ready exploration with thick sidewalls, wide rims, and durable components for remote trips. +A commuter with quick-release rear panniers, integrated frame lock, and a clean matte finish that resists showing dirt. +High-performance time trial bicycle with integrated ventilation, deep section carbon, and single-sided fork for minute aerodynamic gains. +A commuter with elegant brass badge, internally routed lights, and a low-profile rear rack to keep the silhouette clean and functional. +Folding electric commuter with quick-release handlepost, small folding footprint and an intuitive control pad for power. +Electric commuting bike with compact hub motor, long-lasting battery, integrated lights, and a comfortable saddle for daily use. +Vintage-style road bicycle with campagnolo hubs, cotton-wrapped handlebar tape, and classic button-top quill stem. +A stripped, raw-finish single-speed with clean welds, no-frills hardware, and a short wheelbase for zippy, predictable urban performance. +Vintage road frame rechromed, fitted with modern bar tape, serviceable brakes and period-correct decals restored to gloss shine +Racing time trial bike with full carbon cockpit, integrated hydration, wind-tunnel shaped tubing and aerodynamic optimization for speed. +Electric cargo bike with rear bench, child harnesses, and integrated speakers for family outings. +A vintage lugged steel road bicycle with classic paint fade, shifter downtube mounts, narrow 700c wheels and leather bar tape for Sunday jaunts. +A commuter with large-capacity rear rack, integrated light cluster, and an internal gear hub that shifts while stationary for urban stops. +Gravel endurance bike with 42mm tubeless rubber, vibration-damping seatpost, and a steady, comfortable fit. +Electric cargo trike with insulated box, dual-battery option, and hydraulic disc brakes designed for deliveries in any weather. +Lightweight commuter with carbon fork, sealed bearings, quiet belt drive, and a tidy cable routing for polished city use. +A single-speed track enthusiast build with polished chainring, flipped rear hub, and a deep-veneer wooden grip for classic flair. +A children's balance bike with wooden frame, grippy rubber wheels, low center of gravity and cheerful primary color finish. +Folding commuter with durable hinge, low-step frame, and compact folded dimensions suited for train carriage storage. +Single-speed urban track bike with polished steel frame, aero bars removed, and a compact saddle for city sprints +A neon green BMX freestyle bike with short cranks, triple clamp stem, gyro brake for bar spins and bold sticker art. +Lightweight aluminum hardtail trail bike with 120mm fork, short chainstays, 29" wheels, and a 1x10 drivetrain for nimble climbing. +Elegant steel city bike with swept-back handlebars, chrome fenders, and classic headtube badge. +Gravel bike with aero-inspired tube shaping, fender compatibility, and built-in top tube strap points. +A race-ready cyclocross bike with carbon fork, short top tube, and precise shifting for quick accelerations in muddy fields. +A fixed-gear commuter with track dropouts, deep-section front wheel, narrow saddle and simple utilitarian charm. +A bespoke steel cyclocross bike with detailed lugwork, immaculate brazed joints, and a durable textured paint that stands up to winter mud. +Gravel bike with titanium frame, disc brakes, 700x45mm knobby tires, flared drop bars, and internal storage mounting points. +Steel frame single-speed with ornate lugwork, polished headset, and chestnut leather saddle for vintage aesthetics. +A commuter with integrated front basket, smooth internal hub, and a child-seat mount on the rear rack for family use. +A gravel-looking road bike with endurance geometry, a pair of bottle mounts, 32mm tires and modest aero improvements for long rides and comfort. +Endurance gravel with vibration-absorbing seatpost, flared bar drops, and lots of bottle mounts for remote traverses. +A classic cruiser with sweeping frame, chrome plated fenders, wide white saddle and candy-apple red finish with gold pinstripe. +A cargo trike with rear flatbed, heavy-duty loading hooks, reinforced forks, and upright bars for safe, slow-speed maneuvering. +Gravel race build with flared drop bars, wide rims, 38mm tires, and a lean kit focused on speed and reliability. +A commuter with solar-powered rear light, internally routed cables, hub dynamo bottle light, and reflective paint flecks across the frame. +Commuter electric bike with step-through design, hydraulic brakes, and an integrated taillight in the seatpost. +Commuter with mixte frame, enclosed chaincase, and large wicker basket for shopping runs. +Electric commuter bike with step-through frame, mid-drive motor, integrated battery in downtube, LED lights, fenders, and a rear rack. +A steel cyclocross frame with extra-clearance fork, flattened top tube for shouldering, and a light, resilient feel meant for short high-intensity races. +Track fixed-gear with high-flange hubs, polished chainstay, and minimalist black-on-black scheme to emphasize form. +Custom titanium commuter with anodized accents, internal cable routing and solid alloy racks for lightweight load-carrying. +A gravel racer with a highly stiff bottom bracket, flared bars for cornering, and tubeless tires tuned for both speed and puncture resistance. +Lightweight urban fixie with polished chrome and deep-section rims for weekend alleycat style. +Recumbent tadpole trike with reclining seat, aerodynamic fairing, and chainline optimization for high average speeds on flats +A dual-purpose gravel/commuter with dynamo hub, mudguard mounts, 35mm semi-slicks and discreet cable routing for clean looks. +Adventure mountain bike with frame-integrated storage, heavy-duty forks, wide tires and adaptable suspension for multiday off-road trips +Gravel-focused titanium frame with brazed fender mounts, discreet paint and supple ride quality for long miles. +Hardcore downhill mountain bike with massive 200mm travel fork, coil shock, and reinforced wheelset for podium runs +Retro steel cyclocross bike with tan sidewall cyclocross tires, cantilever brakes and minimalist framebag mounts. +Gravel all-road alloy with stealth fender mounts, relaxed headtube angle and a geometry that balances speed and comfort. +Classic steel touring bicycle painted British racing green, with chromed racks, full-length fender mounts, triple chainrings for low climbing gears, and leather saddle with sprung rails. +A durable kids’ BMX with thick chromoly frame, no-slip platform pedals, and bright orange paint to stand out in the skatepark crowd. +Gravel adventure bike with multiple bottle mounts, lightweight aluminum frame and wide rubber for mixed surfaces. +Electric cargo bike with reinforced frame rails, large platform deck, and dual batteries for extended range while hauling loads. +Touring bicycle with triple crankset, low-rider front rack mounts, full fenders, and steel brazed frame. +Children’s balance bike in lightweight aluminum with foam tires and low saddle height. +A commuter with integrated rear light, reflective sidewalls, commuter rack, and puncture-resistant tires for daily urban reliability. +Mountain fatbike with front suspension fork, bolt-on bottom bracket protector, and tan sidewall tires for retro flair. +Road bike with endurance geometry, vibration-tuned carbon, and 28mm tires for a plush ride on rough roads. +Mountain downhill frame with triple-butted steel tubing, long travel and reinforced pivot bearings for rugged use. +Mountain freeride with aggressive geometry, plenty of chainstay protection, and a paint job that camouflages dirt. +A folding city bike with quick-release wheels, single chainring drivetrain, upright handlebars and compact folded footprint for commuters. +A gravel racer with carbon fork, 700c wheels, 38mm gravel tires, flared drops for control and earthy stone-gray paint flecked with metallic chips. +A commuter with wide platform pedals, stepped-through frame, and a low-maintenance belt drive to minimize hassle on rainy days. +A commuter with integrated rear light cluster, puncture-proof tires, belt drive and a compact rack that accommodates panniers and backpacks. +A cyclocross-friendly steel frame with internal downtube storage, 1x drivetrain compatibility, and mud-optimized tube shaping for winter training. +Mountain freeride steel with stout tubes, spring-friendly geometry and a thick-walled dropout for heavy park use. +Cargo bike with front-loading wooden box, electric assist, adjustable bench, and reflective decals for safe urban carrying. +Classic road bicycle with downtube shifters, narrow 23mm tires and period-correct chrome lugs and badge +Track sprinter with high-flange hubs, tubular tires and an ultra-stiff frame for explosive power. +Adventure gravel with carbon fork, integrated fender mounts, and a wide 1x drivetrain for technical routes. +Gravel bike with integrated sensor suite, subtle paintwork, 42mm tyres, and a burly frame for adventure support. +Mountain enduro with mixed-wheel layout option, burly frame, long-travel fork, and components chosen for durability on rough descents. +Vintage city cruiser with gloss enamel, chrome accents, wide balloon tires, and a soft spring saddle for summer afternoons. +Classic leather-wrapped roadster with steel fenders, comfortable upright posture and a deep green enamel coat. +Folding commuter with hidden latch, quick-release pedals, and a secure carry strap for fast multi-modal commutes. +A touring bicycle with custom welded pannier mounts, dynamo lighting system, and wide, tough 700c tires for extended remote rides. +A BMX race bike with 20-inch wheels, short top tube, anodized gold hubs, and a glossy black frame for sprint circuits. +Urban utility bike with integrated lock, front courier basket, seven-speed hub, and puncture-proof tires for maintenance-free use. +A race-ready gravel rig with carbon wheels, electronic shifting, and a light paint that masks small abrasions from frequent use. +Gravel tourer with triple bottle mounts, wide gearing and sturdy wheelset for loaded travel. +A commuter with U-lock mount integrated into the frame, battery-free dynamo light, belt drive and subtle pearl white paint. +Road climbing frame with featherweight carbon, narrow profile seatpost, and elegant coach-painted pinstripes. +Cargo longtail with child seats, reflective side tape, and modular hooks for securing unpredictable loads. +Performance mountain bike with full-carbon frame, 140mm travel, wide rims, and a tuned suspension platform for fast flow trails. +A titanium commuter with belt drive, internally routed cables, integrated rear rack, and subtle bead-blasted finish for low maintenance. +Vintage touring mixte with leather-wrapped grips, charming powder coat, and a low-step frame for easy mounting. +A high-volume enduro sled with coil-sprung shock compatibility, 180mm front travel, and beefy stanchions for rugged downhill sections. +A high-travel enduro mountain bike with 170mm fork, burly swingarm, chain guide and a paint scheme that hides rock chips and trail scuffs. +A classic steel road racer restored with new cables, polished chrome, skinny tubular tires and rich burgundy lacquer with cream pinstripe. +A gravel adventure frame with triple-bottle capability, integrated fork racks, and a rugged powdercoating that hides dings from long trips. +A children's balance bike with powder-coated frame, low saddle, soft foam grips and a playful decal set for early confidence. +Road climbing build with low-mass wheelset, 52/36 compact crankset, and cassette choice tuned for steep alpine stages. +Gravel racer with tubeless tires, wide-range cassette, and micro-adjustable stem for quick handling tweaks mid-ride. +Classic steel fixed-gear with polished headset, narrow pedals and a soft chromed finish for vintage appeal. +A gravel rig with adjustable rear triangle for changing wheel sizes, internal storage tube, and a muted desert-sand finish. +Lightweight chrono bike with integrated hydration, narrow tail section, and anti-squat chainstay profile for time trialists. +A vintage track bike restored to period specs, tubular tires, leather saddle and deep black lacquer with white script logos. +Touring gravel with reinforced dropouts, mud-proof fenders, and dynamo hub for long-range reliability. +A performance gravel bike with flared drops, shallow carbon rims, 700x40 tires and cream-to-teal gradient livery. +Commuter with corrosion-resistant hardware, puncture-proof tires, and a single-button hub to toggle headlight modes. +A gravel touring rig with sloping top tube, multiple braze-ons, wide tubeless tires, and a well-fitted handlebar bag for long days. +Kids' BMX race bike with number plate, rigid forks, and knobby 20-inch tires for track racing. +A vintage track bike with tubular wheels, leather saddle, minimalist decals and gloss black finish with white number block. +A compact trail hardtail with modern geometry, 140mm fork option, and wide 29-inch rims for modern off-road quickness. +Tandem recumbent with broad tires, low center of gravity, and a sail-ready mounting point. +Cyclocross specialist with ultra-short chainstays, large tire clearance, and a durable carbon layup for seasonal racing. +Lightweight gravel path racer with carbon fork, tubeless tires, and a 46/30 compact crank for rolling fast on mixed surfaces. +A bespoke steel track frame with rounded lugs, polished chrome, and a narrow profile tuned for sprint track performance. +All-mountain 29er with slack head angle, 150mm front travel and burly rubber for technical trails. +A minimal urban fixie with slender rim brakes, small profile tires, and monochrome paint for a stealth look. +Urban utility step-through with chaincase, integrated frame lock, rear rack, and puncture-resistant tires for daily work commutes. +A cobble-friendly endurance bike with extra-damped seatpost, wide-volume tires, and a slightly longer wheelbase to smooth out rough pavement. +A modern city e-bike with torque-limited motor, low-step design, and integrated child seat compatibility on the rear rack. +Classic commuter with chaincase, built-in basket rack, reflectorized fenders and upright swept bars for comfort city cruising. +Lightweight aero road bike with full internal routing, hidden cables, aero cockpit and race-oriented stiffness distribution. +Gravel endurance racer in stealth matte with tubeless 42mm tires, wide handlebars and a comfortable touring saddle. +A commuter with built-in handlebar phone mount, integrated lock, and a clean single-speed drivetrain for short efficient trips. +A folding cargo bike with long deck, sturdy rear rack, electric assist, and a high-visibility safety red finish for last-mile deliveries. +A vintage city bicycle with restored brass fittings, leather handlebar wrap, front wicker basket, and a rear hub dynamo for lights. +A tour-ready steel frame with triple bottle cages, three-gear granny ring, and large fenders to keep riders dry in the rain. +Cargo longtail bicycle with low step height, passenger footrests, and a sturdy welded frame for reliable load carrying. +Vintage road bicycle restored with period decals, polished chrome, leather saddle and classic skinny tires for Sunday rides +A urban cargo trike with enclosed cargo box, lockable lid, and hydraulic disc brakes to manage heavy loads through city traffic. +Classic city bicycle with roller brakes, chaincase, front lamp and comfortable upright stance for errands and social rides. +Gravel endurance aluminum with mid-mounted battery option, long wheelbase and a geometry designed for comfort on long days. +Leisure cruiser with extra-wide saddle, swept-back bars, wicker basket and chrome-trimmed fenders for relaxing rides. +Touring tandem with adjustable stem lengths, sturdy wheels, and low gearing for tandem travelers tackling long distances. +Low-step urban e-bike with large integrated battery, torque sensor, upright posture and anti-theft locked compartment +Titanium gravel frameset with extra bottle mounts, brushed finish, comfortable compliance and stealthy anodized headset for understated class. +Gravel endurance frame with carved carbon fairings, integrated tool storage, and disc brakes for fast unsupported races. +A lightweight time-trial bicycle with aero bars, teardrop tubing, integrated hydration behind the saddle, and solid-disc rear wheel for velocity. +A compact folding commuter with low-step hinge, 16-inch alloy wheels, internal gear hub and gunmetal finish with neon highlights. +Road aero finesse bicycle with deep-section rims, hidden cable routes, and a tight cockpit for echelons and fast group rides +Steel framed touring mixte with leather touches, integrated pump clip, and long chainstays for stable loaded rides. +A gravel adventure set up with wide flat handlebars, flared drops converted into stationary positions, and robust luggage rails. +A commuter with integrated fold-out child seat, quiet hub motor, and durable canvas rear bag for carrying small cargo items. +A slick urban single-speed with polished frame, matching deep-section rims, and a minimalistic cable stop for a clean, fast look. +Classic British mixte with swept bars, wicker basket, and chrome-plated fenders for a gentle ride. +Vintage-inspired steel roadster with swept-back bars, chrome fenders and creak-free solid hub for easy rides. +A vintage road racer fully restored with period-correct period-correct components, slim steel tubes, and original decals preserved. +Electric folding commuter in midnight black with quick-fold frame, center-mounted battery, and puncture-resistant tires. +Touring expedition bike with extra-strong wheelset, heavy-duty spokes, and triple-bottle mount for extended self-supported travel. +A hardy utility bike with front cargo rack, chunky tires, wide gearing and a broad, cushioned saddle for delivery routes. +A kids' balance bike built from lightweight alloy, colorful decals, and cushioned grips to keep tiny hands secure while learning balance. +A children's cruiser with small training wheels, soft-grip handlebars, bright graphics and a durable chainguard to keep fingers safe. +Gravel mountain cross with 700x47c tires, keyed thru-axles, and a rugged alloy frame for endurance. +A modern trail hardtail with 130mm fork, wide handlebars, and aggressive grippy rubber for confident descents and quick climbs. +Electric-assist commuter bike with integrated battery in the downtube, belt drive and hydraulic disc brakes. +Vintage-inspired steel road frame with understated decals, restored bar-end shifters, and soft-touch bar tape. +A gravel pro racer with full-carbon frame, 1x wireless shifting, tubeless 700x36 tires and a geometry tuned for sustained speed on rough roads. +Gravel endurance machine with 2.35" tubeless tires, flared drops, and a relatively upright cockpit for day-long comfort. +Gravel endurance build with generous tyre clearance, comfy geometry, dynamo lighting, and a resilient alloy frame. +A steel utility trike with oversized cargo bed, easy step-through, and wide handlebars for confident steering with heavy loads. +A touring tandem with built-in dual mudguard system, heavy-duty spokes, and a double-crank configuration for synchronous pedaling on long treks. +A high-clearance cyclocross/gravel frame with curved chainstays, long wheelbase and a dedicated mud-shedding rear triangle. +A high-end road bicycle with full carbon fiber layup, ultra-narrow chainstays, and integrated power meter for precision training. +Urban practical hybrid with front suspension, 700x38 tires, versatile rack, and USB-powered front light for everyday use. +Track sprint steel frame with classic lugwork, high-flange hubs and a clean single-color paint scheme for minimalism. +Cross-country full-suspension bike with remote lockout, 100mm travel, and feathery climbing character. +Recumbent two-wheeler with elevated backrest, aerodynamic fairing option, and low rolling resistance wheels for long comfortable rides. +Cargo longtail with convertible seating, fold-out side racks, and reinforced axle to carry heavy loads safely. +Lightweight steel road frame with slender seatstays, drilled brake mounts, and classic quill stem for period-correct restoration. +A kids' balance bike with fun cartoon graphics, adjustable seat height, and wide base tires for stability while learning. +Urban cargo tricycle with large front platform, electric assist for heavy loads, and powerful hydraulic brakes for controlled stops. +Electric freight bike with large capacity battery, reinforced bottom bracket and powered steering assist for hefty loads. +A commuters' folding electric bike with compact folded footprint, front-wheel motor, and a durable latch system for frequent public transport users. +Urban single-speed with custom powdercoat, alloy deep-section rims, and polished chrome chainring for stylish city runs. +Touring steel bike with long tail rack mounts, solid brazed joints, and an enamel deep forest paint with gold pinstriping. +Commuter with integrated rear light in the rack, quick-release wheel, belt drive and a damped seatpost for added comfort. +A dual-suspension trail bike with asymmetric stays, 150mm travel, high-volume shock and an adjustable geometry headset for tuning handling. +Cyclocross race bike with quick-release thru-axles, lightweight cockpit, and geometry tuned for rapid shoulder carries. +Urban cruiser with comfortable sweeping bars, wide-profile tires, minimal gears and soft cushioning for easy rides. +Retro-styled single-speed cruiser bicycle with sweeping handlebars, banana seat, pastel mint paint, and whitewall tires for beach promenades. +A coastal cruiser with corrosion-resistant stainless links, sealed hubs, and marine-grade polish for salty seaside rides. +An electric commuter with torque sensing mid-drive, integrated lights, internal racks and a pale gray minimalist frame. +Downhill freeride bike with reinforced head tube, large rotor brakes and aggressive geometry for steep descents. +Downcountry mountain bike with 120mm travel, light chassis, and playful geometry for fast climbing. +A vintage city bike with chaincase, coaster brake, spring-saddle, and swooping downtube livery in a faded teal finish. +Endurance road machine with endurance geometry, wider rims, 32mm tires, and vibration-damping handlebar tape for long days. +Compact folding electric bike with under-saddle battery and torque-sensing motor concealed in rear hub. +Urban fixie with anodized components, short reach brake lever, single front brake, and a high-polish finish for city style. +Electric cargo bike with three child seats, low-step deck, hydraulic brakes and rear-view mirrors for family transportation +Clean single-speed city bike with flash-polished frame and sealed-bearing headset that rolls smoothly. +Lightweight carbon road frame built for sprinting with short head tube, stiff bottom bracket, and deep rear wheel clearance. +Road endurance frame with multi-zone compliance, generous tire clearance up to 32mm, and a satin pearl paint. +A touring gravel bike with integrated pump mounting, multiple bottle bosses, and a cavernous downtube to tuck away tools and a small bladder. +City commuter with spring-loaded kickstand, integrated pump in the frame, fenders and reflective sidewall bands. +Steel touring bicycle with lugged frame, triple crankset, 32mm touring tires, and welded eyelets for full-pannier loading. +Urban commuter with belt drive and hub gear, upright handlebars, integrated lights, and a weatherproof rear rack for daily commuting chores +Mountain trail hardtail with reliable air fork, tubeless-ready rims and reinforced spoke pattern for rough days. +Touring mixte frame with low standover, triple chainring, pannier-friendly rear triangle, and three-bolt rack mounts for long comfort. +A cargo e-bike with reinforced rear platform, fold-down side panels, and a modest electric motor for uphill city transport tasks. +Classic Dutch-style city bike with upright bars, coaster hub, chaincase and integrated lock for relaxed commuting +Tandem touring bike with three-bottle capacity, reinforced fork, and matching pannier sets. +Urban single-speed with flared handlebars, tight steering geometry and a glossy black powder coat. +Gravel endurance build with compact crankset, wide-range cassette, cushioned handlebar tape and flared drop bars +Mountain downhill bike with adjustable travel linkage, super-wide rims and long-travel suspension to conquer steep parks. +A utilitarian folding electric cargo bike with longtail deck, electric-assist, child seats, and locking kickstand for family errands. +Model-specific endurance road bike with relaxed geometry, clearance for 32mm tires, and vibration-damping seatpost for long rides. +Adventure-ready hardtail with 29+ wheels, framebag mounts, and a stable geometry for multi-day dirt-road tours. +Urban single-speed cruiser with wide saddle, retro tank welds and a shiny candy apple red coat for eye-catching rides. +Fixed-gear single-speed with matte navy finish, anodized chainring, flip-flop hub, and a small rear brake for urban safety +A minimalist single-speed cruiser with narrow riser bars, deep black paint, and a slim saddle for short, stylish city journeys. +A low-profile commuter with clean lines, internal hub gears, and matte black finishes for a discreet daily ride. +All-terrain fatbike with studded tires for winter, reinforced fork and a powder-coated frame for salt resistance. +A low-slung recumbent bicycle with long wheelbase, mesh seat, and floppy fairing for aerodynamic cruising. +Cross-country race hardtail with tapered steerer, carbon fork, dropper post, and a lightweight alloy frame for climbs and sprints. +A modern city single-speed with flip-flop hub, minimalist brake lever, and powder-coated pastel frame for trendy urban cyclists. +Touring tandem with reinforced fork, dual bottle mounts, long-chainstay stability and comfortable touring saddles for long days. +A vintage city bicycle with repaint in classic two-tone, leather saddle, and an integrated rear hub dynamo to power a modern LED headlamp. +A mountain enduro bicycle with mixed 29/27.5 wheel setup, coil shock compatibility and a rock-solid drivetrain for aggressive trails. +Adventure gravel bike with copper paint, reinforced chainstays, and triple bottle mounts for bikepacking. +Commuter bike with step-through aluminum frame, front basket, hub dynamo and LED matrix headlight. +Youth BMX park bike with reinforced gussets, short top tube, 20-inch wheels and worm-drive pedals for park tricks. +Gravel bike with stainless steel frame, polished finish, 650b wheels, and classic cable routing for mechanical simplicity. +Handbuilt lugged steel road bike with classic geometry, enamel paint, downtube shifters, and leather saddle for timeless rides. +Folding commuter with low center of gravity, compact folded shape, and a subtle anodized finish on the hinge. +Urban cargo bike with electric boost, quick-release child seat, and puncture-resistant deck coating. +A polished aluminum road frame with shallow-depth rims, compact geometry, and a pearl blue finish that highlights smooth welds. +A red and white cyclocross bike with 1x drivetrain, disc brakes, mud-shedding stays and welded chainstay protector for off-road reliability. +A single-speed mountain bike with thick 2.35-inch tires, rigid fork, and a short top tube for nimble dirt-road carving. +Gravel touring bicycle with painted framepack, flared drop handlebars, and long-ish chainstays for a stable loaded ride. +Race-ready time trial bike with integrated hydration, super-smooth electronic groupset and full carbon cockpit for aero advantage. +Classic track bike restored with period-correct components, tubular wheels, and a high-polish finish reminiscent of velodrome glory. +A classic touring tandem bicycle with two sets of shifters, reinforced fork, matching saddles, and large-capacity panniers for extended trips. +A child-sized mountain bike with durable frame, simple 8-speed drivetrain, and a geometry that balances stability with the ability to learn skills. +Mountain downhill frame with replaceable axle inserts, stiff forged yokes, and a bold graphic that hides scuffs. +A mountain hardtail with wide bars, grippy 2.4-inch tires, and a geometry that favors confidence on technical climbs and descents. +A cyclocross bike with fender mounts, shallow rim brake calipers, and flared bar tops to provide control in long cyclocross races. +A hybrid city-rider with 700c commuter tires, suspension seatpost, oversized stem bag, and muted steel-blue paint. +Folding electric bike with mid-drive motor, collapsible pedals, and a safety-locking hinge for crowded trains. +A modern gravel race frame with integrated GPS mount, aerodynamic tubing, and quick-release thru-axles to minimize pit-stop time. +A mountain hardtail with progressive geometry, 140mm fork, tubeless tires and military gray paint with orange micro-accents. +A vintage-style roadster bicycle with wicker basket, leather brooks saddle, chain guard, and pale cream paint with pinstripe accents. +A kid's balance bike with lightweight aluminum frame, rubber anti-slip footrests, and a low-slung saddle for safe starting confidence. +High-volume gravel rig with 650b wheels, 47mm tires, dropper post, and long-range gearing for bikepacking. +City utility bike with step-through frame, wide cushioned saddle, full fenders, and chaincase for clean clothing on rainy commutes. +City folding electric with compact handlebar, 18-inch wheels, quiet belt drive and quick-charge battery for repeated urban hops +A titanium gravel touring bicycle with extra bottle mounts, internal routing for a dynamo, and chainstay protection for long miles. +A city cruiser with wicker front basket, step-through frame, upright swept bars and low gearing for easy neighborhood rides. +Gravel adventure plus with 650b rear plus wheel compatibility and a 700c front for a balanced ride feel. +A custom city fixed-gear with mirror-polished frame, retro leather grips, single front reflector and slim-profile tires for cleaner urban aesthetics. +A commuter with full chaincase, 3-speed hub, low-step frame, and a wicker basket bolted to the front for small errands or coffee runs. +Gravel e-bike with long-travel fork, mid-drive motor and full-frame bag compatibility for remote self-supported rides. +Mountain cross-country bike with lightweight frame, efficient suspension, and narrow handlebars for tight singletrack. +Retro-styled roadster with chrome fenders, upright handlebars and a sprung leather saddle for leisurely rides. +Urban folding fixie with single-speed drivetrain, quick release folding latches, and a compact profile for apartment storage. +Folding city bicycle with low center of gravity, 16-inch wheels and durable aluminum hinge for daily commuting. +A folding electric bike with kickstand-integrated battery, stable three-gear hub, and lightweight frame for multimodal commuters. +A carbon downhill race bike with twin-crown fork, 200mm travel, and crash-resistant protective film on vulnerable areas. +Electric mountain hardtail with dropper seatpost, thru-axles and torque-sensing assist for steep trails. +Aerodynamic time trial machine with integrated hydration, disc front wheel, TT bars and a long tail section +Mountain enduro bike with adjustable geometry, 170mm rear travel, 29" wheels, and a dropper post for technical descents. +A cyclocross race frame with carbon layup, glossy finish, and a short rear triangle for quick accelerations out of corners. +Urban compact folding bicycle with 16" wheels, tilt-adjustable stem, and a low-maintenance single-speed drivetrain for commutes. +A modern endurance road frame with 32mm tire clearance, vibration-absorbing layup, and an iso-elastic seatpost to smooth long rides. +A restored vintage racer with modern brake calipers retrofitted, polished lugs, and period-correct graphics for classic club rides. +Compact commuter folding bike with 20" wheels, rapid-fold hinge, integrated lock, and color-contrasting grips for personality. +A modern gravel racing frame with integrated cockpit, stealth bottle mounts, and a race-geometry that encourages sustained high-speed efforts. +A vintage touring bicycle outfitted with modern wheels, disc brake conversion, and leather panniers for a retro-modern touring vibe. +Performance-oriented e-mountain bike with big-battery, 150mm suspension and geometry dialed for downhill control. +A gravel endurance rig with compliant carbon seatstays, 700x40 tires, and integrated fender mounts for year-round use. +Retro-inspired cruiser with leather saddle, swept-back bars, and a front wicker basket for sunset rides and errands. +Commuter with upright posture, easy-mount step-through, and comfortable padded saddle for office rides. +A custom steel frame with engraved lugs, classic pump peg, leather saddle, and 28mm tires for elegant club rides. +Adventure touring bike with brazed-on rack mounts, low gearing triple chainset and durable steel tubing. +Mountain enduro with progressive travel curve, sturdy chassis, wide rims, and a chain guide to control chain slap on rough courses. +Kids' mountain slayer with disc brakes, soft grips and a friendly bright green frame for beginner trails. +Kids' pedal bike with training wheels removed, colorful decals, front basket and chainguard for safe neighborhood rides +Time-trial weapon with integrated hydration, steep seat tube angle, aero basebar, and shallow-profile front wheel for quick handling. +Mountain freeride frame with oversized tubing, dual-pivot brakes, and heavy-duty bearings for stunt-heavy park sessions and big drops +Cyclocross race-ready carbon frame with high mud clearance, disk brakes, and a geometry that favors fast run-ups and quick accelerations +A step-through electric cargo bike with toddler seat, enclosed chaincase, low standover height, and throttle assist for easy neighborhood rides. +A gravel racer with a wireless shifting system, aerodynamic seatpost, and wide rims set up tubeless with fast-rolling 35mm tyres. +Track-inspired fixed-gear with aero seatpost, minimalist headset cap, and color-matched chainring. +All-road carbon bike with flared drops, integrated fender mounts, and stealth graphics. +A single-speed urban runabout with flip-flop hub, polished steel rims, leather saddle and a candy-apple red lacquer. +Mountain cross-country full-suspension with lightweight checks, 120mm rear travel, and a race-ready setup for marathons. +BMX freestyle bike with reinforced gussets, short wheelbase, and a responsive geometry for popping off stairs and rails. +A gravel endurance build with slightly taller head tube, padded bar tape, and an upright yet aggressive stance for long rough rides. +A lightweight touring bike with chromed steel fork, stable handling, triple-chainring capacity and a leather saddle for comfort on the road. +A precision-engineered titanium commuter with an integrated lock mount, hydraulic disc brakes, and vibration-damping tube profiles for long rides. +Fixed-gear track-inspired urban bike with gleaming chrome, deep-dish rims, and a functional front light for early starts. +A track sprint bike with aero tubing, fixed gear, deep carbon wheels, and specially tuned frame stiffness for velodrome power transfer. +Touring bike with double-butted steel tubing, polished lugs, and narrow tires for fast rolling on pavement. +Touring steel frame with reinforced fork ends, low bottom bracket, and a compliant ride for multi-day loaded trips. +Electric folding longtail with child bench, dual-battery plug-in, and foldable handlebars for urban family mobility. +A performance cyclocross frameset with carbon top tube, alloy chainstays, and reinforced disc brake mounting for stiff cornering. +A quiet commuter with internal hub and belt drive, integrated rear taillight, and a polished silver frame that resists visible grime. +A boutique titanium road frameset with unique bevelled joins, discrete logo engraving, and a light-brushed finish that reveals use over time. +A rock-solid cargo bicycle with reinforced dropouts, super-wide rear axle, and a cargo box designed for child seats and bulky loads. +Touring tandem with robust wheels, adjustable cockpit, multiple racks, and a relaxed geometry for long comfortable expeditions. +Retro cruiser bike in pastel mint with swept-back handlebars, wide saddle and balloon tires for beach rides. +A BMX street bike with reinforced downtube, sacrificial chain tensioner, and tough tires for grinding rails and stairsets. +Gravel endurance bike with compliance-enhancing features, neutral handling, and a long-range gear spread for touring. +Lightweight hill-climb bike with oversized chainstays, low stack, narrow rim brakes, and a weight-weenie parts list. +A BMX park build with chromoly cranks, strong hub bearings, and an adjustable gyro to tune handlebar rotations for tricks. +Gravel race bike with 40mm tubeless tires, gravel-specific carbon fork, and ergonomic flared drops. +A city e-cargo trike with front platform for deliveries, three-wheel stability, electric assist and reflective safety striping. +Commuter with hub gears, roller brakes, upright bars, pannier rack, and reflective tape along the frame for safety. +Classic Dutch-style cargo bike with elongated front box, child bench and easy-to-use handbrakes for city errands. +A vintage road bicycle with period-correct headset, chrome fenders, and a classic leather saddle that breathes history each ride. +A vintage-inspired city bicycle with spring saddle, Brooks leather touches, and soft grips for classic comfort on slow rides. +A gravel-adventure bike with two-bolt seatpost clamp, large tyre clearance, and a stealthy sand-colored paint that masks dust. +A cyclocross bike with cantilever-compatible frame, ample tire clearance for 33mm knobby tires, and a robust geometry tuned for muddy races. +A full-carbon cross-country race bike with 100mm travel suspension, tapered headtube, lightweight cockpit, and tire clearance for 2.1 inches. +Cargo longtail with child bench, foot straps, and tubeless-ready rims to handle heavier loads comfortably. +A gravel frame designed for steep, rocky mountain passes with durable 650b wheels, 47mm tires, and reinforced chainstay plating. +Enduro race bike in neon orange with 170mm rear travel, coil-sprung shock, stout chainstays, and a wide, grippy handlebar. +A commuter bicycle with step-through aluminum frame, integrated rear light, chain guard, and adjustable stem to suit multiple riders. +A dirt-jump frame with integrated chain tensioners, sealed headset, and a powdercoated finish that masks scuffs from rails and ledges. +A city utility bicycle with heavy-duty rack, integrated lock loops, dynamo lighting and practical steel-gray powdercoat. +A commuter with a sturdy integrated top rack, weatherproof electrical system for lights, and an internal gearbox for low upkeep. +Single-speed urban cruiser with high-rise handlebars, long chainstay, and detailed pinstriping for a personal touch on city streets +A titanium cyclocross bike with stealth internal routing, seat tube-mounted water-bottle bosses, and clearance for 700x40 tires. +Urban single-speed with bulbous paint, riser bars and platform pedals designed for quick errands and nimble handling. +Mountain cross-country race bike with 100mm full suspension, featherweight carbon frame and 1x12 gearing for quick accelerations. +Trail shredder with aluminum frame, coil shock, burly chainstays, and wide 2.5" rubber for aggressive riding. +Cargo bike with passenger bench, reinforced seatbelts, and integrated rain cover for family runs. +Lightweight carbon road frame with integrated seat mast, time-trial geometry, and rim brakes for classic braking response. +Gravel endurance frame with spacious tire clearance, flared bars, lower stack height and light-reflective decals for safety at dawn +Roadtime trial machine with steeply sloped top tube, integrated aerobars and aerodynamic water bottle placement. +A cyclocross bike with steel tubing, vintage-style lug work, single-ring conversion, and knobby 33mm tires for muddy Sunday mudfests. +A cyclocross race-ready bike with light flared bars, tubeless 700x33 tires and a durable alloy frame for frictionless race days. +Racing track tandem with short wheelbase, specialized chainline and aerodynamic seat positions for team pursuit practice. +A lightweight alloy road aero frame with integrated seatpost, shallow rim wheelset, and a clean white paint with contrasting chevrons. +A fatbike with 4.8" balloon tires, rigid frame, lowered gearing for sand and snow, and bolt-on plate pedals for traction in cold conditions. +All-road carbon rig with endurance geometry, 650b wheel option and subtle reflective logos. +Electric urban cruiser with low-step frame, cushy saddle, basket and pedal-assist motor. +A family cargo bike with child harness points, heavy-duty frame, and a comfy bench fit for two small passengers on city runs. +Rigid mountain bike built for bikepacking with frame-mounted tent straps and a bar bag. +A retro-inspired gravel bike with leather bar tape, steel frame, mud clearance and discreet rack mounts for weekend touring. +A gravel machine set up for mixed speed with 700x38 tires, compact double crankset, and flared bar flare for confident handling on washboard. +Gravel-ready cyclocross bike with reinforced fork crowns, mud-friendly geometry, and 38mm tires. +Fixed-gear conversion with flip-flop hub, bright orange frame and platform pedals for urban style. +Touring bike with candle yellow paint, triple racks, and fitted panniers for multiday journeys. +Classic road racer with restored chromed fork, period-correct shifters, leather saddle and vintage decals for nostalgia rides. +Lightweight carbon time trial bike with integrated hydration, narrow chainstays, and a pointy nose tube. +Lightweight time trial rig with integrated tail fairing, electronic groupset and race-grade deep carbon rims. +Mountain enduro bike with progressive geometry, dropper post, and carbon fiber linkages. +A lightweight alloy road bike with a racy geometry, thin-walled tubing, and a performance-focused component kit for weekend racers. +All-road adventure bike in olive drab with framebag loops, underbottle mounts and clearance for 45mm tires. +A gravel all-terrain machine with integrated mudguard mounts, 700x45c clearance, and a long-range hydration cage in the fork. +Mountain downhill rig with serious frame protection, 200mm travel, and maintenance-friendly components for race demands. +A downhill race bike with asymmetric chainstay, external routing for heavy-duty brakes, and glossy electric lime paint. +Road endurance bike with endurance geometry, slightly wider rims, tubeless setup, and a matte sand finish. +Cargo tricycle with low cargo bed, kids’ harness points and industrial powder coat for durability. +Lightweight climbing-focused road frame with low stack height, narrow 23mm tires, and a purposeful stripped-down parts list. +Touring tandem with steel tubing, wide-range gearing and double tail racks for shared long-distance adventures. +Road endurance frame with relaxed geometry, carbon layup tuned for comfort, and silk-screened classic stripes. +Fixie with deep-profile rims, minimalist paint, flip-flop hub for a coaster option and a single brake for safety-conscious urban riders +A high-performance gravel frameset with monocoque carbon layup, aerodynamic tubing, and 700x45 compatibility for fast improvised routes. +Commuter e-bike with rear hub motor, throttle cut-off, front basket, and integrated lock mounting points for quick errands. +All-mountain full-sus with 160mm travel, adjustable geometry, reinforced swingarm and wide 30mm rim for rugged terrain control. +A classic beach cruiser with banana seat, high-rise handlebars, and retro teal paint under a high-gloss finish for sunny boardwalk rides. +Cargo electric trike with weatherproof canopy, LED running lights, and heavy-duty racks for commercial use. +A commuter with integrated small-battery front light, puncture-proof tires, internal-gear hub and a neat low-step alloy frame for quick urban hops. +Gravel touring with stainless hardware, leather bar tape, full fender compatibility, and a wide-range rear cassette for loaded climbs. +Gravel bike with metal flake paint, wide gear ratios, 700c x 42 tires, and plenty of bottle mounts for multi-day unsupported rides. +A stripped gravel conversion with frame-bag mounting points, low gearing for loaded climbs, and slightly upright bars for all-day comfort. +Steel singlespeed track-style bike with brushed raw finish and classic quill stem for simplicity. +Adventure-ready titanium frameset with extra bottle mounts, stealth hard-anodized finish and dropped-seat-tube for comfortable clearance. +A commuter with a removable battery cloak, discreet electric assist, and a pinned chainstay to prevent chain slap noises in urban riding. +A handcrafted steel fixed gear with clean brazing, narrow dropouts, leather saddle and a raw hammered silver finish. +Track-inspired urban single-speed with deep V rims, minimalist matte finish and fixed cog for low-maintenance commuting. +Vintage roadster with chrome fenders, classic headlamp, and upright bars combining old-world style with modern convenience. +A utilitarian city bike with upright riding position, wide sloped frame, heavy-duty basket, and integrated locking system for convenience. +Compact folding e-bike with central battery, torque sensor, fold-flat handlebars and bright safety paint for commuters on the move. +A race-inspired time trial bike with integrated hydration, narrow profile, and steep seat tube for optimized aerodynamics. +Cyclocross race rig with open, mud-friendly geometry, tubular tires, and a low stack height for nimble handling on courses +Classic single-speed city bicycle with slim tires, elegant frame lines, and a minimal toolkit tucked under the saddle. +A rugged touring frame with heavy-gauge steel, oversized rack mounts, and a paint finish meant to survive long-term expedition scratches. +Touring steel bicycle with multiple rack eyelets, triple bottle mounts, and durable mid-fat tires for cross-continental trips. +A wooden-framed bicycle with laminated ash tubes, natural varnish, and leather-wrapped bars for a handcrafted aesthetic. +Urban single-speed with bright enamel paint, classic coaster brake feel, comfortable saddle and a small front rack for errands +Full-suspension trail bike with progressive geometry, 150mm travel, coil-tuned shock and chainstay-mounted bash guard. +Track keirin bike with aggressive geometry, steel lugged frame, and brushed aluminum finish. +All-mountain carbon rig with 160mm fork, robust lower legs, and capable geometry for technical enduro stages. +Cyclocross-specific frameset with asymmetric chainstay clearance, mud-shedding arch and chain retention for tough CX courses +Vintage mixte with floral decals, leather grips, chrome fenders, and a rear coaster brake for leisurely park rides. +Folding city bike with a hinge-frame, 20-inch wheels, rear carrier and quick-release folding latch. +A gravel adventurer with dropper post, 47mm tires, and frame para-mounts for solar-powered navigation electronics and remote camping gear. +Aggressive gravel bike in olive drab with 40mm knobby tires, wide drop bars, disc brakes, and storage mounts for multi-day rides. +Gravel bike with bright geometric decals, endurance geometry, and 42mm tubeless tires for rough surfaces. +A titanium gravel frame with smooth welds, stealth finish, and modular rack mounts for long rides that require reliability and comfort. +Gravel friendly carbon frame with internal dropper routing, 2.2-inch tire clearance and stealth upper cable ports. +Cargo longtail with passenger rails, padded seat, electric assist and reinforced wheels built to carry heavy loads. +Cargo long john bicycle with low cargo box between wheels, electric assist motor and reinforced chassis. +Gravel bike with mixed-material frame, carbon back half, alloy front triangle, and distinctive paint. +A retro track-style single-speed with polished lugwork, narrow tubular tires, and a simple uncluttered aesthetic for city sprints. +A kids' balance bike painted in cheerful orange, low seat height, wide base and rubberized foot pads for safe practicing. +A commuter with low maintenance internal gear hub, integrated lights, and a recyclable powder-coated steel frame for longevity. +Tandem commuter with low center of gravity, reinforced fork, and matching pannier racks for shared rides. +A classic cruiser with swept bars, springy saddle, wide paddle pedals, and a two-tone enamel paint job for laid-back coastline rides. +Road endurance frame with laid-back geometry, soft-touch finish and seatpost compliance for smoother rides. +Gravel touring bike with triple chainset option, triple bottle mounts, and clearance for 2-inch tires and fenders. +A practical utility cargo bike with removable side panels, step-through frame, and a 500W electric motor for steady assistance under load. +A women-specific geometry road bicycle in pearl lavender, shorter reach, narrower handlebars and 28mm tires for comfortable miles. +Electric mountain bike with heavy-duty mid-drive, large capacity battery, and dual suspension tuned for steep trails. +A gravel race bicycle with balanced compliance, electronic shifting, and tubeless tires in a mid-volume 38-42mm range for speed and comfort. +A gravel-specific e-bike with a torque-sensing drive, wide 700x45mm tires, and plated stainless fittings to resist the elements on long adventures. +A gravel bike custom-painted in matte olive drab with tactical mounting points for lights, GPS, and tool storage on the top tube. +Full-carbon trail bike with 140mm travel, kinematic suspension linkage, 1x12 drivetrain and wide handlebars for control +Track-inspired fixed gear with flip-flop hub, narrow 23mm tires, minimalist frame, and polished silver finish. +Mountain fat-semi-fat bike optimized for mixed snow and dirt with 3.0" clearance, low gearing and burly rims. +A flatbar gravel commuter with 650b wheels, disk brakes, and a low stack height for confident handling in fast urban traffic. +A gravel endurance frame with compliant seatstays, 700x45 tire clearance, integrated fender mounts and comfortable endurance geometry. +Urban utility bike in matte army green with integrated lock, heavy-duty rack and puncture-proof tires. +Mountain trail bike with 140mm travel, progressive geometry, dropper post, and tubeless-ready rims for aggressive trail riding. +Beach town cruiser converted to electric with discreet rear hub motor, wicker basket and comfortable cruiser saddle +Lightweight carbon road frame with subtle flake, integrated headset, and narrow seatpost for aerodynamic efficiency. +Cyclocross training bike with stiff fork, 1x11 drivetrain, and large toe clearance for fast remounts during muddy races. +A cross-country race hardtail with carbon seatpost, stiff fork, 29-inch wheels and sunburst orange paint accents. +E-mountain bike with stereo suspension, dual-battery option, and large torque output for steep terrain. +A high-pivot enduro frame with tuned kinematics, adjustable head angle, and long-travel fork to ride steep roads and technical chutes fast. +Mountain enduro rig with lower link design, extra tire clearance, and a burly 30mm inner rim width. +Gravel racer with carbon fork, wide tyre clearance, 1x wireless shifting, and a stealth black gloss finish for performance. +Single-speed touring bicycle with fixed cog, reinforced wheels, leather saddle, and frame bag for stripped-down expeditions. +Vintage mixte frame city bike with step-through top tube, wicker basket and a faded teal lacquer. +Gravel trailblazer with tan-wall tires, brushed aluminum frame, leather bar tape and a sub-compact crank for varied terrain. +A retro-styled fixed-gear with crimson frame, slim drop bars and single-sided front wheel for an aggressive alleycat-ready stance. +Road climbing aero build with deep chainset, low rolling-resistance tires and a narrow cockpit to slice through the wind uphill. +Gravel adventure alloy with stealth battery option, internal routing, and long-travel fork compatibility for mixed-surface adventures. +Touring tandem with extra tall stems, dual water bottle cages per seater, and a deep lacquer finish. +Minimalist rigid mountain bike with steel fork, 29x2.25 tires and a single ring 1x drivetrain for simplicity on trails. +A trail-ready full-suspension mountain bike with 140mm travel, a long wheelbase for stability, and an efficient pedaling platform. +A boutique framebuilt titanium touring bicycle with custom geometry, brazed-on fittings, and oiled-finished raw finish that will age with use. +Kids’ BMX with padded top tube, bold decals and low clearance for tricks at the park. +Compact trail bike with 27.5" wheels, stout frame, precise geometry, and a dropper post for confident off-camber lines. +Vintage-inspired beach cruiser with ornate pinstripes, leather seat and wide balloon tires for relaxed Sunday rides. +A commuter e-bike with integrated cargo basket, throttle-assist option, and safety features like a wide side stand and chain guard. +Fixed-gear commuter with track drops, single front brake, wide tyres and matte graphite paint for a stealth look. +A minimalist city single-speed with compact geometry, polished chain, and a small rear rack for light daily errands. +Single-speed urban track bike with polished steel frame, minimalist branding, and a compact front rack for small items +Gravel race rig with 40mm tyres, carbon cockpit, and a signature paint gradient that shifts under sunlight. +Fixed-gear urban commuter with clean lines, polished chrome components, and minimalist fenderless look for lighter city rides. +A performance road frame with tapered steerer, direct-mount brakes, aero-optimized downtube and a crisp race-ready paint job designed to cut through wind. +Compact folding cargo bike with quick-release rear rack, powerful lights and a foldable passenger bench for kids. +A powerful mountain trail e-bike with stout motor, 150mm travel, and robust components for repeated heavy use in the hills. +A cyclocross race bike with mud-optimized frame shapes, 33mm knobbed tires, and a bright team-contrast paint that stands out in dreary conditions. +A fat-tire adventure bike with bright orange frame, 26x5-inch tires, and a rigid fork for desert and snow expeditions. +Cyclocross endurance bike with modern disc brakes, wide tyre clearance, and a geometry tuned for mixed-condition speed. +Vintage touring bicycle with chrome fixtures, wide leather saddle, and a paint palette that screams classic charm. +Mountain bike with bolted-on bash guard, clutched derailleur, and wide handlebars for control in rock gardens. +Mountain enduro rig with carbon linkage, coil option, and matte stone finish with orange accent lines. +A steel commuter with flared drop handlebars, low-maintenance hub gear, and discreet reflective piping on the frame. +A cargo e-bike with front and rear racks, low center of gravity, and a torque-assist system tuned for heavy loads. +Classic steel racer with polished lugs, period-correct components and a distressed enamel finish that tells a story. +Touring tandem with low-maintenance gearing, robust racks, and durable tires for long days with two riders and luggage. +A stripped racing track bicycle with deep-section carbon wheels, no brakes, aerodynamic seat tube and a crisp glossy black finish. +A small-wheeled folding bike with single hinge, compact footprint, 16-inch wheels and a matte finish for understated portability. +Gravel-plus bike with 650b wheels, 47mm tires, dropper post, and a slack head angle for rough technical gravel descents. +A gravel commuter with 650b wheels, wide tires for stability, and a simple clutch derailleur to keep chains in place on rough roads. +A commuter with low-maintenance planetary hub gearbox, belt drive, and a slim-integrated rear light for safety. +A commuter with internal storage in the top tube, discreet rear light, and a mid-mounted motor optimized for gentle assist in hilly cities. +Cargo e-bike with fold-out side walls, hydraulic disc brakes, reinforced chassis and bright safety yellow finish +A cyclocross race bike with cantilever or cyclocross disc brakes, knobby 33mm tires, and short chainstays for quick accelerations and remounts. +A commuter with mid-drive electric assist, low-step frame, rear luggage rack and a large integrated headlight for night safety. +Mountain trail full-suspension with air shock tuning, modern geometry, dropper post and tubeless tires for technical singletrack fun +A mountain enduro frame fitted with 170mm travel, adjustable geometry, and a beefy tire clearance to run wide rubber confidently. +High-volume touring bike with triple chainset, sturdy spokes, and extra frame reinforcements to handle expedition weight. +Performance gravel frame with asymmetric stays, internal cable routing, and generous tire clearance for rough-riding speed. +A rigid trail hardtail with progressive tubing, 29-inch wheels, and modern tire clearance to fit a variety of trails and conditions. +A high-performance track pursuit machine with NACA-shaped tubes, integrated stem, and a tuned stiffness profile for optimal aero speed. +A steel frame gravel bike with hand-finished paint, framebag-specific shapes, and integrated mounts for a front lowrider rack. +Fitness hybrid with flat handlebars, 10-speed cassette, hydraulic disc brakes, and reflective decals for evening training in the city. +Performance gravel bike with asymmetric chainstays, thru-axles and a flared headtube for stiffness. +All-road carbon bike with clearance for 40mm tyres, internal storage compartments and flared drops for control. +Retro-track inspired fixie with polished silver accents, narrow tires and a minimalistic saddle for clean street style. +A gravel bike with stainless steel hardware, integrated fender mounts, 38mm semi-slick tires and subtle camo patterning for adventure looks. +BMX race bike with 20-inch race wheels, narrow handlebars, and a stripped-down weight-focused build. +Minimal cafe racer single-speed with drop bars, skinnier tires, and flush headset for vintage vibe. +Lightweight cyclocross training bike with alloy frame, shallow crown fork and mud-specific clearance. +Vintage folding bicycle with lugged steel frame, small 20-inch wheels and a leather saddle showing aged character. +Touring tandem with built-in navigation mount, triple-bolt water cage spacing, and heavy-duty spokes for long-haul durability. +A gravel-adventure touring bike with reinforced dropouts, chain-suck guard, triple bottle mounts and wide clearance for 2-inch tires. +Classic two-tone road racer with steel frame, downtube shifters, toe clips and polished alloy rims. +Family cargo e-bike with rear child seats, flatbed cargo platform, pedal assist, and wide stable tires for errands with kids. +A fixed-gear track-inspired commuter with riser bars, minimal guard, and high-volume slick tires for winter city traction. +A trail enduro bike with adjustable geometry, 170mm travel front-end, and burly components for all-mountain exploration. +Cargo-bike with long platform, adjustable passenger harnesses, and reinforced tail section for kids and goods. +Cross-country race hardtail with minimal weight, stiff rear triangle, and race-ready wheelset. +Adventure touring bike with Rohloff-compatible spacing, triple-bottle mounts, reinforced rear rack mounts and rugged protective paint. +A touring bike with low-gear triple setup, reinforced wheelset, full fendering and brass-accented head tube badge for robust long-distance travel. +Gravel commuter with mudguard mounts, dynamo front hub, and small front rack for daily essentials. +Urban compact single-speed with sleek lines, matte finish, and a minimalist front light for clean city aesthetics. +A modern monocoque gravel bike with integrated seatpost, minimal bottle mounts and a discreet matte navy finish. +Mountain downhill with reinforced tubing, massive rotors, and a robust top-tube protector to prevent paint damage. +Mountain enduro with modern kinematics, long-travel fork, and a muted factory finish engineered to hide chips and dents. +A vintage racer restored to period-correct spec with friction shifters, tubular 700c wheels and tan cork handlebar tape. +City mixte with low step-through frame, wicker basket, integrated headlamp and fender set for classic urban commuting +A commuter with stealth black finish, reflective graphics, belt drive, and internal hub that requires little maintenance between rides. +Mountain-alloy enduro bike with external cable routing, protective bash guard and burly 27.5-inch tires. +Gravel-specific titanium frame with brushed finish, classic tubing silhouettes and built-in mudguard mounts. +Modern road aero bike with hidden cables, lightweight carbon seatpost and a matte finish with contrasting logos. +A versatile gravel and touring bike with titanium hardware, removable fender mounts, and a comfortable but aggressive geometry for long, mixed-surface adventures. +Folding e-bike with variable pedal assist, USB charging port in the display, and a quick-fold hinge for compact travel. +A cyclocross training frame with reinforced fork crown, straight-gauge tubing, 35mm clearance, and a durable powdercoat for frequent muddy workouts. +A mountain enduro frame with adjustable rear travel, strong linkages, and a geometry that keeps the rider confident at high speed. +A classic fillet-brazed steel road frame with hand-applied pinstripes, small lug accents for subtlety, and polished silver components. +A lightweight touring titanium frame with 650b readiness, multiple small braze-ons, and a bead-blasted finish that stays timeless. +A short-wheelbase street BMX with reinforced gussets, gyroscopic cable, 20-inch rims and metallic cobalt blue paint. +A gravel-touring machine with reinforced rack mounts, long-range gearing, and heavy-duty spokes to survive remote, rough roads with full loads. +Gravel race machine with disc brakes, wireless shifting, wide tyres and a polished, hand-applied logo on the downtube. +A touring-ready gravel bike with robust steel frame, multiple bottle mounts, heavy-duty racks, and room for sleeping gear for extended tours. +Mountain trail hardtail with modern alloy frame, wide rims, tubeless tires and a dropper post for smoother technical terrain navigation +A gravel-oriented alloy frame with triple cage bosses, steel-reinforced dropouts, and 2.2" tire clearance for multi-day bikepacking trips. +Electric folding commuter with removable battery, quick-fold mechanism and padded travel cover for commuting. +Tour-ready bicycle with triple downtube mounts, heated grips optional, and map-patterned paintwork around the seat tube. +Gravel adventure bike with titanium bolts, stealthy matte finish, and a chainstay protector welded in place. +A stripped-down city single-speed with minimalist fenders, polished frame, and a narrow saddle to cut through morning traffic. +Endurance road bicycle with vibration-damping seatpost, wide rake fork, and comfortable reach for mellow handling. +Urban cruiser with retro whitewall tires, chrome sissy bar, large cushioned saddle and an enamel paint finish for curbside style +A gravel bike with custom two-tone paint, Solstice bronze fade, and matching headset and stem accents for boutique aesthetics. +Lightweight titanium cross-country race bike with 100mm fork, race-specific geometry and sub-9kg build +A stripped-down mountain hardtail with rigid fork, 29-inch wheels, single-speed conversion and burnt orange powdercoat. +Gravel racer with tubeless setup, flared drops, and a reinforced bottom bracket shell for aggressive riding across mixed terrain. +Racing tandem time trial bike with integrated fairings, aero wheels, and coordinated electronic shifting for team speed records. +A compact folding electric cargo bike with long rear platform, quick-fold hinge, 20-inch wheels and high-visibility safety orange paint. +Mountain e-bike with torque-sensing motor and long-range battery, designed to climb like a rocket and descend with confidence. +A gravel racer with full electronic shifting, tubeless-ready rims, and a compact crankset for versatile gearing on climbs. +Steel gravel bike with double-butted tubing, subtle fillet work and polished headbadge for understated craftsmanship. +Kids' cruiser with training wheels, bright streamers and safety reflector strips for visible neighborhood rides. +Gravel adventure titanium frame with generous mounts, brushed finish and a long wheelbase for stability when loaded. +Compact folding bike in cream color, small-diameter wheels, quick-fold hinge, and a carrying latch for subway commuters. +Electric mountain bike with full suspension, torque-sensing motor, robust tires, and dropper post for technical climbs. +A touring recumbent bicycle with comfortable reclined seat, luggage racks, and a long chain path with idlers for efficient pedaling. +A full-suspension trail e-bike with torque-sensing motor, reinforced swingarm, and a balanced center of mass for playful climbs and descents. +Vintage road racer with downtube shifters, leather saddle and polished steel wheels reminiscent of classic races. +Adaptive handcycle-style bicycle with ergonomic cranks, reinforced low frame, and seating optimized for arm-powered travel. +Classic steel city bike with Sturmey Archer hub, chaincase, and high-volume balloon tires for a cushioned urban ride. +City commuter with low step-through frame, mudguards, internal hub, and a quiet belt drive for smooth urban mornings. +A steeped-in-style Parisian city bike with basket, leather saddle, upright bars, and minimalist paint for elegant urban transport. +Electric cargo trike with hydraulic brakes, refrigerated box option, and GPS-integrated display. +A mountain enduro bicycle with adjustable chainstay length, multi-mode shock, burly tires and an all-mountain orientation for variable terrain. +Minimalist track pursuit bike with deep-section front wheel, solid disc rear, integrated stem, and time-trial bars. +A fat-tire trail rig in retro yellow with 4.8-inch balloon tires, rigid fork, and rugged platform pedals for beach or snow exploration. +A premium titanium gravel bike with custom geometry, internal cable routing, 650b+ compatibility and bead-blasted finish with blue anodized headbadge. +A cargo trike with a tilt-locking bed for loading bulky items, a stable wheelbase, and a low center of gravity for predictable hauling. +Cyclocross steel race frame with handpainted swirl finish, shallow chainstays and durable braze-ons for bottle cages. +Modern dual-suspension trail bike with short chainstays, progressive geometry, and modern wheel size options for agility. +A mountain freeride bike with burlier chainstays, replaceable swingarm hardware, and enduro-style geometry balanced between pedaling and descending. +Vintage mixte city bike with gloss paint, wicker basket, and chrome-plated fenders for charming weekend trips. +A kids' balance bike built from lightweight alloy, foam tires, ergonomic grips and bright yellow paint with big friendly letters. +A gravel-capable cyclocross bike with tubeless 35mm tires, dropper post compatibility, and a slightly relaxed head angle for stability. +A lightweight steel road bike with polished meniscus lugs, short wheelbase, narrow tubular tires and classic cream paint with maroon stripes. +A rugged gravel e-bike with internal battery, mid-motor torque sensor, 1x12 gearing and matte storm-gray finish with orange highlights. +A commuter with integrated battery in the down tube, torque-sensing motor, full fenders and hydraulic disc brakes. +Cyclocross commuter hybrid with mudguards, 33mm knobby tires, and a light rack for urban adventures and weekend rides. +A fat-bike with 4.8" balloon tires, painted camo frame, rigid carbon fork, and belt drive for beach sand and snowy winter rides. +Folding electric moped-style bicycle with throttle and pedal assist, 20" wheels, and a commuter-friendly upright seat. +A gravel e-bike with ultra-quiet motor, removable storage, and a long-range battery hidden in the downtube for stealth touring. +Lightweight cyclocross bike with carbon frame, steel dropout inserts, and quick-release thru-axles. +A compact folding bicycle with small 20-inch wheels, hinge frame clamp, rear rack, and reflective decals for commuter convenience. +A fixed-gear alleycat bike with narrow bars, high flange hubs, and an aggressive forward riding position for urban sprinting. +Electric cargo bike with modular boxes, long wheelbase, and a strong motor capable of towing heavy loads uphill. +A folding e-bike with long-range battery, 16-inch wheels, quick-fold frame and a single-button folding latch to get on trains without fuss. +A gravel bike with full-carbon monocoque frame, integrated fender mounts, and an understated slate-gray finish. +A lightweight track pursuit bike with deep section front rim, clip-on aero bars, and a high-polish mirror finish. +Urban cargo e-bike with long low deck, hydraulic disc brakes, and bright LED running lights for nightly runs. +Touring tandem built for comfort and reliability with matching saddles, long wheelbase and heavy-duty rack mounts. +Classic steel track pursuit bike with fixed gear, long reach, high gearing and polished lugwork for the velodrome +A commuter with a low-maintenance shaft drive, sealed bearing hubs, and large canopy fenders for sheltered riding. +A mountain enduro rig with burly 27.5" wheels, 170mm travel, coil shock ready frame and a reinforced headtube for heavy landings. +Modern commuter e-bike with step-through frame, integrated battery in downtube, belt drive, and full fenders. +Mountain cross-country full-suspension with 100mm rear travel, tuned linkage, lightweight shock, and fast-rolling 29ers. +A mountain cross-country rig with lightweight carbon frame, fast-rolling 29x2.1 tires, and minimalistic cockpit for climbing-focused riders. +Classic cruiser with custom chopper-style rake, long wheelbase, flamboyant paint and chrome highlights for statement rides. +Gravel bike with stealth camo finish, 42mm tires, wide handlebars and sealed-bearing hubs for low-maintenance exploration +Touring steel frameset with hand-filed lug work, brazed-on pump pegs, and a classic cream-and-red paint scheme. +Retro folding bicycle with small wheels, chromed fenders, upright bar and a compact folded footprint for tight spaces. +Kids' mountain bike with training wheels removed option, small suspension fork, and easy-to-use twist shifters for learning. +Mountain cross-country hardtail with tapered headtube, stiff bottom bracket, and a 1x11 drivetrain for racing. +Gravel race time-trial hybrid with aero cockpit, narrow tubing, and 35mm tires for fast mixed-surface time trials. +A commuter with integrated head-and-tail lights, stealth battery box in the downtube, wide touring saddle and reflective tape on the frame. +A sleek folding commuter with hingeless clamp, low center of gravity, small wheels and brushed aluminum finish. +Urban e-bike with step-through frame, low center of gravity, integrated lights and throttle-assist for stop-and-go traffic +Compact folding e-bike with 20-inch wheels, rear suspension, small battery and bright LED headlight for night commuters +City cargo bicycle with front-loading box, heavy-duty frame, and integrated reflectors for deliveries. +A lightweight aluminium triathlon bicycle with steep geometry, integrated hydration holders and a narrow profile front fairing. +A touring tandem built for endurance, with triple crank options, sturdy wheelset, reinforced frame joints and classic tourer paint. +A commuter with fully integrated bike computer mount, chaincase, and a rear-facing cargo platform for quick pickups. +A sleek time-trial bicycle with full aero fairings, aero bars, disc wheel, and a deep-section front wheel for triathlon performance. +Gravel grinder with 48T chainring, wide 11–42 cassette, and knobby 45mm tires for rough tracks. +Folding commuter with belt drive, integrated fenders, kickstand and minimalist graphics. +Commuter bicycle with step-through frame, easy-shift hub, integrated lock, and a subtle cream finish for approachable daily riding. +Retro track-style commuter with polished steel, narrow saddle, and toe strap pedals for classic city sportiveness. +A dual-suspension enduro bike with adjustable leverage curve, 160mm rear travel, and coil-sprung shock option for downhill aggressiveness. +Gravel timetrial hybrid with aero bars, 38mm tires and a chromoly frame for mixed-road speed. +Gravel adventure rig featuring an integrated top tube bag channel and paint inspired by topographic maps. +A performance road bike with aero seatpost, integrated cockpit, tubular wheels and pearl-black metallic paint with subtle striping. +Gravel touring bicycle with robust wheelset, 700x45 tires, and threaded bottom bracket for durability. +Road bike with progressive aero tube shaping, shallow rim brakes, and a slightly relaxed head angle for enduring comfort. +Adventure hardtail with 29-inch wheels, reinforced chainstay protection and wide handlebars for balance. +Gravel bike with alloy frame, carbon fork, and a set of 40mm gravel tires for rough roads. +Fatbike with aggressive studded tires, lockout-compatible fork, and salt-resistant hardware for winter trails. +A recumbent trike with reclining seat, aerodynamic fairing, rear-wheel drive and a comfortable couch-like saddle for long miles. +A beach-ready cruiser with pastel mint paint, extra-wide saddle, omega-shaped handlebars and a chrome chain guard. +A commuter with sleek step-through design, built-in reflectors, smooth hub gear and a softly cushioned saddle for comfort. +A gravel touring frame with steel construction, elegant paint, threaded BB shell, multiple framebag anchors and wide tire clearance for expedition travel. +BMX street bike with 20-inch wheels, pegs, gyro rotor and a short rear end for technical tricks. +Triathlon sled with steep seat tube, aero handlebars, and integrated hydration and nutrition compartments for long-course races. +A modern track bike with deep-section front wheel, tubular rear for sprinting, and a polished finish revealing brushed metal texture. +A velodrome sprinter with aerodynamic chainring, stiff bottom bracket, and ultra-short wheelbase for explosive acceleration. +A gravel grinder with 1x drivetrain, tubeless-compatible rims, flared drops for control and charcoal speckle paint with mint accents. +Beach cruiser with extended handlebars, gloss cherry paint, comfortable foam saddle and matching grips for coastal cruises +A smooth-touring steel bicycle painted buttercream with triple bottle mounts, full fenders and a sprung leather saddle. +Touring bicycle with extra-durable wheels, wide-range triple gearing, and welded braze-ons for long unsupported travel. +Classic road racer restored with period-correct decals, cloth-bar tape, and a cheerful red lacquered finish. +Longtail cargo e-bike with modular seat options, high-capacity battery and hydraulic brakes to haul kids. +Electric cargo bike with low center of gravity, long-range battery, and anti-tip geometry for safe urban logistics duties. +A classic town bicycle with step-through frame, stylish chain guard, leather grips and brass lamp mounts for city ambiance. +Vintage mixte city bike with floral pinstriping, wicker basket, full fenders and a comfortable upright riding position +Kids' BMX bike with 20-inch wheels, gyro detangler, pegs and a flame decal on the top tube. +Retro step-through city bike with spring saddle, chrome fenders, upright swept handlebars and a step-friendly low top tube. +A folding cargo bike with long rear deck, reinforced hinge, electric-assist and bright safety yellow powdercoat for city deliveries. +A matte olive-green military-style bicycle with puncture-resistant tires, blackout fenders and robust racks for utility-focused transport. +Vintage-style porteur with front platform, classic brass fittings and a leather strap to hold parcels secure while riding. +A lightweight gravel touring bike with wide tire clearance, multiple rack mounts, and a comfortable endurance geometry for multi-day rides. +Lightweight racing road bike with carbon weave show through, minimal paint, and a focus on responsive handling for Race Day. +Mountain enduro bike with flip-chip geometry, coil shock option and 27.5 mullet compatibility for aggressive lines. +City foldable with 18-inch wheels, comfortable saddle, and a one-handed fold for quick elevator rides. +Classic folding folder with retro livery, single-hand fold latch, and padded saddle for comfort on public transport. +Urban cargo bike with electric assist and a low cargo bed, dual kickstand, and secure straps for transporting fragile items. +Family cargo bicycle with bench seat, child footrests, reinforced braking system and canopy mount for weather protection. +A folding commuter with 20" wheels, internal gear hub, and a walk mode that eases movement through stations and crowded platforms. +A performance road bike with tapered headtube, direct-mount brakes, short chainstays and a paint scheme designed to hide chainstay rub. +A coastal cruiser with sea-foam green lacquer, chrome details, whitewall tires and a friendly bell for relaxed boardwalk spins. +A cyclocross bike with roughcast paint that hides mud, a taut 1x drivetrain, and flared drops for better control while sprinting out of corners. +Classic Dutch cargo bike with wooden bench seats, enclosed chain covers, and bright reflective paint for child safety +Road sprinters’ bike with ultra-stiff bottom bracket, shallow carbon rims and sprint-friendly gearing for bunch sprints. +A kids' balance bike with lightweight bamboo frame, rubber grip handlebars, and adjustable seat for growing riders. +Commuter bike with front and rear reflectors, integrated bell, and mudguard tabs for wet weather commuting. +Gravel racer with flared drops, 38mm rubber, and a short stem for technical control. +Beach cruiser with oversized tires, wide handlebars and retro-striped frame in sunset orange. +A long-travel enduro mountain bike with coil shock option, 170mm travel, and burly 27.5+ tires for aggressive trails. +Commuter with hub dynamo, waterproof pannier rack, and leather grip wrapping for tactile comfort and longevity. +Mountain downhill sled with extra frame guards, aggressive geometry, and a bold, race-number-ready paint scheme. +A commuter with a leather saddle, panniers, weatherproof jacket loops, and integrated theft-deterrent skewers for parked safety. +Touring steel bike with reinforced hubs, wide tires, and a relaxed headtube for comfortable multi-day loaded rides. +Mountain downhill sled with reinforced pivot bolts, long travel and aggressive protective plates to handle abuse. +A city folding bike with adjustable stem height, quick-release seat post, and puncture-resistant commuter tires. +Vintage road bicycle with narrow ribbed fenders, slender chrome fork crown, 9-speed friction shifters and aged patina. +BMX race cruiser with peg-less rear hub, quick-release wheels, and light aluminum frame optimized for sprint acceleration. +A classic lugged touring steel bicycle with polished chrome rack, leather mudguard straps, and a rich deep maroon finish for long journeys. +A modern trail bike with stepped-down chainstay clearance for 2.6-inch tires, 150mm fork, and a tuned shock for balanced mid-stroke support. +Kids' road-style bike with caliper brakes sized down for small hands, 20" wheels, and playful graphic decals. +Handbuilt lugged steel cyclocross bike with classic decals and a no-nonsense low-key aesthetic. +An ultralight triathlon bike with steep seat tube, integrated hydration bento box, V-shaped fork and foam saddle for aero comfort. +Touring bike with steel fork-mounted bottles, S-bend handlebars, and leather frame pump. +High-traction mountain fat-bike with studded tires option, belt-compatible drive and robust drivetrain for winter. +Fixed-gear street cruiser with fat tires, anodized components, and a minimalist single-speed drivetrain for urban style. +A cargo longtail with kid bench, reinforced frame, hydraulic brakes, low center of gravity, and puncture-resistant 26-inch tires. +Classic step-through cruiser with wicker basket, chrome bell, wide saddle and cream-whitewall tires for relaxed short trips. +A long-tail cargo bike with integrated child seat and weatherproof cover for family-friendly urban transport. +Mountain hardtail with modern geometry, lightweight alloy frame, and tubeless-ready rims for confident cornering. +Touring bike with leather-wrapped handlebars, dynamo-powered headlamp and spacious saddlebag. +Family cargo trike with bench seating, rain canopy and safety belts to carry two children safely. +High-volume fatbike with 5" tires, low gearing, and a steel frame to absorb impacts while cruising sand trails. +Electric cargo bicycle with longtail deck, integrated battery, hydraulic brakes and dual child seats. +Steel hardtail with fillet-brazed joins, elegant tapered seat tube and hand-rolled headbadge. +A stealth gravel e-bike with battery integrated into downtube, 1x drivetrain, and matte graphite with small reflective logos. +A kids' mountain bike with front hydraulic disc brake, durable frame, and kid-specific geometry for easier handling. +E-gravel bike with discreet integrated battery, torque-sensing assist, and comfortable endurance geometry for longer mixed-surface rides +Lightweight city folder with step-through geometry, 8-speed hub and compact storage latch for transit use. +Gravel plus hardtail with heavy-duty rims, burly spokes, and a flexible frame pack mount for long miles. +A road time-trial bike with steep seat angle, integrated hydration, and a continuous smooth surface between bars and frame for laminar airflow. +A carbon gravel frame with integrated seatpost clamp, internal cable routing, subtle gloss finish and plenty of mud clearance for rough days. +A city folding e-bike with sharp hinges, short wheelbase for agility, and a quiet hub motor for discreet power assistance. +A commuter with internal battery, torque-sensing motor, puncture-proof tires and a neat rear rack that doubles as a seat for small cargo. +A utility trike bicycle with three-wheel stability, rear cargo bed, and a lockable cover for safe transport of parcels and tools. +Gravel training rig with 650b compatibility, progressive geometry and fast-rolling tires for mixed surface racing. +High-end mountain enduro bike with carbon rear triangle, coil-tuned shock, longer reach and capable descending geometry. +Classic mixte with powder-coated enamel, wicker basket, full chainguard and comfortable upright riding position for errands. +Durable kids' mountain bike with simple 7-speed drivetrain, front suspension, disc brakes, and kid-friendly ergonomics for budding trail riders. +A kids' mountain bike with sturdy aluminum frame, 26" wheels, disc brakes, and a lower standover height to encourage independent trail riding. +A gravel bike with clearcoat over raw carbon, discreet logoing, and room for two large bottles inside the main triangle. +Electric trail hardtail with mid-drive motor, boost hub spacing and dropper post for technical singletrack. +Porteur-style city bike with large front platform, dynamo headlamp and polished brass hardware accents. +Lightweight carbon road bike in matte black with deep-section wheels, electronic shifting and an aggressive aero frame. +Cyclocross race bike with tubular tyre setup, mud-clearance chainstays and reinforced downtube for rugged conditions. +A commuter e-cargo trike with wide stable platform, twin front wheels, electric assist, and weatherproof cargo box for grocery runs. +A BMX flatland bike with narrow frame, low-slung top tube, and freestyle-friendly pegs for tricks and spins. +City commuter with internal 7-speed hub, sealed bearings, integrated dynamo lights, and step-through convenience for daily trips. +A modern dirt jumper with a tapered head tube, short chainstays, and flat-style pedals for controlled landings and pop-heavy tricks. +A children's BMX bike with tough chromoly frame, small pegs, padded crossbar, and bold flake paint for park sessions. +A durable utility cargo bike with wide cargo deck, hydraulic brakes, and a built-in locking system to secure parcels during transit. +A time-trial bicycle featuring a full fairing, integrated bar extensions, very small frontal area and shiny metallic copper finish. +Steel frame urban commuter with internal cable routing, discreet dynamo hub and polished stainless steel mudguard mounts. +A stripped-down commuter with fixed-single, puncture-resistant 700x28 tires, fenders removed for a sleek look, and a single bottle holder. +A city cruiser with swept-back chrome bars, wicker basket, shiny fenders and soft pastel yellow finish with vintage script. +Lightweight gravel race bike with a compact crankset, integrated power meter and discreet team striping. +A touring bike with dynamo hub, full racks, leather grips, low-gear triple and army-olive frame paint with matte finish. +A vintage-style cruiser with spring saddle, sweeping chrome fenders, wide-grip bars and candy-apple red paint with white pinstripe. +Urban electric commuter with vertical battery in seat tube, quiet motor and regenerative braking for stop-and-go traffic. +Electric folding commuter with ultra-compact fold, removable battery, small wheels, and a rear rack for light cargo. +Urban commuter with front and rear racks, modular basket mounts, belt-drive drivetrain and a quiet hub motor for hills. +A classic steel touring frame with extra-reinforced rear triangle, stable geometry, and gloss black paint accented by brass brazing for vintage appeal. +Lightweight carbon time-trial bicycle with integrated hydration, bubble-free aero cockpit and TT-specific saddle for solo efforts against the clock. +A commuter-friendly compact folding e-bike with removable battery, quick-acting latch, and reflective decals for safety on shared transit. +Touring bike with modular rack mounts, extra bottle bosses, and campfire-themed paint for long adventures. +Steel frame randonneur with low-trail geometry, leather saddle, large front rack and dynamo headlamp. +Gravel touring rig with cargo mounts, wide tyre capacity, and rugged protective frame tape for long exploratory trips. +A kids' dirt jumper with 26-inch wheels, single-speed freewheel, alloy cranks, and a short stem for park confidence. +A carbon fiber time trial bicycle with aggressive aero, integrated hydration and molded fairings to reduce frontal area. +A road bike with vibration-damping seatpost, wide 30mm tyres, and a comfortable endurance cockpit for all-day gran fondos. +City commuter with integrated smartphone mount, low-step frame, internal hub gearing, and puncture-resistant tires for reliability. +Classic Dutch-urban bicycle with upright ergonomics, full chaincase, heavy-duty kickstand, and a sturdy rear saddlebag rack. +Modern hardtail mountain bike with 120mm travel, internal cable routing, 29er wheels and lockout fork for cross-country efficiency. +Mountain enduro hardtail with slack geometry, wide tires and a comfortable saddle for long, aggressive downhill days. +Track single-speed commuter with steel fork, polished hubs, and hammered lug detailing for subtle handcrafted appeal. +Classic city bike with step-through frame, retro bell, and leather-wrapped grips for smooth neighborhood commutes. +A gravel endurance frame with breezier geometry, comfy saddle height, and an understated reflective paint pattern for stealth night visibility. +An electric-assist cargo bike with mid-drive motor, longtail deck, integrated child seat mounts and sturdy platform for neighborhood deliveries. +A cyclocross bike with stepped chainstays, reinforced tire clearance, and a clean cable-free cockpit for muddy race days. +Gravel bike with stealth black metallic paint, flared drops, and a wide cassette for varied terrain pacing. +A downhill machine with coil shock, heavy-duty headset, and reinforced chainstay protector for daily bike park abuse. +A gravel touring frame with reinforced dropouts, multiple rack bosses, and a stealth finish that hides the signs of long travel. +Road climbing frame with hollowized carbon layup, narrow seatstays and barely-there graphics for professional minimalism. +Minimal commuter with belt drive, single-speed gearing, puncture-proof tires, and a compact rear rack for light loads. +Gravel e-bike with belt drive, wide-range hub, and cargo compatibility for loaded gravel routes. +A full-suspension cross-country race bike with fast rebound tune, 100mm travel, and efficient pedaling platform for long climbs and quick flats. +Electric cargo trike with roof canopy, climate-poof cargo box, and industry-grade braking system for commercial use +A tidy commuter with internal battery, walk-assist mode, puncture-proof tires, and an easy-to-use rear rack for daily packages. +Touring bike with modular front rack system, triple ring chainset, and high-volume tires for loaded stability. +Gravel bike designed for winter rides with fender mounts, studded-compatible clearance, and heat-reflective paint. +Single-speed beach cruiser with whitewall tires, banana seat, high-rise handlebars, and retro metallic blue paint. +Track-inspired fixed-gear with deep-section wheels, short wheelbase, and a stretched top tube for sprint stability. +A boutique titanium road frame with brushed finish, hidden bolt-on rack, and fillet-brazed-style smooth joints for understated elegance. +A gravel commuter with discreet pannier mounts, reflective piping on fenders, and a comfortable geometry that rides well in rain or shine. +Electric cargo longtail with dual passenger bench, reinforced frame and kid footrests for safe rides. +Lightweight carbon time trial bike with integrated aerobars, disc wheel and electronic shifting optimized for TT stages. +A gravel race build with flared drops, 1x shifting, tubeless 700x36 tires and a subdued stealth-gray finish for stealth attacks. +Cargo trike designed for deliveries with reinforced cargo bed, water-resistant seal, and easy-access tie-down points. +Training mountain bike with hardtail frame, reliable 1x8 drivetrain, heavy-duty rims and platform pedals for skill sessions +A kids' mountain bike with training-wheel mounts, chunky 20-inch tires, soft grips and a cartoon-themed decal set. +A downhill race bike with triple clamped fork, 200mm travel, oversized chainstays and race sled geometry for gravity runs. +A polished white single-speed with track dropouts, bullhorn handlebars, and a minimal seatpost clamp for an uncluttered street aesthetic. +A cyclocross frame with short chainstays and long reach for stability, paired with a glossy protective coating for muddy seasons. +A retro freestyle BMX with chrome plating, colorful grips, double-walled rims and funky geometric stickers on a white gloss frame. +A commuter with reflective sidewall tires, integrated lights running off a dynamo, belt drive and an elegant matte finish for low-key style. +Touring steel bike with rugged rack attachments, long chainstays, and mudguards for all-weather reliability. +Road race bike with ultralight alloy frame, ceramic bearings, high-end cassette and deep-section wheels for time gains on flats +A cargo tricycle bicycle variant with two front carry platforms, low center of gravity, and hydraulic brakes for heavy urban deliveries. +Urban folding cargo with telescoping handle, fold-flat pedals and integrated pannier hooks for convenience. +Fixed-gear commuter with colorful paint fade, deep-V rims, bullhorn handlebars, and leather bar ends for comfort. +A classic steel road frame with traditional lugwork, narrow tires, period-correct components and elegant paintwork for leisurely club rides. +A classic city bicycle with steel frame, swept bars, wide saddle, and a simple 3-speed hub for leisurely neighborhood cruises. +City single-speed with matte pastel finish, sealed bearings, reflective rims and a small detachable front rack for daily convenience +A full-suspension enduro mountain bike with 170mm travel, coil shock, 29-inch wheels and burly 2.5-inch tires in forest camo. +Touring tandem with wave seat rails, extra bottle mounts, and flat-bar compatibility for long comfortable tours. +Vintage steel track frame with classic horizontal dropouts, polished fork crown, and a period-appropriate headbadge. +Folding cargo bike with longdeck and modular load points, thick tires, sturdy hinge and low center of gravity for stability. +Titanium touring bicycle with full rack and fender mounts, dynamo hub lighting, triple chainring and leather Brooks saddle +Mountain enduro bike with adjustable geometry headset, burly 30mm rims, and ample protection for chainstays. +A race-oriented cyclocross machine with tubeless-ready rims, fast-shifting 11-speed groupset, and a minimalist decal set for weight-conscious racers. +Track sprint frame with shallow seattube, aggressive angles, and a glossy paint finish that gleams under the velodrome lights. +Urban single-speed commuter in pastel pink with fenders, rear rack and resilient paint for daily urban wear. +A polished steel cyclocross frame with lightweight braze-on FSA parts and a smooth black gloss finish that repels grime between washes. +Mountain all-terrain hardtail with slacker headtube, wide rims, and fast-rolling 29-inch tires for technical singletrack rides. +A downhill-specific full-suspension bike with reinforced front triangle, long fork travel, and massive rotor sizes to provide braking control on steep trails. +A gravel racer with aero seat tube, 38mm tubeless tires, integrated computer mount and a paint finish that hides scuffs on the trail. +Commuter e-bike with torque-sensing motor, integrated rear rack, hydraulic brakes, and full fenders for year-round commuting. +A vintage-style steel mixte with ornate decals, brass bell, leather saddle and cream-colored tires that evoke an old-world aesthetic. +A winter fat-bike commuter with studded tires, sealed bearings, and fender options for slush-free rides. +A retro-futuristic gravel bike with bulbous headtube, integrated storage compartments and two-tone olive-and-gray paint. +Road aero frame with integrated brakes, stealth cables and a sculpted seat tube for time-trial posture. +Kids' BMX with oversized handlebar pad, bright spoke beads and reinforced stem for extra durability. +A classic Dutch-style upright bicycle with coaster brake, chaincase, integrated rear skirt guard and comfortable saddle for city commuting. +Electric folding commuter with 250W hub motor, quick-fold mechanism, throttle assist, and compact carry case for trains and buses. +Electric cargo longtail with passenger bench, stepped frame, and a powerful motor tuned for frequent stop-and-go urban deliveries. +Cargo tricycle with enclosed cabin option, comfortable bench seat, and electric assist for neighborhood errands in all seasons +A restored steel touring frame with original paint, chrome fenders, leather saddle and an all-weather brazed rack. +A heavy-duty cargo bike with wooden deck, reinforced tubing, wide handlebars and an easy low-gear crawl for hauling loads. +A polished aluminum road touring bicycle with triple racks, bombproof wheels, and a subtle brushed-silver finish. +A full-suspension mountain bike with progressive geometry, 160mm fork travel, 29x2.5 tires, and a burly 34mm stanchion fork. +Mountain freeride aluminum frame with reverse-mount disc caliper tabs, stout chainstays and wide handlebars for control. +Cyclocross rig with tubular tires, quick-release skewers, and a splatter camo finish reminiscent of muddy races. +A classic steel road racer with Campagnolo groupset, tubular 25mm tires, ornate lugwork, and a hand-painted celeste color reminiscent of Italian heritage. +Urban fixed-gear with glossy black paint, bold white accents, and a minimalist saddle for daily errands. +Folding electric commuter with compact frame, rear-hub motor, quick-fold mechanism and removable battery +Gravel tourer with extra frame bottle mounts, stainless-steel bolts, and a sturdy carbon fork. +A vintage-style roadster with swept handlebars, chrome-plated fenders, sprung leather saddle and a classic British racing green lacquer. +A carbon time-trial frame with steep dropouts, integrated hydration, deep-tubed chainstays and minimal graphics for a race-focused look. +A single-speed mountain bike with wide 2.35" tires, a rigid fork, and a simple low-maintenance drivetrain for winter trail simplicity. +A track sprinter with box-section down tube, tapered head tube, fixed gear cluster, and a narrow-time-trial saddle for efficient riding posture. +Compact urban folding bike with belt drive, low pivot fold, integrated lights, and a comfortable saddle for quick trips. +Mountain trail bike with progressive geometry, 29-inch wheels, sensitive rear suspension and internal cable routing for a clean look +Urban fixie with polished steel frame, riser bars, and single-coil bell for a minimalistic, reliable city companion. +Classic cruiser with sweeping frame, large inner-tube tires, comfortable saddle and retro chrome accents for leisurely cruises. +Folding electric commuter with hub-motor, removable battery pack and an easy-to-use folding clamp on the frame. +A minimalist single-speed cruiser with whitewall tires, cruiser handlebars, and laid-back lounge geometry. +A compact folder with mid-drive electric assist, compact fold, small 16-inch wheels and a secure latch for quick commuter transitions. +Vintage-inspired mixte with wicker basket, classic paintwork, leather accents, and a spring saddle for gentle city rides. +Lightweight racing fixie with high-flange hubs, minimal paint, deep rims, and a stiff headset for track-like confidence. +Lightweight carbon time trial bike with integrated hydration and a razor-thin aero profile for low-drag performance on fast courses. +Cyclocross-specific frameset with 1x chainset, carbon fiber shell and asymmetric stays for stiffness. +Lightweight carbon gravel bike with 1x drivetrain, stealth matte finish, and compliance-focused seatpost for long-day comfort. +Electric commuter bike with mid-drive motor, 500Wh battery integrated into the downtube, hydraulic disc brakes and fenders. +A vintage steel touring bicycle with lugged frame, Brooks leather saddle, pannier rack, 36-spoke wheels, and a faded British racing green paint job. +A nimble urban single-speed with light alloy frame, deep-section front wheel, and a short-travel fork for snappy city handling. +A compact folding bike for urban commutes with 20" wheels, magnetic latch folding system, and a lightweight aluminum frame that reduces carry weight. +A kids' trail bike with easy-to-use gearing, generous frame protection, and bright safety orange paint for visibility. +Gravel day-race build with semi-aggressive geometry, 700x38 tubeless tires, and a low-profile aero seatpost for comfort at speed. +Performance road bike with aero seatpost, carbon fork, and a balanced stiffness-to-weight ratio for racing and long climbs. +A folding cargo bike with wide stable platform, quiet hub motor, and a foldable canopy option to protect loads from rain. +A commuter with integrated saddlebag, built-in bike computer mount, and a low-profile rear light baked into the seatpost for unobtrusive safety. +Gravel e-bike with reinforced frame, dual-battery option, and ergonomic grippy bar tape for long tours. +Road aero race machine with integrated storage compartments, deep aero bars and fast rolling tubular tires. +Performance urban single-speed with anodized single-piece crank, narrow profile saddle and rigid fork for quick sprints. +A lightweight gravel race bike with aero-optimized frame, 700x38c tires, and a carbon cockpit to shave seconds on long days. +Lightweight road time trial bike with shaved down tube, integrated hydration, and a narrow aerodynamic fork for slicing wind. +Gravel touring bicycle with extra braze-ons, wide cassette range, and a hand-applied topographic paint motif. +Folding commuter with quick-release folding stem, compact rear wheel, and a bright safety reflector integrated into the frame. +A commuter with full-length mudguards, heavy-duty kickstand, and a cargo-worthy rear platform in matte graphite. +A commuter with belt drive, internal-geared hub, integrated taillight and kickstand for fuss-free transportation. +Tandem recumbent with individual chain tensioners, comfortable leg reach adjustments and aerodynamic fairing option for extended pacelines. +Touring steel bicycle with low maintenance hub gears, full fender set, and a practical rear rack for panniers. +Rigid 29er mountain bike with wide handlebars, 2.4-inch tires, steel frame, and tubeless setup for trail durability. +A handmade lugged steel road frame with custom curvature at the top tube and a paint scheme inspired by classic racing liveries. +A commuter e-bike with step-through frame, mid-drive motor, removable battery and discreet integrated rear rack for shopping trips. +A touring gravel bike with integrated tool storage, three-pack bolt bosses on the fork, sturdy rack eyes, and resilient powdercoat finish. +Classic city bike with leather-wrapped basket, full chainguard, and elegant cream paint with gold pinstripe. +A compact urban single-speed with polished frame, bullhorn bars, toe clips and shallow-profile rims for swift city navigation. +Urban commuter with swooping step-through design, chaincase, hub dynamo, and puncture-resistant tires for carefree daily rides. +A gravel adventure bike with rugged alloy frame, 45mm tubeless tires, integrated lighting mounts, modular racks and a resilient, adventure-ready geometry. +Vintage-inspired city cruiser with mint green paint, wide comfy saddle, and chrome-trimmed fenders for laid-back style. +A classic Dutch upright bicycle with coaster brake, step-through frame, integrated chaincase, and comfortable wide saddle for leisurely rides. +Touring steel mixte frame with step-through twin lateral top tubes, leather saddle and pannier-friendly wheel size for easy loading. +Mountain freeride with thick-walled tubing, heavy-duty rotor mounts, and a camo-green scratch-resistant coating. +Mountain freeride bike with burly frame, long dropper post and reinforced wheelset for big hits. +A vintage road touring bicycle with brass fittings, down-tube shifters, and leather bar wrap for a period-correct restoration. +Classic road frame restored with new cables, polished steel chrome, and reproduction period decals. +Trail hardtail with modern slack geometry, wide handlebars, tubeless tires, and a durable alloy frame for weekend trail missions. +A commuter with quick-detach rear rack, integrated taillight, and belt-drive for a clean, quiet, and low-maintenance ride. +City utility bike with hub gearing, chaincase, and a sturdy front rack for carrying daily essentials reliably. +A classic upright city bicycle with leatherette saddle, chrome bell, full chainguard and simple three-speed hub for reliability. +Matte black carbon road bicycle with integrated cockpit, disc brakes, Shimano Ultegra groupset and 50mm aero wheels +Performance cyclocross bike with stiff carbon fork, compact crankset, and flared drops to handle off-camber courses. +A titanium gravel frame with hand-formed welds, integrated seatpost clamp, and an understated brushed-metal look that mellows over time. +All-road adventure bike with mounts for three bottle cages, wider tires, and robust disc brake calipers for rugged exploration. +Touring gravel bike with leather pannier straps, brass bottle cage screws, low-gear triple crank and reinforced dropout plates +Gravel bike designed for cold weather with fender mounts, stud-friendly tires and insulated framebag pockets. +A small-wheeled folder with hinge-locking handlebar, 16" wheels, lightweight alloy frame and a fold-in-one-step mechanism for commuters. +A cyclocross racer with carbon forks, disc brakes, 33mm tread for muddy fields and matte charcoal paint with bold white number panel. +A gravel adventure bike with framebag, top-tube straps, and oversized water bottle mounts for long bikepacking trips. +Urban commuter with built-in lock, reinforced chainstay, and bright reflective decals for night safety. +Mountain freeride bike with stout chassis, long travel, post mount brakes, and aggressive protective frame guards. +Electric gravel touring bicycle with integrated solar charger option, rugged tires and long-range battery for multi-day adventures. +Urban single-speed with disc brake conversion, sealed hubs, and a matte forest green finish. +Gravel adventure with hidden bag mounts, stealth paint, and multiple fastening points for long unsupported trips. +A compact 24-inch wheel mountain bike for juniors with coil-sprung fork, V-brakes, and wide knobby tires for off-road confidence. +Endurance road bike with carbon compliance zones, 32mm tires, and quiet internal cable routing for calm long rides. +Road race frame with tapered head tube, raked fork, carbon seatpost clamp, and a glossy pearl finish for pro-level performance. +A classic touring bicycle in polished steel with leather saddle, full fender set, heavy-duty racks and reinforced spokes for loaded reliability. +Folding mini velo with small 20-inch wheels, efficient single-speed gearing and a clean, simple paint job. +Lightweight cyclocross training bike with durable alloy frame, tubeless-ready rims, and single-ring drivetrain. +Gravel all-road bike with dual-purpose gearing, stealthy matte finish, and integrated frame protection for long mixed-surface routes. +Electric folding bike with telescoping frame, mid-motor assist, puncture-resistant tires, and quick-release folding pedals. +Cargo bike with double kingpin steering, low cargo bay, reinforced frame, and hydraulic disc brakes for large hauls. +Lightweight aero road frame with shallow rim brakes, integrated seat clamp, and polished carbon finish for a race-day aesthetic. +Touring tandem with third-party luggage mounts, reinforced fork and custom-stitched bar tape for extended travel. +Steel commuter with hub dynamo, classic bell, and broad tires for absorbed city vibration. +Touring steel frame with extended wheelbase, triple-rack compatibility, and a low-maintenance hub-drive option for global touring. +A commuter with integrated smartphone mount on top tube, GPS-friendly layout, hub dynamo charging and steel-blue finish. +A classic town bicycle with elegant curved top tube, polished steel fenders, leather saddle and brass headlamp mounts for a timeless city presence. +A lightweight cyclocross racer with carbon fork, disc brakes, 700x32 tubeless tires, and a race geometry optimized for quick dismounts. +A gravel-touring bike with customizable rack mounts, fender compatibility, and a kickstand boss for loaded overnight trips. +A cyclocross race bike with modern disc brakes, 33mm tubular tires, flared hood ergonomics and a matte finish that masks road grime. +A gravel bike engineered for bikepacking with multiple braze-ons, framebag-friendly geometry, 650b compatibility and moss-tone paint. +Lightweight alloy commuter with polished finish, chromed fenders and classic rear rack for errands. +Downhill mountain bike with 200mm travel, stout aluminum frame, coil shock and massive 27.5-inch wheels for high-speed descents. +Triathlon bike with steep seat angle, integrated bar-end aerobars, dual-bottle storage, and a power meter crankset for time trial pacing. +A kids' BMX bicycle with 20-inch wheels, sturdy chrome frame, thick pegs for tricks and a padded handlebar crossbar for skatepark sessions. +A BMX race sled with race-spec crankset, short wheelbase, and an aggressive graphics package to represent team pride on the start line. +A practical commuter bike with integrated frame lock, rear rack platform, belt drive and puncture-resistant tires for minimal fuss. +A classic road bike with narrow steel tubing, polished chrome fork crown, 9-speed indexed shifters, and vintage-inspired badge work. +Electric off-road fatbike with sealed connectors, high-traction rubber, and an integrated display on the top tube. +Aero road bike in glossy midnight blue, integrated cockpit, 60mm carbon wheels, electronic shifting, and hidden cables. +A commuter with puncture-resistant tires, integrated chaincase, hub dynamo lights and deep bronze metallic frame. +Handbuilt lugged steel cyclocross frame with subtle celeste lugs and polished stainless rivets. +A cyclocross frameset with asymmetric dropouts, mud-shedding bridge, and a stealthy matt black paint for serious racers. +A classic British roadster with deep-sprung saddle, polished fenders, alloy chaincase and a tasteful muted green finish for rainy city streets. +Compact folding electric bike with a removable battery, small-diameter wheels and a rigid folding hinge. +A modern mountain bike with dual-suspension tune, short chainstays for snappy handling, and a durable protective coating over the downtube. +Lightweight gravel racer with carbon frame, shallow-section wheels, SRAM Rival groupset and race-oriented geometry +Commuter with rear luggage strap, puncture-resistant sidewalls, and ergonomically shaped saddle to reduce pressure points. +Mountain trail bike with 140mm travel, tubeless-ready rims and a tuned shock for balanced all-day trail performance. +Vintage track bike restored with period-correct components and a highly polished steel finish. +Handbuilt lugged steel frame with hand-polished lugs, custom fillets, leather accents and classic 700c wheels. +City hybrid with ergonomic grips, front suspension, puncture-proof tires and internal hub gearing for low-fuss commuting. +Electric cargo trike with hydraulic disc brakes, reinforced frame, and integrated child seats for family-friendly errands. +A classic lugged steel road bike with hand-painted pinstripes, toe-clip pedals, and a 9-speed group for nostalgic Sunday spins. +BMX race bike with chromoly top tube, precise geometry for pump tracks, and a low-profile seat clamp. +Mountain full-suspension with forward geometry for steep descents and mid-travel tuned for pedaling efficiency. +A classic beach cruiser with chopper-style extended fork, orange flame paint, and wide tires for leisurely seaside rides. +A mountain trail hardtail with slack head angle, 130mm fork, and stout wheelset for confident handling on technical terrain. +High-performance time-trial machine with integrated wheel fairings, super-stiff bottom bracket and aerodynamic helmet storage compartment. +Vintage cruiser with chrome headlamp, leather saddle, whitewall tires and soft pastel paint for charming beachside rides. +A downcountry bike with 130mm front and rear travel, 29-inch/27.5-inch mixed-wheel setup, and smudged graphite paint for stealth. +Folding commuter with step-through geometry, stable 20-inch wheels, and quick-pull folding latch. +A retro-inspired mixte city bicycle with wicker basket, low step-over, leather saddle and creamwall tires for timeless commuting style. +Track pursuit bike with aero seatpost, solid rear disc wheel, and a stiff shell ensuring every watt goes into forward motion. +Touring mixte with step-through access, full fenders, Brooks saddle and a simple three-speed hub for ease. +A practical folding cargo bike with a long rear deck, folding handlebars, and a low center of gravity for tight urban delivery routes. +A beach cruiser with wide surf-style bars, big balloon tires, surfboard rack and aqua-turquoise paint with wave graphics. +A mountain enduro bike with adjustable travel, reinforced downtube protection, aggressive 29x2.5 tires and a geometry that invites speed. +A lightweight single-speed drop-bar bike made for alleycat races with slim tires, narrow handlebars and minimalistic brake setup. +Urban foldable with integrated basket, anti-pinch frame hinge and reflective decals for safe public transit travel. +A classic single-speed cruiser with sky-blue frame, banana saddle, and coiled spring fork for soft cruising along promenades. +A BMX flatland bike in pearlescent white with gyro detangler, short cranks, 20x2.4 tires and custom pinstriping. +A lightweight aluminum mountain hardtail with tapered head tube, rigid carbon fork option, and tubeless-ready rims for responsive trail riding. +A gravel adventure bicycle with integrated top tube stowage, clever pump mounts within the frame, and a durable powder coat to resist scratches. +A race-focused cyclocross bike with dropper post provision, 11-speed cassette, and a raised top tube to assist running over barriers. +Four-season studded-tire commuter with fender mounts, full chainguard and an oversized rear rack for cargo. +High-performance time trial bike with integrated rear light, wide aerodynamic fairings, and electronic shifting for TT supremacy. +A steel fixed-gear with polished chrome, deep-v rims, minimal branding and a taut, no-nonsense urban build. +A petite touring bike with reinforced triangles, heavy-duty rack mounts, and 650b wheel compatibility to handle smaller riders on global tours. +Mountain hardtail designed for lightweight climbing with skinny seat tube, race geometry and tubeless tires. +A boutique hand-painted steel road frame with unique artistic motifs, smooth welds, and an understated badge to indicate bespoke craftsmanship. +A longtail cargo bike with aluminum frame, extended rear deck, kid seats, hydraulic disc brakes and navy cargo bags. +Urban cargo bike with long wooden deck, reinforced frame, and modular side panniers for deliveries. +A cargo longtail with bench seat for two kids, reinforced frame, hydraulic brakes and olive drab paint with safety reflective panels. +A gravel endurance machine with a geometry tuned for comfort, 700x40 tubeless tires, and subtly placed reflective accents for night rides. +Steel commuter with subtle weathered patina, low-maintenance hub gear, heavy fenders and a long saddle for comfortable posture +Urban fixed-gear with drop bars, thin slick tires, polished aluminum frame and minimalist saddlebag for essentials +A trail hardtail with 120mm fork, steel frame, 29er wheels, and tubeless-ready rims for durability and simplicity on singletrack. +Gravel bike with stealthy black-on-black decals, 44mm gravel tires and minimal branding for understated look. +Cyclocross race bike with carbon fork, disc brakes, 33mm cyclocross tires, and a slightly higher bottom bracket for muddy racecourses. +Urban e-bike with step-through chassis, integrated cargo rack, and multifunction LED display. +Gravel bike with fun graphic decals, beefy tires for stability, and a simple single-bolt seat clamp for quick adjustments. +A mountain full-suspension trail bike with lower-link-driven shock, wide handlebars, and a durable clearcoat to resist trail scratches. +A rapid triathlon bike with steep seat tube angle, integrated hydration, time-trial aerobars, and a full carbon aero fork for minimal frontal area. +Gravel endurance bike with comfortable touring geometry, flared drops, and multiple mounting points for gear. +Performance track pursuit bike with deep-section front wheel, solid disc rear and super-aggressive time trial geometry. +Vintage mixte restored with new components, wicker front basket, and a tasteful two-tone paint job for nostalgic charm. +Performance cyclocross bike with flared drops, integrated cable routing, durable bar tape and quick-release thru-axles for fast pit stops +A 650b gravel bike with large-volume tires, flared handlebar drops, and a custom bag-mounted system for lightweight bikepacking. +Mountain trail bike with modern dropper, 1x12 chainset, and 2.4" tires for confidence on rough technical trails. +A tandem cruiser with banana seats, chain tensioners, and classic chrome fenders for leisurely synchronized rides. +A lightweight cyclocross bike with carbon fork, alloy frame, quick-release thru-axles and a two-bolt seatpost clamp for simple adjustments. +A modern road bike with disc brakes, hidden cable routing, compact double crankset and a finish that shimmers from navy to purple. +Gravel bike with polished paintwork, brushed aluminum accents and discreet rack mounts for stylish touring. +A compact folding e-bike with low-step frame, 16-inch wheels, quick-release fold and satin graphite finish with reflective decals. +Cargo trike with heavy-duty deck, reinforced forks, and neat chain guards to protect cargo and drivetrain. +All-weather commuter with hydraulic disc brakes, fender mounts, dynamo hub, and a low-maintenance internal gear hub. +Touring steel frame with generous tire clearance, multiple rack mounts, and a traditional quill stem for classic appeal. +A commuter with plastic fenders, integrated basket, front light, and easy-to-use coaster brake for simple utility. +A cyclocross frame with dramatic flared downtube, gravel-specific geometry, and a textured coating to reduce glare in mud. +A cyclocross race bike with stiff bottom bracket, wide brake calipers, 35mm tubeless tires and a geometry tuned for fast dismounts and remounts. +Urban cargo trike with electric assist, flatbed design and easy step-through access for marketplace hauling. +A kids' BMX with 18-inch wheels, low standover height, colorful grips and a short crank for easy handling at the skatepark. +A folding electric bicycle with compact battery, quick-fold hinges, integrated display and a small basket for quick errands. +A modern road race frame with reduced frontal area, aero tubing, and tubeless-ready wheels to suit high-speed criteriums and road sprints. +Classic cruiser with chrome accents, spring-loaded saddle, swept handlebars, and a glossy lacquer finish for relaxed rides. +A mountain downhill race bike with triple-clamp fork and reinforced swingarm for brutal, push-the-limits tracks. +A practical city cargo bike with modular side panniers, low center of gravity, and an easily adjustable kickstand for quick loading. +A compact folding e-bike with torque-sensing mid-drive, small battery, and a reinforced folding hinge for daily commuters with limited storage. +A gravel racer with asymmetric frame shaping, loud accent color on the fork, and a tire clearance of up to 50mm. +Modern gravel racer with carbon thru-axles, flared drops, 700x40 tubeless tires and race-focused cockpit ergonomics. +Kids' BMX bike with 20-inch wheels, reinforced chromoly frame, gyro brake cable system, and thick peg axles. +A gravel-specific hardtail with 700x45 tires, wide 35mm internal rims, dropper post and subtle mud-shedding geometry. +Touring expedition bike with reinforced dropouts, triple bottle mounts, and a hand-applied geometric paint motif. +E-cargo longtail with dual batteries, reinforced frame rails, passenger deck and hydraulic disc brakes for commutes with kids +Vintage steel touring bicycle with 26" wheels, triple crankset, linen-wrapped bar tape, and classic steel racks for panniers. +A mountain enduro bike with split-pivot suspension, long reach, and sculpted frame to protect from rock strikes. +A rugged BMX street bike with chromoly three-piece cranks, gyro cable routing, and reinforced head tube gussets to survive hard landings. +Adventure-ready titanium frame with versatile tire clearance, integrated headset storage and brushed raw finish. +Mountain enduro race bike with 160mm front travel, hollow crank, and ABS-like traction control on the rear wheel. +A trail-oriented full-suspension bike with progressive geometry, 140mm travel and wide-angle headtube for confident descending. +Gravel frame with meticulously routed cables, three-bolt fork mount, and a versatile geometry that balances speed and comfort. +A mountain enduro bike with 160mm travel, adjustable geometry, coil shock option and matte army green with orange detailing for visibility. +A mountain e-enduro bike with 170mm rear travel, aggressive geometry, and a hidden battery that balances the frame. +A modern e-gravel bike with torque-sensing motor in bottom bracket, long-range battery, 700x40 tires and graphite-matte paint. +Urban commuter with upright bars, integrated fender set, full chaincase and a rear child seat mount. +Gravel bike with frameset designed for mixed-terrain endurance and painted in a sunrise gradient. +A touring bike set up for bikepacking with framebags, handlebar roll, lightweight racks, and 42mm gravel tires for remote multi-day routes. +Cyclocross pro-level bike with carbon frame, quick-release thru-axles and an aggressive lower stance for off-camber sections. +BMX dirt jumper with gusseted chromoly frame, gyroless setup, and extra-thick rims to absorb landings. +A recreational hybrid with suspension seatpost, upright bars, puncture-resistant tires, and chaincase to keep clothing clean on commutes. +A minimalist urban fixie with polished rims, short-reach brakes, and a leather-wrapped bar for a classy café ride. +Lightweight cross-country frame with tapered head tube, short chainstays, and a focus on low mass and explosive climbing performance. +Touring bike built for bikepacking with custom framebags, triple-bolt fork mounts, and a low-slung geometry for loaded stability. +A modern e-mountain bike with torque-sensing motor, long-range battery, and stealthy matte black finish. +A cyclocross pro-level bicycle with full carbon monocoque, 33mm tubular tires, quick-throw shifters and precise brake modulation for race days. +A practical hybrid bike with upright riding position, suspension seatpost, and reflective sidewall tires for safety. +Urban electric cargo trike with enclosed cargo box, regenerative braking, and a swappable battery pack for continuous delivery shifts. +A downhill-oriented frame with reinforced headtube cluster, longer chainstays, and a forged rear end for high-energy rides. +A mountain enduro bike with thick-wall alloy tubing, progressive geometry, and a long-travel fork for commanding downhills. +Adventure fat-bike with burly tires, frame mounts for gear, and a sturdy front cargo cage for overland travel. +A compact folding commuter with 18-inch wheels, ergonomic saddle, foldable handlebars and brushed aluminum finish with anodized details. +A mountain e-bike with dual-battery option, 170mm travel, coil shock compatibility and a tuned power curve for all-day ascents and descents. +A gravel endurance bicycle with micro-suspension features in the seatpost, 700x45 clearance, and a geometry meant for riding all day off the beaten path. +A commuter bicycle with internal battery for rear light, integrated smartphone charging port, and spacious mudguards for rainy days. +Urban folding bike with minimalist hinge, single-speed simplicity, and a small packed footprint for apartment living. +Gravel-focused titanium frame with tapered headtube, generous tire clearance and hidden water bottle access. +Classic fixed-gear with polished chrome, retro badge, bullhorn bars, and bottle cage mount for a dash of utility. +A downhill race build with heavily braced frame, long travel, gigantic brakes and neon pink paint for maximum visual impact during races. +Low-standover folding e-bike with step-through latch, small wheels and a quiet hub motor for city trips. +Cyclocross-inspired gravel bike with a flared handlebar, 38mm gravel tires, and a flexible seatpost for smoother rides. +A utility cargo bicycle with long front rack, integrated bungee anchors, and a low standover for frequent loading and unloading. +Electric mountain hardtail with low-series motor, trail-tuned geometry, and a subtle integrated battery for clean lines. +Gravel all-rounder with painted frame art, 700x38 gravel tires, 1x drivetrain and comfortable bar geometry for long mixed-surface days +A gravel-specific frame with 2.2" tire clearance, bolt-on front rack points, threaded bottom bracket and a hard-wearing powdercoat. +A folding commuter bicycle with 16-inch wheels, compact hinge frame, integrated carry handle, and quick-folding pedals for public transit compatibility. +A gravel bike with steel frame, clearance for 42mm tubeless tires, wide flared drop bars, multiple braze-ons for bottles and frame bags, and a sand-colored enamel paint. +Retro commuter with enamel paint, leather-covered springs, and heritage chrome fenders. +A mountain bike with dropper post, 140mm travel, 29-inch wheels and a modern slacked-out geometry for flow trails. +A vintage road racer restored with new cloth bar tape, polished chainsets, and narrow tubular tires for classic speed feel. +A cross-country mountain race bike with lightweight alloy frame, 100mm fork, fast-rolling 29x2.1 tires and a precise steering geometry. +A mountain freeride bicycle with heavy-duty gusseting, reinforced swingarm, and chain guide for big jumps and soil-thrashing trails. +Road racing frame with highly tuned stiffness-to-weight ratio, shallow seatstays and a mirror-gloss paint finish. +A mountain e-bike with torque-limited motor, 160mm travel, high torque cassette, and reinforced rim roofing for sustained climbs and descents. +A handbuilt steel touring bike with extra bottle mounts, braze-on rack and fender mounts, and a tough powdercoat ready for miles. +Lightweight aluminum road frame with responsive BB, carbon fork for compliance, and compact geometry for aggressive riding. +Gravel all-road with colorful enamel paint, handmade leather saddle, full decale kit and a multi-tool strapped beneath seat +Steel cyclocross frameset with traditional geometry and subtle modern reinforcements in high-stress areas. +Steel touring bike with triple cage mounts, long chainstays for stability, and subtle hammered finishing near the joints. +A gravel e-bike with torque sensor assist, plus-sized 2.6" tires, and a long-range battery for extended backcountry loops. +Mixed-terrain hardtail with dropper post, 120mm fork, 29er wheelset and modern geometry for playful off-road days. +Mountain enduro bike with slack head angle, long reach, and a stout 34-tooth chainring option. +A retro steel mixte frame city bike with swept-back handlebars, wicker basket, and cream-colored balloon tires. +A titanium endurance frameset with bespoke brazed bottle bosses, internal light wiring, and a subtly wavy top tube for comfort and stiffness balance. +Compact commuter with lightweight alloy frame, internal cable routing, belt drive and single-speed simplicity for urban life. +A mountain hardtail with modern slack geometry, 130mm fork, 29-inch wheels and charcoal matte finish with neon lime accents. +Long-travel enduro bike with adjustable geometry, coil shock compatibility, wide rims, and a burly build for downhill stability. +Fat-bike with 4.8-inch balloon tires, steel frame, rigid fork, wide handlebars, and single-ring drivetrain for snow and sand. +Cargo bike with front-loading bucket, low step-through deck, and kid harness mounts for family transport. +A lightweight gravel frameset with a tapered headtube, compact rear triangle, and vibration-damping features for long-distance efficiency. +Urban step-through e-bike with low standover, belt drive and sturdy kickstand for quick errands and market runs. +A mid-school mountain hardtail with steel frame, retro paint, 26-inch wheels, and simple cantilever brakes for nostalgic trail rides. +Gravel plus bike with 650b x 47 tires, slack geometry, and frame protection for rocky singletrack bikepacking routes +Modern gravel bike with integrated storage behind the stem, stealth matte finish and oversized downtube for stiffness. +Mountain hardtail built for bikepacking with frame bag fitment, extra bottle mounts and 27.5+ tire clearance for cushion on rough trails. +Modern utility e-bike with torque sensing, pedal-assist levels, and hydraulic brakes for reliable city stopping. +A classic steel cyclocross bicycle with cantilever brakes, mud-shedding clearances, knobby 35mm tires, and a slightly relaxed geometry for stability off-road. +Gravel rig set up with high-volume 650b tires, protective down tube skid, and extra brake power for steep descents with heavy packs +Road race bike with ultra-short headtube, steep seat angle, and an aerodynamic bottle bay. +Adventure gravel with asymmetric frame, kickstand mounts, and robust axle hardware for multi-terrain exploration. +Gravel endurance carbon with integrated fender mounts, wide tire clearance, and a low-maintenance cable routing scheme. +Steel fixed-gear track-inspired single-speed with horizontal dropouts and polished chrome finish. +Gravel titanium frameset with integrated steering stops, wide tire capability, and elegant brushed metal aesthetic. +Urban single-speed with cropped handlebars, blacked-out components, slim saddle, and wide tires for pothole tolerance. +Track pursuit bike with deep-section front rim, solid rear disc, and a glossy paint that reflects the track lights. +Downhill bike with 200mm rear travel, dual-crown fork, coil shock and aggressive geometry for steep runs. +A heavy-duty cargo tricycle with three wheels, wooden deck, hydraulic brakes and utility straps for market deliveries. +Road endurance alloy frame with tapered headtube, comfort-oriented seatpost clamp and medium-depth wheels for all-day rides. +Sleek time-trialer with fully integrated brakes, hidden cabling and an aggressive forward position for sustained power. +A cozy cruiser with teal paint, low-slung frame, oversized saddle and rear coaster brake for easy coast-and-relax rides. +A city cruiser with wooden rear rack, swept chrome bars, large balloon tires and soft mint-green enamel with white pinstripe. +A gravel-specific wheelset-equipped bike with 700x45 clearance, tubeless tape pre-installed, and reinforced spoke patterns for durability. +Lightweight aluminum road racer with anodized components, short wheelbase, and race geometry for crit and group sprint performance. +Track pursuit bike with exacting geometry, extremely stiff frame and a time-trial oriented saddle for prolonged aero holds. +A commuter with a matte stone finish, integrated headlight bar, durable rear rack and modern hydraulic brakes for stop-and-go city riding. +A lively fixed-gear commuter with track drops converted to tops for street control, polished rims, and vibrant neon brake calipers. +Kids' balance and training combo bike with removable pedals, adjustable seat and friendly animal decals. +A steel touring bicycle with a brazed lugged frame, front and rear racks, leather saddle, wide 32mm touring tires, dynamo hub lighting, and braze-ons for fenders and bottles. +A modern e-cargo bike with tandem passenger bench, rear child harness points, large-capacity battery and center-of-gravity-optimized frame. +Track-style sprint machine with crisp paint, threaded headset and drilled chainring for weight savings. +A mountain full-suspension enduro bike with 170mm travel, integrated chain guide, and a stout geometry for big hits and big smiles. +A recumbent trike with full fairing, recumbent seat, recumbent pedals, chain idlers, and low-slung aerodynamic profile for comfortable long rides. +Touring steel bicycle with strong rear rack, triple-bottle mounts, and a geometry tuned to carry panniers without flex. +Urban cargo e-bike with removable rear rack, in-built locking clamp and load-sensing geometry for safe hauls. +Performance gravel frame with carbon layup tuned for vibration reduction, 700c x 38 tires, and discreet racks for light packing. +A tiny wheeled folder with adjustable stem, comfy saddle, and robust double-clamp hinge designed for daily multi-modal commutes. +A sweeping-curve beach cruiser painted sunburst orange, with chrome fenders, wide handlebars and a plush double saddle for comfortable tandem rides. +Fixed-gear commuter with polished chrome frame, bullhorn bars and minimalist lighting for sleek nights. +Vintage racer with lightweight steel frame, low-profile handlebars, polished spokes and toe straps for classic criterium vibes +A touring tandem with brazed-on rear rack mounts, comfortable saddles, and a classic two-tone enamel finish. +A steel road racer with glossy candy paint, traditional geometry, and classic quill stem paired with leather-wrapped bar tape for a timeless look. +A gravel bike with bombproof alloy frame, 11-34 cassette, and 700x45c tires for heavy-duty adventure and pack-carrying. +A comfortable city bike with upright posture geometry, full-length chain guard, step-through frame paint in warm terracotta, and wide saddle. +A versatile commuter with 2.0" puncture-resistant tires, integrated front light, and a removable battery pack for occasional electric boost. +Gravel racer with lightweight tubeless wheels, 40mm gravel tires, and a short stem for nimble handling on mixed surfaces. +Commuter bike with hub dynamo, reflective sidewalls, fenders and a mounted front pannier rack. +Road aero triathlon bike with integrated aerobars, hydration reservoirs, and a short-wheelbase for stable solo time trials. +A commuter with step-through frame, integrated pannier rack, belt drive and matte midnight-blue powdercoat for understated urban runs. +Touring bike with large-watt generator hub lighting, cushioned bar tape, and a powder-blue factory finish. +Gravel endurance bike with comfortable geometry, 650b adaptability, stealthy finish and frame protection for long off-grid days. +Utility cargo tricycle with insulated delivery box, electric assist, and foldable tailgate for urban courier work +Modern adventure gravel e-bike with full-suspension seatpost, long battery life and heavy-duty pannier-compatible racks. +A kids' BMX with chromoly frame, pegs, gyro and graffiti-style bright purple paint for park tricks and neighborhood shredding. +A boutique fixed-gear with anodized parts, minimalist frame, and a handcrafted leather saddle adding a touch of elegance to city rides. +A utility cargo tricycle with large wooden deck, weatherproof canvas cover, robust steel frame and safety yellow paint. +A modern downhill frame with reinforced shock mounts, a long reach, and an aggressive head angle for controlling steep, fast descents. +A commuter with belt-drive and nine-speed internal hub, a discreet rear rack and TOT-friendly foldable design to fit small storage spaces. +Commuter roadster with upright swept bars, Sturmey-Archer hub and sprung leather saddle. +Pinion-geared expedition bike with belt drive, lugged steel frame, Rohloff-compatible mounts and heavy-duty racks. +A modern downhill frame with narrow-pitched linkage, heavy-duty pivots, and an integrated protective skid plate to survive rock gardens. +Touring tandem with matched saddles, twin rear racks, and gloss enamel paint featuring faint route markers. +A lightweight touring bicycle with brazed-on bottle bosses, sturdy rear rack, leather saddle, and a subtle satin finish for understated endurance. +Gravel speed commuter with lightweight alloy frame, urban tires and hidden fenders for unexpected weather. +A touring-ready alloy frame with brazed lugs, triple-bottle clearance, and 36-spoke wheels to handle heavy loading and daily challenges. +A casual cruiser with swept-back handlebar, wide saddle, coaster brake and pastel peach paint with floral decals on the chain guard. +A full-suspension trail bike with 140/140mm balanced travel, responsive rear linkage, and nimble steering for tight singletrack. +Track-inspired urban racer with polished handlebars, single-speed drive, and a hip minimalist paint job for street cred. +Lightweight aluminum road bike with Shimano 105 groupset, shallow-section rims, and race geometry. +A mountain-enduro machine with adjustable geometry, 170 mm rear travel, and a robust drivetrain to handle steep technical terrain. +A modern trail full-suspension bike with 140mm travel, aggressive MX geometry, 29-inch wheels, and burly tires for technical singletrack. +A lugged steel frame with art deco accents, hand-painted filigree, and classic thin-walled tubing for a vintage feel. +Vintage city classic with swept bars, sprung chrome saddle, and a built-in bell for charming neighborhood rides. +A gravel bike with a stealth matte black finish, hidden fender mounts, and a 36-tooth chainring for climbing steep dirt roads. +A compact city folding bike with integrated luggage rack, puncture-resistant tires, and a tie-down strap for carrying loads when folded. +Kids' training bicycle with stabilizers, low-step frame, durable tires and child-friendly, colorful graphics to encourage practice. +A cyclocross bike setup for CX with tubeless 33mm tires, cantilever brakes swapped for discs, and a tight geometry for quick shoulder carries. +Lightweight titanium single-speed bike with horizontal dropouts, brushed finish, and leather saddle for a minimalist city ride. +A commuter e-bike with pedal-assist cadence sensor, rear hub motor, internal cable routing and comfy ergonomic grips. +A performance cyclocross racer with 35mm tubular tires, stiff carbon fork, and minimal stack height for aggressive handling. +A classic child's cruiser with training-wheel mounts, simple coaster brake, low step-over height and bubblegum-pink paint with star decals. +Old-school road bike with quill stem, steel fork crown, leather saddle and classic Campagnolo-style aesthetic. +A stealth matte-black single-speed mountain bike with plus tires, simple gearing, and modern slack geometry for playful trail rides. +A city commuter bicycle with low step-over frame, integrated front and rear lights, full chaincase, and a practical rear rack for daily duties. +Mountain trail bike with adjustable dropper height, stiff crankset, modern fork offsets, and tubeless-ready wheels for confident handling. +A modern plus-sized mountain bike with 27.5-plus wheels, 2.8-inch tires, dropper post, and trail-oriented progressive geometry for confidence. +Retro folding step-through bike for city commuting with swept-back handlebars, small wheels, and a low-maintenance hub gear. +A cargo longtail electric bike with twin batteries, hydraulic brakes, reinforced tubing and matte army green cargo bays. +City e-bike with retro styling, step-through frame, and gentle torque assist for relaxed commutes. +Compact commuter with internal hub, step-over frame, and ornamental bell for quick downtown errands and coffee runs. +Retro-inspired steel track-style fixie with minimal decals, polished headset and a deep red enamel finish. +Vintage track frame updated for urban use with toe-clip pedals, polished chrome, and modern wheelset for daily riding. +High-performance cyclocross frameset with oversized tubing, disc brakes, and an integrated seat clamp for reduced weight. +A trail mountain bike with dropper post, internally routed cables, and protective clear film in rock-hit areas. +A commuter with integrated LED taillight, internal cable routing, belt drive and satin silver finish with reflective side stripes. +Mountain trail hardtail with modern slack geometry, internal cable routing, and upgraded hubs for durability on rough trails. +Enduro e-bike with powerful mid-drive motor, integrated battery, long-travel suspension, and a rugged downhill bias. +Gravel endurance bike in earthy brown with leather accents, 650x47 wheels and mudflap protection. +Single-speed beach cruiser with pastel mint paint, wide handlebars, and coaster brake charm. +A long-haul touring bike with triple chainrings, multiple bottle mounts, reinforced rims and a comfortable, stable geometry. +A folding cargo bike with long tail, fold-flat deck, and a low center of gravity for stable hauling. +Mountain cross-country full-suspension with efficient pedaling kinematics, 120mm travel, and lightweight components for race pace. +City cruiser with bright candy-red enamel, retro chain guard, balloon tires and a comfortable saddle for easy neighborhood errands +Mountain XC race hardtail with light alloy frame, 100mm fork travel, and a tight geometry for fast, technical cross-country tracks. +Kids' trail bike with scaled-down suspension fork and simple twist shifter for easy operation. +Steel city commuter with practical frame-mounted lock, puncture-proof tires, and reflective graphics. +A practical cargo trike with front cargo box, adjustable handlebars, comfortable saddle and industrial-strength brakes for urban hauling. +Recreational beach cruiser with pastel mint paint, wide chain guard, upright seat, and a relaxed geometry for cruisy rides. +Cyclocross training rig with spare tubulars strapped to top tube, durable bar tape and robust brake setup for practice laps +Folding e-bike with quick-click hinge, integrated security cable, and a compact battery tucked into the frame. +Modern CX bike with cantilever or mechanical discs depending on build, tapered steerer, and a stealth matte finish. +Roadtime trial aero bike with raised tail, integrated seatpost clamp, and elliptical chainring for marginal gains in speed. +A mountain hardtail with 29" wheels, 120mm fork, tubeless setup and race-oriented geometry for fast, flowy singletrack. +Touring expedition with triple chainrings, wide-range gearing, heavy-duty racks and integrated fender mounts. +A competition-ready downhill frame with adjustable geometry, long reach, and heavy pivot bearings to handle big jumps at speed. +A classic road bike with polished chrome fork, detailed head badge, and thin tires for efficient pedaling on smooth pavements. +Road endurance aluminum frame with carbon fork, comfortable saddle, wide tire clearance and relaxed stack for multi-hour comfort +A classic military surrey-style bike converted into a single-speed commuter with blackout paint, sturdy carrier rack, and heavy-duty tires. +Touring tandem bicycle with two sets of shifters, triple crank compatibility, sturdy steel frame, and matching panniers for two-up travel. +Classic steel tandem with matching paint, rope-style handlebar tape, triple chainset and durable spokes for touring couples. +20-speed hybrid bike with suspension seatpost, front rack and ergonomic grips for light touring. +A utility commuter with integrated bike lock, fendered wheels, and low-step alloy frame for reliable daily errands. +A gravel-adventure bike fitted with dynamo hub, full-coverage fenders, rack for overnight gear and army-olive finish with tan accents. +Long-travel enduro mountain bike with oversized downtube, adjustable geometry, and robust thru-axles for heavy use. +A cyclocross-ready steel frame with classic feel, modern clearance for wider tires, and a geometry that keeps it competitive in mixed conditions. +Commuter e-bike with torque-sensing motor, step-through frame and integrated rear lights for everyday use. +Cyclocross race winner with electronic groupset, fast-engaging hub and high-traction tires for muddy races. +A retro-modded road bike with modern disc brakes, vintage-inspired paint, and modern wide-tyre clearance for contemporary comfort. +A commuter with full fenders, chaincase, and magnet-mounted front light that clicks off when parked to conserve battery life. +Utility cargo trike with hydraulic brakes, adjustable bench seat, and heavy-duty pannier rails. +A modern aluminum road frame with aero tube shaping, internal cable routing, 28mm tire capacity, and a glossy metallic paint finish. +Gravel enduro hybrid with dropper-compatible seatpost, robust headset, and slack geometry for charging down rough descents. +Cyclocross training bike with slightly relaxed geometry, mud-shedding tubing, and knobby cross tires ready for winter workouts. +A commuter with integrated aluminum fenders, reflective pinstriping, and a sealed bearing headset that resists urban grime. +Steel randonneur bicycle with dynamo hub, Sturmey-Archer hub gear, and lights for night touring. +A modern trail bike with adjustable kinematics, 150mm rear travel, 29-inch wheels and matte eclipse blue finish. +A carbon aero time-trial machine with integrated disc brakes, deep 80mm wheels, and a sculpted downtube designed to reduce drag. +A lightweight trail hardtail with stable geometry, internal cable routing, and a reliable 1x drivetrain for simple trail enjoyment. +A downhill race bike with coil-shock rear, 200mm fork travel, massive 900mm bars, and a reinforced slack-chained aluminum frame built to absorb big hits. +A performance carbon mountain bike with 150mm travel, tapered headtube, Sram 12-speed Eagle and matte petrol-blue finish. +A custom painted touring bike with map-themed decals, hand-stitched leather grips, and a handmade wooden pannier rack for long trips. +Adventure-ready 29er with beefy 2.6" tires, dropper post, and multiple frame-mount bosses for long, remote rides. +Mountain enduro bike with mixed-wheel options, adjustable geometry, and burly components for aggressive descending. +A commuter with quiet belt drive, sealed hub, integrated rear light in the seat tube and a corrosion-resistant finish. +A gravel-adventure frame with multiple braze-on points, broad tire clearance to 45mm, and a resilient powdercoat for long remote expeditions. +Adventure touring bike with Rohloff hub, belt drive compatibility, extra-large tire clearance and reinforced rack mounts. +A hand-tuned steel touring bike with eccentric bottom bracket option for chainline adjustments and a satin enamel paint job. +Electric mountain bike with high-torque motor, robust chain guide, long-travel suspension and aggressive tread tires for powered singletrack. +Gravel explorer with titanium frame, discreet welds, and multiple mounts to support long unsupported overnights. +A gravel e-bike with torque-sensing motor, long-range battery, rack mounts, and 42mm semi-slick tires for mixed-surface touring. +Gravel race machine with aggressive tyre choice, tubeless setup, wide cockpit and a stealth wrap with hidden logos. +A beach cruiser with pastel ombre paint, chrome trimmings, plush saddle and oversized handlebars for comfortable coastal style. +A factory-fresh gravel pro bike with integrated top tube bag, two-bolt seatpost clamp and a subtle geometric camo paint. +A commuter with low-maintenance hub gearing, belt drive, puncture-resistant tires and a locked rear rack for secure parking. +A high-volume fatbike with bright neon decals, 4.8-inch tires, and a reinforced fork for adventurous winter or beach excursions. +A handmade fillet-brazed lugless steel tandem with classic proportions and subtle pinstripe detailing along the top tube. +Modern rigid mountain bike with plus-sized 2.8 tires, slack head angle and confidence-inspiring handling. +All-mountain enduro bike with robust pivot bearings, reinforced downtube, and a wide bar for confidence at high speed. +All-terrain fatbike with studded tires, 4.5" rubber, and matte army green frame for winter commuting. +Gravel race bike with asymmetrical chainstay, 700c wheels, tubeless setup, and a 1x drivetrain for simplicity and speed. +A gravel bike with tactically placed splash-guard paint, 40mm tubeless tires, and a modular rack system for front and rear loads. +Urban weekday cruiser with step-through frame, enclosed chaincase and soft suspension saddle for comfort. +Gravel touring frame with full brazing, fork eyelets, top-tube bag included, and 700x45 tire clearance for rough paths. +A kid’s BMX race learner with low seat, strong pegs, and simplified brake setup to build confidence on ramps and tracks. +Bicycle with step-through aluminum frame, child seat mounts, rear rack, dynamo lighting, and puncture-resistant tires for family use. +Lightweight gravel frame with integrated stealth frame bag pockets and a comfortable geometry for all-day rides. +Kids' balance bike with wooden frame, rubber grip tires, ergonomic seat and minimalistic paint for early learning. +Classic mixte roadster with wicker basket, chaincase, umbrella holder, and a two-tone cream-and-brown finish. +Vintage mixte city bicycle with step-through frame, wicker basket, coaster brake rear hub, and pastel paint for casual neighborhood rides. +A sleek track pursuit bike with carbon monocoque, integrated stem, deep-section front wheel and a composed matte finish for velodrome focus. +A commuter with retro flare, swept handlebars, vintage saddle, and wide comfy tires for leisurely city rides. +Road endurance bike with taller headtube, relaxed geometry, and vibration-damping carbon layup for long comfort. +Gravel-packing bicycle with external battery pack, extra chainstay protection, and wide 650b tires for remote scouting missions +Classic step-through city bike with pastel cream color, integrated bell, and comfortable upright geometry. +A mid-fat plus hardtail with 27.5+ wheels, 2.8" tires, steep seat tube angle and a crisp blue powdercoat for playful trail handling. +A matte forest-green full-suspension trail bike with dropper post, 29" wheels and aggressive tread for technical climbs and descents. +A handmade lugged steel touring bike with classic leather saddle, brass bottle cages and dark cherry lacquer with subtle gold striping. +Mountain enduro with robust frame, 170mm rear travel, adjustable headset angle and proven climbing efficiency for marathon gravity days +A commuter with fenders, integrated front light, and a low-profile rack that supports a messenger bag without spoiling the bike’s sleek silhouette. +Gravel all-rounder with a 1x gravel drivetrain, alloy cockpit, and reversible dropout for alternator hubs. +A cyclocross training rig with steel frame, 34mm mud-wedge tires, breathable handlebar tape and extra reinforcement at the dropouts. +A modern gravel bike with hidden fender mounting points, internal routing, and a comfortable sloping top tube for day-long comfort. +A city e-bike with a low center of gravity, mid-drive motor, and easy-access battery for simple, everyday power assistance. +A precision gravel race frame with asymmetric chainstays, integrated computer mount, and aerodynamically profiled tubing for speed on rough roads. +A folding e-bike with seatpost battery, rear hub motor, and a compact frame that collapses for easy apartment storage. +A mountain fatbike with rigid carbon fork, 26x4.8 tires and lower gear ratios for plowing through deep snow. +Mountain trail hardtail with adjustable travel fork, direct-mount front derailleur and all-mountain tire clearance. +A cyclocross race frame with aluminum fork, flared top tube, and an emphasis on easy shoulder carry during run-ups. +Gravel touring bike with titanium frame, long-range gearing, wide tire clearance, and discreet mounting points for racks and bottles. +Kids' mountain bike with coaster brake option, bright graphics, and knobby tires to build trail confidence young riders. +Gravel tourer with wide handlebars, frame pump, and reversible thru-axle for varied terrain. +Gravel race bike with nanotube carbon layup, integrated top-tube bag slot and compact cockpit for speed. +Electric cargo longtail with child seats, reinforced deck, high output motor and long-lasting battery for family transport. +Cyclocross bike with durable carbon fiber seat tube, flared bars for control, and increased clearance for muddy conditions during races +A stealth carbon road bike with internal cable routing, wider tire clearance, and subtle glossy-black brandless decals. +A kids' adventure bike with 20-inch wheels, stabilizing geometry, easy-reach brakes and a comfortable saddle for growing riders. +Cyclocross special with lugged steel frame, mud guards optional, and a hand-applied pinstripe down the top tube. +Road performance build with aero-optimized tube profiles, comfortable seatpost compliance and medium-depth wheelset for sprinting. +A utilitarian folding cargo bike with fold-flat loading deck, electric assist, and modular inserts for different cargo types. +A touring bike set up for winter expeditions with studded tires, insulated bag mounts, and fuel canister racks for frigid self-supported travel. +Lightweight racing carbon frame with tapered headtube, BB386 bottom bracket and ultra-stiff chainstays for sprinting. +A handbuilt Columbus steel road frame with decorative lugwork, elegant slender tubes, and a classic road feel under power. +Adventure mountain bike with 29+ conversion, wide-range cassette, reinforced bars and multiple framebag mounting points +A modern trail bike with tuneable damping, 29-inch wheels, and an integrated chainguide to keep the drivetrain aligned during rough descents. +Classic Dutch cargo bike with enclosed box, child bench, wooden floor and upright steering geometry. +A classic lugged road frame refinished in original colors, with a durable modern wheelset for comfortable weekend rides and social groups. +Race-ready track bike with stiff bottom bracket, high-pressure tubulars, and aerodynamic seatpost. +A commuter with integrated GPS mount, subtle reflective accents, and a fully enclosed chaincase to minimize maintenance. +A gravel bike with 1x drivetrain, clutch rear derailleur, and a hand-applied splatter paint technique for a unique look. +Beach cruiser with woven wicker basket, two-tone paint and wide spring saddle for beachside sessions. +City electric folding bike with a compact folded package, removable battery and a clear display for assist levels. +A commuter with full-length fenders, integrated lock, wide cushioned saddle and a discreet low-step frame for easy daily use. +Gravel race build with wide tyres, aerodynamic cockpit, and light weight prioritization for fast mixed-surface events. +A durable cargo trike with weatherproof, lockable cargo box, low platform for easy loading, and reflective paint for night safety. +Trail hardtail with 29" wheels and aggressive 2.35" tires, lockout fork, and short stem for responsive handling in the woods. +Gravel touring frame with wide tire clearance, integrated seatpost clamp, and top-tube bag compatibility for off-grid expeditions. +A hand-painted track bike with metallic fleck in the paint, polished dropouts, and a minimalist decal set for a refined competition look. +A road endurance bike with compliance-focused seatstays, 28mm tires, flared drop bars and mounts for two bottles on long rides. +A dirt road crusher gravel bike with steeply stacked geometry, long reach, and a confidence-boosting wheelbase for high-speed gravel descents. +Kids' cruiser with training wheels removable, bright cartoon decals, steel frame, and easy-to-use coaster brake for learning. +A kids' balance bike with rubberized pedals (optional), a wide base for stability, and a bright lacquer finish to stand up to playground use. +Urban single-speed with paint-splatter finish, deep-section rims, modest saddle, and a rear coaster brake for carefree city spins +A dirt-jumping BMX with rigid fork, reinforced frame gussets, short chainstays and bold graffiti-style paint for park tricks. +Practical city bike with internal 8-speed hub, roller brakes, and chaincase for low maintenance. +City fixie with deep V rims, polished spokes, minimal decals, and a narrow racing saddle for nimble urban movement. +A race-ready fixed-gear track bike built for sprints with an aggressive saddle position, ultra-stiff bottom bracket and gloss black finish. +Gravel plus rig with robust alloy frame, 2.4-inch knobbies, and a rack mount for light touring duties. +Mountain trail full suspension with tuned kinematics for traction, sturdy bearings, and matte metallic highlights. +A gravel commuter with disc brakes, mudguard-compatible frame, 700x40 tires and an integrated rear light for rainy mornings. +Classic step-through commuter with basket, coaster brake and floral paint job for casual city rides. +Urban folding cargo bike with reinforced hinge, foldaway pedals and a lockable carry case for extra security on commutes. +A cyclocross bike with shallow drop handlebars, tubeless-ready rims, and a stealthy black-and-olive camouflage finish for muddy weekend races. +A classic Dutch transport bicycle with heavy-duty frame, coaster brake, long chain guard and a big wooden crate on the front. +Commuter folding bike with magnets to lock when folded, puncture-proof tires, and reflective rim tape. +Road endurance machine with endurance-focused compliance, slightly taller headtube, and wider tire clearance. +Folding commuter with easy-carry handle, quick-fold release and compact folded dimensions suitable for public transport. +Gravel commuter with reflective sidewalls, integrated lights, fender mounts, and a comfortable upright geometry for daily rideability. +Gravel bike with aluminum frameset, carbon fork, and a paint job that fades like dawn to dusk. +Competitive track bike with tubular tires, aero handlebars, steep seat angle and track-specific stiff bottom bracket. +A track pursuit frameset with low-surface area tube profiles, stiff BB shell, and a tapered headtube optimized for an aero fork. +A classic cruiser with soft two-tone seat, chrome sissy bar, wicker basket and a long, low frame ideal for coast-side parade riding. +Cargo bike with electric assist and large front-loading box, low center of gravity, and child harnesses in the box. +Handmade steel lugged road frame with bespoke paint, polished finishing touches, narrow 28mm tires and classic ride quality. +Road race bike with integrated bar-end computer mount, minimal paint for weight savings, and quick-shift levers. +Gravel adventure bike with titanium bolts, stealth cable routing, and a subtle two-tone color split. +Gravel endurance with carbon fork, subtle micro-suspension in the seatpost, and discreet reflective accents for night rides. +Urban cargo electric bicycle with modular deck, fold-up child seat, and strong braking system for safe city loads. +Road endurance frameset with compliance-engineered seatstays, integrated seatpost clamp, and warm beige colorway. +Electric cargo longtail with enclosed wiring, integrated lights, and locks on both the wheel and frame for security. +A track endurance bike with tall gear ratios, deep-section front wheel, and a half-carbon rear wheel to balance aerodynamics with stability. +Full-suspension trail mountain bike with 150mm travel, 29-inch wheels, and a coil shock for big hits. +Time trial bike painted electric blue with integrated extensions, steep seat tube and a rear disc for maximum speed. +A modern track pursuit bicycle with integrated front fairing, aero tubing, deep-section front wheel, and stiff bottom bracket for power. +A touring tandem bicycle with two sets of drop bars, downtube shifters, matching saddles, and reinforced frame joints for long-distance pair rides. +Steel cyclocross frame with chainstay protection, wide mud clearance, and elegant lugwork. +A handbuilt lugged steel road bicycle with classic proportions, polished chrome fork crown, and elegant three-color paint scheme. +A full-suspension cross-country race bike with 100–120mm travel, ultralight frame, and fast-rolling 29ers for marathon events. +Mixed-terrain e-gravel bike with mid-drive motor, long-range battery, dropper post and reinforced rack mounts. +Lightweight endurance road bike with vibration-absorbing layup, 30mm tire clearance and ergonomic seatpost for long distances. +Enduro mountain bike with slack head angle, 170mm front travel, beefy chainstays, and a bashguard for rocky descents. +Endurance road frameset with compliance-focused seat tube, hidden cable routing, and clearance for 30mm tyres for rough pavements. +A kids' balance bike with bright red paint, low center of gravity, cushioned grips and front-mounted nameplate for personalization. +A low-slung cruiser bicycle with sweeping chrome bars, oversized seat, whitewall tires and a low top tube for easy step-over. +Mountain freeride frame with stout tubing, extended travel and reinforced pivot hardware for extreme terrain. +A low-slung cruiser with chopped fenders, chrome springer fork, wide swept bars and a surf-inspired teal paint scheme for beach rides. +A gravel-specific bike with dropper post, 1x wide-range cassette, and a unique burnt orange paint that hides scuffs well. +Gravel speed machine with aero profile tubing, 36mm tires, and an electronic shifting system for crisp gear changes. +Electric mountain trail bike with torque-controlled motor, reduced weight battery, and a supportive saddle for long climbs. +Lightweight time-trial bike with integrated hydration, deep carbon wheels, and adjustable aerobars for optimized ergonomics. +Gravel competition frame with internal storage tube, wide tire clearance and a firm but comfortable ride feel for racing. +A minimalist single-speed road bike with polished frame, narrow tubulars, and a track-style stem for pure simplicity. +A sleek aluminum time-trial bike with integrated hydration, ceramic bearings, aero cockpit and minimal paintwork to reduce weight. +Road aero frame with integrated seatpost clamp, deep-section front and rear wheels and shaved cable ports. +A classic British-style roadster with chaindrive, Sturmey-Archer hub, upright rake, and brass bell for leisurely pub-to-home rides. +A commuter with integrated rear hub lock, wide tires, comfortable saddle and a softly sloping top tube for easy mounting. +Mountain enduro with progressive travel, adjustable geometry, and thick protective guards to withstand rock strikes. +Full-suspension trail bike with progressive geometry, lockout feature, dropper post and tubeless-ready rims for varied terrain. +Racing gravel bike with aero-optimized tubing, integrated mounts for aero bottles, and 40mm high-pressure tires for speed on dirt +A race-tuned time trial bicycle with integrated hydration and a sculpted seatpost designed to minimize drag on flat, windy courses. +Electric step-through city bike with mid-motor, walk-assist mode, full fenders, and a comfortable swept-back handlebar. +A chopper-style cruiser bicycle with swooping frame lines, elongated fork, and a hand-stitched leather saddle for showy weekend rides. +A performance road frame with aero profile, integrated seat clamp, 28mm tires and glossy black finish with subtle chromed accents. +A nimble trail hardtail with quick geometry, 29" wheels, and a confident front end that encourages technical line choice and playful riding. +Trail hardtail with 130mm fork, slack head angle, bolt-on chain guide and wide tires for confident trail riding. +Mountain trail rig with short chainstays, low bottom bracket, 140mm travel and burly tires for playful yet capable rides +A lightweight aero-climbing road bike with exaggerated seat tube taper, 34mm rim profile and a pearly white finish. +E-gravel with torque sensor and discreet battery, designed to handle long mixed-surface charity rides and remote days out. +A gravel-adventure bike in desert tan with 700x45c tires, frame-mounted pump, and extra bottle mounts for long desert rides. +A welded titanium gravel frame with smooth joints, burnished finish, internal headset cable stops, and clearance for 50mm plus tires for roughroads. +Commuter with integrated rear rack, keyless locking system, and a comfortable saddle for quick errands. +A performance gravel bike with disc-brake optimised frame, stealth internals, and a pearl white paint that hides dust. +E-gravel bike with long-range battery, motor-assisted climbing, flared bars and comfort geometry for extended mixed-surface days. +Trail bike with 130mm dropper, tubeless-ready rims, and a snug, confidence-inspiring geometry. +Retro-racer steel frame with traditional lugwork, downtube shifters, and a polished chrome fork crown for vintage charm. +A carbon commuter with belt drive, hub dynamo, and internal lighting that turns on automatically with motion sensors. +A flared-bar gravel racer in stealth black with splash accents, tubeless-ready wheels, and wireless shifting to make the cockpit tidy and functional. +A mountain trail bike with playful geometry, 140mm travel, and a lively rear end for quick manualing and poppy features. +A classic mixte city bicycle in pastel teal with swept-back handlebars, chaincase, skirt guard, and a comfortable upright riding position. +Mountain enduro bike with adjustable geometry, sturdy 27.5+ tires and big-piston brakes for technical descents. +A mountain enduro frame with custom chainstay guards, heavy-duty rocker arm, and geometry optimized for steep tech descents. +Mountain all-mountain with progressive geometry, 150mm travel, and secure chain guide for rough trails. +Lightweight touring bicycle with titanium fork, low maintenance hub gearing, and multiple luggage mounting points. +A steep-angled track bike with ultra-short chainstays, aggressive geometry, narrow drop bars and sleek black finish. +Gravel grinder with olive drab paint, SRAM 1x12 drivetrain, dropper post, and low-profile mudguards for mixed terrain. +A commuter with quiet belt-drive, automatic hub lighting, and integrated rear carrier for everyday reliability. +Lightweight single-speed commuter with polished chrome frame, leather saddle, and narrow tires for zippy urban transport. +Gravel commuter with integrated mudguards, disc brakes, dynamo light, and a comfortable geometry for mixed-surface commutes. +Mountain enduro rig with adjustable chainstay length, long travel fork, and a stout downtube for rough terrain. +Folding commuter with belt drive, 16-inch wheels, compact folded footprint, and quick-release seatpost clamp for bike storage. +A custom hand-welded chromoly mountain bike with tapered tubing, internal cable routing, and bespoke headtube badge hand-stamped. +Classic Italian road racer with Campagnolo shift levers, steel chromed fork, and tubular rims for spirited group rides. +City utility bike with integrated U-lock holster, chaincase, and comfortable upright posture. +City electric step-through bike with torque-sensing motor, comfortable slippers-style saddle, full fenders, and rear cargo rack. +Touring bike with Rohloff hub, Gates belt drive, and a polished steel frame built for decades on the road. +A cyclocross machine with carbon monocoque frame, 33mm tubular tires, flared drops, and stiff bottom bracket for sprinting out of corners. +A BMX pro-level frame with chromoly tubing, strong sealed hubs, and a geometry tuned for pro contest lines. +High-traction mountain bike with 29x2.6 tires, wide rim profile, plush rear damping and modern low-slung geometry. +Lightweight aluminum hardtail with thru-axles, modern geometry, and 29-inch wheels for efficient cross-country rides. +A titanium touring frame with multiple cargo attachments, a corrosion-resistant finish, and a geometry designed for stable loaded touring over long days. +A modern trail bike with flip-chip geometry, 150mm travel, and large-volume tires to handle long descents and flick off berms with ease. +A restored road classic with period decals, renewed brake cables, and modern disc brakes discretely fitted for safety and classic looks. +Lightweight carbon gravel bike with thru-axles, toughness-focused layup, and mud-shedding frame shapes. +A kids' BMX mini with tiny 16-inch wheels, bright stickers, gyro and padded handlebars in neon pink. +Steel gravel bike with waxed drivetrain, silent chain tensioner, leather saddle, and a subtle brushed finish for understated style +Cyclocross race-inspired with carbon fork, tubeless tires, and a frame designed to keep mud from baking into the stays. +Classic cruiser with exaggerated swept bars, foam-padded saddle, chrome-plated chainring and bright candy paint for Sunday parades. +A beach cruiser with soft pastel gradient paint, chrome piped fenders, cruiser bars and extra-wide saddle for maximum comfort. +A lightweight commuter with belt drive and single-handed hub gear for hassle-free shifting and smooth silent performance in town. +A commuter loaded with commuter accessories: phone mount, stainless rack, bread-basket, and puncture-resistant tires for reliability. +Gravel e-bike with frame-integrated accessories, dual bottle mounts, and an understated matte terra paint for outdoor blends. +A beach cruiser with sunburst orange paint, deep saddle, coaster brake and sweeping bars for relaxed coastal rolling. +A city cargo tricycle with roomy front box, electric assist, low center of gravity and high-visibility yellow paint with reflective striping. +Gravel touring bike with framebag compatibility, durable finish, ample tire clearance, and low gearing for long adventurous rides. +Mountain downhill bike with custom shock tune, thick-walled chainstays, and protective skid plates under the down tube. +A skinny-tire track bike with sleek profile, deep-section rims, and a high gear ratio for the velodrome. +A step-through urban bike with swept bars, chain cover, large wicker basket, and puncture-resistant whitewall tires for market runs. +A high-performance carbon commuter with stealth paint, disc brakes, and a vibration-damping seatstays for longer daily commutes. +A compact city folding bike with 20" wheels, easy-to-stow latch, and a low center of gravity to navigate crowded trains and apartments. +A rugged winter commuter with studded tires, triple-layer paint for corrosion resistance, and a full chaincase to limit grime buildup. +Minimal fixed-gear track bike with polished hubs, cable-less single-speed drivetrain and glossy black paint for city finesse. +Electric commuter with Bluetooth connectivity, pedal-assist sensor and low-step aluminum frame. +A compact children's balance bicycle with anodized aluminum frame, soft rubber tires, and a low-slung center for early balance practice. +Fixed-gear track-style commuter with polished deep-rim wheels, minimal graphics and flush-mounted fender for light rain. +A hand-painted frame with unique watercolor gradient, lugless steel construction, disc brake compatibility and custom headbadge. +Gravel all-rounder with two bottle mounts, moderately slack head tube angle, and 700x40 tyres for mixed pace adventures. +A gravel endurance build with wide tire clearance, vibration-absorbing seatpost, 700x42 tires and matte stone finish with faint speckles. +A performance time trial machine with optimized aero tubes, disk wheels, hydration system and ice-white pearl paint with subtle sponsor logos. +A trail-specific short-travel full-suspension bike with playful geometry, 130mm rear travel, and wide handlebars for technical trail riding. +A fast cyclocross bike with tapered headtube, flat-mount disc calipers, and bar-mounted number plate bracket for race-day practicality. +Road aero frameset with hidden seatpost clamp, refined tube profiles, and lightweight layup for a stiff yet compliant ride. +An urban electric-assist commuter bicycle with mid-drive motor, integrated battery in the downtube, hydraulic disc brakes, and pannier rack. +A fast road bike optimized for criteriums with responsive steering, 32mm wheels, 11-28 cassette and vibrant flame-orange paint. +A touring tandem with Rohloff hubs, belt drives, and rack-and-pannier optimized mounting points for extended two-up travel. +A commuter with built-in cargo clip system, low-maintenance belt drivetrain, and a bright powder coat that hides day-to-day scuffs and scratches. +Touring lugged steel with brass fittings, hand-stitched leather bar tape and triple bottle mounts for expeditions. +Urban single-speed with polished steel track frame, narrow handlebars and a bold painted chainring as a visual accent. +A cyclocross training rig with tubeless-ready rims, wide-range cassette, and a paint finish that hides salt and mud from rainy rides. +A lightweight XC race bicycle with tapered forks, sub-1,000g frame, and fast-rolling 29x2.0 tires for marathon races. +A durable workbike with steel frame, reinforced rear rack, drum brakes and reflective safety striping for nightly deliveries. +A lightweight gravel bike with racy geometry, 700x35c tires, and a tuned layup to balance flexibility and pedaling stiffness. +Sleek gravel race frameset in smoked silver with integrated headset, flat-mount brakes, and 42mm tire clearance for speed. +A commuter with quiet belt drive, internal gearing, and integrated raincover for the seat and bag during sudden storms. +Fixed-gear track throwdown with brushed aluminum frame, deep V rims and minimal saddle for weight savings. +A custom steel fixed-gear with polished dropouts, narrow leather saddle, and a single-ring crank designed for urban simplicity. +Track fixed with single-tooth cog, glossy clearcoat over bare aluminum and aggressive seat tube angle for sprinting. +Electric folding cargo bike with removable battery, easy-fold latching system and sturdy panniers. +A performance road bicycle with tall 32mm deep rims, a 52/36 compact chainring, and a 11-28 cassette for lightweight climbing. +Urban step-through commuter with chaincase, hub-based shifting, and puncture-resistant tires tailored to stop-start city travel. +A beach cruiser with two-tone sea-glass paint, wicker basket, stainless steel fenders and comfy wide saddle for sunshine rides. +A commuter with internal cable routing, frame-integrated mounts for racks, and wide 35mm tires to smooth out pothole-heavy routes. +A compact folding cargo bike with a small footprint, large-capacity detachable front box, and an electric assist for heavier errands. +Cyclocross-inspired commuter with clearance for 35mm tires, mudguards, dynamo lighting, and a steel touring frame. +Gravel race-friendly bike with stiff bottom bracket, integrated seat clamp and light-mounting points for endurance events. +A downhill slayer in gloss orange with heavy-duty linkage, long travel, and reinforced chainstays for brutal bike park punishment. +Road endurance frameset with vibration-absorbing layup, comfort-oriented geometry, and subtle hand-painted accents along the head tube. +A commuter with retro paint, leather saddle, front wicker basket, and a relaxed upright geometry that prioritizes comfort over speed. +A cyclocross training bike with wider brake calipers for modulation, flared bars for leverage, and a tough frame for rigorous practice. +A cheerful kids' balance bike in sky blue with wide handlebars and a low saddle height for confident learning. +A gravel endurance bike with long wheelbase, 700x45 tires, and a vibration-damping handlebar for long days in the saddle. +A sleek carbon aerobic road machine with a focused geometry, internal cable routing, and light-weight wheels optimized for gradient sprints. +Kids balance bicycle in bright red with no pedals, low frame and foam grips to teach balance and coordination. +Road sprinter with stiff rear triangle, shallow disc wheels and short wheelbase for explosive acceleration. +Mountain gravity bike with 200mm rear travel option, heavy-duty cranks, and proven downhill geometry. +Single-speed mountain bike with tough chromoly frame, wide handlebars, and an extra-large chainring for flow trail fun. +Classic road bicycle with hand-lugged steel, polished head badge, chrome fork crown and narrow tubular tires for vintage club events +Gravel e-bike with clutch-style rear mech, heavy-duty chainring, and long-range touring gearing. +Kid’s balance bicycle in bright red with no pedals, lightweight frame, foam tires, and low center of gravity for learning to balance. +A touring bike with steel frame, triple chainrings, and heavy-gauge spokes designed to chew miles with full panniers and tent. +A commuter cargo bike with electric assist, long wheelbase, box cargo bay and heavy-duty hydraulic disc brakes for hauling kids. +Electric cargo bike with long-tail deck, hydraulic brakes, powerful motor and child-specific harness attachments. +Road race aero frame with hidden cables, integrated bar clamp, and shallow boat-tail seatpost for aerodynamic advantages. +Gravel adventure rig with durable alloy frame, long-range gearing, wide tire clearance and protective chainstay plating. +Gravel racer with progressive geometry, high-volume tires set up tubeless, and a compact cockpit for endurance speed. +A titanium gravel grinder with flared bars, 650b wheels, 47mm tires, titanium bottle mounts, and raw brushed finish with laser-etched logos. +A classic urban city bike with full fenders, a heavy-duty rear rack, and an easy-handling geometry that invites everyday errands and market trips. +Lightweight track bike with ovalized chainstays, glossy red paint, and narrow aero seatpost. +Gravel race frameset with asymmetric chainstay, clearance for 700x42 tires, and integrated cockpit for clean lines. +A comfortable cruiser with oversized banana saddle, swept-back bars, detailed pinstriping and a chrome chain guard for classic styling. +Pace bike for criterium training with responsive aluminum frame, shallow rim wheels and short chainstays for snappy handling +A carbon ultralight climbing road bike with low stack, shallow rim depth wheels, and a feather-light component selection for steep ascents. +Road climbing bike with ultra-light components, pared-back paint, and a high stiffness-to-weight ratio for steep ascents. +Gravel endurance bike with ergonomic drops, micro-suspension seatpost, and fender-compatible clearance. +City step-through electric bike in cherry red with basket, wide saddle, low gears, and simple twist-grip throttle. +Mountain bike with dropper post, 2.35-inch aggressive tires, and tubeless setup for rocky trails. +Electric-assist city bicycle with low-step frame, integrated battery in the downtube, hydraulic disc brakes, and a rear hub motor tuned for commuter acceleration. +Mountain downhill race bike with reinforced swingarm, coil-sprung shock, massive rotors and a low-profile cockpit for control at speed +City bike with internal hub gearing, low-maintenance belt drive, coaster brake, and a soft leather-look saddle. +A commuter with low-maintenance belt drive, fully enclosed chaincase, integrated lighting and a subtle two-tone paint job for city stealth. +Vintage socialites' bicycle with brass accents, wicker basket, cream tires and a curving top tube for graceful looks. +Electric cargo bike with tilt-adjusted seat, secure front box, and a heavy-duty motor allowing steep hill climbs with loads. +Mountain trail full-suspension with 140mm travel, lockout capability, dropper post and progressive modern geometry for diverse trails. +A mountain enduro rig with 170mm travel, adjustable chainstay length, and a burly 35mm fork ready for aggressive trail riding. +Single-speed mountain bike with rigid fork, wide handlebars, chunky tires, and a minimalist gear ratio for trail simplicity. +A folding commuter with belt drive, low-fold geometry, and integrated lights for last-mile multi-modal transport. +A BMX street bike with 20-inch wheels, reinforced top tube, gyro cable system, and pegs on both wheels for grinding rails. +Gravel racer with aero-optimized tubing, deep-section wheels, 1x drivetrain, and a companion frame bag for long events. +Mountain trail bike with switchable geometry headset, 150mm front travel and slacker angles for confidence going down. +A stripped-down steel commuter with coaster brake, comfortable swept bar, and a basket that blends with a glossy cream paint finish. +A rugged cyclocross commuter crossover with mounting points for fenders, 35mm knobby tires and disc brakes for year-round utility. +A lightweight triathlon bike with integrated hydration, time-trial cockpit, disc brakes, and wide gear range for climbing on varied courses. +Commuter with chaincase, integrated dynamo lights, and a quiet internal hub for fuss-free use. +A vintage folding bicycle restored with new bearings, repolished chrome, and a fresh paint job that complements original decals. +A commuter with integrated rear rack and lock, low-step geometry, and a warm sunset orange paint that improves visibility in traffic. +A gravel endurance bike with an emphasis on rider comfort, wider tires, and adjustable cockpit for long multi-day stages. +Electric fat-tire surf-style bike with surfboard rack mounts and corrosion-resistant hardware for beach use. +Gravel bike with electronic shifting, stealthy integrated cables, and a frame built to accept racks and fenders. +A mountain enduro rig with burly construction, 170mm fork, dropper post, reinforced hubs and a livery that hides scuffs from rock strikes. +Lightweight cyclocross carbon frame with tapered headtube, thru-axle wheels, and mud-shedding geometry. +Folding e-cargo bike with reinforced rear triangle, battery-integrated rack and child harness attachment points. +A lightweight carbon endurance road bike with endurance fit, 28mm tires, and vibration-damping layup for long rides. +A contemporary mountain hardtail with 120mm fork, aggressive geometry, wide 29er rims, and a dropper seatpost for rough singletrack. +A BMX freestyle bicycle with 20-inch wheels, pegs on both axles, reinforced chromoly frame, and gyro brake setup for park tricks. +Road race frameset with aerodynamic tube shaping, 25mm tire clearance, and clear-coat that shows fine carbon weave. +Electric mid-drive mountain bike with torque sensing, battery integrated into downtube, and sturdy chain protection. +A matte black carbon road bicycle with aero tube shaping, integrated cockpit, deep-section 60mm wheels and a compact double crank for fast group rides. +City single-speed with glossy neon decals, fixed cog on rear hub, small front rack and retro bell for personality +Gravel tourer with titanium bolts, forged rack-compatible lugs, and a durable anodized finish for longevity. +Electric cargo bike with twin bench seating, powerful mid-drive, sturdy frame, and integrated passenger restraints for family safety. +A long-travel all-mountain bike with 160mm fork, mullet setup, and reinforced dropouts for aggressive trail days. +Adventure gravel rig with flared 3T bars, dropper post compatibility, and safety orange paint for remote routing visibility. +Compact folding commuter with step-through hinge, comfortable ergonomic grips, and small-footprint fold for easy transit storage. +Mountain freeride bike with short chainstays, extra-wide handlebars, and heavy-duty drivetrain for punishing park sessions. +City hybrid with flat bars, suspension fork, built-in rear rack, and puncture-resistant commuter tires. +A vintage steel road bike with chromed fork crown, narrow tires, leather saddle and deep maroon lacquer with subtle decals. +A commuter with integrated smartphone mount, stealth wiring for lights, and a robust rear rack ready to hold a week of groceries. +A handmade lugged steel road bike with brass headbadge, gloss cherry lacquer, and thin classic 25mm tires. +A modded cruiser with single-speed drivetrain, chopped fenders, and a chopped rear frame for a shortened custom silhouette. +Touring tandem built for loaded expeditions with reinforced racks, triple bottle mounts and disc brakes for reliability. +A discreet commuter with internal hub, full chainguard, and clean welds for a corrosion-resistant low-maintenance ride. +A mountain cross-country bike with narrow seatstays for compliance, tapered head tube for steer precision, and a light cockpit for weight savings. +A classic road racing bike repainted with period-accurate decals, chrome fenders, and polished steel handlebars. +Cyclocross training build with reinforced fork, aluminum rims, and a durable crankset to survive winter's abusive conditions. +A cyclocross steed with blacked-out components, internal mud flaps, and 33mm knobbed tires for sticky conditions at the back of the field. +Electric mountain fatbike with torque-controlled motor, wide handlebars, and a grippy saddle for rough terrain. +A commuter with built-in cargo platform, child seat mounts, low center of gravity and safety orange paint with reflectivity. +A kid’s training bicycle with adjustable saddle and handlebar height, easy-to-use coaster brake, and colorful graphics to make learning fun. +Urban electric cargo bike with front-loading box, low step-through frame, pedal-assist motor and child harnesses. +A raw-aluminum gravel frame with polished welds, integrated GPS mount, and 650b compatibility for mixed-surface expeditions. +Cyclocross race-ready machine with mud-shedding top tube, rapid engagement hub and a low-slung geometry for bursts. +A commuter with full-length chaincase, integrated tail light, and a low-step frame to make mounting easy even when carrying cargo. +A touring tandem with two spacious pannier racks, triple chainrings, reinforced spokes and a stable long-wheelbase for comfortable two-up touring. +A professional cyclocross machine with top-shelf carbon layup, semi-integrated cable routing, 33mm knobby tyres and matte black paint with lime accents. +Road endurance commuter with disc brakes, wide tyres, and an upright position to combine fitness and commuting efficiency. +High-performance road bike with electronic shifting, carbon cockpit integration, and pearlescent white paint. +Gravel sleeper bike with matte gray paint, stealth fender mounts, 42mm tires, and a practical handlebar bag. +A cyclocross race bike with staggered frame tubes, disc brakes, and a custom paint job with high-contrast chevrons. +Gravel adventure frameset with full-coverage chainstay protection, multiple rack mounts and generous 650b clearance for mixed-terrain touring. +Touring gravel bike with rugged steel frame, triple chainring, and pannier-ready rear triangle for unsupported expeditions +Adventure bike with Pinion gearbox, Gates belt drive, sturdy racks and weatherproof finish for low-maintenance expeditions. +Gravel touring frame with built-in fender mounts, heavy-duty bolts, and adaptable fork for racks. +A gravel race bike with aero bars clipped on for speed, 700x32 tires, electronic shifting and pearly white paint with subtle metallic glitter. +Cyclocross-specific steel frame with wide tire clearance, traditional geometry and modern disc compatibility for classic feel. +A gravel explorer with S-shaped top tube, internal cable routing, wide bottle mounts, and 2-inch tubeless tires for cross-country rides. +A gravel adventure tandem with bar-end shifter options, reinforced fork, and durable protective film on the downtube to survive long trips. +A gravel endurance build with 650b wheels, fatter casing tires, and a longer wheelbase for confidence on rough descents. +A modern enduro bike with 170mm travel, adjustable geometry, and a durable carbon-reinforced downtube to defend against rock strikes. +A titanium cross-country frame with featherlight tubing, precise geometry for climbing, and a soft, bead-blasted finish that resists minor scratches. +Mountain all-mountain with adaptable geometry, 150mm rear travel, and robust braking system for long aggressive runs. +Lightweight mountain XC frame with asymmetric chainstay, carbon layup, and geometry focused on quick acceleration. +Gravel exploration frame with stainless steel tubing, custom water-bottle bosses, and full-length mudguards for rough-weather travel. +A gravel racer with aggressive fairing shapes, slim yet strong 700x32 tires, and a tasteful gradient paint scheme that signals competition intent. +Cyclocross race frame with reinforced stays, short chainstays, and mud-shedding paint for autumn and winter competition. +Track-focused carbon bike with stiff headtube, race-quality bearings and an aerodynamic seatpost clamp for marginal gains. +Classic step-through city bike with woven basket, spring-loaded saddle and subtle floral decals for charm. +A carbon fiber mountain bike with 130mm travel, 29er wheels, remote lockout, and tapered steerer for precise handling. +Road ultralight build with shaved rim braking surfaces, ceramic bearings and a shaved cockpit to minimize grams on climbs. +A classic lugged steel road bicycle with gloss British racing green paint, quill stem, downtube shifters, and 700c tubular wheels for criterium charm. +A cargo longtail with extended platform, integrated passenger footrests, and weather-resistant coating for family rides and errands. +Steel commuter with Brooks saddle, polished stainless fittings, reflective strips and single-speed simplicity for everyday reliability +A cyclocross race bike with electronic shifting, monocoque carbon frame, tubeless setup and flared bar reach to manage muddy courses. +High-end road bike with hand-laid carbon, electronic shifting, and tuned layup for the ideal balance of stiffness and compliance. +Classic randonneur bike with low-rider rack, generator lamp, wide rubber mudguards and long-distance geometry. +Touring tandem with long-chain tensioners, comfortable handlebars for both riders, and a carefully applied enamel finish. +A retro city bike with swept bars, big balloon tires, and a spring-loaded leather saddle for a soft ride. +Classic lugged road bike with cotton-tape handlebars, 7-speed friction shifting and gently aged patina on the chrome. +A modern freestyle BMX with chromoly bars, sealed bearings, and a slim saddle to facilitate long sessions at the park. +Track bike with pursuit geometry, deep rim front wheel, aluminum frame, and a polished finish for track nights. +Touring e-bike with long-range battery, low step-through frame, pannier rack, and an easy-shift internal hub for loaded rides. +All-road commuter with reflective paint accents, integrated tail light, and puncture-resistant tires for daily reliability. +Mountain trail bike with updated kinematics, 150mm front travel and a quick-shifting 12-speed cassette for demanding trails. +Mountain hardtail with welded dropouts, remote lockout lever, tubeless-ready rims and aggressive tread for technical climbs +A kids' balance bike with wooden frame, rubberized tires, playful decals and a lower center of gravity for confidence building. +A sleek carbon endurance bicycle with a slightly raised headtube for comfort, micro-suspension layup, and clearance for 32-35mm tires. +A tandem recumbent with two reclined seats, belt drives, dual disc brakes and a low center of gravity for smooth road touring. +Urban folding electric bike with clean battery integration, 7-speed hub, and a compact folded footprint. +Gravel light bike with low-profile decals, titanium bolts, and a satin finish designed to hide small scratches. +Cyclocross training hardtail with steel fork, mud clearance, and bold red mudguards. +Mountain all-mountain trail bike with adjustable shock tune, efficient pedaling platform and wide handlebar for descending control. +Road race bike with asymmetric aero downtube, deep-section carbon rims, and a race-ready cockpit. +Mountain bike with mixed wheel sizes, dropper post, and tubeless carbon rims optimized for weight savings. +Compact kids' mountain bike with 24" wheels, front suspension fork, wide knobby tires, and a reinforced frame for trail confidence. +A matte black stealth commuter with hidden cables, integrated lights and a flat silver belt drive for nearly silent pedaling. +Enduro mountain bike with adjustable leverage rate, forged dropout replacements, and a two-tone metallic paint. +A gravel e-bike with long-range battery, ergonomic grips, and a mid-drive setup for natural-feeling pedal-assist on long haul rides. +Mountain hardtail with dropper post conversion, 29" wheels, 2.4" tires, and sealed bearings on all pivots for reliability. +Vintage racer restored with period components, hand-laced wheels, cotton cables and a gentle patina on the chrome bits. +Classic lugged steel racer with hand-filed lugs, twin water bottle mounts and a leather saddle for long rides. +A folding city bike with integrated luggage platform, 20-inch puncture-resistant tires, one-handed folding latch and a neat minimal aesthetic. +Lightweight alloy triathlon bike with integrated hydration, disc brakes, and steep seat tube for aggressive positioning. +Electric folding commuter in matte gray with torque sensor and anti-theft integrated alarm system. +Hardtail trail bike with slack headtube, 120mm fork, chainstay-mounted fender, and tubeless-ready wheels for trail reliability. +A folding commuter with tri-fold frame, integrated front carrier, quick-fold pedals, and a compact roll-away bag for multi-modal travelers. +Time-trial TT bike with monocoque carbon frame, integrated hydration bento and seven-speed cassette tuned for speed. +A gravel adventure frame with extra bottle mounts, low-compression damping forks, and a wide tire clearance for all-day exploration. +Mountain downhill bike with extra-long travel, reinforced linkage, massive rotors and burly tires for extreme gravity runs. +Handmade titanium road bike with brushed finish, discreet internal routing, and tasteful enamel pinstriping for a refined aesthetic. +Aero road bike with sharply truncated tube profiles, shallow rim wheels and internal cable routing for clean aesthetics. +A gravel touring frame with robust steel tubing, multiple braze-ons, 650b plus compatibility and desert sand finish with tan decals. +Mountain enduro carbon frame with associative shock layout, replaceable rocker arm, and matte charcoal finish. +Downcountry mountain bike with light frame, 120mm fork, and nimble handling for fast singletrack. +Modern steel racer with tapered headtube, integrated seatpost clamp and a tasteful metallic flake paint finish. +A full-suspension trail bike with 140mm travel, low-slung top tube, dropper post and a modern slack geometry for fast technical descents. +Old-school BMX park bike with 20" wheels, welded chromoly frame, pegs on both axles, and a short top tube for tricks and spins. +A commuter with concealed battery pack, quiet hub motor, integrated rear light and pannier-compatible rack for errands. +Handbuilt titanium commuter with internal headtube spacers, understated badges, and bead-blasted finish. +A vintage touring bicycle with stamped steel racks, period saddle bags, and a three-chainring crankset for conquering mountain passes. +A restored classic track bike with steel fork, pursuit geometry, and polished chrome highlights for velodrome meets. +Commuter e-bike with smartphone docking port, regenerative braking, and discreet rear light integrated into the seatpost. +A handmade lugless steel touring frame with extra-long chainstay clearance, multiple bottle bosses, and a practical slate-gray enamel finish. +A downhill race-oriented full-suspension with massive travel, reinforced suspension linkages, and wide handlebars for maximum control. +A single-speed urban fixie bike in glossy candy red with flip-flop hub, narrow riser bars, minimalist frame, and slender 700c wheels. +Lightweight aluminum road bike with modern cable routing, aero fork, disc brakes and 28mm tires for versatile performance. +Lightweight road frameset with ovalized downtube, integrated stem, and deep-section wheels for sprint-focused rides. +Classic road touring bicycle with stitched leather saddle, three-bolt pump peg and sturdy rear rack for easy pannier mounting. +A hardtail mountain bike with 29er wheels, 120mm suspension fork, dropper post, and aggressive trail geometry. +Touring recumbent with luggage mounts, comfortable backrest, durable chainring protection and reliable hub gears for long days in the saddle. +A performance hardtail XC bike with 100mm fork, sub-1000g alloy frame, lightweight wheelset and skinny 29x2.0 tires for racing. +Mountain e-bike with 170mm front travel, 160mm rear, heavy-duty wheels, and a torque-sensing motor to conquer alpine tracks. +A fixed-gear with full anodized components, narrow handlebars, and a bold gradient paint that shifts from purple to blue. +Urban single-speed with tall front wheel and small rear wheel for a unique silhouette and nimble city handling. +Mountain enduro with adjustable kinematics, robust links, and puckered chainstay protection to fight rock strikes. +Road aero commuter with shallow rim wheels, 28mm tires and discreet mudguard compatibility for occasional rain. +Road time trial bike with hidden cockpit cables, integrated hydration, and low, aggressive stack for aerodynamic performance. +Vintage city bike with full chaincase, swept-back bars, chrome fenders and a graceful paint detail across the top tube. +Gravel commuter with lightweight alloy frame, puncture-resistant 38mm tires and a discreet, integrated rear light in the seatpost. +Handbuilt custom steel frameset with subtle torch-finished lugs and brazed-on braze-ons for racks. +Classic road racer with steel tubes, drilled brake calipers, and a tasteful deep maroon paint matched with silver decals. +A BMX race speed machine with tapered chromoly frame, race hubs, red anodized components and sponsor-style race graphics. +A commuter with low step-through, integrated rear light in the rack, and reflective sidewall tires to boost safety on early morning rides. +A classic lugged road frameset with delicate pinstriping, deep metallic paint, and new sealed bearings to make it resilient for daily riding. +Adventure gravel frameset with randonneur-style mounts, long chainstays for stability, and a versatile tire clearance. +A vintage mixte with restored lacquer, wicker basket, and comfortable upright position for city promenades. +A folding e-bike with long-range battery, quiet hub motor, and a compact folded package for public transit. +Lightweight aero road bike with integrated cockpit, high-aspect wheels, and a clean cable-free aesthetic for speed seekers. +Mountain freeride with reinforced top tube, chain guide, and double-ring compatibility for aggressive lines. +Carbon gravel bike with stealthy decals, wide chainstays, and triple-bolt bottle mounts. +Modern urban single-speed with carbon fork, hydraulic brakes, and a matte finish street look. +Lightweight cyclocross bike with carbon fork, mud-specific tire tread, and frame geometry tuned for shoulder-carry sections. +City single-speed with minimal parts, deep-rimmed wheels, and a clean head tube emblem for understated style. +A carbon time trial bike with integrated hydration and a barely-there seat tube for improved aerodynamics. +Steel touring bicycle with sturdy racks, extra bottle bosses, and a chipped paint finish that shows decades of use. +A freestyle BMX with chromoly frame, removable gyro, and low-profile pedals for road-park tricks and grinds. +A cyclocross bike built for muddy courses with flared axle spacing, longer wheelbase, and a top-tube carry-friendly shape. +Mountain trail full-suspension bike with 150mm travel, adjustable geometry, dropper post and wide handlebars for technical singletrack. +A lightweight road race frame with carbon layup optimized for sprint stiffness, minimal vertical compliance, and quick handling. +Fat-bike built for snow and sand with 4.8-inch tires, rigid aluminum frame, and a single-ring drivetrain for simplicity. +Lightweight carbon time trialer with integrated storage, specially sculpted hydration and steep front end for aero posture. +Electric-assist folding bike with a removable battery, small wheels and a compact fold that fits under a desk. +Mountain enduro with stiff alloy frame, long-stroke shock, wide contact patch tires, and a slammed cockpit for control. +Gravel-plus commuter with 650b wheels, low tire pressures and a practical rear rack for hybrid commuting and gravel excursions. +Cargo e-bike with long-tail deck, durable braking system, and modular bench seats for flexible passenger configurations. +A mountain enduro build with 160mm front travel, burly 27.5+ wheels, reinforced dropouts and neon green highlights on matte black. +Mountain all-rounder with mullet setup, 29 front 27.5 rear, and grippy Maxxis rubber. +A gravel touring rig with extra wheel clearance, framebag-optimized openings, and a durable steel fork for gasketed reliability. +A city cargo bike with longbox front-loader, child harness attachments, mudguards, and an easy-to-maneuver short wheelbase. +High-volume gravel bike with 700x45 clearance, carbon fork with rack mounts, and comfortable endurance geometry. +Gravel plus with large-volume tyre clearance, sturdy rims, and a stable geometry for loaded adventure days. +Folding mini velo with single-speed simplicity, secure folding handle and a clean matte finish that resists scuffs. +Electric mountain beast with 750W peak motor, dual suspension, oversized brakes and reinforced drivetrain for technical expeditions +Modern track sprinter with oversized down tube, carbon bars, solid rear disk, and a toothy chainring for explosive acceleration. +A full-suspension enduro bike with 165mm travel, coil-compatible shock, and burly 35mm inner-width rims to prevent pinch flats on rock gardens. +Lightweight commuter with disc brakes, internal cable routing, rack and integrated LED taillight. +A high-volume enduro mountain bike with adjustable geometry, 170mm front travel, and a reinforced rear triangle for demanding descents. +Commuter with belt drive, integrated lights, and an elegant stainless-steel badge with subtle logo embossing. +Urban cargo trike with three wheels, flatbed rear platform, electric assist and low step for easy loading and unloading. +A commuting cargo bike with side panniers, drivetrain guard, and reinforced racks that convert easily between goods and kid carriage. +A lugged steel touring bike with brazed-on bosses, low-profile fenders, and a focus on serviceability on remote roads worldwide. +A gravel grinder with micro-shimano group set, dropper post, and oversized tire clearance to tackle multi-surface events and backcountry routes. +Electric longtail cargo bike with modular child seat inserts, integrated lights and an easy-to-shift drivetrain. +Sturdy cargo bicycle with wooden deck, long-reach brakes, reinforced spokes, and a low mounting platform for heavy loads. +Fixed-gear urban rocket with glossy black finish, minimal decals, and a focus on stiff power transfer for stop-and-go city races. +A full-suspension cross-country race bike with featherweight build, 120mm travel, and fast-rolling 29" tires. +A vintage touring frame with lugs, brazed-on rack mounts, stout steel tubing and a long wheelbase for stability under load. +A gravel race rig with 1x12 drivetrain, flared gravel bars, and a minimal strip of reflective paint along the downtube for night commuting. +A single-speed fixed-gear track-style bicycle with a minimal urban build, narrow riser bars, and flip-flop hub for freewheel or fixed riding. +A nimble 29er hardtail with fast-rolling tires, 100mm travel, light alloy frame, and geometry optimized for cross-country pace. +A performance gravel bike with dropper post, 42c tubeless tires, electronic shifting and custom hand-painted splatter camo on the seat tube. +A mountain enduro frame with flip-chip geometry options, piggyback shock for cooling, and oversized pivot bearings for durability. +Gravel endurance machine with titanium frame, flexible seatpost, integrated GPS mount and capacious 700x45 tires for remote adventures +A kids’ cruiser with training wheels and easy-pedal gearing, painted in sunshine yellow with polka-dot seat covers for cheerful rides. +A mountain enduro sled with carbon linkage, 170mm travel, and a tuned shock to handle high-speed rock gardens and big drops. +A lightweight titanium road frame with brushed finish, internal cable routing, disc brakes, full Ultegra groupset, and 32mm endurance tires. +Steel winter commuter with fender mounts, fat studded tires, and disc brakes tuned for cold-weather stopping power. +Cyclocross commuter with mud-blocking chainstays, hooked dropouts for chain retention and robust cantilever-style mounts. +Gravel expedition bicycle with titanium frame, large-volume tires, multiple racks, and a weatherproof frame bag system for remote touring +A trail-ready enduro bike with adjustable geometry, coil-sprung rear shock, 170mm fork, and wide carbon bars for control at speed. +Cyclocross training rig with wide clearance, all-weather hydraulic discs and grippy 35mm tires. +Cargo longtail bicycle with extended rear rack, dual passenger pegs and reinforced frame for hauling kids or goods. +A boutique steel mountain hardtail with polished welds, tapered headtube, and a color-matched fork for a premium custom look. +Mountain trail full-suspension bike with modern progressive geometry and burly tires to tackle aggressive terrain. +A drop-bar commuter with belt drive and 11-speed internal hub, full-length fenders, and a large front basket ready for market treks. +A gravel all-road machine with a little extra frame clearance, a subtle integrated pump mount, and a balanced ride for extended exploration. +Lightweight cyclocross with fast-rolling tires, a comfortable bar shape, and subtle frame reinforcements for durable racing. +A gravity slopestyle bike with chromoly frame, gyro head set, wide bars, and vibrant graffiti-style paint. +A BMX flatland bike with short frame, slender seatpost, and incredibly responsive steering for spinning and balance tricks on smooth surfaces. +A compact urban folding bike with shoulder strap, 16" wheels, hinge in the top tube and minimalist lines for commuters. +Track sprinter with zero-stack headset, minimal seatpost setback and a bright anodized finish on the fork crown. +Gravel bike with electronic 1x shifting, color-matched cockpit, and micro-suspension seatpost. +Gravel bike with mixed-color paint splatter, wide handlebars, and dynamo hub for powering lights on long rides. +Electric mountain bike with full-suspension, long-range battery, torque-sensing motor, and sturdy alloy frame for big rides. +Performance cyclocross race bike with stiff chainstays, hydraulic disc brakes and fast-engaging freehub for quick accelerations. +Gravel machine with bolt-on fender mounts, bright orange accents, and reinforced downtube. +A Belgian-style cyclocross bike with mud relief fenders, flared drops, and vintage-inspired candy-apple lacquer. +Touring tandem with extra eyelets, reinforced headtube and a simple low-maintenance gearset for remote routes. +A budget-friendly hardtail with hydraulic disc brakes, 120mm travel fork, 2.35-inch tires, and a tough paint job for consistent weekend use. +City commuter with belt drive, single-speed hub, fenders and an integrated rear rack for daily essentials. +Cyclocross training bicycle with clear protective coatings, reinforced points, and a setup that repels mud and grime. +Mountain fat-tire e-bike with suspension seatpost, adjustable power modes, and removable battery pack. +City bike with upright handlebars, integrated basket, three-speed hub, and a step-over frame for general urban use. +Touring expedition rig with aluminum frame, oversized bottle cages and rough-road rugged tires. +A high-visibility commuter with reflective paint, wide 35mm tires, dynamo lighting and comfortable swept-back bars. +Aero triathlon bike in fluorescent yellow with integrated hydration, shallow rear stays, and a raised aero seatpost for aero tuck. +A commuter with integrated storage in the frame triangle, quiet belt drive, and a refined baking-lacquer paint that endures the seasons. +A handbuilt steel road frame with ornate paint, small integrated tube pump cradle, and a geometry tuned to steady touring distances. +Modern urban single-speed with sealed bottom bracket, clean welds, and a quick-release seat post for easy adjustments. +A folding commuter with quick-release hinge, 20-inch wheels, compact cockpit, and a rear carrier suitable for tumbling into elevators. +A lightweight carbon time trial bike with integrated hydration, narrow aero seatpost, and a focus on aggressive forward weight distribution. +Race-ready carbon road bike with aerodynamic tube shaping, ceramic bearings and tubular tires glued on carbon rims +A performance road frame with aero seatpost, integrated brake routing, and a paint job emphasizing airflow and a minimal frontal area. +Vintage mixte with enamel decals, chaincase, upright bar and a comfortable saddle for city cruising. +A compact kids' BMX with dustproof sealed bearings, lower top tube, and durable frame for consistent progression at the skatepark. +Urban folding bike with one-handed fold, magnetic latch, and quick-detach front wheel for compact storage. +A commuter with internal battery light, belt drive, and a rear rack designed to accept quick-release panniers for smooth city errands. +A fixed-gear commuter with riser bars, low-profile tires, matte black paint and a tiny chrome logo on the top tube. +A chopper-style elongated cruiser bicycle with banana seat, ape-hanger handlebars and exaggerated rake for low-slung boulevard cruising. +Retro city cruiser with chrome details, tassel grips, and pastel turquoise paint for easy neighborhood charm. +A mountain trail frame with adjustable geometry inserts, dropper post compatibility, and a stealth camouflage paint job. +A commuter e-bike with mid-drive motor, torque sensor, integrated rear rack, and a rainproof battery enclosure for year-round commuting. +A carbon downhill bike with adjustable link ratio, 200mm travel, and a reinforced downtube to withstand the roughest tracks. +A commuter with quick-release rear rack, foldable basket, and puncture-proof tires for reliable daily errands. +A gravel-plus bike with flexible tire sizing, clearance for up to 2.4-inch rubber, and a solid alloy frame for reliability on rough tracks. +A highly polished chromoly steel BMX with custom plated frame, small 20" wheels, gyro steering, and a one-time show finish for collectors. +Gravel endurance machine with tubeless 42mm tires, flared compact handlebars, and a geometry that favors comfort over long distances. +A classic touring frameset with brazed rack mounts, reinforced lugs, and a polished finish that masks scuffs from many miles. +A vintage upright city bike with a sprung leather saddle, enamel-flaked paint, and an old-world charm that rides like a memory. +Lightweight titanium city bike with polished welds, slender tubes and a minimalist rear rack for light loads. +All-road bike with mini-v brakes, steel fork, and a delicate fade paint job from sky blue to teal. +A restored vintage mixte frame with wicker basket, powder-coated fenders, and a leather-sprung chair saddle for elegant city travel. +A dirt jump sled with reinforced gusseting, short wheelbase, and a matte black finish applied to absorb scuffs from regular park use. +A commuter with integrated phone mount, pannier hooks, and reflective decals across the downtube for night safety. +A commuter with hydraulic rim brakes, integrated rack, and a retro-style enamel headbadged steel frame. +Performance road bike with electronic Di2 shifting, ultra-light rims, aero seatpost and gloss red accents. +A carbon all-road machine with clearance for 40mm tires, flared drop bars, and a comfortable saddle for long-distance reliability. +Mountain hardtail with 120mm fork, welded aluminium frame, tubeless-ready rims and aggressive side knobs +Road time trial frameset with integrated hydration, narrow frontal area, and a glossy finish with subtle pearlescence. +A kids' balance bike with colorful anodized frame, easy-grip saddle, and slightly wider rear hub for gentle stability while learning balance. +A modern e-gravel bike with internal battery, discreet controller, 1x electronic shifting and a quiet pedal-assist motor for remote exploration. +Cyclocross classic steel frame with reinforced top tube, braze-ons for mudguards and a geometry tuned for shoulder carry. +A folding bicycle with 20-inch wheels, lightweight aluminum frame, and suitcase-style fold that tucks easily under a desk or in a train. +Mountain fatbike with belt-driven single-speed option, fat knobs and rigid fork for beach and snow exploration. +Single-speed BMX with micro drive gear, chromoly frame, pegs, and a gyro for clean bar spins and tailwhips. +A kids' balance bike in powder blue with rubberized tires, soft padded grips and a friendly cartoon decal on the downtube. +Road aero bike with integrated seatpost clamp, 25mm tires for a balance of speed and comfort, and metallic paint. +All-mountain bike with 150mm front travel, 140mm rear, stout alloy frame, and wide bars for confidence on steep singletrack. +Classic Dutch transport bike with large front rack, chaincase and upright seating position for easy neighborhood errands. +Titanium commuter with disc brakes, fender and rack mounts, internally routed cables, and a brushed finish that resists corrosion. +A titanium road vector with subtle bead-blasted finish, smooth welds, and clearance for 32mm tires to pair low weight with compliance. +A gravel-adventurer with wide 650b tires, dropper post, and a selection of modular mounts for lights, spares, and food for multi-day journeys. +Classic cruiser with two-tone paint, reclining saddle, swept bars and chrome headlamp for leisurely weekend cruises. +A gravel racer with aero-shaped chainstays, tubeless-ready rims, and an understated cream-and-charcoal paintwork for quiet confidence. +Urban foldable electric bike with compact design, hydrophobic seat fabric and integrated front-and-rear LEDs for visibility. +A commuter with integrated GPS and anti-theft alarm, reflective sidewall stripes, and a belt-drive system for clean passage through clothes. +Mountain all-mountain rig with 160mm fork, 1x12 wide-range gearing and burly tires for aggressive trail abuse. +Road aero machine with hidden cables, time-trial geometry and disc brakes tuned for criterium racing. +Electric city bike with quiet hub motor, long-range battery, integrated taillight, and a comfortable upright riding position. +A modern all-mountain bike with mullet wheel setup, 170mm fork, 160mm rear travel and a geometry optimized for descending confidence. +Electric cargo bike with long deck, side rails, torque-sensing mid-motor, and a reinforced frame for heavy loads. +A vintage-inspired road frame with slender seatpost, leather-wrapped stem, and a warm cream finish that suits leisurely cafe rides. +Gravel endurance machine with relaxed geometry, vibration-damping features, and 40mm tires for remote gravel roads. +Mountain enduro machine with adjustable travel fork and a coil-over rear shock tuned for downhill. +Commuter bike with belt drive, internally geared hub, integrated fenders and chaincase for low maintenance +A mountain bike with coil shock, piggyback reservoir, and stealth chainstay protection, wrapped in camo graphics. +Track-derived fixed-gear bike with drop bars, single brake for legal riding, deep-section front rim and narrow 23mm tire +Gravel race build with direct-mount derailleur clamp, tubeless-ready rims and flared drops for confident control on loose descents. +A gravel bike built for the volcano gravel races with reinforced fork, 700x45c tires, and volcanic ash black finish with orange speckles. +A commuter bike with internally geared hub, belt drive, full-coverage fenders, integrated dynamo lighting and a navy powdercoat frame. +A cyclocross race-ready steel frame with flared chainstays, mud shedding features, and a light but durable build for the season. +A classic mountain hardtail with rigid fork, steel frame, and ornamental enamel headbadge restored to vintage shine. +Lightweight carbon-clad mountain bike with 130mm travel, crisp handling, and 29-inch wheels for efficient climbing and descending. +Urban commuter with belt drive, hub motor assist, and small panniers for groceries and laptop. +A commuter with classic steel frame, full fenders, front basket and a warm marigold finish with whitewall tires. +Electric folding bike with low center of gravity, internal cable routing and a secure folding latch for commuting. +A compact folding city bicycle with strong hinge locking, large carry handle built into the frame, and a 7-speed internal hub for ease of use. +Race-ready track bike with stiff alloy frame, high-flange hubs, and short cranks for quick cadence acceleration. +Touring gravel hybrid with dynamo hub, removable pannier mounts, and stable geometry for loaded miles. +A mountain sled with 29x2.6 tires, slack head angle, coil shock option and a stealthy stealth-gray paint job. +A budget-friendly utility bike with 7-speed hub, step-through aluminum frame, and cargo-friendly geometry for practical daily use. +A rust-velvet patina steel town bike restored with modern sealed bearings, new cables, and subtle period graphics on the downtube. +A full-suspension enduro machine with 170mm rear travel, mixed-carbon construction, and a high-spec drivetrain for steep technical races. +A mountain trail bike with 140mm travel and progressive geometry that encourages aggressive corner exit speeds without sacrificing pedaling efficiency. +City cruiser with swept chrome bars, soft saddle, whitewall tires and a calming pastel finish for neighborhood cruises. +Classic fixie with polished chrome frame, whitewall tires, and minimalist leather saddle. +Lowrider cruiser with elongated frame, springer front end, custom paint flames and whitewall tires for show-stopping style. +A kids' BMX race bike with reinforced frame, race-spec cranks, 20-inch wheels and neon yellow paint with sponsor-style graphics. +A gravel bike built to take larger 650b wheels, fitted with a 1x drivetrain, short stem, and flared drops for rough descents. +Fixed-gear alleycat-ready machine with stiff frame, deep rims, and a compact cockpit for quick, precise city riding. +A mountain trail full-suspension with refined kinematics, 150mm travel, idler pulley system and stealth matte black paint. +A full-suspension trail bike with modern layup carbon, 140mm travel, and a reassuringly predictable handling balance. +Touring steel frame with wide-gear triple, fender compatibility, and low-maintenance wheels for loaded long-distance travel. +A cyclocross-inspired winter trainer with studded cross tires, triple crank compatibility, and salted-road resistant paint. +A low-step city bike with integrated child seat mount, broad tires for stability, and a discreeted frame lock for quick errands and school runs. +Urban commuter bicycle with low-step frame, integrated lighting, internal hub gearing, puncture-resistant tires, and a practical rear rack. +Compact folding commuter with 20" wheels, internal gear hub, and a robust hinge to handle repeated folds. +Gravel-plus bike with massive clearance, 27.5+ tires, and a dropper post to make steeper descents more manageable. +Lightweight adventure road bike with endurance geometry, wide tires, integrated lighting, and discreet top-tube storage space. +A city commuter with front hub motor, integrated display, battery in-frame, and puncture-resistant rubberized tires for predictable stops. +Classic British city bicycle with chaincase, upright handlebar, and enamel gloss black finish. +A commuter with corrosion-resistant paint, full fenders, easy-access front basket and a simple three-speed internal hub for stress-free riding. +Cargo e-trike with enclosed cargo box, kid-safe harness mounts and hydraulic disc brakes. +Racing road bike with 105 mechanical groupset, mid-depth wheels, short stem and a responsive alloy frame for criteriums. +Cyclocross steel frame with modern cable routing, tubular-compatible rims, and a long-reach brake set for technical courses. +A high-mileage commuter with dynamo lighting front hub, stainless spokes, puncture-resistant tire casing and reflective rim tape. +Vintage roadster with brass bell, sprung leather saddle, chrome mudguards, and a classic chaincase for leisurely rides. +A kids' balance bike featuring hardwood frames, carved grips, and a smooth finish designed to inspire confidence and durability. +Vintage randonneur with a Brooks saddle, 650b wheels, brass lamp and original leather straps on the framebag. +Urban folding single-speed with quick-release hinge, compact folded size, comfortable geometry and vibrant anodized paint for visibility. +Trail enduro hardtail with 29" wheels, 2.5" tires, and geometry biased toward descending confidence while pedaling efficiently uphill. +Performance road disc bike with 28mm tires, lightweight alloy frame, Shimano 105 groupset and tubeless setup +Mountain bike with 29" wheels, dropper post, tubeless tires, and a modern alloy frame for cross-country adventures. +Touring gravel bike with 650b option for higher-volume tires, rugged fittings and touring geometry for loaded comfort. +A low-slung chopper bicycle with long rake, bean-shaped tank, and plain gloss finish for show and leisure riding. +A mountain cross-country race bike with featherweight build, stiff crankset, narrow tires and aggressive climbing geometry. +Road race bike with lightweight composite layup, integrated cockpit, and subtle gold accents on the decals. +A mountain enduro bike with quick-recovery coil shock, burly handlebars, and crashed-scrawl paint that hides dings and abrasions. +Cyclocross-specific machine with mud-hating shapes, internal cable routing and 34mm race rubber for fast courses. +Mountain hardcore hardtail with oversize down tube, tapered headtube, and durable frame finishes for trail abuse. +A high-performance gravel frame with asymmetric chainstay, integrated chain catcher, and a stealthy raw carbon finish. +A gravel e-assist bike with torque-sensing motor, long-range battery, full-coverage mudguards, and a bifurcated luggage rack. +A kids’ training bike with removable pedals, low center of gravity, and cheerful cartoon graphics to make learning to ride fun. +Classic Dutch-style city bike with basket, long chainguard and upright swept-back bars for dignified neighborhood cruises. +Lightweight commuter with carbon fork, disc brakes, 28mm tyres, and a discreet rack mount for practical speed. +A kids' trail bicycle with 24-inch wheels, front suspension, simple gear range and durable paint for rough-and-tumble fun. +A compact folding e-bicycle with long-range battery, belt drive, 16-inch wheels and black satin paint with matte accents. +Lightweight commuter with integrated taillight in seatpost, belt drive, and a one-piece forged alloy stem. +Gravel-plus trail bike with 650b+ wheels, wide handlebars, and capable suspension fork for fast off-road exploration. +Cyclocross racer with hydraulic disc brakes, carbon fork and 33mm mud-prone tires. +A commuter with integrated GPS mount, left-side chainstay guard, and reflective sidewall tires for visibility. +A gravel-specific race bike with lowered stack height, stiff bottom bracket, and a rim/tire combo tuned for speed without sacrificing comfort. +A steel-framed road bicycle with polished lugs, copperhead logo inlay, narrow tires and classic geometry for fast group rides. +A gravel machine built for mixed-surface events with aggressive tread tires, carbon fork, and a reliable mechanical groupset for easy service. +Urban commuter with fold-away pedal, integrated reflectors, and classic upright handlebars for daily errands. +Electric folding travel bike with compact folded size, 250W hub motor and puncture-resistant tires for airport travel. +Road aero race bike with aero seatpost, hidden cables, and race-tuned wheelset for crits and sprints. +Mountain freeride hardtail with triple-butted tubing, short chainstays and wide handlebars for playful jumps and drops. +Touring tandem with robust wheelsets, strong chainset and durable racks to carry baggage across continents. +Cargo trike with long flatbed, reinforced frame, hydraulic disc brakes and electric assist option. +Tandem with comfortable upright geometry, sturdy triple-butted steel, and wide tires to ensure stability on shared journeys. +Gravel-friendly hardtail with 29+ wheels, modern geometry, and a reinforced fork for mixed-terrain adventures. +Time-trial bike with integrated power meter, aero seatpost, deep rim wheelset, and hydration system tucked into the frame. +A lightweight endurance frame with slightly relaxed head angle, layered damping in chainstays, and 35mm tires for comfort on long routes. +A classic single-speed cruiser with soft-sprung saddle, balloon tires, swept bars, and a subtle patina paint to suggest many relaxed afternoons. +A gravel hardtail with clever cable routing, integrated top tube loops for bungees, and wide clearance for aggressive gravel tires. +A gravel adventure bicycle with custom water-bottle warmer sleeve, long-range gearing and a sophisticated olive-green matte finish. +A commuter with built-in U-lock slot, internally routed cables, belt drive and matte slate finish with subtle metallic flecks. +Gravel bike painted sunrise orange with high-volume tires, long-range gearing and alloy stem for stiffness. +Electric folding cargo bike with low center of gravity, foldable child seat and a long-range motor for heavy urban use. +A modern trail bike with 140mm front travel, dropper post, 29-inch front/27.5-inch rear mullet setup and smoky charcoal paint. +All-road carbon bike with endurance posture, disc brakes, and fender mounts for year-round versatility. +Steel gravel bike with tan wall 40mm tires, dorsal rack mounts, 1x drivetrain and clearcoat showing raw lugged frame details +Mountain cross-country race bike with carbon frame, lightweight wheels, and an efficient suspension tuned for climbing and sprinting. +A winter commuter with studded tires, fendered frame, bright reflective accents, and weatherproof drivetrain protections. +Retro-styled hybrid with leather saddle and grips, combustion-wood fenders, and a relaxed rake for scenic urban rides. +A polished titanium commuter with discreet e-assist, reflective head tube inlay, and narrow 28mm tires to balance speed with comfort. +A city electric folding bike with quick-fold mechanism, small-range pedal assist, and a durable powder-coated frame. +Road bike with electronic shifting, ceramic bearings, deep clincher rims and stealthy matte charcoal paint. +A titanium cyclocross frame with tapered headtube, bolt-on mudguards, and hand-finishing to marry lightness and durability. +Kids mountain bike with front suspension, safety chain guard and colorful star decals. +Folding city cruiser with vintage aesthetics, leather grips, and 20" balloon tires for cushioned rides. +Gravel endurance alloy frame with vibration-absorbing seatpost, extra water mounts, and an integrated GPS handlebar mount for navigation +A classic city bike with upright geometry, long chaincase, and an enamel paint finish that resists chips and keeps a polished appearance. +A gravel-capable cyclocross bike with slightly relaxed geometry, tubeless 40mm tires, and discreet fender mounts for messy conditions. +A classic beach cruiser with pastel hues, whitewall tires, and a comfortable saddle to glide slowly along promenade lanes. +Beach cruiser with custom paint, chrome accents, thick foam saddle and wide-rise handlebars for relaxed coastal cruising +Electric folding commuter with fold-flat pedals, integrated saddlebag, and low maintenance hub for city life. +Race-specific time trial bicycle with cantilevered front wheel mount, integrated hydration, and maximized aero shaping. +Gravel e-bike with torque-sensing mid-motor, full-coverage fenders, and long-range touring battery for multi-day trips. +Gravel racer with stealth black decals, 1x drivetrain, and carbon clinchers for lightweight performance. +A cyclocross race bike equipped with tubeless setups, a high standover, and an emphasis on lightweight components for quick course acceleration. +A commuter with reflective paint flecks, dynamo front hub, low-step frame and ergonomic saddle built for early morning rides. +A commuter with anodized accents, hub dynamo headlight, and puncture-resistant tires designed for low-maintenance daily urban travel. +Handmade cromoly dirt jumper with short wheelbase, reinforced headtube, 26-inch wheels and vibrant graffiti-style paint. +A commuter bike with integrated dynamo lights, belt drive, internal hub gearing, and puncture-resistant city tires. +Mountain cross-country race bike with 100mm travel, rigid rear triangle and race-ready 1x12 drivetrain. +A carbon mountain bike with modern geometry, dropper post, and stealthy graphics that reflect little light for a low-key look. +A handpainted frame with floral art, steel tubing, stamped headbadge, leather bar tape, and a classic road geometry bicycle. +Gravel machine with stealth matte finish, 1x drivetrain, and extra watercage mounts for long gravel days. +A cyclocross build with leather bar tape, grippy tubeless tires, and reinforced chainstay protector to prevent paint damage from boot rub. +A boutique handmade steel mountain bike with custom geometry, hand-painted frame art, 29-inch wheels and brushed chrome accents. +Touring bicycle with three-bolt racks, downtube shifters for tradition, and a comfortable 48/36/26 gearing for steep roads. +Commuter with integrated keypad lock, internal cable routing, battery-powered headlight, and a sprung saddle for comfort. +A folding commuter with quick-fold technology, adjustable cockpit, and a bag that doubles as a carrying case. +A vintage-inspired steel commuter with enamel paint, enamel pinstriping, and a classic Brooks leather saddle. +A high-end time trial bicycle with full integrated cockpit, single-piece bars, and a paint job that reduces aerodynamic drag by smoothing transitions. +Urban electric bike with throttle assist, built-in rear light, and simple twist shifter for casual riders. +A steel-framed fixed-gear with fillet-brazed joints, faded patina, leather saddle and slim profile tires for classic street style. +Gravel adventure cargo with built-in rack, modular panniers and heavy-duty tires for self-supported trips. +A cyclocross endurance frame with a slightly relaxed top tube, flared aero bars, wide clearance and disc brake integration for versatile performance. +A handbuilt steel frame with fillet-brazed joints, custom lugwork, and subtle turquoise enamel decals. +A durable cargo e-bike with reinforced frame, extra-large battery, and ergonomic grips for frequent heavy hauling in all weather. +Gravel riding bike with aero-friendly tube shapes, 38mm tires, and discreet lights integrated into the stem for stealthy long rides. +Classic town bike with swept bars, chainguard, rear coaster brake and a basket mounted to the front for errands. +A painted-frame gravel racer with asymmetric chainstay, SRAM Red eTap AXS, and carbon clincher wheels for high-speed gravel sprints. +A utility cargo bike with removable box, quiet belt drive, and hydraulic brakes sized for heavy loads and safety. +A commuter with low-step steel frame, hub dynamo, integrated LED strips, mudguards, and a leather saddle for daily comfort. +Classic step-through commuter with spring-loaded saddle, chrome fenders, and quiet coaster brake for low fuss. +Gravel speedster with 28mm tires for fast gravel sections, aero bar attachments, and a racy setup. +A cyclocross race machine with SRAM wireless shifts, tubeless-ready rims, and a gritty graphite mud-sheddable finish. +A road endurance bike with micro-suspension seatpost, vibration damping inserts in the bars, and a wide compatible tire range for comfort. +A gravel adventure bicycle with generous front-fork mounts for extra cargo, long wheelbase for stability, and a low BB for loaded handling. +Cyclocross-inspired gravel bike with short wheelbase, aggressive tread tires and low-maintenance mechanical disc brakes. +A gravel endurance racer with endurance geometry, 28mm tires, full-carbon fork and deep charcoal paint with faint metallic speckle. +Adventure touring bicycle with steel frame, wide 2.1-inch tires, lots of rack mounts and dynamo lighting for multi-day expeditions. +A single-crown freeride mountain bike with 27.5+ tire clearance and burly chainstays for heavy landings. +Mountain trail 29er with fast-rolling rubber, short chainstays, and a tidy internal routing for a clean cockpit. +All-city bike with reflective paint, upright bars, dynamo hub and coaster brake for low-key urban mobility. +A restored vintage road bicycle with downtube shifters, chrome lugs, patina handlebars and cloth-wrapped handlebar tape. +A vintage-style commuter with brass headlight, polished steel fenders, and a wide spring saddle for comfortable commutes. +Urban fixie with bold matte color, deep-groove tires, and minimalist front light for safe nighttime street riding. +A rugged cyclocross race bike with flared levers, reinforced fork crown and a two-bolt WTB saddle for durability and control. +Mountain hardtail with thru-axles, tapered head tube, and compatibility for a dropper post upgrade for dynamic riding. +Vintage-inspired chopper with long front fork, banana seat, chrome sissy bar and flame paint job. +Mountain cross-country race frame with carbon layup on key sections, light components, and a responsive feel for fast courses. +A gravel touring bike lacquered in olive drab with wide tire clearance, triple racks, and thumb-shifters for reliable off-grid travel. +A titanium track sprint bike with short chainstays, stiff bottom bracket, single fixed gear, and minimalist seatpost for track use. +Gravel touring bike with wasted no braze-ons, integrated GPS loop, and multi-day gear clearance. +A trail hardtail with 120mm fork, stiff alloy frame, tubeless-ready rims and a stealthy matte camo paint job for forest rides. +A trail hardtail with 130mm fork, dropper post, 29er wheels and a multi-color geometric camo pattern. +A minimalist fixed-gear commuter with polished rim brake mount conversions, short reach brake lever, and a narrow saddle for city commuting. +Touring tandem with low-slung frame, supportive saddles and extra-sturdy wheels for two-person loads. +Touring steel frame with extra mudguard clearance, brazed-on lowrider mounts, and comfortable bar bends for all-day riding. +A modern gravel bike with stealth dropouts, integrated stealth rack mounts, 700x44 tires and a full-carbon fork. +Track sprint machine with oversized seat tube, aerodynamic headtube, and a stiff monocoque shell for explosive accelerations on the velodrome. +Commuter with hydraulic disc brakes, belt drive, internal hub, and rear rack with integrated lighting for safe night rides. +A mountain enduro bike with reinforced handlebars, protective frame skins, and a double-crown fork for big-hit stability. +Fixed-gear urban commuter with matte grey frame, toe clips, narrow riser handlebars, and a small LED taillight. +Lightweight touring bike with triple chainset, steel fork, and high-volume 32mm tires for comfort. +A whimsical kids' cruiser with quirky graphics, fat polymer tires, and a low-step frame so little ones can dismount easily during play. +Electric folding commuter with integrated rear light, 250W hub motor and safety flag for urban transport. +A modern trail hardtail with 120mm fork, tubeless rims, and a crisp paint job tailored to weekend trail thrashing. +A rugged touring e-bike with belt drive, gear hub, and a torque-sensing assist tuned for carrying heavy panniers over gravel passes. +A robust utility cargo bike with reinforced front platform, hydraulic disc brakes, and bright safety flags for urban deliveries. +A utility bicycle with heavy-duty front rack, bolt-on child seat mounts, reflective tape, and puncture-resistant cargo tires. +Commuter with integrated cargo basket, comfortable upright geometry, and puncture-resistant tires for daily errands. +Classic road racer with chrome lugs, narrow handlebars, and delicate period decals for a vintage feel. +A city folding bike with one-handed fold mechanism, 16-inch wheels, and an upright handlebar that locks for easy carrying into cafes. +Touring expedition frameset with reinforced dropouts, triple bottle mounts, and 26-inch wheel compatibility for remote trails. +A mountain enduro bike with 170mm travel, adjustable geometry, burly wheelset and a quiet chainstay protector for silent technical runs. +Electric cargo trike with stable three-wheel chassis, powerful mid-drive, large cargo box, and ergonomic bench seating for safe hauling. +Vintage-inspired cafe racer bicycle with leather grips, slim steel frame and minimalist decals. +Classic randonneur with long wheelbase, center-pull brakes, low gearing and leather-wrapped handlebars for all-day comfort. +Urban utility bicycle with modular cargo attachments, quick-release rack bits and puncture-resistant tires for delivery runs +Cargo longtail with passenger footrests, modular mounting points, reflective decals and hydraulic brakes for safety +Classic Dutch-style bike with fully enclosed chain guard, integrated rear reflector, and comfortable upright handlebar geometry. +Lightweight all-road bicycle with 35mm tires, disc brakes, and a slightly relaxed frame for multi-surface exploration. +Cyclocross bike with mud-shedding frame, cantilever-style clearance, knobby tires, and a compact geometry for race agility. +A folding commuter bicycle with compact 20-inch wheels, instant-release hinges, upright stem and a carry handle for easy transit on trains. +Lightweight alloy road racer with compact crankset, 25mm tires, quick-release skewers and anodized accent colors. +A polished steel road frame with classic geometry, traditional quill stem, and thin 23mm tires to replicate old-school road racing handling. +Lightweight gravel racing bike with full carbon frame, 700x35 tubeless tires, and race gearing for multifaceted performance. +A young teen BMX with colorful spokes, padded top tube, short cranks, and reinforced pegs for skatepark sessions. +A compact folding folding commuter with low step frame, small 16" wheels, and a reliable hub gear for last-mile connectivity. +Lightweight racing road bike with full carbon cockpit, 50mm carbon rims and Ultegra mechanical groupset. +Electric folding cargo bike with robust rear rack, integrated lights, throttle assist and fold-flat handlebars. +A colorful kids' balance bicycle with adjustable saddle, low center of gravity, and cartoon-themed graphics to develop confidence. +Full-suspension downhill bike with adjustable chainstay length, long-travel fork and massive 200mm rotors for heat control. +A sleek carbon road bike with disc brakes, hydraulic shifting, aero seatpost and champagne-gold metallic finish. +A cyclocross-specific steel frame with classic lug work, mud-shedding clearances, 1x compatibility and a durable matte black paintjob. +A lightweight race-ready gravel bike with 700x35 tires, SRAM Red kit, and a subtle raw carbon finish to keep weight minimal and performance high. +A handbuilt titanium frame with signature smooth welds, subtle headbadge etching, and anodized dropouts for a refined look. +Single-speed city cruiser with coaster brake, spring saddle, and glossy baby blue finish for leisurely neighborhood rides. +A gravel adventure bicycle with full framebag triage, handlebar roll, and a hammock saddle for multi-day dirt touring comfort. +Electric urban cargo trike with lockable box, powerful mid-drive, and cushioned bench seating for short-haul deliveries. +A modern aero time trial bicycle with integrated hydration, steep seat tube, and disc wheel compatibility. +Performance road bike with ultra-stiff carbon chainstays, power meter crankset, and short headtube. +Compact city bike with step-through frame, integrated cargo rack, LED display, and hub motor offering effortless riding. +Road training bike with steel frame, reliable mid-range groupset, durable wheelset and comfortable saddle for daily mileage accumulation +A touring tandem with rugged steel frame, wide low gearing, dual racks and classic British racing green with polished chrome accents. +Touring expedition bike with stainless steel components, triple bottle cages and a simple, field-serviceable drivetrain. +Lightweight commuter with carbon fork, internal cable routing, 11-speed drivetrain and small rear rack for light loads +Saddlebag-equipped touring bicycle in forest green with triple-butted steel frame, low gearing, and reinforced rack mounts. +A compact city folding single-speed with rapid-fold hinge, upright bars, integrated carry handle and simple aesthetics for daily flexibility. +A cyclocross-inspired gravel bike with dropper post, 650b wheels and 47mm tires for technical singletrack and rough backroads. +Retro-inspired road bike with down tube shifters, threaded headset and a rich burgundy enamel paint. +A boutique hand-painted track bike with bespoke typography, minimalist forks, and a gloss lacquer that highlights the paint. +Gravel all-road with steel frame, discreet paint, and endurance geometry tuned for prolonged comfort. +A commuter with removable panniers, integrated lights, and a comfortable upright geometry that makes daily errands easier. +A bikepacking-friendly gravel frame with three small accessory bosses on the seat tube, top tube pocket compatibility, and a matte desert tan finish. +A gravel adventure bike with off-road gearing, flared bar drops, and a custom framebag set-up for self-supported journeys into the backcountry. +Touring gravel bike with Rohloff hub, dynamo lighting, and triple chainring for long-range travel. +Mountain hardtail for XC racing with race-ready fork, lightweight alloy frame and aggressive 1x gearing. +A matte-black carbon road bike with deep-section 50mm wheels, electronic shifting, aero integrated cockpit, and a race-oriented aggressive geometry bicycle. +Lightweight commuter with carbon fork, disc brakes, internal hub gearing, and a narrow profile for lane-splitting agility. +A road endurance bike with high-stack geometry, 30mm tires for comfort, carbon fork and milky gray finish with embroidered-looking decals. +Commuter single-speed with matte navy frame, puncture-resistant tires, and a small front rack for everyday convenience. +A heavy-duty BMX cruiser with thick downtube, 24-inch wheels, one-piece crank, and exaggerated chrome accents for showy park sessions. +A commuter with hidden frame lock, built-in front basket, and puncture-protected 28mm tires ready for daily grocery trips. +Minimalist single-speed with matte gray paint, narrow riser bar, and a slim saddle for park cruising. +Beach cruiser with retro stripes, coaster brake, swept-back bars and an oversized cushioned saddle for comfort rides +Full-suspension enduro mountain bike with 170mm rear travel, 180mm fork, slack head tube angle, coil shock, and a 1x drivetrain for aggressive descents. +A racing gravel bike with electronic shifting, sub-7kg build spec, and race-fit geometry for fast gravel circuits. +Road time trialer with anti-rotation seatpost, aero hydration system and full tuning for marginal gains. +A cyclocross winter racer with stud-friendly tires, sealed bottom bracket, fender mounts and a deep charcoal paint with icy blue pinstriping. +Folding electric bike with secure latch, 250W rear hub motor, and a display showing battery percentage and assist level. +A vintage step-through with brass headlamp, wicker basket, and a cushioned bench seat perfect for leisurely neighborhood tours. +Cyclocross bike with steel stem, semi-gloss paint, and a comfortable saddle for extended muddy races. +Trail-capable e-MTB with active suspension tuning, large-capacity battery, and torque-rich mid-drive for steep technical climbs. +A vintage-inspired touring bicycle with ornate brass fittings, leather tool roll, and period-correct lugwork on a tough steel frame. +A modern gravel hardtail with carbon fork, 2.25-inch tires on 29er wheels, and discreet frame mounts for lights and GPS devices. +Lightweight road bike with deep V rims, aerodynamic tube shaping, 2x wireless shifting, and a bold team decal kit. +Electric folding cargo bike with low center of gravity, powerful hub motor, and fold-flat handlebars for compact storage. +Compact kids' BMX with reinforced frame, small pegs, bright decal kit, and durable grips for early park progression. +A performance track sprinter with oversized chainring, narrow saddle, and polished steel track drops for speed. +A commuter with hydraulic disc brakes, mid-drive e-assist, and frame-integrated locks for security in dense urban areas. +A retro-inspired cruiser bicycle with banana seat, high-rise chrome handlebars, and whitewall tires for a relaxed beach vibe. +Lightweight climbing road bicycle with minimal paint, narrow tubular tires, triple crankset option, and carbon seatpost. +Vintage-inspired roadster with Brooks saddle, chromed fenders, leather strap on top tube and classic enamel paint. +A carbon cross-country race bike with superlight frame, 110mm fork, narrow-profile tires, and quick acceleration for technical climbs. +Gravel adventure bike with hand-applied enamel, 650b x 47mm tires, and wide handlebars for stable, long-distance travel. +A compact folding e-bike with 14-inch wheels, detachable battery pack, folding stem and a carry handle molded into the frame for commuting. +A fixed-gear track-inspired city racer with polished steel fork, alloy deep rims, slim saddle and matte silver frame. +Mountain cross-country fiberglass forked bike with efficient pedaling platform, sub-11kg potential and narrow tires for speed. +Mountain bike with split-pivot design, 160mm travel, and heavy-duty components for serious trail use. +Cross-country race hardtail with lightweight alloy frame, tapered steerer tube and race-ready 1x drivetrain. +A gravel-friendly cyclocross bike with tubeless-ready rims, wide clearance, 700x40 tires and army-finish paint with subtle camo. +Handmade chromoly track bike with fillet-brazed joints, polished frame, narrow saddle and track-specific gearing for velodrome sessions. +A commuter with rear-wheel-drive motor, built-in pannier rack, reflective sidewall and a low-maintenance belt system. +A touring bike with dynamo lighting, full racks, leather grips and triple-ring gearing for remote route reliability and deep blue paint. +Touring steel frame with S&S couplers, full mudguards, triple-chainset, and luggage mounting points for global travel. +A hammered-bronze patina steel frame touring bike with leather saddle, brass headbadge and low-maintenance internal-gear hub. +A lightweight track sprint bike with stiff carbon frame, short wheelbase, and an optimized chainline for maximum power on the velodrome. +Gravel bike kit with integrated frame lock, discreet GPS mount, and a stealth matte finish for low-visibility touring. +Mountain downhill bike with dual-crown fork, extensive bearing service, aggressive geometry and beefy brakes for steep descents +High-end road bike with integrated electronic shifting, carbon clinchers, aero cockpit, and a weight-focused component selection. +Mountain enduro rig in camo finish with coil shock, long travel fork and wide handlebars for control. +Lightweight track-style fixed with single-bolt chain tensioner, polished headset and a bright anodized chainring. +Beach cruiser with hand-painted pinstripes, comfortable sprung saddle, chrome-plated spokes and retro twist shifter for simple riding +Youth BMX bike with 20" wheels, gyro detangler, reinforced chromoly frame, and pegs for tricks at the skatepark. +Gravel endurance build with flared bars, 40mm tubeless tires, and a vibration-damping stem for rough road comfort. +Gravel adventure with metal flake paint, wide bars and reinforced chainstays for rough-and-tumble touring. +Touring bicycle with triple chainring, robust frame, full fenders and a soft-satin paint that hides road scuffs with grace. +Mountain trail bike with mixed 29/27.5 wheel sizes, supportive suspension kinematics and wide 2.4-inch tires. +A kids' trail bike with small 20-inch wheels, front suspension coil fork, simple hub brake, and colorful decals for outdoor play. +Cyclist-friendly fitness hybrid bike with flat bars, 700x32c tires, 10-speed drivetrain, and a comfortable ergonomic grips setup. +A pocketable folding bicycle with hinge-mounted handlebars, quick-fold pedals, and bag-friendly compactness for commuters. +Steel hardtail with classic slender tubes, fillet-brazed joins and a gloss oil-slick finish that shifts color. +A gravel/touring frame equipped with front and rear rack mounts, three bottle cages, and a neutral dark olive powdercoat to hide grime on long trips. +Folding electric bicycle with compact frame, 250W hub motor, clasped folding hinge, and removable battery for apartment storage. +Gravel e-bike with torque-sensing motor, integrated LED mode indicator, and robust alloy frame capable of loaded tours. +A retro BMX dirt jumper with salt-and-pepper fade paint, reinforced top tube gusset, and wide 26-inch wheels suited to hopping berms. +Touring tandem with matching steel frames, synchronized shifters, reinforced wheels and duffel straps for extended travel +A carbon time-trial bike with torpedo-shaped down tube, integrated hydration in the frame, aero bars and a metallic gold finish. +A carbon time trial machine with full disc braking, storage integrated into the frame profile, and a stealth finish that hides sponsor logos. +Lightweight race frame with tapered head tube, aero seatpost, and a glossy ceramic white finish with subtle decals. +A cyclocross steel frame with reinforced fork crown, multiple boss mounts, and a simple drivetrain designed to take abuse and shed mud. +BMX park bike with reinforced fork crown, lightweight alloy frame and bright neon paint suited to tricks. +Cyclocross training bike with durable alloy frame, 35mm clearance, and single-ring simplicity. +A mountain e-bike with durable alloy frame, 160mm fork, and mid-drive motor programmed for trail-compatible power curves. +Gravel plus adventure rig with 27.5+ wheels, reinforced fork, and single-ring simplicity for remote trail exploration. +Gravel bike with stealthy matte black finish, wide flared drops for control and tan wall tyres for classic appeal. +Road endurance bike with raised stack height, 32mm tires compatibility, and a soft-touch paint for grip in gloves. +A retro single-speed with candy blue paint, wood-rimmed basket, and whitewall tires for charming Sunday rides. +Fast road bike with short wheelbase, stiff BB, and an aggressive fit for criterium results. +A robust cargo trike with enclosed box, electric assist, low center of gravity and safety yellow paint with reflective striping. +Electric folding commuter with compact fold, user-friendly display, 20-inch wheels and a durable frame for mixed-mode transport. +A modern all-mountain bike with 160mm front travel, longer reach geometry, chain guide, and aggressive tire tread for technical descents. +Vintage French mixte with chrome-plated lugs, small 650c wheels, and original headbadge that speaks to timeless elegance. +City commuter with polished stainless fenders, belt drive, internal gear hub and ergonomic grips for relaxed daily rides. +Classic heritage road bike with Campagnolo-style aesthetics, polished aluminum rims and leather bar tape. +A gravel bike with titanium fork, frame-mounted GPS cradle, and a two-bottle accommodation for remote exploratory tours. +Modern gravel drop-bar bike with internal storage in the top tube, stealthy graphics and 38mm clearance for mixed terrain. +Gravel bike with bright lime decals, 700x45 tires, and low-standover geometry for comfortable handling. +Single-speed beach cruiser with banana saddle, sissy bar, coaster brake, and sky-blue enamel paint for casual summer rides. +A youth BMX styled bike with short top tube, padded crossbar, 20-inch wheels and colorful graffiti-inspired decals. +A lightweight steel track bike with polished chrome fork crown, tubular tires, deep navy finish and classic racer decals. +Road race-ready frameset with aerodynamic tube sections, internal routing, and a focus on saving grams while maintaining stiffness. +A commuter with aggressive matte charcoal paint, integrated hidden cables, and a small rack for a slim-profile urban look. +Mountain trail hardtail with slack head angle, modern dropout spacing, wide handlebars and lockout fork for cross-country bursts +A boutique gravel racer with half-matte, half-gloss paint, electronic shifting, and tubeless tires fitted with lightweight foam inserts. +Classic step-through Dutch bike with chain guard, upright riding position, hub dynamo, and a wicker basket on the front carrier. +A steel frame cyclocross rig painted in bright neon green, with knobby 33mm cross tires, dropper post, and GRX-level gearing. +A compact cargo e-bike with hidden battery, low step-through access, long rear deck and mellow sea-glass green powdercoat. +BMX freestyle bike with gyro system, reinforced top tube, and bold graffiti-style decals for the park. +A single-speed urban bike with polished steel frame, minimal graphic elements, and a narrow saddle for efficient city commutes. +A commuter with integrated front rack, stabilizing kickstand, triple-chainring gearing and reflective sidewall stripes for safety. +Mountain downhill race rig with coil rear shock, 27.5" wheels, reinforced pivots, and long-travel fork for steep descents. +Vintage Dutch-style bicycle with large coach lamp, chaincase, and upright swept-back handlebars for relaxed urban cruising. +A polished titanium commuter with handmade brazed-on racks, discreet dynamo lighting, and skinny 28mm tires for a refined urban ride. +British-style city bike with enclosed chaincase, three-speed hub, coaster brake and wicker basket for errands. +Alloy cross-country bike with narrow Q-factor crank, slimline frame and 29" fast-rolling tires. +A cross-country carbon race bike with 100mm rear travel, light alloy wheels, and aggressive tires for World Cup-style climbs and sprints. +A bikepacking-ready drop-bar bike with multiple braze-ons for bags, titanium rack mounts, and reinforced fork crown. +Touring bike with 650b wheels, comfortable geometry, lightweight steel frame and a robust rear rack for long tours. +A durable kids' mountain bike with front suspension, easy-rolling 24-inch wheels, bright safety lime paint and fun character decals. +A dirt-oriented hardtail with short chainstays, wide handlebars, 130mm fork and aggressive 2.35" trail tires for playful singletrack. +A gravel racer with electronic wireless groupset, 700c carbon wheels, tubeless-ready tires and metallic moss-green frame. +Gravel adventure build with bikepacking frame bags, wide handlebars and a resilient mechanical drivetrain for repairs. +Gravel bike with integrated top-tube bag, stealthy matte olive finish and wide flared bars for long gravel days. +Trail bike with 140mm travel, adjustable shock tune, dropper seatpost, and grippy tires for mixed descents. +Tandem recumbent with dual steering options, padded seats, and low drag profile for smooth two-up touring. +Gravel adventure bike with dropper post compatibility, progressive geometry and 700x45mm tire capability for rough routes. +Vintage-inspired randonneur with low-rider front rack, generator lamp, leather saddle and long-day comfort geometry. +Gravel bike with contrast-colored fork and seat tube, adventure gearing and frame-mounted pump for remote rides. +Cargo longtail with multi-position tie-down rails, reinforced rear axle and kid-friendly footrests for family use. +A downhill-ready machine with 200mm travel, reinforced cockpit, wide bars and fluorescent orange paint with bold graphics for line visibility. +Fatbike with studded tires, low gearing, and steel frame for winter commuting on icy paths. +A cargo longtail with dual passenger seats, optional canopy, and heavy-duty suspension for carrying kids safely across the city. +Commuter with robust rear rack, integrated dynamo, full fenders, and a leather saddle for daily comfort and utility. +Mountain enduro build with long-travel suspension, beefy wheels, and a pocket-friendly dropper remote for quick access. +Road race bike with stiffified BB shell, aerodynamic stays, and a racing cockpit set up for flat-out sprints and breakaways. +Mountain all-mountain with burly frame, 150mm travel, wide rims, and a 12-speed drivetrain for technical singletrack. +A titanium commuter with machined dropouts, modular rack mounts, and a hand-applied satin finish that becomes more personal over time. +Steel frame single-speed with raw finish, belt-driven drivetrain, and coaster-brake compatible rear hub for simplicity. +Utility city bike with wide platform pedals, durable steel frame, chainguard, and a spring-steel rear rack for groceries. +A full-suspension enduro bike in forest camo, 170mm travel front and rear, and a dropper post for rough descents. +Lightweight carbon gravel frameset with compressed seatpost, integrated frame saver, and room for 40mm rubber for comfort. +A simple steel single-speed with matte olive paint, fender mounts, and city tires designed for everyday resilience. +A rigid gravel bike with steel fork for compliance, 700x40 tubeless tires, and a wide-range cassette for steep dirt climbs. +Folding commuter with micro-fold hinge, robust locking clasp, and 9-speed gearing for hilly cities. +A minimalist urban fixed-gear with anodized components, narrow saddle, and a subtle matte black frame for understated street style. +A cargo long-john front-loading bike with secure box, hydraulic brakes, and a low center of gravity for safe heavy loads in traffic. +A gravel adventure bicycle with double-saddlebag capacity, low-slung center of gravity, and a reinforced headtube for heavy front loads. +Electric touring bike with swappable batteries, integrated GPS charger and long-legged geometry for loaded comfort. +Urban folding e-bike with quick-fold mechanism, small-wheeled agility, and a clear LED readout for assist levels. +Classic aluminum road frame with polished brake calipers, threaded headset and 25mm tires for retro racing. +A gravel racer with flared drops for control, tubeless-ready rims, 38mm rubber and faded teal paint with subtle metallic flecks. +A gravel-specific full carbon bike with 1x focused drivetrain, torque-optimized crankset and integrated seatpost clamp. +Track sprint bike with stiff track frame, deep carbon bars, and single fixed gear optimized for velodrome acceleration. +Folding electric cargo bike with long-range battery, foldable child seat, and reinforced hinge. +Touring frameset with recessed braze-ons for neat pannier fitment and a hand-painted mountain scene across the top tube. +A folding cargo bike with reinforced folding points, extra-large rear platform, and an easy-to-operate safety lock for heavy loads. +Mountain enduro bike with mullet wheel setup, 170mm rear travel, 180mm fork and coil-sprung shock tuning for big hits. +A rigid mountain bike with modern slack geometry, 29-inch wheels, and tubeless-compatible rims for low-maintenance trail fun. +Performance road bike with integrated power meter, aero handlebars, 28mm tires and race-optimized stiffness distribution for criteriums. +Classic city cruiser with pastel accents, basket, and chrome plated mudguards for polished weekend rides. +Touring bike built for cold climates with mounting points for fenders and studded tire clearance. +A commuter with integrated locking mechanism, puncture-resistant tires, and a low-maintenance internal transmission for ride simplicity. +Electric folding e-bike with compact battery, quick-release stem fold, and a cargo clip for backpacks and grocery bags. +A compact folding e-cargo bike with long rear deck, electric assist, small 20-inch wheels and bright safety red paint for urban freight. +Long-wheelbase cargo bike designed for stability with reinforced dropouts and long deck. +A gravel endurance build with generous tire clearance, vibration-damping seatpost, 700x42 tires and earthy ochre paint with speckles. +Commuter hybrid bike with flat bars, hydraulic disc brakes, and a belt drive for low maintenance. +Steel road bike with classic geometry, polished chrome lugs, and comfortable long-distance gearing for centuries. +A winter fat-bike with 4-inch tyres, studded tread, and powder-coated frame to resist road salt and slush. +A versatile commuter with rack-integrated battery, whisper-quiet motor, and a modular basket that clips on and off for errands. +A cyclocross frameset with progressive geometry, 12mm thru-axles, and a clearcoat that leaves the raw carbon weave exposed for rugged appeal. +Vintage mash-up city bicycle with restored enamel paint, modern pads on caliper brakes and a small front carrier rack +Lightweight touring bike with steel frame, dynamo lighting, wide-range cassette and comfortable touring geometry. +E-gravel bike with battery integrated into downtube, long-range assist, sturdy frame, and low-slung top tube for balance. +A kids' balance bike with anodized frame, sealed bearing headset, and adjustable handlebars for growing toddlers learning steering basics. +Mountain trail rig with 29-inch wheels, modern slack geometry and a responsive suspension platform for fast, fun descents. +A titanium mountain hardtail with artful bead blasting, stealth internal routing, and a geometry tuned for confident climbs and descents. +A classic steel road frame with narrow tire clearance, quill stem, and decorative decals for period-authentic presentation. +A pure commuter fixie with flip-flop hub, riser bars, matte grey finish, and a minimalist front light for early morning rides. +Classic Dutch-style cargo bike with deep box, double-kickstand, and flat platform for hauling heavy shopping loads. +Lightweight cyclocross bike with tubeless-ready rims, full carbon frame, and a generous tire clearance for mud-laden courses. +A classic randonneur with low-trail geometry, generator front hub light, leather saddle, and racks for long-distance brevet events. +A single-speed fixie with matte graphite frame, small front basket, toe clips, and minimalist headtube badge for urban style. +Adventure e-gravel bike with large torque motor, 45mm all-weather tires, and long-range battery for multi-day exploration. +A lightweight gravel endurance build with 700x42 tires, compact double crankset, and a forgiving geometry to make long-day distances easy. +A gravel enduro bike with dropper post, wide handlebars, and robust 40mm gravel tires for aggressive off-road exploration at speed. +Road aero bike with skin-smooth paint, integrated computer mount, and hidden bottle mount behind the seat tube. +Folding urban bike with integrated carrying strap, small form factor and 6-speed cassette for city hills. +Electric folding commuter with high-torque motor, rigid frame, and a fold that makes it easy to carry into offices. +BMX racing bike with lightweight chromoly frame, a four-piece handlebar, and narrow-profile tires for sprint tracks. +Track fixed-gear with single chainring, minimalist cockpit, polished frame and classic, understated decals for a clean silhouette. +A kids' balance bike with wooden frame, quick-grip rubber tires, and low-stance to foster early confidence on two wheels. +A mountain enduro bike with adjustable geometry, 160mm rear travel, dropper post and matte army green with bright orange accents. +High-performance track sprinter with stripped-down cosmetics, drilled chainring and durable steel frame for short-burst power. +Gravel bike with inter-layered carbon layup, sculpted downtube, and stealthy matte grey lacquer with orange accents. +A race-ready cyclocross build with stiff aluminum frame, purpose-built tires, and fast, reliable mechanical disc brakes for consistent stopping. +A commuter with stealth internal battery, hub dynamo backup, and a quiet belt drive for maintenance-free daily use. +Urban utility bike with built-in fold-down bench on the rear, integrated bungee straps, and reflective piping. +Classic cruiser with single-speed simplicity, wide saddle, sweeping chrome bars, and a sunny retro paint job for summer rides. +Utility bicycle with welded front basket, wide touring bars and puncture-resistant tires for grocery runs. +A touring bicycle with brazed-on eyelets, triple chainring options, cottered-style simplicity, and a matte forest green paint job. +Urban folding commuter with magnetic latch, 20-inch wheels, and simple one-handed fold for rapid transitions. +A modern gravel e-bike with wide-range gearing, torque-sensing motor, integrated mudguards and matte desert tan color. +Lightweight aluminum cyclocross frame with wide tire clearance and single-ring ready chainline for mud-heavy courses. +Gravel tandem with disc brakes, 2x drivetrain, and reinforced tubing for multi-day touring. +Gravel racer with carbon seatstays, dropped chainstays for tire clearance and a quick-reacting alloy fork. +A touring bike with triple chainrings, stainless steel racks, full fenders and deep mahogany lacquer with cream pinstripe. +Folding mountain bike with 20-inch wheels, front suspension, compact fold and sturdy hinge for multi-terrain commuting +Compact folding commuter with in-handlebar light, belt drive, and a solid latch mechanism for frequent folding. +Adventure-loaded touring bicycle with full-length fenders, triple chainrings, touring tires and heavy-duty dynamo lighting system +A commuter with integrated smart lock, app-connected lights, belt drive and a low-step frame designed for secure city parking and easy mounting. +A boutique handpainted city bike with floral motif, enamel headbadge, and polished steel fenders. +A handpainted steel road frame with small floral motifs, clear lacquer over brushed metal, and thin classic tubing. +High-performance time trial bike with fully integrated hydration system and aero-optimized fork. +Mountain bike built for bike parks with heavy-duty rims, reinforced spokes, and gravity-oriented components. +Mountain downhill machine with double crown fork, massive rotors, burly tires and stiff frame designed for heavy impacts. +Vintage-inspired cruiser with cream fenders, glossy lacquer, chrome accents, and a plush saddle for Sunday cruising. +Mountain trail bike with a friendly geometry, dropper post, 130mm travel and reliable, easy-to-service parts. +A cyclocross cross-country rig with stiff BB shell, 1x drivetrain, fast-rolling 33mm tires and flared bars for stability on rough terrain. +A track pursuit bicycle with steep seat tube, deep-section carbon front and rear discs, single fixed gear and glossy white paint with minimal branding. +Lightweight alloy road bike with ceramic bottom bracket, tapered headtube, and satin silver finish. +Gravel endurance machine with long wheelbase, wide handlebars and 42mm tubeless tires for rough endurance rides. +Gravel bike with stealth graphics, 1x11 drivetrain, and carbon handlebar for vibration damping. +A city cruiser with wicker basket, chrome fenders, upright posture and gentle baby-blue enamel with cream pinstripes. +Modern gravel steel bike with rust-colored paint, Shimano XT 1x, and wooden cockpit grips for a warm aesthetic. +A modern dirt jump hardtail with 26-inch wheels, beefy 100mm fork, and reinforced chainstays built to withstand rail slides and big landings. +A sleek time trial bike with deep-section carbon wheels, integrated hydration, aero bar extensions, and a glossy red finish stamped with subtle sponsor decals. +Folding e-bike with 3-speed internal hub, long-range battery, and a discreet integrated headlight in the hinge. +A bespoke painted touring frame with map-inspired graphics, heavy-gauge rack mounts, and stamped serial coordinates on the chainstay. +City utility bike with wicker basket, full chaincase, and an upright silhouette for easy mounting and dismounting. +A downhill mountain bicycle with long travel, coil-sprung linkage rear shock, dual crown 200mm fork, 27.5-plus tires, and heavy-duty brakes. +City folding cargo bike with long deck, hydraulic brakes, hidden battery and modular racks for customizable urban transport. +A classic road bike with downtube shifters, quill stem, steel frame, and a patinaed finish showing decades of use and care. +A commuter with a stealthy frame-mounted lock, integrated lights, and durable puncture-resistant tires for daily dependability. +A modern trail hardtail with 130mm fork, tubeless set-up, 29" wheels and a balanced geometry for all-day comfort on singletrack. +Gravel e-bike with discreet battery integration, 1x drivetrain, and reinforced forks for loaded weekend adventures. +Road aero bike in glossy neon green with hidden cables, aero seatpost clamp and 28mm race tires for high-speed group rides +Fixed-gear street bike with anodized bolts, glossy lacquer paint, and a narrow aero saddle for sleek urban posture. +A modern trail bike with 150mm travel, balanced progressive geometry, 29-inch wheels and a grippy tire profile for ripping descents. +Folding city bike with small wheelbase, adjustable saddle height, and quick-release clamp for multi-modal commuting +Electric cargo bike with center-load box, three-wheel stability and throttle-controlled hill assist. +Road training bike with steel construction, high-volume 28mm tires and classic quill stem for retro training. +Fixed-gear track conversion with hip matte paint, polished crank, and solid urban street patina for everyday riding. +A steel frame chopper with extended fork, low-slung seat, and stylized painted flames running along the downtube. +Electric commuter with flat bar, torque sensor, hydraulic brakes and cargo-ready integrated rear rack for groceries. +Cyclocross bike with flared drops, short wheelbase, and satin olive paint accentuated by mud guards. +Mountain trail hardtail with Single Speed option, plus-sized rubber compatibility and a rigid steel fork for throwback toughness. +A high-performance road endurance bike with vibration-damping carbon layup, 32mm tires, and refined geometry for centuries and sportives. +A track sprint bike with thick gauge steel tubing for stiffness, small 700c deep rims and polished chrome paintwork. +Adventure hardtail with boost spacing, bolt-on mudguard, and heavy bolt-on rack mounts for rugged load-carrying capacity. +Classic steel gravel bike with hand-brazed lugs, 700c clearance for wide tires, and discreet rack mounts for versatile all-weather touring +Gravel bike with stealthy matte black finish, chainstay protector, and discreet mudguard mounts to keep the drivetrain clean. +A randonneur-style road bicycle with wide handlebars, a dynamo-powered headlight, and a relaxed geometry that encourages comfort at dawn. +A gravel endurance frame with long chainstays for stability, 700x45mm rubber, three bottle mounts and subtle matte finish for adventure reliability. +Cyclocross race frame with attention to mud clearance, a durable paint that hides scratches and a stiff bottom bracket. +Handbuilt steel single-speed with polished chrome, minimal decals and a leather saddle for urban minimalists. +Track fixed-gear with minimalist aesthetics, polished silver hubs, and a thin leather saddle for vintage-inspired rides. +A gravel bike with metallic flake paint, 700x40mm tubeless tires, integrated tool storage in the top tube, and quick-release thru-axles for easy wheel changes. +A touring tandem built for comfort with relaxed geometry, four-bottle cages, dual fenders and deep navy paint with white pinstripe. +Urban cargo tricycle with boxbed, low center of gravity, and hydraulic parking brake for stable delivery use. +A classic single-speed beach cruiser with whitewall tires, sweeping chrome fenders, banana seat and pearl-cream paint. +Kids' BMX with toddler-sized geometry, low seat height, and extra-wide tires to teach balance and basic skills safely. +A carbon-fiber cyclocross bike with cantilever-clearance geometry, knobby 33mm tires, mud-shedding frame design, and race-ready gearing. +A mountain trail bike with 140mm rear travel, dropper post, tubeless-ready rims, and a seat tube kink to lower the standover. +Vintage track bike with steel fork crown, horizontal dropouts, and a tasteful polished paint finish for classic track days. +A cargo long john bicycle with wooden box, low platform, and a strong frame for neighborhood deliveries and kids' rides. +A touring-ready steel commuter with a triple crank and low gearing, comfortable bars, and durable heavy-gauge rims for carrying kits. +Fatbike with mid-drive motor system, reinforced dropouts, and studded tires for winter touring. +A classic city beach-style cruiser with chaincase, wide swept bars, comfortable saddle, and an easy single-speed for relaxed neighborhood spins. +A winter studded commuter with fat 700c studs, insulated wrap on cables, and a wide fender set to keep slush off the rider. +Classic Dutch-style bike with coaster brake, full chaincase, step-through frame, and a built-in rear rack for baskets. +A commuter with integrated lock, USB headlight charging port, and reinforced pannier mounts for daily errands. +A recumbent tricycle with reclining seat, recirculating mesh backrest, chain idler system, and low-profile fairing for aerodynamic touring comfort. +A carbon gravel bike with asymmetric chainstays, power meter-equipped crank, and integrated GPS mount for performance-oriented adventures. +A gravel all-road titanium bike with generous clearance, understated brushed finish, and longevity-focused geometry for epic rides. +Gravel-focused titanium bike with 700x45mm tire clearance, subtle brushed finish, and custom tube shaping. +Youth trail mountain bike with simple derailleur shifting, grippy tires, and a sturdy frame to handle first off-road adventures. +Mountain all-mountain with progressive suspension linkage, tapered head tube, wide handlebars and durable cassette for endurance descents +Gravel bike with stealth matte charcoal, external routing for easy maintenance, and a comfortable 50mm stem rise. +Fixed-gear urban track bike with color-matched stem, deep rims, narrow saddle, and a minimalist look for alley racing. +A pro-level time trial bike with ultra-narrow seatpost, integrated brake ducts, and a cockpit sculpted for aero performance. +A track sprinter with polished track hubs, solid-disc training wheels, and a single fixed cog for pure speed sessions. +Cargo trike with wooden box, three-wheel stability, pedal-assist and reflective safety striping for deliveries +A commuter with wide balloon tires, upright bars, and cloth-wrapped leather grips for a soft, comfortable city commute. +Touring bike with welded stainless racks, low-maintenance hub gears and three-point pannier system. +Folding urban bicycle with compact folded footprint, quick-release seatpost and a durable hinge lock for daily commuters. +A custom titanium trail frame with an extra-long headtube for adjustability, internal routing, and a raw finish that ages beautifully. +A sea-side cruiser with teak rear rack, wicker basket, chrome-plated fenders and ocean-foam green enamel. +Classic steel touring bicycle with chromed forks, wide comfortable saddle, and smooth low-end gearing for mountain passes. +Compact kid's balance bicycle with adjustable seat height, wooden frame, and colorful hand grips. +A classic road frame with a hand-applied pinstripe, simple 8-speed drivetrain, and a vintage saddle that adds character to countryside spins. +Gravel racer with carbon frame, flared upper drops, 700x34 tires, electronic shifting and a light, compliant seatpost for endurance sprints +A cyclocross centerpiece with top-end carbon layup, race geometry, disc brakes, and integrated frame protection for regular shoulder carries. +A mountain bike with coil-sprung rear shock, burly 27.5-plus wheels, chain guide, and protective bashguard for rocky park runs. +A mountain enduro machine with long-travel fork, dropper post, and secure geometry for steep and rough descents with heavy braking. +A retro touring bike with downtube shifters, steel racks, leather Brooks saddle, and 650B wheels with 40mm tires for vintage charm and comfort. +A foldable cargo bike with longtail bed, quick-release wheel, and electric assist to help with heavy loads through urban streets. +A rigid gravel machine with steel fork, wide clearance for 650b x 47mm tires, and a comfortable upright cockpit for long gravel races. +A gravel commuter with fender-ready fork, integrated mud flap, and wide 35-40mm tires for comfort on rough routes. +Steel touring bicycle with chromed lugs, triple crankset, full fenders and rear pannier racks for long-distance hauling. +Touring bike with fitted GPS mount, gear-specific cassette and triple-bolt rack supports for heavy loads. +A vintage cruiser fully restored with fresh chrome, replaced leather saddle, wide whitewall tires and pastel mint lacquer with pinstripe. +A cyclocross commuter with radial-pattern spokes, 35mm semi-slick tires, dynamo lighting and a robust rack for daily practicality. +A modern enduro bike with mixed-wheel compatibility, 170mm travel, adjustable geometry and daisy-chained chainstay protection for heavy-duty use. +Mountain enduro build with long-travel shock, reinforced headset, and big rotors for descending confidence on irregular terrain +A mountain e-bike with dropper post, 150mm travel, torque-balancing motor, and an oversized downtube battery for long climbs. +A gravel-ready carbon frame with integrated seatpost clamp, 700x45mm tire fitment, and rubberized chainstay protector for long adventures. +Urban folding cargo with two-tier rack, deep dish wheels and robust hinge latches for grocery hauls. +Classic Dutch bike with upright bars, step-through frame, hub gears and built-in chaincase. +Mountain trail bike with twin-ring compatibility, burly tires and crash-friendly frame protection for enduro-style riding. +A full-suspension trail bike with progressive geometry, high-volume tires, and a stealth finish that resists showing mud and dirt. +Aggressive cyclocross setup with knobby 33mm tires, dropped top tube for shouldering and fast-release skewers +Gravel bike with stealth matte finish, 650b wheels, wide handlebars, and sealed-bearing hubs for low-maintenance touring +A folding cargo bicycle with long wheelbase when unfolded, fold-in handlebars, integrated cargo hooks and an electric assist option for heavy loads. +Vintage fixed-gear with polished steel frame, leather saddle, rear coaster option, and classic head tube badge restored to shine +Mountain enduro hardtail with 29" wheels, cushy saddle, and a wide gear range for tackling punchy climbs and descents. +A classic road bicycle with hammered finish lugs, 9-speed group, tubular tires, and a low-profile quill stem for a timeless look. +Touring expedition bike with reinforced fork blades, heavy-duty hubs, triple gearing, and full fender compatibility for remote routes. +Commuter with integrated rear light in rack, flexible pannier clips, and reflective tire stripes. +A compact folder with 20-inch wheels, quick-fold mechanism, reinforced hinge and oversized grips optimized for urban portability. +A beach cruiser with swooping frame, wide handlebars, whitewall tires, powder-blue enamel and seashell decals. +Gravel adventure frame with stealth paint, robust steel construction and threaded bottom bracket for tool-free repairs. +BMX street bike with chromoly frame, heat-treated bars, gyro system and skid plate for ledge riding +Performance time trial bicycle with deep-section front wheel, disc rear, torpedo-style aero frame and integrated hydration system +A BMX street/freestyle bike with tapered aluminum bars, sealed bearings, gyro system and reinforced pegs for trick-heavy sessions. +Adventure-ready mountain bike with adjustable travel, protective frame armor, and wide handlebars for stability. +Bold orange cargo longtail with kid-friendly bench, adjustable footrests, and a highly visible paint job. +A minimalist track-inspired fixed-gear frameset with flipped fork ends, short wheelbase, and a matte slate-gray powdercoat finish. +A compact commuter with step-through alloy frame, hub gears, fully enclosed chain, and integrated USB-charging lights for gadgets. +A high-volume downhill machine with uprated hub bearings, heavy-duty spokes, and large negative travel fork for park days. +A cyclocross bike with rugged geometry, clutch derailleur, and a matte finish that hides accumulated mud on race days. +Steel single-speed with raw polished frame, copper accents, leather grips and thin 28mm road tires for urban sprinting. +A full-carbon XC race bike with 100mm travel, aggressive short chainstays, 29er wheelset, and a vibrant neon yellow accent livery. +Mountain enduro with big-volume tyres, adjustable geometry and a clutch-equipped derailleur for chain stability. +A durable kids’ mountain bike with low standover, small crank arms, and a reliable front suspension fork to teach climbing and descending skills. +Gravel race bike with wireless electronic shifting, carbon clinchers, and a minimalist logo treatment on the downtube. +A minimalist commuter with belt drive, Gates Carbon CDX, internally geared hub, full-length fenders, and a matte gray paint that hides dirt. +Electric folding bike with compact frame, cushioned saddle, and smartphone-integrated display. +Gravel e-bike with removable battery, integrated storage slots, and a frame paint that camouflages dust and dirt. +Cargo longtail bike with rear-facing child bench, adjustable footrests, and a durable powder-coated frame. +Performance mountain bike with modern geometry, integrated chain guide, tubeless-ready rims and grippy 2.35" tires for mixed terrain. +Gravel race bike with electronic wireless shifting, light aero wheels, and flared deep drops for aggressive adventures. +A gravel bike painted army-olive with fender mounts, 700x40c knobby tires, double-bottle bosses, and a steel frame for rough-road durability. +Classic cruisy beach bike with wide tires, steel frame, cruiser handlebars, and a soft low saddle for coasting along the shore. +Gravel-friendly adventure bike with titanium frame, wide tire clearance, and brazed-on expander bosses for extra attachments. +A folding city bike in candy-apple red with quick-release hinge, compact folded footprint, and reflective sidewalls for commuters on the go. +An adventure mountain bike with wide 2.6-inch tires, reinforced rims, and lower gearing for loaded technical rides in the backcountry. +A modern endurance road bike with light vertical compliance, wide tire accommodation, and a refined cockpit that reduces rider fatigue. +A race-ready gravel machine equipped with dual GPS mounts, aero-optimized tubing, 40mm tires, and stealth matte black paint. +A modern e-cargo trike with three-wheel stability, large platform, heavy-duty hydraulic brakes, and a throttle for initial torque when loaded. +A mountain hardtail with semi-slacker head angle, psyllium suspension seatpost, and lockout-equipped fork for trail climbing versatility. +Lightweight time trial bike with integrated hydration, deep-section wheels, and carbon aero cockpit for maximal aerodynamic performance. +A touring tandem with reinforced frame joints, triple chainring capability, rack-mount eyes and a timeless gloss paint finish. +Kids' road bike with small 20-inch wheels, drop bars scaled for children, and caliper brakes. +A titanium commuter with stealthy matte finish, integrated lockbox, hydraulic disc brakes, and a narrow 28mm slick tire setup. +Gravel bike fitted with a front rack, multiple mounts, bright visibility tape, and stout tires for loaded adventures. +Gravel drop-bar e-bike with discreet mid-frame battery, low-mounted motor, and gravel-ready 40mm tires. +Gravel bike with distinctive gold flake paint, sealed bearing hubs, internal battery for lights and 42mm plus tires +Lightweight road commuter with integrated lights, reflective decals, internal routing and a narrow 28mm tire setup. +Lowrider cruiser with exaggerated rake, chrome springs, elongated frame and small-diameter wheels for show rides. +Sleek commuter with integrated lights in frame, stealth battery, hydraulic rim-style brakes and secure lock mount. +A cyclocross training build with stiff alloy frame, mechanical disc brakes, 35mm tires and quick-release wheel axles for frequent race practice swaps. +Fixed-gear urban machine with wraparound handlebar tape, low-profile saddle, and polished chainring. +A matte-white gravel bike with paint-splatter accents, 650b clearance, dropper-post compatible seat tube, and stealth cable routing. +Race-oriented road bike with shaved seatpost, carbon wheels and low profile stem for aggressive handling. +A polished stainless steel fixed-gear with minimal decals, straight-pull spokes, and a matte black anodized chainring for a sleek urban vibe. +Gravel bike with stealth rack bosses, threaded BB, and vibration-damping seatpost for rough days. +Nordic winter fat bike with studded tires, heated grips, insulated frame bag and low gearing for snowy paths +Urban commuter with Gates belt drive, 3-speed hub, and a frame-integrated rear rack for low-maintenance daily reliability. +BMX street freestyle bike with sawed-off seat, reinforced dropouts, gyro system and thick tires for park sessions +A commuter with integrated solar-charged taillight, lightweight alloy frame, and sealed-bearings hubs tuned for low maintenance. +Cyclocross race build with mudguards, fast-rolling tread, lightened cockpit, and a crisp shifting groupset for race days. +Gravel adventure bike with hand-laminated carbon fork, wide 650b tires, custom leather saddle and multiple top-tube zip pockets for tools +Vintage mixte step-through frame bicycle with wicker basket, chain guard and comfortable upright posture +Endurance road bike with relaxed geometry, endurance-specific carbon layup, 28mm tires and vibration-damping seatpost. +A boutique fixed-gear track commuter with anodized components, narrow profile bar tape, and a lightweight alloy wheelset for city sprints. +A touring bike engineered with triple-butted steel tubing, brazed-on low-rider racks, and 40mm tires for comfort over unknown roads. +High-performance gravel racer with volcanic grey finish, 38mm tires, SRAM Force AXS wireless shifting and disc brakes. +An urban cargo bike with longtail deck, kid seat adapters, reinforced frame, and ergonomic grips for family logistics. +A drop-bar commuting bicycle with fender mounts, rack mounts, dynamo light wiring and matte green enamel with subtle metallic flecks. +E-MTB with multiple power modes, trail-tuned suspension, and reinforced chainrings built to handle high torque loads. +A track-focused sprinter with ultra-short chainstays, single-speed drive, and a gleaming chrome finish that reflects sprint lane lights. +Gravel race bike with metal flake paint, flared handlebars and fast-rolling 40mm tubeless tires for classic races. +A gravel bike with comfortable endurance stance, 650b wheel compatibility, and flared handlebars for stability on rutted tracks. +A BMX halfpipe-focused build with short chainstays, responsive geometry, and robust bearings to stand up to heavy landings. +Gravel hybrid with drop bars and flat bar conversion kit installed for versatile commuting options. +Modern road race bicycle with asymmetric chainstays, power meter-integrated crank and 28mm high-pressure tires +Gravel-specific bike with aero-optimized tubing, hidden cable ports and broad tire clearance for endurance races. +Urban folding electric bike with compact fold, removable battery, and a user-friendly control interface for daily commuting. +Electric cargo bike with large front box, hydraulic disc brakes, and adjustable suspension for comfortable hauling. +Mountain trail full suspension with short stem and wide bar for modern trail control and confidence on descents. +BMX racing bike with stiff chromoly frame, UCI-spec geometry, and smooth-rolling 20-inch high-pressure tires. +A classic ten-speed road bike restored with new brake cables, polished chrome, skinny tires and traditional cream paint with red pinstripes. +A modern e-mountain bike with adjustable geometry, long travel suspension, and a stealthy frame-mounted battery pack. +Touring steel frame with smooth welds, heavy-duty racks, and puncture-resistant tires for reliable expedition travel. +Touring bike with center-pull cantilevers, 700x35 tires, and triple bottle cages for loaded adventure. +A high-performance road bike with aerodynamic rim sections, stiff BB area, and a lightweight seatpost to save rotational mass. +Road aero race frame with internal cable routing, integrated stem clamp and lightweight deep-section wheels for weekend race days. +Road all-rounder with disc brakes, 28mm tires, responsive frame and a rational balance of comfort and speed. +A practical utility bike with integrated cargo platform, wide-grip bars, low-ratio gearing and a paint job that hides dirt. +Beach cruiser with rustic wooden rack, teak accents, wicker basket and slow-geared coaster hub for seaside charm. +Classic step-through city bike with rear rack, basket, and an upright riding position for easy daily errands and cafe stops. +Road endurance frame with vibration-mitigating carbon, 30mm tires and slightly taller head tube for comfort. +A performance triathlon bike with superlight carbon layup, fully integrated cables, and an adjustable fork rake for bike-fit optimization. +Urban single-speed with narrow tire, gloss finish, and a clean single-sided chainstay protector for style and function. +High-spec cyclocross bike with disc brakes, SRAM Rival AXS, fast-handling geometry and mud-shedding frame details. +Touring tandem with reinforced seat tubes, matching racks, and laced wheels built for long-term durability. +Cargo long john bike with front box, low center of gravity, steel frame, and hydraulic brakes for safe delivery runs. +Urban commuter with quiet belt drive, roller brake hub and simple paint for low-maintenance commuting. +Gravel-plus adventure rig with wide rims, 2.2-inch tires and durable hubs for forgiving off-road performance. +A mountain bike with carbon front triangle and aluminum rear, 140mm travel, linkage-driven rear, and an adjustable headset cup. +A touring tandem with reinforced frame and dual racks, comfortable stoker spacing, and a dull olive paint that hides dings and scuffs from long-distance tours. +Youth children's balance bike with low seat, wooden frame, and no pedals to teach stability and steering control. +A titanium commuter with polished welds, internal routing for e-assist wiring, and ergonomically shaped titanium handlebars. +Lightweight city bike with clean lines, belt drive, integrated lights, and a rigid fork for quick handling. +A polished track bike with full steel fork, minimal graphics, and a focus on stiffness through the bottom bracket for explosive sprinting. +Gravel endurance frame with full coverage fender mounts, durable alloy tubing, and comfortable cockpit geometry for long rides. +Gravel all-road with handbuilt steel frame, small internal compartments, low-slung framebag and 700x38 tire clearance +A modern cyclocross frame with short wheelbase, plenty of toe clearance, and mounts for mud-shedding fender options if needed. +Gravel adventure machine with top tube bag, bar-end mirror, and a 1x11 drivetrain for simplicity on rough roads. +A cargo e-bike with modular attachments, heavy-duty chainstay reinforcement, and a practical, low-step frame for frequent loading and unloading. +A gravel all-rounder with stealth matte finish, 700x40 tires, and external framebag mounts for quick access to essentials. +A subcompact BMX with 18-inch wheels, reinforced forks, and bright stickers for kids learning tricks in small skateparks. +Urban cargo trike with insulated delivery box, electric assist, and safety harness mounts to protect packages and passengers. +A kids' BMX with low standover height, short top tube, padded crossbar pad, and tough tires for park sessions and backyard shredding. +A gravel race bike with split top tube, modular cage mounts, and a balanced frame-to-wheel compliance for long rugged races. +Retro-styled city bike with swept steel handlebars, chrome fenders, dynamo hub and brown leather grips. +A winter fatbike with studded tires, studded side knobs, and studs spaced for snowy singletrack grip and stable cruising. +A performance road machine with aero seatpost and aluminum stem, designed to shave seconds in criterium sprints and breakaways. +A triathlon bike with steep chainstay angle, integrated hydration system and aero-saving bolt-on accessories for race day. +Electric cargo longtail with reinforced rear triangle, dual batteries, and safety rails for transporting kids and heavy loads. +Adventure bike with frame-mounted tool rolls, twin racks, wide 650b tires, and high-clearance fork for rough roads. +Single-speed urban cruiser with coaster brake, wide whitewall tires and a gloss sky-blue finish. +A classic steel road bicycle with plain gauge tubing, leather saddle, and a period-correct ten-speed group for club weekend blasts. +Classic city step-through with bright enamel paint, integrated basket, hub dynamo light, and a comfortable upright posture. +City e-bike with step-through design, hub motor, hydraulic brakes and an illuminated logo for urban night rides. +A classic Italian-inspired steel road bicycle with ornate lugs, celeste-green paint and narrow 22mm tires for spirited paved climbs. +A gravel adventure rig with tall headset, flared drops, and a small integrated top-tube tool slot for quick repairs on remote dirt roads. +Gravel bike in olive drab with 40mm gravel tires, disc brakes, flared drop bars and wide-range 1x drivetrain ready for bikepacking. +Utility trike with hydraulic disc brakes, enclosed gearbox, step-through platform and big-capacity cargo bin +A touring bike with heavy-duty racks, fender mounts, and additional brazed-on bosses for extra gear and extended endurance. +A gravel endurance machine with comfort geometry, 700x40 tires, framebag-friendly design and matte olive paint with rusty fleck finish. +Lightweight titanium road tourer with disc brakes, 28mm tires, and multiple water-bottle mounts. +A bikepacking-ready titanium gravel bike with multiple framebags, reinforced rigging points, and a dropper post for rough descents with gear. +A beach cruiser with single-speed drivetrain, surfboard rack option, and balloon tires for smooth seaside rides in the sun. +Gravel commuter with belt drive, dynamo light, and integrated phone area for navigation on daily rides. +Road endurance with micro-adjust seatpost clamp, slightly relaxed geometry, and a satin finish that resists showing dirt. +Urban fixed gear with painted chainring, low top tube, and minimal accessories for a stripped-down commuter look. +Touring bike with threaded headset, steel fork for comfort, and three-bolt rack mounts front and rear for gear. +A stripped-down cross-country race hardtail with ultralight frame, narrow handlebars, and performance wheelset to shave grams on climbs. +Gravel race build with high-volume tires, full carbon cockpit and a 1x12 drivetrain for simplified shifting. +Classic mixte step-through frame city bike with wicker basket, brown leather grips, and classic spring saddle. +A mountain downhill bike with reinforced swingarm, adjustable geometry, and a thick bashplate for rock-strewn runs. +Fat-bike for snow and sand with 4.8" tires, rigid fork and single-ring drivetrain in camouflage paint. +Longtail cargo bike with extended deck, secure tie-down anchors and a cushioned bench for child passengers. +Youth balance bike with low frame, grippy rubber tires, foam saddle and colorful graphic decals for learning to ride. +A sleek urban single-speed with polished chainring, anodized headset, shallow-profile rims and minimalist branding for modern metropolitan style. +Folding commuter with oversized handlebar stem, large 20-inch wheels and quick-release for urban portability. +Lightweight titanium gravel bike with subtle brushed finish, thru-axles, and tire clearance for 45mm gravel tyres. +Road endurance carbon bike with compliant rear triangle, endurance saddle, vibration-absorbing bar tape and 28mm tires for comfort at pace +A gravel-adventure frame with robust steel tubing, three bottle mounts, 650b compatibility and matte sandstone paint with mud-flinging decals. +Vintage commuter frame with chainguard, swept handlebars, and a wicker basket that harkens back to leisurely shopping trips. +E-road bike with stealth battery in downtube, 250W motor assist, Shimano Di2 electronic shifting and integrated cockpit +Touring folder with small wheels, adjustable stem, and lockable hinge for airplane travel. +A titanium town bike with subtle polished welds, removable mudguard mounts, and a comfortable short-reach brake lever configuration. +Cargo e-bike with cargo bay, electric assist, low step-through frame, and adjustable suspension for heavy loads in the city. +A commuter with belt drive and internally geared hub, integrated rear light, and reflective sidewalls for increased night visibility. +An adventure tandem with frame bag compatibility, wide tires, and durable touring components for two-up bikepacking experiences. +A gravel-capable touring bike with full braze-ons, long-range gearing, heavy-duty rims and a paint job that resists road grime. +Classic road racer with downtube shifters, toe clips, patina chrome fork crown and narrow 23mm tires. +A 29er hardtail with 120mm fork, trail-tuned geometry, and fast-rolling 2.25-inch tires to handle mixed terrain during after-work rides. +A longtail cargo bicycle with extended rear platform, modular passenger rails, and a reinforced rear triangle to support heavy loads. +Track keirin-style sprint bike with single-speed hub, deep-section carbon rims and minimal weight. +Urban cargo e-bike with synchronous rear-wheel drive, heavy-duty tires, and customizable modular racks for deliveries. +A cyclocross racer with stiff carbon fork, 1x drivetrain, flared drops and tubular tires tuned for muddy, technical courses. +Cyclocross pro-level alloy frame with light weight, stiff chainstays, and optimized clearances for fast race pacing. +A gravel-focused 1x mountain road hybrid with 650b wheels, wide rubber, dropper post and stealth black ceramic paint. +A commuter with hydraulic disc brakes, integrated light strip in the downtube, and a low-maintenance hub for predictable stop-and-go traffic. +A minimalist track fixed-gear bicycle with aerodynamic deep dish front wheel, deep tubular rear, and pursuit-style handlebars. +E-cargo bike with insulated delivery box, secure locking lid, powerful motor and long wheelbase for stability under heavy loads +A commuter with Bluetooth-enabled lights, integrated USB charging, and reflective paint dots on the downtube for city safety. +Rugged winter bike with studded tires, fenders, low-pressure fat tires and heated grips for icy commutes. +Urban fixed gear with bold neon chain, deep rim wheels and urban survival kit strapped under the saddle. +City utility bike with sealed hub, belt drive, integrated frame lock and a low cargo rack for grocery runs. +Mountain freeride carbon with strong pivot bearings, shock reservoir compatibility and geometry that encourages big tricks. +Track pursuit frame with monocoque carbon, integrated stem and a polarized metallic paint finish. +Mountain downhill steel frame with coil-friendly shock mount, long-travel fork and reinforced headtube for heavy abuse. +A lightweight aluminum cyclocross bike with tapered headtube, CX geometry, and removable fender mounts for winter training. +A full-suspension trail bike with tuneable shock, dropper post, and 2.4-inch tires on 29-inch rims for modern trail performance. +Modern recumbent trike with three wheels, recirculating seat mount, and aerodynamic fairing for speed. +A compact folding bicycle with quick-release folding pedals, low-profile hinge, and a light, stiff frame that rides surprisingly well for its size. +A steel cyclocross frameset with classic lug details, stainless-steel water-bottle bosses, and a freshness-sustaining semi-gloss finish. +Cargo trike with three-wheel stability, large wooden bed, and an electric assist mode for heavier loads without strain. +A steel mixed-terrain commuter with dynamo lights, pannier rack, internal shifter routing, and a puncture-resistant 35mm tire. +A restored vintage cruiser bicycle with balloon tires, swept handlebars, a spring saddle, and retro surf-style paint for seaside rides. +A touring tandem with reinforced fork, two sets of water-bottle mounts, and matching nameplate plates on the headtube for identity. +A modern track bike with aero seat tube, narrow bottom bracket, and a color scheme that screams competition at the velodrome. +A touring gravel bike with wide tire clearance, S&S couplers for travel, downtube storage compartments and durable stainless hardware. +A gravel bike with an emphasis on comfort, thicker seatpost, and slightly taller head tube for less fatigue on long routes. +A classic Dutch transport bicycle with long-chaincase, upright bars, full fenders and a large front basket for market runs. +Gravel touring bike with welded brass accents, long-range gearing and mudguards for multi-surface journeying. +A kids' mountain bike with front suspension, easy-reach shifters, and colorful stickers on the frame. +Gravel commuter with rando bangles, 35mm tires, mudguard mounts, and a resilient aluminum frame for city-to-dirt rides. +A cyclocross-specific build with reinforced mudguard eyelets, quick-release wheels, and a high-stance frame to ease shoulders during shouldering. +BMX street with reinforced top tube gusset, gyro-free cables, and graffiti-inspired paint for street cred. +Durable alloy hardtail with reinforced welds, versatile 27.5 wheels, and trail-friendly 2.6-inch tires. +Urban electric folding cargo bike with removable battery, flatbed option and quick-folding handlebars for space-saving storage. +Urban folding utility bike with lint-free hinges, integrated cargo rail and comfortable upright posture for quick errands. +A modern trail hardtail with dropper post, 29" wheels, 2.35" tires, and a geometry optimized for fast technical descents and confident cornering. +A lightweight aero road frame with integrated spacers, disc brakes, full-carbon fork and a satin pearl finish. +A sleek city single-speed with bright metallic powdercoat, minimalist chainring guard, and upright grips for short, stylish commutes. +Urban cargo bike with low deck, child harness attachments, and a sturdy alloy frame for frequent loads. +Classic Dutch city utility with robust frame, front rack, and a straightforward three-speed hub to go anywhere on flat streets. +A road commuter with dynamo hub, integrated lighting, and a full chaincase for a neater look and low maintenance. +Electric commuter bike with mid-drive motor, step-through frame, integrated battery, and rear cargo rack. +A gravel tandem designed for stability, extra bottle mounts, and a low-slung frame chassis optimized for two-up adventure mileage. +Gravel race frame with semi-aero tubing, internal cable routing, and a stout rear triangle to sprint out of corners confidently. +A gravel adventure machine with steel frame, three-bottle mounts, framebags pre-fitted and dusty-olive paint with hand-splattered accents. +A gravel commuter with reflective logos, internal cable routing, and a thoughtful combination of comfortable endurance fit with sporty handling. +Gravel bike with carbon layup tuned for compliance, stainless-steel hardware, and subtle turquoise logo. +Retro mixte city bike with leather saddle, chrome fenders, three-speed hub, and a delicate floral decal along the top tube. +Electric mountain bike with high-capacity battery, torque-sensing motor, long-travel suspension, and heavy-duty tires for rugged terrain. +Compact kids' balance bike with no pedals, low seat, lightweight wooden frame and brightly colored grips. +Urban mini-velo with small wheels, tight geometry, upright bars, and a bold two-tone paint split for city fashion. +A commuter with sweeping curved tubes, integrated frame lock, puncture-resistant tires and gel saddle for comfort. +A commuter with belt-driven hub, full-length fenders, and a child seat-ready rear rack to simplify daily family logistics around town. +Mountain freeride frame with short chainstays, massive bottom bracket stiffness, reinforced head tube and thick-walled tubing for big-park riding +Retro-style European city bicycle with basket, chaincase, upright geometry, and a step-through frame for easy mounting. +A race-ready cyclocross bicycle with flared drops, electronic shifting, and stiff carbon fork for quick accelerations. +A long-tail cargo bike with child bench and integrated footrests, sturdy rails for foot security, and a modest mid-motor to ease day-to-day loads. +Road endurance bike with slightly higher stack, softer rubber, and a flexible carbon layup tuned for comfort. +Gravel tourer with double-braze-on frame, heavy-duty hubs, and an upright bar position to save the neck on long days. +A modern gravel frameset with stealthy integrated mounts, 650b/700c compatibility and a design that favors comfort over pure speed. +Tandem recumbent with differential drive, comfy bolstered seats, and large-volume tires for touring. +A cyclocross practice bike with knobby tubeless tires, reinforced fork, and a frame that resists mud build-up to stay race-ready. +Classic lugged-steel randonneur with 28mm tires, flared drop bars, dynamo front hub, and leather saddle. +Lightweight steel touring frame with mudguard eyelets, long chainstay for pannier clearance, and stainless bolts. +Track bike with fixed gear, high-flange hub, aero spokes, deep-section rim, and steep geometry built for velodrome sprinting. +A city step-through with chaincase, hub dyno-powered light, comfortable swept-back bars, and a child seat mount on the rear rack. +A carbon hardtail optimized for gravel with 40mm tires, 110mm fork, and a stealthy black-on-black decal layout. +Forged-aluminum commuter with integrated lock mounting, puncture-proof tires, and a modular rear rack for deliveries. +Mountain enduro bomber with adjustable travel fork, reinforced bottom bracket and heavy-duty tires for big drops. +A mountain freeride bike with chainstay bash guard, large-volume tires, and a low-slung profile for confidence on drops and jumps. +Modern titanium gravel bike with monostay, thru-axles, 40mm tires and stealth raw finish that will patina over time +A beach cruiser with retro whitewall tires, flame-sculpted fenders, large chrome chain guard and comfortable two-up saddle for social rides. +A handmade lugged steel frame with elegant fillet-brazed joins, custom paint swirl, and hand-cut headtube for classic craftsmanship and ride compliance. +A full-suspension downhill rocket with reinforced swingarm, massive 200mm travel, and electric-orange paint with monster decals. +A cyclocross-friendly all-road bicycle with oversized tire clearance, integrated cable routing, and a robust steel frame to shrug off rough training. +A compact electric commuter with hub motor, internal battery pack, and a nylon belt drive that stays clean through dress gear. +A carbon gravel bike with slack head tube, long reach for stability, and 42mm gravel tires for all-terrain confidence. +A flamboyant orange fixed-gear with colored spokes, matching grips and a lightweight rim for city crit style. +A lowrider cruiser bicycle with banana seat, high-rise bars, chrome sissy bar, and a candy-apple paint job for showy street style. +Mountain all-mountain bike with 160mm travel, adjustable geometry, and wide handlebars for aggressive trail riding. +Mountain downhill rig with 200mm travel, coil shock, dual crown fork and DH-specific brakes and cockpit +Track racing bike with fixed gear, deep-section carbon wheel, aero seatpost and steep fork rake for velodrome sprints. +A gravel endurance machine with integrated seatpost clamp, wide bar flare, and micro-suspension post to cut down saddle chatter. +A compact folder with belt drive, small wheels, and a hinge that collapses in seconds for easy trunk storage. +Cyclocross training bike with semi-integrated headset, mudguard mounts and reinforced chainstays. +Lightweight chronograph road bike with carbon fork, 25mm racing tires, and an aggressive geometry for criterium attacks. +All-mountain carbon bike with tuned suspension kinematics, 160mm front travel, mullet wheel compatibility and progressive geometry for big lines. +BMX street shredder with gyro, reinforced chainstay, and slim profile tires for sticky tricks on urban architecture. +Full carbon downhill bike with 200mm travel fork, long reach, and burly tires for race courses. +A track-inspired urban single-speed with deep-section carbon front wheel, alloy rear, leather saddle and matte black paint. +A gravel endurance rig with slightly flared drops, 38mm tire clearance, and a compact handlebar for long-day comfort on varied roads. +A gravel endurance bike equipped with comfortable geometry, vibration dampening seatpost, and a soft-touch top tube for long days in the saddle. +Mountain downhill bike with massive travel, heavy-duty alloy frame, coil shock linkage, and 27.5" wheels built for speed. +Retro roadster with swept-back chrome handlebars, cream fenders, and a cushy saddle for relaxed city cruising. +A performance gravel racer painted matte slate gray with race geometry, integrated frame protection and tubeless 38mm tires for speed over dirt. +Gravel bike with stealth topcap-mounted computer and flared handlebar drops for long gravel descents. +E-gravel bike with discreet frame battery, hydraulic disc brakes, and 45mm tires for long unsupported days. +Urban cargo longtail with weatherproof child seat option, electric assist, and reinforced deck for weekly market runs. +A modern drop-bar bike with stealth internal battery for an auxiliary motor, blending e-assist subtly into an otherwise classic silhouette. +Full-suspension enduro bike with modern geometry, beefy tires, and dropper post for technical descents. +A folding electric cargo bike with modular cargo boxes, front and rear hydraulic braking, and a step-through frame for loading and family use. +Touring tandem with long chain, dual racks, and matching bottle cages for two-up expeditions. +A city cargo bike with front-loading bucket, two-kid bench, and stable low-slung chassis for carrying kids and groceries safely. +A modern urban cargo e-bike with longtail frame, passenger foot pegs, Bosch-style motor and a reinforced alloy platform for child seats. +A handbuilt titanium road frame with brushed finish, discreet cable ports, endurance geometry and a focus on compliance and longevity. +Gravel race frame with aero-optimized tubes, integrated cockpit and wide tyre clearance for high-speed off-road sections. +A commuter-friendly flat-bar road bike with 28c tires, ergonomic grips, relaxed geometry, and hydraulic discs for stopping power in traffic. +Track sprinter with wide chainset spacing, stiff bottom bracket, and a classic glossy enamel paint that reflects the velodrome lights. +Steel adventure bike with hand-brazed lugs, triple-pivot rack mount and durable powder coating. +Classic single-speed with flip-flop hub, polished steel frame, and leather grips for a clean minimalist look. +Folding electric bicycle with quiet hub motor, integrated battery, and quick-release folding pedals for compact storage. +A vintage road frame restored with period decals, slender chrome fork crown, and a leather saddle that breathes history. +Gravel-focused steel frame with titanium dropout inserts, 650b compatibility, and smooth-weld finishing for durability. +Touring bike with triple chainring, long wheelbase, and leather-wrapped handlebars for comfort on long days. +Classic Dutch city bicycle with upright bars, coaster brake, and full chaincase for urban commuting. +A compact folding commuter with a singular hinge, 16" wheels, and a low maintenance drivetrain for frequent door-to-platform-and-back city trips. +A classic road bike with chrome fenders, leather saddle, period-correct components and a filigreed head badge that tells a story. +Vintage mixte step-through bicycle with woven leather saddle and wicker basket on the handlebar. +Commuter folding bike with integrated bag, 20" wheels, Shimano Nexus hub, and lightweight frame for tighter storage in apartments. +E-cargo trike with low center of gravity, hydraulic brakes, powerful motor and waterproof cargo bay for deliveries. +A modern trail shredder with asymmetric chainstay design, 150mm travel suspension, and 29x2.6 tires to float over obstacles. +Mountain hardtail with 29-inch wheels, straight-pull spokes, and fast-rolling 2.25 tires. +A custom hand-built steel frame bicycle with fillet-brazed joints, hand-cut lugs, and a hand-painted enamel fade for a boutique feel. +A gravel racing machine with aggressive cockpit, wide tire compatibility, and a stealth matte finish that minimizes reflective glare during races. +A polished steel frameset with filigree lugwork, subtle clearcoat that shows the brushed metal grain, and classic geometry. +A classic touring steel bike with full braze-ons, triple bottle mounts, wide low gears and a deep burgundy shellac finish. +A gravel-laden adventure rig with pannier mounts, frame bag-compatible main triangle, and a rugged steel fork to carry extra gear. +Urban utility bike with sturdy rear rack, integrated bell, full fenders and simple low-maintenance gearing for everyday tasks. +A high-performance gravel bike with carbon fiber layup, tubeless 700x42 tires, and a race-tuned geometry for aggressive off-road pacing. +A compact trail hardtail built for smaller riders with 27.5 wheels, 120mm fork, and nimble handling for local singletrack adventures. +A low-step electric cargo bike with hydraulic brakes, child bench, and a wide rear deck for groceries or multiple small passengers. +A BMX race rig with light alloy frame, sealed hubs, race-specific gearing and grippy tires for dirt track sprints. +Classic town bicycle with sprung saddle, swept-back bars and a built-in front lamp for charming city rides. +Gravel endurance frameset with vibration-damping layup, SRAM 1x groupset, and room for dual bottles in the main triangle. +A classic colonial-era style roadster with sprung saddle, alloy chain guard, step-through frame and pale yellow enamel paint. +A gravel racer with electronic drivetrain, 700c carbon wheels, tubeless tires and gradient paint moving from olive to sand. +A cyclocross frameset with carbon fork, short chainstays, and reinforced top tube for shouldering in muddy races. +Mountain trail bike with extra-wide handlebars, short stem, and pronounced dropper post for technical sections. +Electric cargo bike with dual child seating, reinforced frame, integrated harnesses and wide deck for family outings. +Cyclocross commuter hybrid with mudguards, sponge grips and puncture-resistant tyres for wet seasons. +Gravel endurance bike with supple carbon construction, 700x40 tires, and a slightly relaxed geometry for daylong comfort. +Bikepacking-specific frame with dedicated framebag space, low-slung geometry for stability, and extra mounts for long trips. +A classic cargo bike with front box, wooden slats, step-through frame, coaster-braked rear wheel and painted nameplate for local deliveries. +Belt-drive urban bike with Gates Carbon Drive, internally geared hub, sealed chaincase, and maintenance-free drivetrain for daily commuting. +Folding city bike with low-maintenance internal gear hub, leather grips and compact folded dimensions for commuting. +A polished steel track bike with aero seatpost, high-flange hubs, and a tight racy geometry for competitive velodrome sessions. +Commuter hybrid with upright geometry, puncture-resistant tires, front suspension fork and rack for daily practicality +A cyclocross training frame with rocker-compatible geometry, 1x setup readiness, and durable paint that resists chips from constant use. +Simple single-speed cruiser with coaster brake, swept-back bars, and pale yellow paint for easy neighborhood spins. +Track pursuit bike with teardrop tubing, deep-section disc wheel and a paint job matching team kit. +Urban folding cargo bike with reinforced rear rack, child seat option, small electric assist and quick fold for transit +A gravel tourer with triple-bolt fork, S&S couplers available, rugged alloy frame, wide tire clearance and a mounting system for long-distance gear. +Cyclocross commuter hybrid with upright bar ends, reflective tape on forks and resilient puncture-resistant tires. +A retro-inspired chopper bicycle with long reclining handlebars, minimalist seat, and show-grade paint details for weekend parades. +A trail-honed full-suspension bike with tuned shock, wide handlebars, dropper post and progressive geometry for modern singletrack. +A bikepacking-specific frame with integrated battery for lights, top-tube map pocket, and lug-style brazed eyelets for custom racks. +Urban commuter with built-in frame lock, puncture-resistant tires, ergonomic grips and a wide stable platform for effortless daily use. +Mountain all-mountain bike with 150mm travel, adjustable geometry and aggressive treaded tires for fast descents. +Gravel racer with carbon fork, synthetic leather bar tape, and fast-rolling 35mm tires. +A durable workhorse bicycle with reinforced frame, heavy-duty chain, drum-brake rear hub and a broad steel rack for deliveries. +A gravel endurance bike with lower gearing, wave-cut seatstays for compliance, 700x40 tires and faded olive finish with specks of bronze. +Gravel endurance alloy frame with flared drops, tubeless-ready rims and generous clearance for diverse tire sizes. +Mountain trail machine with aggressive 2.5-inch tires, clutched derailleur, and adjustable chainstay length. +Time trial specialist with front fairing, deep-section front wheel, and narrow bars for optimal aerodynamics over flat courses. +A commuter with low-step frame, rear rack, and a built-in cable lock that tucks into the top tube when not in use. +Folding travel bike built for compactness with 14-inch wheels, quick-fold hinge and reinforced handlebar stem. +Cyclocross steel frameset with traditional geometry, modern disc mounts, and neatly filed lug joints. +Electric cargo trike with built-in GPS, weatherproof controls, and wide deck for reliable city deliveries. +A compact folding e-bike with a mid-drive motor, 16-inch wheels, and an elegant hinge system that locks rigidly for safe rides. +Electric mountain bike with full suspension, powerful mid-drive motor and 150mm travel for bike parks. +Gravel endurance frame with added compliance in the seatstays, multiple mounting points and a subtle metallic flake finish. +A lightweight race road bike with integrated seat clamp, 28mm tires, and a stiff carbon layup to convert every watt of power into speed. +Steel single-speed track bike with tapered seatpost clamp, subtle pinstripe, and waxed leather saddle. +Performance road bike with aerodynamic seatpost, disc brakes and an integrated cockpit for clean lines. +Steel singlespeed track-style commuter with powder-coated cherry red finish and leather saddle for comfort. +Cyclocross training bike with semi-upright position, 33mm cross tires, and formation-compatible gearing for fast group workouts. +Urban electric step-through with automatic transmission-style hub, low-step entry, and integrated rear child seat mounts. +A lightweight gravel racer with alloy frame, carbon fork, 700x32 tires and a glittering copper fade on the headtube. +A mountain enduro build with coil-tuned shock, long-travel fork, 29-inch wheels and stealth matte black with neon orange highlights. +Classic steel cyclocross bike with mud-shedding stays, short chainstays, and a timeless lugged aesthetic. +A fat-tire commuter designed for sand and snow with wide platform pedals and a sturdy kickstand for parking on soft ground. +A lightweight cross-country race bike with 100mm travel, 29-inch wheels, carbon frame and sunburst orange accents over matte black base. +Gravel drop-bar bike with comfortable geometry, flared bars, triple-mount downtube and generous tire clearance for mixed terrain. +Track pursuit frame with high stiffness, minimal compliance, deep-section wheel compatibility, and a focus on aerodynamics. +Lightweight endurance road frame in pearl white with comfort-tuned seatstays and clearance for 35mm tires to smooth rough tarmac. +A retro-inspired city bike with swept-back handlebars, basket on the front, coaster brake hub, and cream-colored fenders over 700c wheels. +Lightweight time trial bike with super-aero profile, elongated head tube and integrated electronics for data monitoring. +A compact electric folding bike with 350W rear hub motor, quick-fold frame, small 16-inch alloy wheels and integrated lights for last-mile travel. +Classic folding bike with steel frame, 20" wheels and chrome trim for cottage-town promenades. +A classic steel road frame with period-correct components, hand-laced wheels, and a vintage leather saddle that patinas beautifully with time. +Kids 20-inch mountain bike with front suspension, coaster brake optional, colorful graphics and knobby tires for trail play. +Full-suspension enduro mountain bike with long travel, coil shock, 27.5" wheels and aggressive slack geometry. +Speed-focused fixie with aerodynamic frame angles, bladed spokes and a tall gear for flat urban cruising. +Electric mountain bike with full-suspension, powerful mid-drive, adjustable geometry, and heavy-duty brakes for steep trails. +Cargo e-bike with hydraulic disc brakes, anti-rollover stabilizers, and welded-on tie-down points for secure hauling. +Performance cyclocross bike with carbon fork, quick-release drops and high flange hubs for muddy starts. +Touring expedition bicycle with oversized racks, external cable routing for easy service and strong brazed-on lugs. +A gravel racing model with painted camo flush, 38mm tires, and wireless shifting tuned for rapid climbs and fast flat sections. +Cyclocross race machine with light aluminum frame, cantilever stops, quick-release skewers, and grippy knobby tires. +Urban folding hybrid with comfortable seating, 9-speed cassette, and fold-flat pedals for compact transportation. +Gravel-specific carbon bike with subtly flared drops, chainstay protection and a nicely balanced ride for both speed and comfort. +A classic road frame with a threaded headset, elegant top tube, and a set of polished steel rims for a pleasing retro ride. +A track bike with aero-profile tubing, fixed cog, and a stiff bottom bracket region for maximal power on short pursuits. +Vintage roadster with upright geometry, full fenders, coaster brake and hand-painted pinstripes on the skirt guard. +A city bike with upright bars, double-legged kickstand, pannier rack and reflective wheel decals for safe urban errands. +A sloping geometry road bike with compact crank, aerodynamic seatpost, 25mm tires and a graduation paint that moves from purple to pink. +Touring steel frame with brazed-ons, triple bottle mounts, and stout rear rack for heavy packing. +Classic city bicycle with swept-back bars, springer suspension, roller brakes, and a deep navy lacquer for understated style. +Classic Italian-style racing bicycle with Columbus tubing, steel headset, frayed cotton tape, and ornate decals. +Retro-styled fixed-gear with chrome fenders, white-wall tires, bullhorn bars, and a small saddlebag for tools. +A folding electric bike with mid-drive motor, long-range battery, hinged frame and foldable pedals to stash on public transit. +BMX race bike with lightweight chromoly frame, stiff fork, and narrow seat for sprint-oriented tracks. +Kids' commuter with chain guard, low gearing and easy-reach brake levers for safe neighborhood rides. +Race-ready aluminum cyclocross frame with flared stays, flat-mount brakes, and clearance for large-diameter tires. +Vintage cruiser with balloon tires, retro chrome fenders, swept-back bars, and two-tone pastel paint for slow coastal days. +A high-end titanium cyclocross bike with full carbon fork, mud clearance, ultralight wheelset, and minimalist decals for a clean look. +A touring bike with extra-strong frame lugs, multiple water-bottle mounts, and an appropriate gearing spread for mountainous terrain. +City bike with retro headlamp, leather saddle, integrated frame lock and wide tires for comfortable short trips. +Urban single-speed cruiser with bright candy finish, comfortable saddle and playful geometries for city fun. +Adventure-focused fat-tire bike with reinforced fork, wide deck mounts and a geometry tuned for loaded touring. +Folding bike with small 20-inch wheels, quick-fold hinge, rear hub dynamo light, and compact basket for urban storage. +High-spec mountain enduro with coil-tuned rear shock, 170mm fork, and reinforced hardware for big hits and drops. +Cargo e-bike with full weather cover, dual battery option and sturdy alloy deck for persistent deliveries. +A vintage-style racer refurbished with modern hubs, 25mm rubber, alloy brake calipers and a glossy cherry lacquer reminiscent of classic bicycles. +Modern fixed-gear urban bike with riser bars, wide tires, flip-flop hub and a matte army green finish. +A city cargo e-bike with electric-assist geared for low speed torque, heavy-duty platform, and reinforced spokes for daily freight. +A custom painted urban bike with mural-style frame art, wide tires, upright handlebar and vintage leather grips. +A commuter with integrated front and rear racks, protected chaincase, and a lockable downtube compartment for small items. +A gravel-specific frame with asymmetrical chainstays, durable BB shell, 700x45 tire fitment and integrated GPS mount for rugged exploration. +A lightweight road frame with aggressive geometry, flat-mount disc brakes, and aero tube shaping where legal to keep weight and drag low. +A compact folding commuter with quick-release wheelset and a single-speed drivetrain for easy maintenance and compact parking. +A retro fixed-gear with tall riser bars, comfortable saddle, and a friendly upright geometry for relaxed city spins. +A carbon comfort road bike with shallow geometry, vibration-damping layup, and 32mm tires for long group ride comfort without sacrificing speed. +Electric cargo bike with integrated turn signals, modular bench seating, powerful mid-drive motor, and a low center platform. +Cyclocross race bike with tubeless setup, flared gravel drops and a wide-range cassette for variable race conditions. +Retro-styled folding mixte with wicker basket, chrome accents, and 20-inch wheels for an urban vintage vibe. +Gravel adventurer with integrated framebag shape, discreet mounts for lights and GPS, and resilient paint for scuffs. +Folding commuter with 20-inch wheels, compact frame, simple single-speed hub and reflective accents for evening commutes. +A stripped, stealthy urban single-speed with gloss black finish, deep-section wheels, and a short wheelbase for nimble city carving. +Vintage cruiser with balloon tires, swept spacing, chrome rack, and an easy-to-use coaster brake for relaxed weekends. +Gravel endurance machine with bar-mounted GPS, flared drop bars, and a layout for long-day nutrition and hydration storage. +Leisure cruiser with oversized foam grips, spring-mounted saddle, candy-coated paint and matching fenders for smooth neighborhood rides. +Mountain marathon race bike with 120mm travel, lightweight wheels and a fast-rolling tire profile for long races. +A gravel bike set up for endurance racing with a comfortable stack height, wide flared bars, and a tubeless wheelset tuned for long days on rough courses. +A touring bike with steel fork, large panniers, long wheelbase, and a comfortable bar tape wrap for cross-country touring. +A retro single-speed city cruiser with wide whitewall tires, chrome fenders, and an extra-large wicker basket for weekend markets. +A high-end road racing bike with lightweight thermoplastic resin carbon, electronic wireless shifting, and a dedicated sprint geometry. +A cargo longtail with reinforced dropouts, integrated lights, child seat anchors, and a low-slung rear deck for stability under load. +A carbon aero racer with disc brakes, integrated bar/stem, and a sculpted downtube that channels airflow for reduced drag. +A modern track bike with deep-section aero front wheel, fixed rear, and a stiff monocoque frame for sprint events. +A classic road frame with fillet brazing, polished seat cluster, narrow tires and vintage-style decals for Sunday group rides. +A modern gravel race bike with aero-shaped tubes, 35mm tires, and wireless electronic shifting tuned for rapid sprint accelerations. +A cross-country race hardtail with tapered head tube, 100mm fork, 29-inch wheels, and a minimal cockpit for weight savings. +Vintage steel road frame with polished lugs, leather saddle, and period-correct components restored for classic club runs. +Performance gravel bike with wide chainstays for tire clearance, 1x electronic shifting and a carbon gravel handlebar with flare. +A modern urban commuter with belt drive, hydraulic rim-less disc brakes, integrated fenders, and a minimalist powder-coated frame. +Gravel adventure frame with discreet rack mounts, comfortable compliance, and 700x45 tire clearance for remote trips. +A vintage city bicycle updated with modern brakes, sealed hubs, and a freshly wired dynamo headlight for practicality with style. +A city tourer with robust aluminum frame, wider tires for comfort, and a sturdy rear rack ready for weekend picnics and groceries. +Road endurance bike with integrated storage in the fork crown and composite seatpost to dampen road buzz. +Fat-tire beach bike in sand yellow with 4.8" tires, single-speed hub, coaster brake option, and a stable low-pressure ride. +A high-performance track sprinter with ultra-stiff bottom bracket, polished aero frame, and vintage chrome details for style. +Vintage road frame restored with polished stainless steel accents, leather saddle and period-correct downtube shifters. +Gravel plus touring bike with robust rack mounts, 2.1" tyres, and greater stability for carrying gear on rough roads. +Mountain enduro with 170mm front travel, slack head angle, chainstay protection, and a burly wheelset for tough descents. +Gravel adventure frame with stainless steel tubing, bead-blasted finish, and QR mounts for light rack use. +Electric gravel adventure bike with discreet range extender, waterproof connectors and durable anodized components for rugged use. +Urban commuter with integrated smartwatch mount, quiet belt drive, front and rear lights and puncture-resistant tires. +Cargo longtail with expanded rear deck, passenger cushions, and integrated tow hitch for trailers. +Sturdy downhill bike for park riding with massive forks, coil shock, and extra-long travel for big drops and landings. +Mountain enduro with carbon swingarm, coil-tuned shock, beefy headset, and 170mm travel for high-speed technical runs +Mountain downhill frame with reinforced linkage, replaceable shock mounts, and a distinctive matte black-and-orange scheme. +A custom painted mountain hardtail with 29-inch wheels, tapered headtube, fast-rolling tires and meticulous cable routing for aesthetic polish. +A cyclocross training rig with wide flared bars, premium grip tape, and a quick-release seatpost for rapid handling adjustments. +A custom titanium gravel bike with hammered finish, discreet badge, and drivetrain tuned with a wide-range cassette for off-grid climbs. +Gravel marathon racer with light frame, wide rims, 40mm tires, and an emphasis on endurance geometry for long events. +Gravel commuter with fender mounts, mud-shedding chainstay protector, and modular front rack system. +A folding e-bike with a robust mid-drive, easy-fold latch, and an all-day battery for long commutes combined with trunk storage. +A high-end titanium endurance road bike with subtly tapered tubes, comfort-focused geometry, and a finish that reveals brushed metal under bright light. +Vintage steel touring bike with ornate lugwork, comfortable saddle and a robust rear rack ready for panniers and long distances. +A classic road racing bike restored with period-correct Campagnolo groupset, tubular tires, and leather-honey saddle for club rides. +Cargo e-bike with child seat rails, easy-on step-through design, and front platform for quick package loading. +A performance road bike with compact geometry, aero seatpost, carbon seat tube and a glossy red lacquer finish. +A city cargo bike with an easily removable deck, foldable side panels, and a mid-drive motor to help while loaded in tight routes. +A lightweight aluminum cyclocross machine with mechanical disc brakes, high bottom bracket, and tubeless-ready 33mm cyclocross tires. +A comfortable commuter with swept-back bars, gel saddle, aluminum fenders and a discreet rear rack for groceries. +A utility bike with wide platform pedals, robust crankset, and reinforced dropouts designed to carry heavy loads through town. +A cyclocross training rig with steel frame, durable fork, 700x33 tires and a simple 2x drivetrain optimized for range and repairability. +Mountain trail bike with aggressive 29er wheels, cushioned saddle, and protection tape around likely impact zones. +Touring expedition bike with oversized cargo mounts, steel frame, and triple-crank gearing for remote terrain. +Gravel speedster with flared drops, stealth tape, and fast-rolling tires for mixed-surface competitions. +Gravel race bike with SRAM Rival, 700x38 tires and aerodynamic shallow-section wheels. +Performance gravel racer with electronic shifting, tubeless rims, 700x38 tires and stealth matte finish for competitive events. +Road racing bike with aggressive fit, oversized down tube, and minimal paint for weight savings. +Gravel race rig with aggressive stance, 42mm tires, flared cockpit and minimalist paint for focused performance. +A kids' BMX with reinforced top tube, colorful sprocket guard, and pegs ready for park riding and street tricks. +Classic Belgian cyclocross bike with steel frame, short wheelbase, and eyelet-mounted mudguards. +Gravel-adventure alloy bike with frame protection strips, generous bottle mounts and versatile tire compatibility up to 2.1". +Road commuter with durable alloy frame, fender mounts, internal hub compatibility and a comfy saddle for dailies in mixed weather +Full-suspension trail mountain bike with 140mm rear travel, aggressive 29er tires, and wide handlebars for control. +Steel randonneur bicycle with dynamo-powered lights, fenders, low gearing and comfortable endurance geometry +Lightweight time trial frameset with integrated cockpit, aerodynamic bottle storage and discreet gapless fairings for speed focus. +Urban single-speed with leather-wrapped saddle, polished crankset and smooth sealed bottom bracket for quiet rides. +A dh race sled with protective bash guard, reinforced headtube, massive 203mm rotor brakes, and coil over shock for race runs. +Youth mountain bike in neon green with 24-inch wheels, mechanical disc brakes and simple 8-speed drivetrain. +Gravel e-bike with torque-sensing motor, internal battery, and a comfortable geometry for extended mixed-surface excursions. +Youth BMX freestyle bike with reinforced top tube, gyro detangler, pegs and mid-size pegs for park tricks +A gravel adventure bike with frame-mounted tent straps, low gear ratios for steep climbs, reinforced fork and desert sand paint. +Ambitious bikepacking rig with custom framebags, handlebar roll, and extra water bottle cages for multi-week treks. +Classic randonneur bicycle with dynamo lamp, leather saddle, low-geared hub, and steel mudguards for long days in the countryside. +Classic Dutch-inspired electric commuter with basket, cushioned saddle, and torque-sensing hub for smooth pedal assistance. +Mountain trail hardtail with a slack head angle, short stem and grippy tires for fast, confident cornering. +Gravel touring bike with stainless-steel fasteners, luggage-friendly geometry and durable bead tires for long gravel roads. +A performance time trial bike with very deep sections, integrated hydration, and a sealed cockpit to minimize aero drag. +City step-through bicycle with integrated lights, rear rack, cushioned grips, and a comfortable low step for ease of use. +Cargo longtail with rainproof cover, modular benches, and low-step load for easy child and cargo handling. +A classic alloy cyclocross frame with reinforced headtube, 35mm tire clearance and olive drab finish with white race number panel. +A lightweight aluminium crit bike with triple-butted tubing, short wheelbase, and quick steering for navigating tight urban circuits. +Cyclocross frameset with robust clearance, cable guides for quick maintenance, and an earthy matte green livery. +Classic cruiser with swept bars, chrome accents, balloon tires, and an extra plush saddle for easy neighborhood meanders. +A commuter with internal electronic connectivity, built-in phone charger, and a discreet rear rack that blends with the frame silhouette. +A commuter with internal battery, discreet LED taillight, hydraulic rim brakes, and a subtle paint finish that hides dirt from daily use. +Gravel commuter with mudguard-compatible frame, robust tires, integrated rack mounts, and a quiet internal hub for city travel. +Road aero frame with hidden cables, truncated airfoil tubes, and shallow-section wheelset for everyday fast group rides. +Cyclocross carbon frameset with tapered headtube, oversized seatpost clamp, and aggressive clearance for mud-laden courses. +Commuter with center-mounted lock, integrated lights and rustproof paint for year-round urban duty. +Gravel endurance build with disc brakes, 1x drivetrain, and 42mm tubeless tires for rough roads. +Gravel race build with ultra-clean internal routing, wireless shifting, and a short stem for quick steering inputs on loose roads. +Gravel bike with titanium frame, delightful brushed finish, wide 650b tire compatibility and silky-smooth ride quality. +Lightweight titanium road bike with semi-integrated cables, discreet graphics, and endurance-focused geometry. +Cargo tricycle with insulated cooler box, low center of gravity, electronic pedal assist and a robust lock for secure deliveries +Cyclocross training bike with steel fork, winter-ready geometry, and hand-sanded paint finish for an artisan touch. +A children's trail bike with front suspension fork, 24-inch wheels, simple 8-speed cassette and fun starburst decals on a bright red frame. +Touring bicycle with full fender set, robust rack, triple-chainset, and leather saddlebags for extended self-supported trips. +Road endurance bicycle with relaxed geometry, vibration absorbing inserts, and wide 28mm tires for comfort. +Road bike painted in gradient teal with fine pinstripes, downtube shifters and tubular tires ready to be glued. +Full-suspension trail bike with switchable geometry, sealed pivot bearings, and a two-tone metallic colorway. +Touring tandem with wide touring tires, low maintenance drivetrain, and reinforced frame lugs for long mileage. +A low-slung recumbent bike with reclining mesh seat, long wheelbase, mirror mounts and a folding fairing for aerodynamic cruising. +Youth mountain bike with front suspension, easy-shift drivetrain, and bold green highlights. +Light trail hardtail with alloy frame, 120mm fork, 29x2.25 tires and aggressive geometry for fast singletrack. +A beach cruiser with soft pastel paint, chrome accents, oversized saddle and smooth-rolling balloon tires for casual seaside loops. +A classic British roadster bicycle with upright bars, polished chrome fenders, rear coaster brake and a leather sprung saddle. +A commuter with secret integrated lights, hub dynamo charger, belt drive and pebble-gray finish with reflective microbeads. +A boutique handmade titanium gravel frameset with engraved head badge, stealth matte finish, and wide tire compatibility for exploration. +Urban utility bike with built-in child seat, integrated lock, and reflective piping for safe school runs and errands. +A lightweight steel frame cyclocross bicycle with horizontal top tube, cantilever bosses, and a traditional threaded headset for classic feel. +A touring tandem made from chromoly, with multiple racks, heavy-duty rims, triple crankset and low-gear options for loaded climbs. +A folding commuter with electric assist, twist shifter on short handlebar stem, quick-fold pedals and compact handlebars for busy city life. +Fast cyclocross build with flared rims, long headtube, and knobby 35mm tires tuned for muddy, twisty racecourses. +Racing road bicycle in bright yellow with asymmetric chainstay reinforcement, rim-brake calipers and aerodynamic seatpost. +A gravel bike with an innovative removable top tube bag, stealthy matte finish, and low gearing for steep, sustained climbs. +Mountain downhill sled with double-crown fork, massive rotors, reinforced chain guide and low-slung cockpit for control at speed +A modern commuter with hydraulic disc brakes, integrated lock mount, and a stepped geometry for easy standovers in crowded streets. +A gravel bike built with a low-slung top tube for easy dismounts, flared bars for control, and light, tubeless 700x38 tires. +Adventure gravel bike with titanium frame, stealth mounting points, wide tire clearance and classic brushed finish for road-to-trail versatility. +Track fixed-gear with polished steel chainstay, narrow saddle, and a minimalist paint job with single-figure decal. +Classic step-through cruiser with pastel accents, wicker basket, and spring-loaded seat for comfort. +Gravel-specific cyclocross bike with flared drops, reinforced fork ends, and 700c x 40mm tires for fast mixed-surface racing. +Urban commuter with step-through frame, quiet belt drive, integrated lock system and reflective side panels for safety. +A vintage-inspired cruiser with large swept bars, spring-loaded saddle, and subtle retro graphics reminiscent of classic boardwalk bikes. +A cyclocross rig with precise geometry, flatter crown fork, and a frame design allowing quick tire swaps between training and race days. +A commuter with low-maintenance shaft drive, internal gear hub, and discrete charging port for small electronic accessories. +Lightweight urban bike with narrow tires, mid-rise handlebars, and retro lamp dynamo kit. +Youth trail bike with reliable V-brakes, 24" wheels, and kid-optimized handlebar reach for confident first singletrack rides. +A gravel-favored bicycle with a refined carbon fork, spacious tire clearance, and a comfortable cockpit for marathon distances. +Touring commuter with integrated pannier rack, chaincase, upright bars, and built-in locking system for secure parking. +A cyclocross race machine with beefed-up bearings, mud-optimized chainline, and discreet tire clearances that help in sloppy fields. +Compact urban single-speed with bright powder coat, short chainstay for quick handling and platform pedals. +A military-style expedition bike with heavy-duty racks, welded mounts for cages, and matte olive paint with stenciled numbers. +E-assist commuter with belt drive, integrated rear rack, and luminescent paint for safe urban commuting after dark. +A gravel adventure bike with additional gear mount points, robust fork crown, and a paint job inspired by mountain ranges. +Urban fixed-gear with upside-down fork, inverted stem, clean single-speed look, and matching colored wheels for pop. +Touring bicycle with integrated pump mount, solid rack bosses, and enamel paint with faintly visible brush strokes for character. +BMX dirt jumper with lightweight frame, short chainstays, and a rugged finish ready for repeated jumps and slams. +Urban folding e-bike with small wheels, low-step frame, and lightweight battery for quick folds and metro portability. +A touring-ready steel frame with extra braze-ons, long-reach brakes, and a deep matte blue finish to reflect long nights on the road. +A lightweight climbing road bike with 34mm rim depth, 105 groupset, 11-28 cassette and a gradient fade paint transitioning from sky blue to white. +Race-ready track pursuit bike with carbon monocoque shell, deep-section fairing, and an emphasis on straight-line speed. +Fat-tire beach cruiser with oversized saddle, patterned rims and bold palm tree graphics. +Trail-friendly hardtail with tapered headtube, 130mm travel fork, and tubeless sealant already preinstalled. +BMX flatland specialized with low stand-over height, stiff geometry, and thin-profile tires for smooth balance tricks. +A gravel bike with asymmetrically-shaped chainstays for improved tire clearance, 700x40mm tires, and a matte olive-green paint to blend into trails. +Folding city cruiser with step-through hinge, small wheels, banana seat, and retro two-tone paint for compact living. +A commuter with internal cable routing, full-length mudguards, and discreet integrated lights for an elegant urban profile. +Gravel adventure bike with carbon-reinforced dropouts, flared bar options, and high-volume tubulars for rough miles. +Youth balance bicycle in primary colors with foam tires, low saddle height and rounded edges for safe learning. +Cargo bike with wooden deck, passenger belts, hydraulic brakes and integrated lock for securing goods. +Gravel adventure bike with integrated top tube storage, wide bars, and a low-slung frame for balanced loads. +A high-volume fat-bike with custom graffiti paint, 5-inch tires, rigid fork, and a single-speed conversion for winter fun. +Powder-blue commuter bicycle with dynamo hub, luggage rack, chain cover and reflective sidewalls for visibility +Mountain enduro with electronic suspension adjustment, powerful brakes, and a coil-oriented rear shock for gravity-focused days +Urban speed bike with polished aluminum frame, flat bars, hydraulic disc brakes, and reflective sidewall tires for night visibility. +Electric folding cargo bike with modular attachments, long-range battery and reinforced folding hinge for hauls. +Compact folding bike with 20-inch wheels, hinge frame clamp, internal cable routing and pannier-compatible rear rack +Cyclocross training bike with spare tubular strapped, durable cables, and a sturdy fork for frequent off-season practice +A commuter e-bike with mid-drive motor, integrated battery along the downtube, hydraulic disc brakes, and puncture-resistant tires for daily reliability. +A gravel racer with top-tier wired electronic shifting, fast-rolling 700x35 tubeless tyres, and a ballistic frame for high-speed chases. +Mountain all-mountain with adjustable geometry, impressive tire clearance, and a reassuringly stiff bottom bracket. +Road bike with disc brakes, 28mm high-volume tires, ergonomically shaped drops, and a comfortable endurance fit. +Bike with belt drive and internal gear hub, fully enclosed chaincase and low maintenance city riding setup. +All-conditions touring bike with stainless steel fender mounts, dynamo hub, sealed bearings and wide gear range. +Mountain hardtail with 120mm fork, tapered headtube, dropper post, and fast-rolling 29" tires for plus terrain. +A minimalist gravel commuter with titanium frame, belt drive, 650b wheels, hydraulic brakes, and discreet rear rack mounts for light touring. +Gravel bike with modular fork mounts, triple bolt top tube, and low-slung geometry for comfort. +Urban cargo long-tail with primavera rack, child's seat, integrated lights, and stable geometry for family errands. +Urban folding single-speed with low profile, quick fold, and small footprint for stowing under desks or in closets. +Minimal single-speed commuter with stainless-steel chain, simple frame silhouette and bright anodized accents. +A gravel endurance bike with progressive head tube angle and comfortable saddle for multi-day rides on technical tracks. +Fat-tyred beach cruiser with wide hub spacing, coaster brake, and flamboyant paint to match sunny boardwalk vibes. +A gravel adventure frame with a unique camo paint job, internal routing for a dropper post, and a long wheelbase for stability under load. +A cyclocross racer with full-carbon fork, 1x shifting tuned for quick transitions, and a versatile setup for mixed-season racing. +Compact folding commuter with 16-inch wheels, quick-release seatpost, and slim frame for tight apartments. +A children's BMX with compact frame, padded crossbar, gyroscopic cable set and neon orange gloss finish with bold stickers. +Adventure gravel bike with plenty of braze-ons, stealth matte paint and long chainstays for stability when loaded. +Classic touring tandem with steel frame, leather-wrapped handlebars, true blue enamel paint and heavy-duty rear rack for gear. +Retro BMX with chrome frame, short top tube, gyro setup, and colorful grips for skatepark flair. +A lightweight road climbing bike with deep tooth chainring, small wheelset option, and a geometry that favors long-legged seated power. +High-traction fat-bike with forged fork, wide studs, full fender compatibility and reinforced hubs for heavy winter use. +Mountain full-suspension bike with 29" wheels, progressive geometry, and dropper post to navigate steep technical trails. +Electric mountain trail bike with 150mm travel, torque vectoring motor control and durable drivetrain for technical terrain. +A lugged-steel track frame repurposed into a single-speed commuter with modern brakes and fenders. +Gravel commuter with flashy neon accents, reflective decals, 700x40 tires and a practical rack-top bag +Gravel plate-style racer with 700x36 tires, flared shallow drops, carbon fork and lightweight alloy frame. +Steel commuter with internal cable routing, stainless fender hardware, and a deep navy lacquer. +A randonneur with long wheelbase, Brooksmoss leather saddle, and polished stainless steel rack with a brass strap. +Fat-tire snow bike with 4.8-inch tires, low tire pressures, rigid fork and steel frame for winter riding on snow. +A compact wheel recumbent with low recumbent seat, small folding front fairing, and bright high-visibility paint. +A purpose-built cyclocross race bike with a short wheelbase, flared drops, ultra-light tubular rims and quick mud-shedding geometry. +Electric mountain trail bike with remote lockout, advanced motor mapping, and a rugged drivetrain for extended off-grid adventures +A modern track pursuit bike with steep seat tube angle, aerodynamic cockpit, and a disc rear wheel for maximum power transfer. +A commuters' electric cargo bike with long-range battery, torque-sensing motor, and adjustable rear deck for heavy-duty hauls. +Racing time trial machine with integrated hydration and narrow rear triangle for optimal aerodynamic posture on long efforts. +A compact folding cargo with reinforced platform, quick-fold hinge, and a comfortable upright handlebar for practical urban load-carrying. +Race-oriented time trial bicycle with elongated tail, integrated tri-bars, hydration loop and lightweight construction for podium sprints +Mountain downhill racer with triple-phase suspension, heavy-duty wheels, and a stiff headtube for big-ride confidence. +Steel road frame with restored chrome lugs, hand-painted head badge, and period-correct aerobars. +A kids' balance bike with composite frame, rubber tires, low seat and friendly animal-face head tube decal in sun yellow. +A titanium framed mountain bike with custom brazed-on bottle bosses, discreet cable routing, and an understated satin finish. +A commuter with built-in frame lock, integrated front light, touring-style saddle and left-side drivetrain to avoid road salt. +Electric cargo longtail with fold-down passenger seats, rain cover option, and built-in child harness anchors for family rides +Electric assist cargo trike with enclosed cargo bay and foldable ramp for easy loading of goods. +A bespoke titanium gravel frame with brushed finish, engraved headtube, and subtle anodized accents on the dropouts. +All-mountain bike with progressive head angle, 160mm fork, dropper post and grippy 2.5" rubber for rowdy descents. +Electric cargo bike with 2,000W hub motor and reinforced frame for heavy loads and steep hills. +Urban folding commuter with internal gear hub, sturdy hinge, and reflective accents for nighttime commuting. +Lightweight track pursuit rig with carbon bars, aerodynamic stem, and splined bottom bracket for maximum power transfer. +Lightweight track bike with single-speed drivetrain, tubular rims, polished steel finish and minimal graphics for pure speed. +A sleek carbon time trial bicycle with integrated hydration, aerodynamic chainrings, and a tucked-in cockpit for reduced wind resistance. +Lightweight alloy road machine with short chainstays and snappy acceleration for punchy climbs. +A titanium commuter with modular rack system, internal routing, and a clean brushed aesthetic that stays elegant in urban environments. +A mountain downhill frame with generous clearance, reinforced bearings, and stealth protective layers on high-impact surfaces. +A mountain trail bike with progressive geometry, 150mm fork, dropper post and knobby 2.35" tires for aggressive singletrack. +Gravel racer with lightweight wheelset, tubeless-ready rims, and a subtle pattern reminiscent of contour lines across the tubes. +Gravel racing build with carbon frame, bladed fork, 35mm tires, and a narrow cockpit for powerful, sustained riding. +A carbon trail bike with 140mm rear travel, remote dropper actuator, wide handlebars, and modern chain retention for technical singletrack. +A cyclocross training rig with steel frame, mud-shedding geometry, tubeless tires and vintage-style dark green paint with white number plate space. +A vintage-inspired city bicycle with wicker basket, brass headlamp, sweptback bars, and waxed leather saddle for refinement. +A downhill race frame with replaceable derailleur hangers, triple-butted chainstays, and a geometry designed to handle big hits and high speeds. +Vintage BMX cruiser with wide saddle, swept-back bars, coaster brake, and balloon tires for boardwalk cruising. +Track sprint frame with reamed bottom bracket, large-diameter tubes and high-stiffness layup for explosive power. +A downhill freeride bike with heavy-duty frame, reinforced chainstays, long travel fork, and a coil shock tuned for big drops. +Hardtail trail slayer with modern wheel size, dropper compatibility, wide handlebars, and an efficient pedaling platform. +A city bike with full fenders, weatherproof paint, dynohub lighting, and a cushioned upright saddle for commuting in all seasons. +A commuter with integrated front and rear racks, soft saddle, and reflective piping for increased visibility on rainy days. +Electric cargo bike with low-step front deck, child bench, and modular extension rails for packages. +A stylish city single-speed with wood veneer fenders, leather-sprung saddle, and a classic cream color to match cafe-style afternoon rides. +Modern trail bike with 29" wheels, 130mm travel, lightweight aluminum frame, and tubeless-ready rims for fast singletrack. +Vintage-inspired cruiser with wide forks, custom paint, sprung saddle, and balloon tires for a leisurely coastal aesthetic. +A commuter with magnetized lights that attach to the frame, wide platform pedals for winter boots, and a reflective rear pannier for safety. +A gravel commuter hybrid with fenders and racks, 700x38 tires, and a durable drivetrain for daily cross-terrain commutes. +A kids' cruiser with low step-through frame, oversized banana seat, and playful sticker kit included to personalize the ride. +Mountain trail rig with short stem, wide bars, and 2.6-inch modern trail tires for traction and control. +A bespoke hand-painted track pursuit bike with metallic flake, custom decals, and a rounded top tube profile. +Compact city folding bike with integrated carry handle, small wheels, and a lightweight alloy frame for multimodal transport. +A modern fixed-gear conversion with flip-flop hub, polished crankset, and matte charcoal paint for an understated street build. +Road racing frameset with steep seat angle, deep-section carbon wheels, and short wheelbase for sprint-focused performance. +Commuter-friendly city bike with low-step geometry, enclosed chaincase and puncture-proof tires. +A modern trail hardtail with a competition geometry, dropper post compatibility, and wide rims paired with fast-rolling tires for spirited singletrack rides. +Mountain cross-country full-suspension with a featherweight carbon frame, nimble geometry and fast 29er wheelset for racing and training +A traditional Dutch bicycle with upright posture, step-through frame, full chain guard, and a sturdy rear carrier for market runs. +A classic steel road bicycle with Campagnolo-era parts, low-profile calipers, and a vintage paint job for nostalgic group rides. +An urban utility bicycle with integrated milk crate rack, puncture-proof tires, platform pedals and reflective stripes for night safety. +High-clearance gravel racer with 700c x 45 tires, single-ring simplicity, and wide handlebars for fast days off the beaten path. +A lightweight single-speed mountain bike with 27.5+ wheels, rigid fork for skill development and grippy 2.6" tires to handle rooty trails. +A modern electric-assist city bike with mid-drive motor, 500Wh battery integrated into the downtube, upright bars, and hydraulic disc brakes. +Commuter with hydraulic disc brakes, reflective frame accents, and a single-button hub to wake the battery. +A matte-black carbon road bike with aerodynamic tubing, 11-speed groupset, deep-section wheels and tubular tires for racing. +A classic European city bicycle with step-through frame, integrated basket, fendered wheels and an internal-gear hub for easy shifting. +Gravel endurance frame with flared drop bars, vibration-damping paint systems, and stealth matte olive paint with tan sidewalls. +A modern gravel bike with pass-through quick releases, fender-compatible tubing, and discreet luggage mounts for stealth touring. +Trail-oriented 29er mountain bike with modern slack headtube, internal dropper routing, and tubeless-compatible rims. +Urban fixed-gear with riser bars, polished chainring, anodized headset, and slim tires for sharp city sprints. +A handbuilt titanium road bike with elegant double-butted tubes, neat welds, and a satin raw finish that shows craftsmanship. +A cargo longtail with child seats and optional canopy, reinforced rear axle, and a low center of gravity to keep kids secure. +A commuter with sustainable bamboo frame, coaster brake hub, belt drive and a hand-woven basket for eco-conscious urban rides. +A polished steel vintage touring mix with downtube shifters, wide touring bars, mudguards, and inscriptions of mileage on the frame. +Road climbing weapon with ultralight frame, narrow seatpost, and a heroic two-tone paint job signaling speed. +Mountain trail bike with tubeless-ready rims, 2.4-inch rubber, and wide-supportive rims for confident cornering. +Road bike with endurance geometry, wide-tire clearance, and stepped top tube for aerodynamic yet comfortable positioning. +A classic road racer with thin chrome fork, steel spindle bottom bracket, 9-speed cassette and a cream-to-red fade paint job. +Gravel adventure bike with triple chainrings, frame bag compatibility and a weathered military green finish. +A sporty hybrid with flat bar, shallow V-brakes, 700x32c tires and a comfortable upright position for mixed roads and light trails. +Mountain all-mountain slayer with 150mm travel, modern geometry, and refined suspension for big-line confidence. +A lightweight touring bike with triple-butted steel tubing, integrated pump mount, and a comfortable bar shape for long days in the saddle. +Adventure touring bike with reinforced forks, heavy-duty spokes, triple-drilled chainstays and removable fender mounts for adaptability. +Kids' BMX with short top tube, robust pegs, and colorful anodized headset for park shredding. +A titanium cross-country racer with featherweight tubes, minimalist graphics, and a geometry tuned to excitedly climb and accelerate. +A gravel-specific drop-bar bike with seat-tube mounted fender boss, double chainring option, and a neutral handling geometry for loaded miles. +Folding city commuter with 20-inch wheels, belt drive, adjustable handlepost, and a carry harness for transit-friendly use. +A classic Dutch transport bicycle with chainguard, upright handlebar, big saddle springs, and wide platform pedals for city errands. +Fixed-gear track-style commuter with straight gauge spokes, narrow urban tires, and a small front light. +A commuter with dynamo hub, subtle integrated lights, ceramic bearings in the hub, and a puncture-resistant belt drive drivetrain. +Mountain downhill racer with massive travel, reinforced welds and a dual-piston rear brake system for steep. +Mountain fatbike with studded tires, snow-ready gearing, and a burly steel frame. +A gravel race frame with burly chainstay reinforcement, 2.0-inch tires, and a bright reflective stripe down the seat tube for night rides. +A lightweight cyclocross bike with carbon layup, semi-integrated cables, mud-optimised geometry and deep forest green paint. +A restored cruiser with minty chrome, whitewall tires, retro saddle and an oversized chain guard with vintage decals. +Comfortable cruiser with swept handlebars, sprung leather saddle, wide tires and soft pastel paint for relaxed rides. +A compact cargo trike with stable three-wheel design, electric assist for steep hills, and a large weatherproof cargo tub. +A mountain enduro bike in matte forest green, 160mm travel, dropper post and beefy 2.4 tires for aggressive descending. +A modern gravel builds with electronic shifting, aero bars for gravel time trials, and wide-volume tires to eat up rough surfaces. +A track-inspired road bike with short head tube, aggressive saddle position, and tubular tires for crit race dynamics on fast urban circuits. +High-end road time trial frame with integrated hydration and a focus on rider aerodynamics for triathlon speed events. +A gravel bike with colorful enamel head badge, three-pack mounts on the fork, and a long-range endurance setup. +Cyclocross bike with cantilever brake bosses, 700x35c knobby tires, and slender chainstays for quick shouldering. +A sharp-looking track sprinter with aero seatpost, steep head angle, and glossy black paint with pinstriped logos. +Gravel-focused alloy bike with flared drop bars, 1x gearing, integrated frame protection, and a rugged matte finish. +Off-road fatbike with staggered tire clearance, full chainguard, and hand-painted desert camo frame. +Trail-focused 29er with modern geometry, 130mm travel, tubeless-ready rims and fast-rolling tires for mixed trails. +A sleek gravel frame with internal top-tube storage, integrated mounting points for extra bottles, and a geometry built for long off-pavement days. +Adventure touring bicycle with wide clearance, framebag-friendly triangle, dropper-compatible seatpost and tubeless-ready rims. +City folding mini velo with quick release folding stem, small-diameter wheels and an urban-friendly single-speed setup. +Urban single-speed with polished aluminum frame, rear coaster brake, and a small rear rack for light cargo loads. +A hand-built steel track frame with tapered fork, polished raw steel finish, minimal decals and an emphasis on clean lines and stiffness. +Urban utility bicycle with luggage platform, internal gearing, robust kickstand, and puncture-resistant tyres for grocery hauling. +Lightweight aluminum commuting bike with rack mounts, puncture-resistant tires, ergonomic saddle, and easy-maintenance hub gearing. +A trail-ready full-suspension bike with floating rear linkage, adjustable geometry, and large-volume tires for flow-oriented singletrack. +A retro-styled Dutch city bicycle with chaincase, sprung saddle, and a relaxed upright geometry for easy short urban trips. +Recumbent two-wheeled road machine with low drag, long wheelbase, and ergonomic mesh seat for all-day comfort. +Mountain downhill freeride bike with double-crown fork, burly axle standards, and heavy-duty wheels. +Cargo bike with sturdy wooden deck, extended frame, and ergonomic handles for pushing heavy loads. +Gravel bike with integrated top tube bag mount and subtle reflective speckle paint for dawn starts. +Cyclocross training rig with alloy frame, quick-release wheels, and flared drop bars for control. +A gravel bike with progressive geometry, flared bars, and tubeless-ready 45mm tires for ambitious off-road centuries. +A performance track bike with minimal stack height, fixed gear ratio tuned to velodrome sprints, and a stiff carbon fork for quick steering. +Cargo bike with longtail wooden deck, twin rear seats, reinforced frame, hydraulic brakes, and heavy-duty tires for hauling kids and groceries. +Kids' BMX with low stand-over, small-frame geometry, and flashy stickers to match a fearless approach to tricks. +A city hybrid with wide saddle, gel grips, and a kickstand-ready rear rack for errands and weekend shopping trips. +Mountain downhill competition bike with reinforced headset, 203mm rotors, and triple-clamp fork crown for maximum stiffness and braking power +Kids' balance bike with adjustable seat, light frame and friendly color palette for easy skill progression. +Gravel bike with vibrant purple fade, 40mm tires, and integrated frame storage pockets. +Folding commuter with single-handed fold, pneumatic tires, small-battery e-assist, and a lightweight frame for portability and speed +Downcountry 29er with 120mm front and rear travel, light yet stiff frame, and fast-rolling 2.25" tires. +Steel cyclocross bike with cantilever brakes, mud-clearance 35mm tires, and reinforced dropouts for aggressive off-road racing. +Track fixed-gear roadster with polished steel fork crown and leather bar tape. +A commuter with integrated child seat at the rear, wide stable pedals, and a low center of gravity to keep kids safe on urban rides. +A modern gravel racer with integrated handlebar storage, stealth seatpost storage tube, and wide tire clearance for mixed terrain. +Gravel plus bike designed for bikepacking with 2.4-inch tires, wide bars, and reinforced frame mounts for heavy gear and rough tracks +A kids' balance bike in light teal with cushioned grips, no pedals and fun animal stickers on the frame. +Gravel bike with triple water bottle mounts, welded-on fender eyelets, and a subtle speckled coat for durability. +Gravel racer with electronic groupset, hydraulic disc brakes, and fast-rolling 700x35 tubeless tires for stage events. +A stealthy black road bike with hidden cabling, narrow 25mm tires, and a stripped fairing on the downtube for slightly more aero. +Mountain downhill race sled with reinforced head tube, DH-specific brakes, coil shock and carbon seatpost clamp for weight savings +Folding electric cargo bike with extended deck, safety rails, and hydraulic brakes for safe, foldable family transport. +Mountain DH race bike with massive headtube, reinforced chainstays, and heavy-duty frame protection. +A mountain full-suspension with tuned kinematics, 150mm travel, and a balanced feel that encourages playful yet confident riding. +A vintage-inspired single-speed cruiser with tan leather grips, curved downtube, and cream fenders for classic weekend rides. +A cargo longtail with low deck, kid harness mounts, and a clean minimalist paint job to match modern urban aesthetics. +A commuter with integrated rear light cluster, low-profile fenders, and a hub-based five-speed system for clean shifting. +A matte-black carbon fiber road bike with aero tube shaping, integrated Di2 shifting, deep-section carbon wheels, and hidden cables for a sleek racing profile. +Cargo longtail with modular deck, military-grade straps and adjustable footrests for secure loads. +A bare-carbon gravel bike with stealth decals, 700c wheels, thru-axles, and a 1x drivetrain for mixed-terrain racing. +A lightweight cyclocross training bike with reinforced fork, high-volume 35mm tires, and a low standover for shouldering obstacles. +Mountain all-rounder with 120mm travel fork, dropper post, 2.35-inch tires, and a wide-range 12-speed cassette. +A touring bike with integrated front and rear racks, Sturmey-Archer hub option, triple chainring for climbing and classic understated paint. +A gravel endurance bike with comfort-focused geometry, vibration-damping seatpost, 700x40 tires and soft graphite paint with light speckle. +Kids' balance bike with bright decals, adjustable handlebar height, and non-slip grips to make first balancing easy and safe. +A nostalgic city cruiser with brass bell, leather saddle, woven front basket and a cream-and-olive paint job for vintage charm. +A commuter with reflective sidewall tires, integrated lights, quiet hub motor and a low-slung frame for secure mounting and dismounting. +Modern gravel bike with raised chainstays, gravel-specific cranks, and a whimsical mountain decal on the seat tube. +A folding low-step utility bike with small wheels, adjustable handlebar stem, and a rear rack that collapses with the frame for storage. +Urban single-speed with flipped headset spacers, anodized seat clamp, and a compact frame for agile city weaving. +Tandem recumbent lowracer with long wheelbase, aerodynamic fairing option and adjustable boom positions for optimized pedaling. +A carbon gravel racer with electronic shifting, integrated bar and stem, and a pearlescent paint finish that changes with angle. +High-capacity cargo bike with electric assist and modular attachment points for businesses and families. +A family cargo bike with multi-rider seating options, safety harness attachment points, and integrated rain cover for all-weather child transport. +A gravel endurance bike with progressive geometry, slack head angle, 700x47 clearance, and gravel-specific gearing for long days. +Track pursuit bike with shaved-down seatpost clamp, sharp aero shaping, and a glossy deep-black finish for velodrome sleekness. +A practical cargo bicycle with welded steel platform, tie-down anchors, roller brakes and reflective strips for evening deliveries. +Smooth-rolling commuter with low friction hub, sealed bearings, and puncture-resistant 28mm tires. +A commuter with low-maintenance belt drive, internally geared hub, and color-matched mudguards for a clean look. +An off-road electric mountain bike with mid-drive motor, 160mm travel, dropper post and mud-shedding tire tread in forest green. +Road racing frame with aerodynamic shaping, full internal cable routing and a deep carbon fork for precise steering. +A kid’s balance bike with low-slung frame and grippy EVA tires to provide a safe, simple introduction to two-wheeled riding. +A classic lugged steel road frame with period decals, a comfortable geometry for club rides, and a ride quality that smooths out long miles. +A kids' mountain starter bike with easy-reach brakes, durable steel frame, 20-inch wheels and bright safety lime paint. +A mountain hardtail with classic steel frame and modern suspension fork, blending endurance with vintage aesthetics. +Mountain hardtail with slack geometry, 140mm fork, and tubeless-ready rims for aggressive trail riding. +Fat-bike commuter with studded tires, insulated grips and weatherproof charging port for winter commutes. +City folding bike with internal hub gear, reflective rim tape and compact locking mechanism for trains. +A compact commuter with hub-based electronic shifter, internal battery, and tubeless-ready 28mm tires to keep rolling in city traffic. +A flattened-gravel endurance frame with elevated chainstays, integrated storage box in downtube, and a wide gear range for alpine passes. +A commuter with stealth belt drive, internally geared hub, minimalist fenders and a charcoal electrostatic finish. +Adventure touring bike with 650b wheels, 42mm tires, triple chainring, and robust rack and fender mounts for multi-day trips. +Compact city folding bicycle with belt drive, low standover, and a quick-release rear wheel for easy storage. +Racing aero road bike with full carbon cockpit, integrated seatpost, and flush cable ports. +Cargo longtail electric bike with passenger footrests, reinforced frame, hydraulic brakes and rain-resistant canopy. +A cyclocross training bicycle with reinforced toe overlap, sealed pivot bearings, and a race-ready tire clearance for aggressive CX training. +A gravel-focused adventure bicycle with reinforced fork-mounted racks, extra tire clearance, and a supple steel rear triangle for comfort on long rides. +Touring-ready steel bicycle with lugged frame, built-in pump mounts, and a spring-loaded rear rack for easy loading. +Lightweight steel cyclocross machine with modern tube profiles, wide clearance and discreet internal routing for clean aesthetics. +Modern chopper-style bicycle with stretched forks, wide front wheel, low-slung frame, and minimalist drivetrain for cruising. +Touring bike with polished steel finish, long-range hubs and full mudguard set for wet-weather touring. +Dirt-jump BMX with short top tube, reinforced gussets, gyro-free stem and knobby 2.3-inch tires for park tricks. +A folding bike with large 24-inch wheels, single-hand latch, rear hub gears and a compact folded height that fits under a desk. +Lightweight track pursuit bike with straight-blade fork, ultra-aero tube shapes, and track-specific gearing optimized for sustained speed. +Off-road hardtail with slack head angle, burly tubing, 29" front wheel, and a tapered head tube for confident steering. +Classic steel road bike with hand-polished lugs, leather saddle, era-specific decal and comfortable but efficient geometry for rides. +A track-style pursuit bike with elliptical chainring, narrow saddle, aerodynamic bars and a glossy jet-black paint scheme. +Mountain trail hardtail with 140mm fork, roomy tire clearance, dropper post and tubeless-ready rims for confident trail shredding. +A dual-suspension trail bike with 140mm in the rear, progressive geometry, and a burly 35mm offset fork for stability. +Enduro 29er with progressive geometry, aggressive head angle, and carbon fiber lower link for weight savings. +A high-travel downhill bike with triple clamp fork, massive 220mm travel, and wide handlebars for tackle park features fearlessly. +A lightweight aluminum commuter with elegant welds, internal cable routing, and puncture-resistant 35mm tires for daily reliability. +Cargo tricycle with flatbed, electric assist, modular load anchors and anti-tip geometry for safe neighborhood hauling +Bike with disc brakes, thru-axles front and rear, and a low-step frame ideal for urban commuting. +A classic randonneur with lowtrail geometry, leather saddle, and generator-powered lights for nights on country roads. +Urban commuter with puncture-resistant tires, internal hub gearing, low-step frame, and a practical rear rack for daily errands. +A hand-painted steel road bike with accent pinstripes, leather-wrapped bars, and vintage-styled decals to celebrate traditional craft. +A commuter with front basket, stable kickstand, and a small step-through frame to make city errands easier on footbridges and buses. +A city cargo long-tail bike in matte gray with child bench, integrated seatbelts, pedal-assist motor, and puncture-proof tires. +Electric cargo bike with front platform, quick-release child seat, and powerful regenerative braking system. +Lightweight aero road bike with integrated stem-bar, Di2 shifting, and a deep-section rear wheel for time-saving speed. +A commuter with integrated GPS tracker, swappable battery, and robust internal cabling to keep the silhouette clean and theft-resistant. +A ride-ready cyclocross bicycle with disc-compatible frame, wide 33mm tubeless tires, and a strong yet light alloy build. +Classic mixte with wicker basket, comfortable saddle, chaincase, and a calm geometry for relaxed neighborhood rides. +Urban single-speed with bright anodized chainring, lightweight crankset, and a comfortable saddle for daily errands. +A classic Dutch-style cargo bicycle with low step-through frame, extended rear deck, and wide tires to smoothly carry weekly groceries. +A BMX race bike with 20" wheels, lightweight aluminum frame, narrow handlebars, and a race-ready sprocket for track sprints. +Single-speed urban fixie with flip-flop hub, glossy candy blue frame, and deep V rims. +Retro-inspired roadster with swept handlebars, wide comfortable saddle, and two-tone paint for leisurely rides. +A touring tandem with long wheelbase, dual panniers, and an array of mounting points for extended two-up travel comfort. +A velodrome sprint bike with track handlebars, stiff monocoque frame, narrow tubular tires, and a high-gear ratio tuned for top-end speed. +A beach cruiser with turquoise paint, balloon tires, swept-back handlebars and a springer fork for laid-back seaside riding. +Lightweight track bike with no-brake conversion, polished alloy frame and aero deep-section rims. +A modern alloy commuter with integrated lighting and cable routing, mudguard mounts, and a durable powder coat for daily fidelity. +Electric folding cargo bike with modular baskets, LED headlamp and regenerative braking. +A dual-suspension enduro bike with long travel, adjustable geometry, reinforced headtube and a rugged matte finish for backcountry efforts. +A cyclocross endurance bike with carbon fork, wide tire clearance, mechanical disc brakes and a forgiving geometry for long muddy races. +Touring tandem with synchronized pedaling, triple bottle mounts and long-chainstay stability for distance. +Gravel endurance bike with endurance geometry, flared gravel bars, 40mm tubeless tires and carbon seatpost for comfort. +A kid-sized mountain bike with 24" wheels, suspension fork, disc brakes, and bright safety graphics encouraging outdoor play. +Race-oriented cyclocross bike with lower bottom bracket, stiff bottom bracket shell, and light tubeless wheels for mud racing. +Recreational cruiser with springer fork, oversized saddle, bold striping and coaster brake for relaxed weekend rides. +Gravel race bike with SRAM red eTap, lightweight carbon wheels, and a precise neutral gray finish. +Gravel exploration build with mixed cassettes, flexible tire clearance and frame-mounted pump for backcountry days. +Cyclocross frameset with high-stance geometry for faster remounts, precise welds, and a matte slate gray paint. +Gravel endurance mixed-material frame with carbon stays, alloy main triangle, and compliance zones for long-day comfort. +City utility step-through with internal hub, full chaincase, and wide comfort saddle for reliable daily commuting. +Classic Dutch town bike with coaster brake, relaxed geometry, and wicker basket for weekend errands and picnics. +A kids' trail bike with robust V-brakes, short cranks, protected chainring, and knobby 24" tires to build trail skills safely. +A women's specific touring bicycle with lower standover, relaxed reach, integrated pannier fittings and plush saddle for long comfort. +A beach cruiser with pearlescent paint, wicker basket, chrome rails, springer fork and a two-tone saddle for sailor-style seaside rides. +Road climber with lightweight tubing, high-gearing compact crankset and shallow-depth rims for quick ascents. +A touring tandem with long wheelbase, comfortable semi-upright posture, dual racks and blue lacquer with white trim. +Cross-country race hardtail with carbon fork, lightweight 29er wheels, narrow 2.1" tires and efficient pedaling geometry. +A children's balance bike with adjustable seat, rubber-coated handlebar grips, and personality-packed bright patterned frame. +Fixed-gear single-speed with powder-coated pastel frame, minimal decals and a flip-flop hub for single-speed freedom. +A compact travel folder with tiny wheels, internal gearing, and a slim carry case for air travel and overseas commuting. +A retro chromoly mountain bike with rigid fork, 26-inch wheels, wide platform pedals, and a distinguished forest-green enamel coat. +Mountain bike with dropper post, SRAM GX Eagle, and a subtly camo frame wrap. +A practical town bicycle with integrated rear child seat mount, sturdy alloy rack, wide saddle and puncture-proof tires for school runs. +A performance steel road bike with hand-brazed lugs, clean internal routing, and a classic paint scheme with pinstripes. +Fat-tire commuter with 26x4-inch rubber, puncture-proof lining, and bolt-on fenders for winter routes. +Touring bike with steel braze-ons, reinforced seatpost clamp, and enamel livery with map decals. +A gravel-capable single-speed with wide 700x40 tires, coaster-free hub for fixed mode, and an earthy olive paint for low-key exploration. +Gravel endurance bike with dampening micro-suspension in seatstays, disc brakes and a satin forest green finish. +Folding commuter bicycle with 20" wheels, quick-fold hinge, compact frame latch, and integrated carrying handle for multi-modal transport. +A gravel grinder with light frame, 700x40 tires, flared drops, and hand-dipped anodized blue headtube. +Custom handbuilt steel frame bicycle with lugged joints, brushed stainless accents, classic geometry, and Brooks leather saddle. +A steel MTB with slacker head angle, 140mm travel fork, and a resilient paint finish that disguises scratches earned on rocks and roots. +Kids' cruiser with training wheels, bright floral graphics, coaster brake and a padded banana seat. +Mountain cross-country race frame with efficient pedaling geometry, 29" wheels, and a precise handling ethos for elite competition. +High-pivot enduro mountain bike with long travel, coil shock option, and burly 27.5+ compatibility. +Road bike with classic steel tubing, modern brake compatibility and supple ride characteristics for old-school enthusiasts. +A light trail hardtail with carbon frame, 120mm fork, dropper post, XM-profile rims, and a race-ready SRAM GX Eagle 12-speed. +Electric step-through commuter with quiet hub motor, walk mode, integrated lights, and a folding rear rack for convenience. +A gravel beast with 650b wheels, massive 2.2-inch tires, single-ring setup and frame-mounted tool storage under the downtube. +Commuter with enclosed chaincase, hub dynamo lighting, and a classic lacquer finish for dependable daily use. +Gravel plus expedition bike with large-volume tires, multiple rack mounts, reinforced fork and internal frame storage for ultra-distance travel +A full-carbon cross-country race bike with 29" wheels, 100mm travel fork, and a featherlight frame optimized for climbing and acceleration. +A folding commuter with magnetic latch, adjustable stem, 20-inch wheels and a built-in bottle cage for portability. +Road aero frame with hidden seatpost clamp, sculpted stays, and a high-modulus carbon weave visible under clearcoat. +A BMX flatland setup with shortened top tube, smooth-rolling tires, and a low-profile seat clamp to facilitate spins and technical moves. +Gravel commuter with 650b wheel option, puncture-resistant tubeless tires, and a slimline dynamo lighting system for early mornings. +Gravel endurance frame with vibration-damping inserts, 35mm tire clearance, and integrated top tube bag mount for long rides. +Vintage porteur bicycle with front wood platform, polished brass fittings and a generous headlamp mount. +Lightweight commuter with carbon fork, hydraulic discs, reflective decals, and a tube-less wheelset for reliability. +A vintage randonneur with polished steel fenders, leather straps on a front rack, and a slightly relaxed geometry for long days. +A custom steel touring bicycle with hand-formed dropouts, swing-out racks, and a geometry tuned for heavy loads and patient long days. +Vintage mixte bicycle with pastel color scheme, floral decals, wicker basket, and a spring-suspended seatpost for comfort. +A compact fixed-gear commuter with deep polished rims, narrow saddle, bolt-on fenders and a crisp monochrome paint scheme for urban simplicity. +A downhill slayer with 200mm fork, massive 203mm rotors, and a carbon-reinforced alloy frame to endure big impacts and steep drops. +A gravel adventure bike with custom welded rack, integrated tire pump mounts, and a subdued earth-tone paint job. +Race-ready road bicycle featuring full electronic shifting, carbon deep-rim wheels, and lightweight race geometry for criterium victories. +Handmade lugged steel road bike with Reynolds 853 tubing, polished finish, classic geometry and downtube shifters. +Kids' cruiser with low top tube, single-speed coaster brake, and bright decals to encourage independent neighborhood exploration. +Kids' training bike with removable stabilizers, cheerful decals and safety chainring guard. +Enduro mountain bike with adjustable chainstay length, 170mm travel and burly sealed bearings. +Vintage commuter with restored chrome-lugged frame, leather saddle, and a wicker basket maintaining classic urban elegance +High-performance road bike with integrated power meter, aero wheelset and a polished two-tone livery. +A versatile hybrid bike with suspension fork, 700c wheels, puncture-resistant tires, and a comfortable upright ride for mixed surface commutes. +Retro-styled single-speed with narrow whitewall tires, chrome fenders and stylized pinstriping on the top tube. +A specialized cyclocross bike with carbon dropouts, slender chainstays, and a light weight for quick shouldering through barriers. +Urban single-speed with compact wheelbase, riser bars, and a carefree upright posture for quick city jaunts. +Urban single-speed with polished steel frame and blacked-out components for a stealthy, minimal city commuter vibe. +Classic mixte bicycle with gentle step-through frame, leather saddle, and a wicker basket strapped to the front rack. +A modern cyclocross rig with ergonomic bar tops, long reach brakes, and an understated matte stone finish that keeps focus on performance. +Mountain downhill sled with massive frame, dual-crown fork, 200mm travel, and heavy-duty wheels to survive race courses. +A mountain downhill monster with double crown fork, heavy-duty 4-piston brakes, and reinforced headset bearings for extreme abuse. +A children's balance bike with matte turquoise paint, comfy saddle, wide rubber wheels and easy-grip handlebars to build confidence early. +A mountain fat-bike with anodized blue rims, 4.0" tires, rigid fork and a single-speed drivetrain for soft-surfacing adventures. +A vintage steel town bicycle with ornate lugwork, brass rivets, and a comfortable sprung saddle for nostalgic neighborhood rides. +City utility bike with integrated cargo box, low-slung step-through frame, hydraulic brakes, and a child seat mounting system. +City commuter with basket mount, easy-step frame, hub dynamo front light and integrated mudguards for all-weather practicality. +A bespoke titanium touring frame built with multiple braze-on extras, dynamo hub wiring, and eyelets for panniers and bottles. +Electric mountain trail bike with big-torque motor, trail-smoothing geometry, and 150mm travel to conquer climbs. +Cargo electric bike with long deck, mid-drive motor, hydraulic brakes and a friendly low step-through entry for easy loading. +Compact commuter with belt drive, integrated hub gears, full fenders, and low-maintenance sealed components for daily use. +A sleek commuter e-bike with mid-drive motor, integrated battery in the downtube, belt drive, and a step-through frame for easy mounting. +A restored vintage town bike with brass accents, long-chaincase, and a very comfortable saddle that invites slow neighborhood exploration. +All-road steel bike with polished lugwork, 650b wheels, 38mm mixed-terrain tires, and an elegant fillet-brazed top tube for centuries and gravel. +A full-suspension trail mountain bike with 150mm travel, progressive geometry, and a lightweight alloy frame for long, fast trail days. +Fixie with anodized stem, polished chainring, and minimalist paint for an urban lifestyle look that’s quick and nimble. +Commuter e-bike with low-step alloy frame, integrated fender set, and intelligent torque sensing to make urban rides effortless. +A dirt jump platform bike with beefy top tube, short stays, and knobby tires to tackle jump lines with confidence. +Touring steel frame with extra tire clearance, brazed-on pump peg, and a custom lug pattern for charm. +A cargo trike with three-wheel stability, electric assist, large load box and ergonomic cargo handles for business deliveries and family hauling. +A sleek road bike with hidden cabling, compact double, disc brakes and a lustrous metallic charcoal finish for understated performance. +A kids' mountain bike with adjustable travel fork, easy-to-operate mechanical disc brakes, and thick knobblies for dirt-track fun. +A city cargo trike with dual front crates, hydraulic brakes, and an electric assist for stable, heavy freight deliveries. +A modern gravelful roadbike with 700x40 tires, disc rotor protection, flared drops and internal cable management for clean lines. +Lightweight cyclocross frame with disc brakes, low standover, and a responsive geometry for fast race transitions. +A retro cafe-racer inspired bike with drop bars, slim leather seat, steel frame, and polished chrome fork crown for classic styling. +Lightweight single-speed road bike with tapered fork, aero seatpost, and a stripped-down parts spec for ultra-low weight. +A bombproof urban mail bike with heavy-gauge steel frame, reinforced fork rake, and a large front basket for daily deliveries. +Gravel bike in matte forest green, flared bars, 42mm tires, and stealthy frame protectors. +A compact cargo foldable with rear platform, electric assist, quick-fold stem and warm red powdercoat for urban deliveries. +A stealthy matte black fixed-gear with discreet logos, blacked-out components, and a minimalistic saddle for city nights. +Touring tandem with steel frame, triple bottle mounts, and robust wheelsets designed to carry two riders and loaded gear. +A hand-painted custom frame with gradient paint, braze-ons for racks, classic geometry and eye-catching enamel finishes for city rides. +Tiny-wheeled brompton-style folding bicycle with screw-on pannier, compact folded size, and an adjustable seatpost for comfort. +Utility cargo bike with weatherproof cargo box, hydraulic brakes, and integrated rain cover to protect parcels and passengers. +Mixed-surface adventure bicycle with belt drive, internal hub gearing, dynamo lighting and full-length fender mounts. +A bikepacking gravel bike with wide tire clearance, external mount points, dynamo lighting and a rugged matte ceramic finish. +A gravel frame built for mixed-terrain races with aerodynamic features, slightly longer chainstays for stability, and 700x40+ clearance. +Mountain freeride bike with extra thick tube walls, 190mm rear travel and reinforced swingarm for big drops. +Lightweight trail hardtail with fast-rolling 29" wheels, dropper-ready seat tube, and progressive geometry for flow trails. +High-performance time trial bike with integrated hydration, full carbon cockpit, and a deep-section rear wheel for aerodynamic gain. +Gravel endurance racing setup with flared drop bars, 40mm rubber and lightweight tubeless rims tuned to gravel. +Bright yellow hardtail mountain bike with 29" wheels, 120mm travel suspension fork, 1x12 SRAM Eagle drivetrain, and tubeless-ready tires for trail carving. +Classic cruiser with banana seat, sissy bar, high-rise handlebars, and retro candy-apple red paint. +Lightweight steel city bike with Thompson stem, leather saddle and polished steel fenders. +Electric mountain bike with adjustable motor assist levels, long-lasting battery, and regenerative braking option for steep descents +A gravel adventure bike with modular bags and frame attachments, durable fork eyelets, and oversized bottle cage mounts. +A gravel bike with integrated GPS mount, triple cage mounts, frame protectors and a matte metallic forest green paint job. +A time-trial machine with integrated aeros, torpedo frame sections, deep disc wheels and luminous metallic copper finish for fast flat courses. +A trail-ready mountain bike with updated geometry, 140mm travel, coil-compatible shock and tubeless-ready rims. +Youth BMX stunt bike with reinforced frame tube junctions, removable pegs, and a gyro-free brake setup for park play. +A modern mountain trail bike with 140mm travel, snappy rear suspension, and a light wheelset built to ascend and descend efficiently. +A performance road bike with stiff carbon layup, electronic groupset, narrow-profile saddle and low-volume aero bars for weekend sprints and group rides. +Mountain trail bike with transformative geometry switch and chainstay protector for direct impacts. +A commuter with hydraulic rim brakes, dynamo-powered headlight, stepped-through alloy frame, and a woven basket for market trips. +A modern road race bike with integrated hydration, low stack height, and seamless cable routing to shave drag and streamline handling. +Cross-country race machine with lightweight alloy frame, 100mm fork and 11-speed cassette for quick accelerations. +Mountain cross-country race bike with light alloy frame, 100mm fork, and quick-rolling tires for marathon events. +Cyclocross race bike with carbon fork, 33mm treaded tires, and short chainstays for snappy handling. +Touring bike with triple-butted tubing, multiple rack mounts, and an extra-long wheelbase for stable loaded riding. +A compact travel folding bike with small wheels, single-ring drivetrain, and a quick-release joint designed for airline carry-on compatibility. +A gravel bike with stainless-steel bolts, wide-range cassette, and a roomy frame triangle for festival-centric bikepacking. +A performance road bike with integrated cockpit, hidden cables, slender seatstays, and a paint scheme that emphasizes speed and stiffness. +A commuter with integrated kickstand, slide-in pannier system, and narrow frame requiring little storage space at home or work. +A track pursuit machine with aerodynamic wheelsets, single-speed drive, and a smooth glossy finish that absorbs the bright velodrome lights. +Classic British roadster with upright riding position, Sturmey-Archer hub, and simple single front chainring for relaxed urban riding. +Lightweight city fixie with integrated headset, minimal decals, and anodized components for a clean, custom look. +A lightweight aluminum time trial machine with box-section chainstays, integrated hydration and a pearlescent white-to-gray gradient. +A city utility with enclosed drivetrain, robust rear platform, and a stepped top tube for easy mounting with packages. +Mountain trail bike with refined linkage, rubberized chainstay pad, and a small storage hatch for tools. +Mountain enduro with dual-crown fork, heavy-duty disc calipers, and sturdy chainstay protection for aggressive lines. +Gravel adventure tandem with dual bottle mounts, wide tires for comfort, and robust racks for extended touring. +A gravel explorer with 2.25" tires, cargo cage mounts, steel fork and extra frame mounts for long self-supported trips through remote terrain. +A handmade lugged steel city fixie with deep metallic blue paint, polished chrome dropouts, and a minimal single cog setup optimized for urban style. +Electric folding commuter with quick-release battery pack, 8-speed cassette and reflective stickers for safety. +Electric cargo tricycle with three-wheel stability, lockable refrigerated box, and push-button gear shifting for effortless stop-and-go deliveries +A gravel-oriented full-carbon frame with modern geometry, multi-bottle capacity, and a light but stiff ride for fast, unstable roads. +High-volume fatbike touring rig with oversized rims, wooden crate cargo option and reinforced fork mounts for heavy loads. +Retro road bike with downtube shifters, steel frame, toe-clip pedals and classic horizontal top tube. +A modern e-cargo trike with heavy duty platform, centrally mounted battery, and three-point turn stability for confident urban transporting. +Urban speed commuter with aero handlebars, hidden cables, and lightweight frame for fast city commuting. +A racing BMX with minimal weight, 20" wheels, smooth hub bearings and a single-piece cranks for sprint-focused track performance. +A modern mountain bike with adjustable geometry flip-chip, 29-inch wheels, 140mm travel front and rear, and a stealth dropper lever. +A BMX racing bike with alloy frame, light race wheels, rear brake only and a crisp race livery tuned for agility on dirt tracks. +A coastal cruiser with turquoise enamel paint, chrome-plated springer fork, oversized grips and wide cruising tires. +Time trial machine with integrated hydration strips, low drag cockpit, and a narrow frontal area for optimal aero performance. +A kids' balance bike in candy pink with no pedals, low seat height, foam tires and whimsical cloud decals. +Endurance road bike with taller head tube, carbon compliance seat stays, 28mm tyres, and a neutral geometry for comfort. +A mountain enduro rig with 170mm travel front and rear, coil shock tuned for heat dissipation, and reinforced 35mm stanchions up front. +Gravel racer with asymmetric chainstay design, 38mm tires, electronic groupset, and a ceramic bottom bracket for efficiency. +A commuter folding bicycle with magnetic latch, compact handlebars, integrated lights and a soft-touch rubberized frame finish. +A modern fixie with aggressive crank arm length, narrow chainline, and micro text decals along the chainstay. +Gravel specialist with 650b plus wheels, aggressive tread, lower gearing, and reinforced fork for rough backroads. +A gravel racer with low stack cockpit, integrated GPS pocket, and a hand-sanded finish that shows subtle brushwork under a clear coat. +A modern mountain bike with adjustable reach stem, S-shaped chainstay for compliance, and a compact rear shock for balanced pedaling. +A handbuilt lugged steel road bike with classic paint scheme, long reach brake calipers and period-correct handlebar tape for club nostalgia. +Touring bicycle with a steel lugged frame, triple racks, and an ample cassette for heavy loaded climbs and multi-day treks. +A luminous yellow cyclocross bike with aggressive tread tires, cantilever brakes and a slightly shorter top tube for nimble handling. +A full-suspension marathon bike with tuned kinematics, light wheels, and a geometry that balances efficient climbs with controlled descents for long events. +Gravel endurance bike with welded aluminum frame, carbon fork, flared shallow drops and endurance geometry for all-day comfort. +Track sprint specialist with carbon fork, stiff monocoque frame and tight wheelbase for quick acceleration out of the gate. +Specialized cyclocross bike with top-level components, tubeless-ready kit, and a frame optimized for quick shouldering. +A retro mountain hardtail with slacker head tube, rigid steel fork, and knobby tires for nostalgic singletrack rides. +Road classic steel racer with downtube shifters, comfortable steel compliance and period-correct decals for retro events. +A downhill race bicycle with extensive frame protection, steel guard plates, and long-travel suspension tuned for heavy impacts. +A family-oriented cargo bike with foldable bench, integrated rain cover, and multiple tie-down points to secure groceries or kids during errands. +Folding commuter with robust hinge, small wheels, low step-over and a front basket for carrying essentials on commutes +A classic mixte step-through with wicker basket, chaincase, and a comfortable upright saddle for leisurely city rides. +Modern reflex commuter with disk brakes, reflective tape, integrated racks and LED headlight for safe night riding. +A downhill freeride mountain bike with coil shock, reinforced rocker link, 200mm travel and removable bash guards for big mountain terrain. +A single-speed mountain bike with chromoly frame, tight geometry, heavy-gauge rims and knobby tires for local trail shredding. +Cargo long-wheelbase bike with low deck, reinforced chainstay, and an optional rainproof cover for weather resilience. +A cross-country race carbon hardtail with tapered headtube, integrated headset bearings, and minimalist graphics. +A titanium road commuter with matte raw finish, integrated headlight, belt drive, and internal routing for a stealthy look. +Cargo longtail with dual battery option, reinforced decking, and child footrests designed for family transport and grocery runs +A BMX park bike with chromoly frame, sealed bearings, plastic pegs and a reinforced top tube for aerial tricks and rails. +Performance cyclocross rig with tubeless-ready wheels, semi-slick tread tires for fast transitions, and a short chainstay for responsive handling. +High-modulus carbon time trial bike with integrated bar extensions, steep seat tube angle, and a single-sided fork for aero gains. +A touring tandem with stability-oriented geometry, dual racks, long-chain routing and British racing green finish with polished chrome bits. +A fixed-gear track-inspired commuter with polished chrome components, flip-flop hub, slim tires and clean minimalist styling for city pride. +Lightweight time-trial bicycle with aero hydration bottle, bladed fork, and rear wheel cover for reduced drag. +Cargo bike with hydraulic disc brakes, extra-wide deck, and adjustable footrests for passengers. +A commuter with hidden lights in the frame, low-maintenance hub transmission, integrated cassette chain protector and graphite matte finish. +A touring titanium frame with full brazing, trio of bottle mounts, and a polished finish that resists salt and road grime for long ocean-adjacent tours. +A modern touring bicycle with titanium frame, vertical dropouts, Rohloff Speedhub internal gear hub, and modular rack mounts. +A gravel race bike with wireless electronic shifting, aero-optimized tubing, and 700x35 tires tuned for fast gravel criteriums. +Gravel plus tourer with wide-volume tyres, robust alloy frame and multiple eyelets for an extensive rack and bottle setup. +Gravel adventurer with rounded tubing for comfort, integrated frame protection, and triple-bottle capability for long rides. +A stripped racer with minimal paint, drilled seatpost, and race-ready wheelset to keep weight down for criterium accelerations. +Urban single-speed with a deep metallic blue coat, thin profile tires and a single front brake for simple stopping power. +Youth trail bike with simple twist shifters, front suspension, wide tires and a friendly geometry for skill-building. +A gravel-friendly alloy frame with stealth storage compartment, flared drops, and a deep emerald paint to match forested backroads. +Mountain shuttle bike with reinforced rear triangle, coil-sprung shock and race-proven components for downhill shuttle laps. +Commuter with internally routed cables, lightweight alloy rack and reflective striping on mudguards for safety. +Touring bike with bosch dynamo lights, large-capacity panniers, and a handlebar bag map pocket. +Vintage lugged road bicycle with butter-yellow paint, narrow handlebars, leather saddle and chromed steel rims for classic rides +Cyclocross race rig with tubeless-ready rims, powerful hydraulic disc brakes, and a one-by setup. +A lightweight aluminium hybrid with flat bars, 700c wheels, 28-speed drivetrain, and rack mounts for versatile commuting. +High-volume fat-bike with extra-wide rims, studded tires, reinforced fork and low-pressure setup for snow, sand and mud adventures. +A city cargo bicycle with reinforced aluminum frame, fold-out child carrier, and reflective decals for safe family trips. +A handbuilt titanium gravel frame with custom fillet-brazed joins, anodized headbadge, and satin brushed finish with subtle decals. +Cyclocross steel frameset with generous tire clearance, fiberglass fork and handbuilt wheelset for classic feel. +All-road endurance machine with steel frame, disc brakes, and gender-neutral geometry for inclusive long-distance comfort. +An e-gravel bike with silent hub motor in the rear, integrated top tube battery, and a torque sensor for natural pedal feel. +A budget BMX park bike with reinforced forks, 20-inch wheels, and thick grips to handle sessions at local skate parks reliably. +A polished steel city bike with a 3-speed internal hub, coaster brake option, and subtle gold accents for classic urban elegance. +A lightweight carbon crit bike with twitchy handling and a build focused on low inertia wheels for quick accelerations. +A track discipline frameset with oversized chainstay for power transfer, polished paint, and a minimal saddle for optimal sprint position. +A vintage road bicycle restored with new bearings, fresh brake cables, and period-correct decals to match the original model year. +A full-suspension enduro bike with 160mm rear travel, coil shock, 170mm fork, and burly 27.5-plus tires for aggressive descending. +Bikepacking cruiser with low-slung handlebars, wide saddle, framebag and multiple strap points for gear. +Road aero disc bike with shallow rear rim, integrated brakes, and a paint job that shifts color in sunlight. +A classic muscle BMX with heat-treated chromoly frame, 3-piece cranks, and glossy purple paint with anodized hub accents. +A city utility bike with a built-in lock mechanism, quick-release front rack, and reflective paint for added safety. +A gravel-focused frameset with built-in mudguard slots, flared drop bars, and a stealthy dropper-post-ready seat tube. +Folding electric commuter with adjustable stem height, 16" wheels, and integrated carry handle for portable mobility. +A gravel cyclocross hybrid with clearance for 42mm tires, modest luggage mounts, and a slate-gray finish suitable for both races and commute. +A kids' BMX with reinforced top tube, padded crossbar, short cranks and colorful paint for confident trick attempts. +A cyclocross-inspired gravel bike with dropper post, 47mm tyres, responsive handling and a slotted top tube for mud clearance. +A touring recumbent with adjustable seating, integrated panniers at the rear, low rolling resistance and a metallic silver finish. +Gravel race bike with aerodynamic tube profiles, slim seatpost, and a wide-range cassette tuned for rolling terrain +A retro BMX freestyle bike with colorful stickerbomb finish, gyro for bar spins, double-wall rims and small 20-inch wheels. +A lightweight steel road bicycle with shallow-section alloy rims, 25mm tires, and a saddle-to-bar drop tuned for endurance comfort. +Folding electric bike with shock-absorbing hinge, small-watt motor, quick-charge battery and ergonomic grips for short urban hops +A hand-lugged chromoly BMX with glossy chrome finish, 20x2.4 tires, and reinforced gussets for park durability and style. +Mountain enduro with progressive leverage rate, replaceable shock hardware, and recessed cable routing for protection. +Mountain trail bike with modern geometry, short stems, wide bars, dropper post and a stealthy matte graphite finish. +A cyclocross competition build with minimalist aero geometry, 11-speed shifting, and a paint scheme that becomes iconic across multiple race seasons. +A classic dutch-style city bicycle with full fenders, chaincase, upright bars and a stainless hub for reliable braking. +A gravel-specific frameset with aerodynamic tube shaping, integrated seatpost clamp, and room for substantial framebag storage. +Vintage Italian road bicycle with Campagnolo downtube levers, leather saddle, and classic red paint with pinstriping +Aerodynamic time-trial bike with integrated hydration, split handlebars, and deep-section carbon wheelset for fast solo efforts. +Compact folding electric bike with mid-drive, long-range battery, small wheels, and a single-handed fold for ease of use. +Urban step-over hybrid with durable alloy frame, suspension fork, 700x35 tires, and a simple seven-speed drivetrain. +A touring-ready steel frame with custom racks, triple chainset, dynamo lighting, and a deep navy enamel that resists scratches over long miles. +A sleek road aero machine with integrated hydration, deep-section carbon wheels, ceramic-bearing hubs and a glossy black finish. +A race-geometry road frame with modern clearance for 28mm tires, shallow head tube, and full carbon layup for competition-level responsiveness. +Mountain enduro bike with aggressive geometry, 170mm fork, coil shock tune and reinforced bearings for rough lines. +A commuter with mini-fenders, reflective sidewall stripes, and a small luggage deck for lunchboxes and tote bags. +A kids' mountain bike with simplified shifting, wide platform pedals, and a tough steel frame that can take learning bumps. +A gravel-specific hardtail with slack head angle, long wheelbase, 2.2-inch tires and a comfortable geometry for all-day rides. +Gravel race machine with electronic shifting, 38mm race rubber and lightweight carbon bars for speed. +Gravel commuter with mudguards, integrated lights, 35mm tires, and reflective decals to enhance visibility at dawn. +A mountain e-bike with long travel, torque-sensing mid-drive, integrated motor cooling and matte charcoal paint with neon highlights. +A vintage-inspired single-speed with cream frame, rose-gold chain, swept-back bars, and matching leather saddle for slow paced neighborhood cruising. +A full-suspension enduro machine with adjustable travel, 29-inch wheels, and a dual-chamber shock for consistent performance on long stages. +Gravel endurance bike with tuned compliance, 700c x 40 tires, and a stable, confidence-inspiring geometry for long days in the saddle. +A steel frame road bike with classic lugs, leather saddle, and a thin-wall seatpost for extra compliance on long Sunday rides. +A cyclocross steel racer with tapered head tube, modern geometry, fast-rolling 700c wheels, and a textured matte finish for grip when shouldering. +A downhill mountain bicycle with massive 200mm travel, adjustable coil shock, reinforced headtube, and generous 27.5” wheels for steep descents. +A tandem touring bicycle with two sets of shifters, dual saddles, extra-strong wheels and mounting points for heavy pannier loads. +Gravel explorer with wide flared bars, portal of braze-ons, and a multi-day range gearing. +A kids' BMX-style bike with reinforced pegs, shorter cranks, and thick paddle tires to boost confidence on ramps and in skateparks. +A kids' balance bike with powder-coated steel, low saddle, foam grips and a cheerful decal set to encourage early riding confidence. +A lightweight commuter with internal cable management, integrated LED rear light in the seatpost, and a foot-down-friendly geometry. +A road bike with titanium hardware, carbon-clad seat tube, and a matte-black stealth finish aimed at understated performance. +A classic city mixte with wicker front basket, polished fenders, and a simple three-speed hub for relaxed daily errands and coffee runs. +City commuter with soft gel saddle, upright stem, puncture-resistant tires, and built-in bell for practical neighborhood use. +A performance cyclocross rig with a carbon fork, stiff alloy frame, and high-volume tubeless tires for sticky traction on muddy racecourses. +A cyclocross long-travel frame with slack head angle, full carbon layup, wide tire clearance and pewter-gray paint with orange bolts. +A chopped-chrome fixed gear with deep-section rims, mirrored headset, low-profile stem, and a minimalistic paint-free raw metal aesthetic. +A mountain fatbike with 5-inch tires, flat-bar configuration, and powder-finish frame resistant to abrasive salt and sand. +City commuter with full fender set, quiet belt drive, internal gear hub, integrated lights and comfortable upright bars for short trips. +Compact single-speed commuter with flip-flop hub, fenders, and a small front rack for a portable cargo solution. +A commuter with integrated rain guards, reflective chainstay striping, and a secure rear seat post clamp designed for city life. +A trail hardtail with 29-inch wheels and a slack head angle designed to keep confidence high on steep, rooty descents. +Lightweight road race bike with minimal paint, hollow-section rims, and a short head tube for an aggressive racing position. +Mountain hardtail with reinforced pivot and oversized tubing to handle rocky, technical climbs. +A minimalist fixed-gear with raw aluminum finish, narrow riser bars, and a simple front brake for a clean, purposeful city machine. +High-performance mountain marathon bike with 120mm travel, light yet stiff frame, race-tuned components and aggressive climbing geometry. +Road endurance bike with longer chainstays, comfort-focused carbon layup and discreet mounts for fenders and racks. +Lightweight titanium touring bike with brushed finish, forgiving geometry and lightweight racks for long days. +Classic road racer with slim tubing, vintage components, and a patinaed paint job restored to rideable condition. +A road endurance machine with endurance geometry, carbon fork, 30mm tires and elegant stone-gray finish with fine pinstriping. +A BMX park frame with reinforced welds, pegs front and rear, and sealed bearing hubs to deliver smooth spins and grinds. +High-volume touring bike with reinforced framebags and houseable sleeping pad mounts for expedition touring. +Lightweight race frame with aerodynamic seatpost, steep seat angle, and race geometry for optimal power transfer. +Gravel bike with mechanized dropper post, 700x50 jaunt tires, and reinforced frame for carrying heavy bikepacking loads +A robust cargo trike with hydraulic disc brakes, reinforced wheel hubs, and a weatherproof cargo box for heavy-duty local deliveries. +Beach cruiser with vintage floral decals, oversized chrome bell, soft foam grips and wide balloon tires for relaxed coastal rides. +Gravel racer with racy position, aero bars, and a bikepacking-ready top tube bag installed. +Touring-ready steel bicycle with hand-brazed lugs, triple bottle mounts, and a geometry that stays stable under weight. +A gravel monster with wide 650b tires, reinforced wheelset, and a cozy endurance cockpit for full-day adventures. +Touring bike with chrome-plated lugs, low gear triple crank and weatherproof pannier attachment points for heavy loads. +A cyclocross race machine built with a focus on weight savings, featuring carbon bars, light tubeless wheels, and a precise mechanical drivetrain. +A classic Dutch commuter with step-through frame, chaincase, and comfy upright posture to glide through city streets without fuss. +A BMX park bike with shallow dropout design, precision sealed bearings, and micro-adjusted geometry for tighter tricks and spins. +Electric urban commuter with low-slung battery pack, integrated lights, hydraulic disc brakes and comfortable ergonomics for city hills. +A lightweight gravel bike with an emphasis on responsive ride quality, narrow 700x36 tires, and a compact cockpit for race focus. +A painted aluminum mountain bike with coil-sprung rear shock, heavy-duty flat pedals and reinforced chainstays for bike park abuse. +A kids' BMX styled for durability with extra thick top tube, short cranks, and robust pedals that withstand hard landings. +Compact hardtail with 27.5-inch wheels, short stem and grippy 2.25-inch tires for playful trail antics. +Commuter with internal gear hub, shaft drive, enclosed chaincase and built-in lock for low-maintenance urban reliability. +Folding kid's bike with simple latch, training wheel compatibility, and a colorful cartoon decal for playful rides. +Gravel race machine with aggressive fork rake, 700x40 tires, and a compact cockpit for aerodynamic positioning. +Bikepacking-ready gravel bike with frame bags, handlebar roll, and low gears for loaded climbs. +Handmade titanium commuter with brushed finish, internal cable routing, wide comfortable saddle and discreet anodized details for urban use. +A sleek urban fixie with fluted stem, minimalist paint, and a polished single-sided chainstay detail to underscore simplicity and speed. +Track fixed-gear with polished lug joints, narrow saddle and single-tooth chainring for pure simplicity. +A cyclocross pro-level bicycle with semi-integrated cables, tubeless-ready rims, 33mm cyclocross tires and matte black with bright sponsor panels. +Lightweight titanium road bike with slender tubing, endurance geometry, 28mm tires, and brushed metal finish. +Gravel endurance bike with endurance geometry, 40mm tires, and vibration-damping seatpost. +A commuter equipped with theft-deterrent screws, reflective wheel stickers, a small front tray, and a low-entry frame for quick stops at shops. +Mountain trail bike with adjustable geometry, 150mm rear travel, dropper post and 29x2.35 tubeless tires for stability +Road time trial frame with integrated hydration behind the headtube, narrow tube profile, and a gloss pearl paint finish. +Commuter step-over bike with plastic fenders, chaincase, and a steady upright seating position. +Gravel endurance machine with cutaway chainstay clearance, 40mm tires and an ergonomic compact cockpit for comfort. +A commuter with integrated GPS navigation on the stem, theft-deterrent hardware, and a low-maintenance hub gear for daily reliability. +Vintage city mixte with wicker basket, cream-wall tires, and a slightly rusted but charming chrome headbadge and fenders. +Gravel sub-24-hour ultra bike with multiple bottle mounts, streamlined cockpit and lightweight steel for durability. +A compact-folding electric bike with 20-inch wheels, hinge in the main tube, rear hub motor, and a quick-release latch for commuters with limited storage. +A vintage-inspired roadster with chaincase, swept-back bars, slim leather saddle and ivory enamel with tasteful pinstriping. +A modern trail bike with 150mm travel, aggressive chainstay length, and an adjustable headset cup for small geometry tweaks. +A polished stainless-steel frame with brushed finish, classic double-butted tubes, and custom-matched headset for a boutique aesthetic. +A modern gravel frame with integrated seatpost clamp, stealth cable routing and a discreet top tube tool hatch. +A touring bike built for gravel expanses with low-gear triple crank, reinforced racks, and splayed-for-load wheels for stability. +A durable touring bicycle with nickel-plated dropouts, triple cage mounts on the fork, heavy-gauge spokes and a classic design for rugged use. +Lightweight alloy road bike with endurance geometry, tubeless-ready rims, and SRAM 11-speed mechanical groupset. +Track pistard with stiff monocoque frame, narrow Q-factor crank, and glittering silver paint for sprint events. +Classic British road bicycle with lugged steel, 3x10 drivetrain, leather saddle and subtle champagne metallic paint. +A classic steel racing bike restored with modern headset bearings and new narrow tubular tires for a lively, responsive ride. +Vintage-style steel mixte with cream paint, wicker front basket, chaincase, and a finely stitched leather saddle. +A longtail cargo bicycle with extended rear rack, dual seats, high-visibility yellow paint and reinforced aluminum frame for family hauling. +Touring tandem with front and rear racks, sturdy spokes, and internally routed cables to keep lines tidy under load. +Urban commuter with quiet internal gearing, magnetic closure saddlebag, and reflective tape along the frame. +A stripped-down track bike for velodrome sprints featuring deep-section alloy rims, aggressive geometry, and a minimalist single cog with a 46:14 ratio. +A track sprint machine with large chainring, single-speed hub, and a custom-painted aero disc for the rear wheel. +A mountain enduro bike with reinforced swingarm, 170mm travel front and rear, and a stout wheelset for rugged durability. +A gravel endurance bicycle with modern carbon layup, vibration-mitigating geometry, and tire clearance up to 45mm for true adventure. +A gravel bike with a titanium frame, carbon fork, 42mm tubeless tires, and mounting points for water and gear for minimalist bikepacking. +A full-suspension trail all-rounder with 140mm rear travel, modern slack geometry, wide handlebars and a dropper post for technical trails. +Bright red hardtail mountain bike with 29er wheels, 140mm travel fork, and aggressive trail geometry. +A mountain downhill machine with adjustable geometry, widely spaced axles, and heavy-duty frame guards preparing it for big impacts. +Endurance gravel bike with lightweight steel frame, endurance geometry, 38mm tires, and custom low-stack cockpit. +A handsome steel touring bike with ornate lugwork, multiple braze-ons for racks and bottles, and a stable geometry for loaded cross-country trips. +A classic racing frame with steel lugs, thin seat tube and 700c tubular wheels dressed in leather handlebar tape. +Road aero frame with sculpted downtube, hidden cable routing, and a top-tier carbon layup for low drag and high stiffness. +Track fixed-gear with minimal paint, polished chainring and a clean single-gear aesthetic for alleycat events. +High-end carbon road bike with integrated computer mount, tubular clinchers, and ceramic bottom bracket. +Lightweight commuter with internal battery, quiet motor, integrated rear light and discreet fender mounts for commuting. +A trail-focused full-suspension bike with adjustable geometry, 150mm rear travel, and trail-specific casing tires for mixed singletrack. +Gravel all-road with bright reflective piping, 700x40 tires, comfortable bar shape and low-maintenance sealed hubs for long-distance reliability +Gravel-plus adventure rig with wide 27.5+ wheels, single-ring drivetrain, and a dropper post for confidence on chunky surfaces. +Mountain downhill frame with extra gussets, long-travel suspension, and cutaway chainstays for tire clearance. +Steel commuter with polished lugwork, dynamo hub, leather saddle and practical rear rack for office-to-home rides +Comfortable commuter with spring coil saddle, swept-back bars, step-through frame and reflective decals for safety. +A sleek urban fixed-gear with anodized components, bullhorn handlebars, polished rims and a minimal paint job for street aesthetic. +Electric cargo trike with low-slung loading area, child harnesses, and a high-torque motor for hill-climbing loads. +A kids' balance bike with a low frame, adjustable saddle, non-slip footrests, and playful decals to encourage early learning of balance. +A 29er trail bike with burly frame, 140mm fork, dropper post, and fast-rolling tires for mixed trail conditions and flowy lines. +A rugged mountain hardtail set up with wide 2.6-inch tires, reinforced rims, and a frame guard against chain slap. +A city cruiser with integrated child seat mount, low-step geometry, and a relaxed handlebar sweep for comfortable family rides. +A touring bicycle with comfortable ergonomic grips, a wide-gear range for loaded climbs, and an integrated frame pump holder for maintenance. +A kids' balance bicycle with low step-through frame, no pedals, small saddle and bright decals to teach steering and balance safely. +A modern endurance road bike with long wheelbase, relaxed head tube height, and vibration-dampening seatpost to minimize fatigue on long rides. +A classic fixie conversion on an old steel frame with polished crankset, narrow saddle, and minimal decals left to show patina. +A classic road racing frame with lugged steel construction, period Campagnolo components, tubular wheels and a heritage paint scheme. +Road endurance frame with slightly relaxed angles, carbon compliance features, and larger-volume tires for comfort at speed. +A lightweight carbon road bike with matte black finish, aero tubing, 700c deep-section wheels, and a Dura-Ace groupset with rim brakes. +Adventure bike with mixed-wheel setup, 700c front and 650b rear, rack mounts, and larger tire clearance for bikepacking. +A fixed-gear urban commuter with polished steel fork, anodized purple hubs, narrow profile tires and an off-white frame. +A gravel racer with light, wide rims, extra-traction tread pattern, and a carbon seatpost tuned for small bump compliance on rough roads. +Electric folding city bike with 250W motor, low fold weight, and a secure latch for quick office storage. +A mid-travel trail bike with modern steep seat angle, long reach, and dropper-ready routing tuned for long trail days. +Gravel plus bike with 27.5+ wheels, slack head angle, and robust frame tubing for confidence on rough routes and technical terrain. +A modern carbon road bike with subtly integrated power meter in the crank and an understated matte graphite finish. +City utility bike with enclosed chaincase, integrated cargo deck, comfortable swept bars and wide puncture-proof tires. +Urban single-speed fixie with glossy black paint, leather saddle, and a flip/flop hub for switching between fixed and freewheel modes. +Gravel e-bike with range-extending battery pack, integrated fender mounts, and micro-suspension seatpost. +Mountain enduro rig with adjustable shock tune, coil compatibility, and reinforced protective plates on high-wear areas. +Touring frame with replaceable hanger, sealed-bearing hubs, and carefully tacked rack mounts for long-term serviceability. +Traditional touring frame with mounting points for both lowrider and highrider racks and three-bottle mounts. +Touring tandem recumbent with dual seats, belt drives, and integrated weatherproof panniers. +A touring bicycle with Rohloff hub and belt drive, robust stainless fixtures, and a wallet-friendly, no-nonsense paint job. +A lightweight gravel hardtail with 700x40 tires, flared bars, stealthy carbon fork and a subdued satin paint job that hides scuffs well. +A city single-speed with gleaming chrome fork, swept-back bars, and retro leather grips for style over function. +Lightweight commuter with internal dynamo, puncture-proof tires, and easy-to-use hub gearing for hassle-free trips. +A hand-painted cruiser with artful fade, restored chrome trim, wide whitewall tires, and a comfortable relaxed geometry for coastal rides. +Electric cargo bike with front loader box, twin child belts, regenerative braking and weatherproof canopy. +A trail-oriented full-suspension bike with 140mm travel, low bottom bracket, and a playful geometry that’s confident on technical singletrack. +Off-road e-gravel bike with robust motor, high-volume tires, and internal battery with long-range capability for mixed-surface adventures. +Gravel-friendly steel frame with hand-painted tiny flecks, reinforced drops, and a dependable 1x drivetrain for singletrack. +A bombproof fixie with track drops, deep V rims, single-speed cog, bright red frame and minimalist saddle for urban cruising. +A carbon gravel bike with aero tube shaping, integrated cable routing, and a subtle satin blue paint that looks different in every light. +Mountain enduro carbon bike with adjustable shock tune, 170mm fork, and burly tires for high-speed rock gardens and drops. +Urban single-speed with polished chrome fenders, bell, and low-maintenance coaster brake for short errands. +A cyclocross rig with modern disc brakes, 33mm tubeless tires, and a low stack height for powerful technical sprints. +A handbuilt steel road bike with compact geometry, classic silk-screened logos, and a leather-wrapped bar tape. +Cargo trike with hydraulic braking, weatherproof cargo bay and low-step access for efficient neighborhood hauling. +A custom painted BMX street bike with reinforced top tube, gyro, and dent-resistant chain tensioners for park abuse. +Gravel bike with double-bottle mounts, heavy-duty wheelset, and an endurance geometry that prioritizes comfort on long routes. +Touring bicycle with narrow pack racks, internal cable ports, and a glossy dark maroon paint with fine pinstripes. +A single-speed urban fixie bike with deep matte black rim, flip-flop hub, horizontal dropouts and minimal clean lines for city cruising. +Vintage mixte with pastel paint, wicker basket, leather grips, and brass bell for a timeless city companion. +A modern trail e-MTB with 170mm front travel, supportive geometry, and battery integrated low in the downtube for balanced handling. +A lightweight carbon road frame with aerodynamic downtube, compliance-oriented seatstays, and 28mm tires to combine speed with comfort. +Light touring titanium bicycle with braze-ons, internal cable routing, triple chainring compatibility, and a satin finish. +Compact folding bike in gunmetal gray with 16-inch wheels, quick-fold hinge and integrated rear rack for urban transit. +A fixed-gear crank-built bike with polished chain, anodized pedals, and minimal front brake for lean urban style. +A race-ready mountain bike with SRAM-style 12-speed cassette, 150mm travel fork, carbon linkages and a bold lime accent paint. +Touring rig with triple bottle cages, front low-rider rack, and a leather-sheathed pump on the frame. +A compact-folding commuter bike with skate tool-free hinge, upright post that folds down, and a lightweight frame for transit-friendly portability. +A kids' balance bike made from lightweight aluminum with rubberized grips, foam tires and sunny yellow paint with friendly decals. +Folding electric cargo bike with mid-motor and quick-fold frame to combine range and compact storage for urban families. +BMX race bike with slim geometry, lightweight alloy frame and minimal accessories for track sprinting. +Urban commuter with cargo trailer hitch, puncture-resistant tires, LED lighting system, and an ergonomic saddle for daily rides. +Touring gravel bike with disc brakes, dynamo lighting, wide-range cassette, and kingpin rack mounts for long unsupported trips. +A boutique handmade steel frame with artisanal paint, elegant stainless hardware, and a geometry tuned for long-distance comfort. +Gravel adventure tandem with robust chainline, double cargo cages, and a slack head angle for stability when loaded. +A cargo e-bike with full suspension on the rear rack, low center of gravity cargo box, and integrated lights for safe evening deliveries. +A gravel racing frame with tuned compliance, low-slung top tube for better handling, and a bold, geometric paint job that reads fast at speed. +A titanium touring bike with extra bottle mounts, generator hub headlight, and 36-hole wheels for durability under heavy loads. +Women's-specific road bike with shorter reach, narrower handlebars, carbon fork, and cherry red paint with floral decals. +Touring classic with jubilee rack, brass bell, leather handlebar wrap and a timeless cream paint job. +City step-through single-speed with coaster brake and rear rack, simple reliable transportation for short rides. +Vintage city cruiser with chrome fenders, spring saddle, and classic balloon tires for relaxed Sunday tea rides. +Steel gravel bike with raw metal finish, rack mounts and hand-filed lugs for classic craftsmanship. +Commuter with integrated smartphone mount, wireless shifting, and discreet LED indicators for turns. +Minimalist fixie with matte gray finish, deep black rims, narrow drop bars and a stripped-down aesthetic. +Gravel race bling with shiny anodized parts, ceramic bearings, wide rims, and a slick two-tone livery for podium appeal. +Sleek track bike with polished steel tubing, long top tube and a skinny profile for velodrome presence. +A twin-triangle BMX race bike with short chainstays, 20x1.75 race tires, magazine-style sponsor stickers and bright neon yellow frame. +Touring steel frameset with triple-braze-ons, full fender compatibility, low-maintenance gearing and relaxed geometry for long-distance comfort. +Electric cargo bike with three-wheel stability, rear canopy, and heat-sealed child harness for safety. +Folding city bicycle with single-step fold, lightweight frame, and a small integrated lock for quick stops in town. +City single-speed with artistic mural paint on frame, riser bars, and an included front cargo tray for errands +Gravel adventure bike with triple water-bottle mounts, heavy-duty racks, and 650b compatibility for loaded touring. +A compact folding commuter with quick-release hinges, small-diameter tires, and a weakly cushioned saddle for short urban hops. +A mountain hardtail with a tuned trail fork, tubeless tires, and a shortened stem for quick handling in technical singletrack. +Kids' balance bike with EVA foam tires, adjustable handlebar height, and bright safety reflectors. +A cyclocross race bike with carbon forks, disc brakes, 33mm tread, and shiny black paint with contrasting white sponsor stripes. +A road endurance bicycle with relaxed angles, cabling hidden in the frame, and plush 30mm tires for long comfort. +A light touring gravel bike with carbon layup, multiple mounting points, 700x38 tires and muted sage green finish. +A longtail cargo bicycle with two child seats, reinforced headtube, suspension seatpost and a high-visibility paint job for family hauling. +Gravel explorer with extra toe clearance, practical water cage positioning, and a grippy textured top tube. +A retro commuter with Sturmey-Archer three-speed hub, classic steel frame, and leather saddle that ages into a warm patina. +Cargo trike with aluminum bed, retractable side panels, and a low center of gravity for stable handling. +City porteur bike with front rack, integrated lighting, and polished fender edges for a classy urban presence. +Gravel race specialist with featherweight carbon fork, narrow 38mm tires, race geometry and wireless shifting for instant response +A classic road bicycle with thin tires, polished steel frame, and leather straps on the top tube for holding spares and tools. +Time trial aero machine with twisted tube shaping, aggressive seat position and integrated hydration for optimal pacing. +A compact city bike with integrated lock, generator hub lights, comfortable saddle and pannier-ready rear rack for practical errands. +A touring trike with heavy-duty rear rack, three-speed internal hub, comfy upright saddle and high-visibility safety flag. +A compact utility bike with short wheelbase, sturdy double-leg kickstand, heavy-duty tires and a tilting cargo box on the front. +A compact folding commuter with small-diameter wheels, quick-release hinge, belt drive and brushed aluminum finish with anodized accents. +Road time-trial frame with integrated hydration and a cockpit designed for long aero holds and comfortable breathing access. +A trail hardtail mountain bike with 120mm fork, reinforced headtube, tubeless-ready wheels and aggressive tread tires for cross-country racing. +Lightweight cyclocross frame with race geometry, precise handling, tubeless compatibility and a quick-release oriented cockpit for pit stops +Gravel adventure bicycle with titanium bolts, under-top-tube framebag space, and a two-tone olive-to-beige fade. +Gravel adventure bike with mixed-terrain tires, external battery mount for GPS, and puncture-resistant sidewalls. +Steel commuter with internal hub gear, chainguard, and a retro two-tone paint split along the center tube. +A rugged mountain bike with 27.5" plus tires, burly rims, and a twin-spar frame design for stability over roots and rocks. +A retro step-through city bike with wicker basket, polished chromed fenders, and a coaster brake for breezy neighborhood rides. +A period-correct steel road frame restored with fresh decals, new cloth bar tape, and polished chrome accessories. +A modern touring tandem with titanium frame, long chainstay, comfortable saddles, and oversized 36-spoke wheels to carry two riders plus gear. +Touring steel bicycle with generous tire clearance, triple chainset, and extraized wheel strength to carry expedition loads. +Urban folding electric bike with hub motor in the front wheel and quick-release folding clamp. +Gravel touring build with disc brakes, 35mm semi-slicks, dual bottle mounts on the fork, and a dropper post for descents. +Compact folding commuter with sporty geometry, quick-fold clamp and 20-inch wheels for mixed transit use. +Electric mountain trail bike with high-displacement motor, long-dropper range, and durable drivetrain components for punishing trails. +Lightweight gravel race bike with carbon handlebars, 700x35 tires, wireless shifting and minimalist paint for weight-conscious racers. +Touring e-bike with extended-range battery, low gear ratios, and robust pannier mounts for extended trips. +A gravel apocalypse-ready bike with wide tires, metal bashbar, and tow points for pulling additional gear across rough terrain. +A touring bike with triple-gear setup, full-coverage fenders, leather grips, stainless steel racks and antique slate paint. +Sport-touring bicycle with mid-compact crank, full rack mounts, robust wheels, and dynamo lighting for long-distance mixed-surface touring. +Cargo e-bike with rear box, adjustable straps, and an LED array for nighttime visibility. +A modern performance gravel bike with full carbon fork, integrated seatpost clamp, wide cockpit and matte stone-gray paint. +A drop-bar gravel touring bike with leather handlebar tape, lugged chromoly frame, and multiple eyelets for lengthy off-grid expeditions. +Kids balance bicycle in bright primary colors with foam tires and low saddle height for learning coordination +A performance gravel race machine with 1x drivetrain, flared bars, and a steep seat tube for efficient climbing posture. +Urban folding utility with single-hand latch, small-wheel suspension, and an integrated carrying handle for portability. +Vintage mixte with pastel paint, wicker basket, high-rise stem and polished steel hardware for classic town cruising. +A commuter with fold-in pedals, bright reflective decals, and a small front basket for everyday errands in the city. +Gravel enduro bike with wide drop bars, short stem, and precise handling for descents and climbs. +A mountain trail full-suspension rig with 150mm front travel, 140mm rear, coil shock tuned for big hits and mud-flinging splatter paint. +Single-speed cruiser with coaster hub painted matte purple, long comfortable saddle and matching purple grips +Dirt jump hardtail with short travel fork, strong chromoly frame, flat pedals and low saddle for popping tricks +A retro-styled town bicycle with double-spring leather saddle, sweeping bars, fendered wheels and an elegant cream-and-gold livery. +Performance cyclocross race bike with lightweight tubular rims, stiff bottom bracket, and quick-handling geometry for sharp course lines +Road endurance bicycle with comfortable handlebars, longer wheelbase, wide tyre clearance and a satin navy paint job. +A mountain freeride bike with reinforced head tube area, long travel fork, and a geometry that invites big lines and massive drops. +A commuter with integrated child seat mount, low-step access, and a handy front basket for groceries. +A vintage town bicycle restored with original chrome fenders, leather grips, and a single-speed coaster hub in pastel mint paint. +Cargo tricycle with weatherproof locking box, wide footprint, and reflective chevrons for urban logistics fleets +A mountain freeride bike with extra-rocker geometry, long travel and stable high-speed handling for aggressive terrain surfing. +Cyclocross rig with carbon fork, generous tire clearance, and a mud-shedding seat tube profile for quick shouldering. +A city cruiser with integrated lock system, soft-sprung saddle, and wide swept-back bars for upright comfort. +Lightweight aluminum track bike with ovalized tubing, deep-section rims and minimal decals for a clean racing look. +A mountain hardtail with modern geometry, 120mm fork travel, and a dropper post for descending confidence after climbs. +A high-volume fat-tire bike with 5" rubber, reinforced rims, and a geometry tuned to float across soft sand and deep snow. +Steel cyclocross frame with classic lugwork, braze-on fender mounts, and reinforced fork crown for durability in winter racing. +A kids' BMX with lightweight chromoly frame, padded crossbar, gyro and hot pink paint with bold stickers for park riding. +Gravel bike with aero-optimized tubing, hidden fender mounts, and a subtle gradient paint job from navy to teal. +A gravel bike with soft metallic mint paint, internal battery for an auxiliary motor, and a threaded bottom bracket for serviceability. +Gravel endurance frame with micro-suspension inserts on the seatstays, generous tyre clearance and a sand-swept finish. +A mountain e-bike with mid-drive motor, 180mm rear travel, dropper post and rugged tires for all-day backcountry rides. +Handbuilt steel road frame with brushed raw finish, lugged joints and custom geometry. +Mountain trail machine with modern kinematics, 150mm travel, adjustable shock tune, and balanced geometry for mixed riding. +Road aero bike with integrated front and rear lights, narrow cockpit, and sculpted seatpost for aero gains. +A stripped race day track bike with polished alloy frame, fixed drivetrain, and a sparse saddle for weight saving in sprint events. +A modern trail hardtail with long front center, short chainstays, and a 140mm fork for a fun blend of agility and stability. +A mountain hardtail with tapered headtube, reinforced headset bearings, 120mm fork and a geometry built for quick technical climbs. +A retro beach cruiser with oil-rubbed bronze finish, heavy chrome fenders, and a comfortable long saddle for seafront promenades. +A full-suspension trail bike with progressive geometry, dropper compatibility, and axle-to-axle tuned for playful yet composed handling. +A modern hardtail built for aggressive trail climbing with 130mm travel fork, 29-inch wheels with fast-rolling tread, and stealth graphics. +A nostalgic road frame with slim tubes, classic paint, and an old-school quill stem revitalized with modern sealed bearings. +Handbuilt steel fixie with lugged joints, polished finish and narrow riser bars for aesthetic simplicity. +A performance gravel frame with asymmetric stays, through-axle security, and a geometry that balances speed and predictability on unpredictable terrain. +A touring tandem with stepped frame, reinforced lugs, and a long-range gearing set for pair expeditions across varied terrains. +A titanium gravel bike with 40mm clearance, 1x drivetrain, thru-axles, and subtle brushed finish built for long off-road adventures. +Road e-bike with discreet rear hub assist, carbon fork, integrated lights, and a compact battery for long commutes. +Classic cruiser with swept-back chrome bars, wide whitewall tires, and a practical rear rack for picnics. +Gravel endurance race bike with 40mm tubeless tires, short 40mm stem and electronic wireless shifting for clean cockpit setup. +Gravel commuter with stainless steel frame, disc brakes, mudguards and a bomber rear rack for shopping. +Cyclocross race machine with carbon fiber fork, flared drop bars, and quick-release thru-axles for mud-prone conditions +A downhill freeride rig with reinforced swingarm, dual-piston brakes, and a geometry set for high-speed stability in rough terrain. +A downhill mountain bike with long-travel fork, dual-crown crown, massive 203mm rotors, and a battered protective bash guard. +A stealthy black electric-assist city bike with integrated battery in the downtube, hub motor, display on the stem and puncture-resistant tires. +A touring tandem with Rohloff hub, triple water bottle mounts, double racks and reinforced wheelsets built for multi-week expeditions. +A utilitarian city bicycle with built-in child seat mounts, sturdy rear rack and puncture-resistant tires for family logistics. +Electric commuter with cog-free belt drive, sealed-bearing hubs and a stealth battery concealed in the frame. +Versatile gravel bike with tapered headtube, internal battery-ready downtube, and clearance for up to 2.0" tires. +Mountain enduro bike with adjustable geometry, remote lockout, coil shock option, and burly wheels for aggressive terrain. +A trail-oriented full-suspension mountain bike with 150mm front travel, 140mm rear, dropper post and wide handlebars for technical terrain. +A commuter with step-through frame, upright handlebars, comfortable saddle and integrated chain guard for stain-free clothes on the ride to work. +Kids' starter road bike with 20-inch wheels, caliper brakes, simple 6-speed grip shift and friendly geometry. +Titanium gravel grinder with clearance for 2.1-inch tires and stealthy bead-blasted finish. +Track sprint machine with high engagement hub, stiff fork, and a short wheelbase for explosive acceleration on the boards. +A cyclocross friendly aluminum frame with extra weld reinforcement, easy-mount fender bosses, and an intentionally simple parts list for reliability. +A modern e-mountain rig with long-travel suspension, powerful torque-sensing motor, large-volume tires and a Spline-friendly cassette for uphill power. +Steel commuter with frame-integrated lock mount, bell, and reflective decals for safe night riding. +Bikepacking-specific touring bike with custom framebags, double-bolt seatpost harness, multiple braze-ons, and 700c x 40mm tires. +A cruiser with chrome-plated bell, scalloped fenders, pastel coral paint and an oversized cushioned saddle for easy neighborhood rides. +Classical steel mixte with floral decals, rear rack and traditional bell for picturesque city rides. +A mountain freeride rig with overbuilt swingarm, long-travel coil shock, and rock-solid head tube to tackle park and backcountry lines. +Dirt jump frame with oversized tubing, welded gussets, and a striking colorway designed to survive hard landings. +Kids' mountain bike with front suspension, coaster brake, and colorful graphics for young riders. +A children's low-step bike with training wheels, easy-to-use coaster brake, bright stickers and a cushioned saddle for early cycling fun. +Kids’ BMX freestyle bike in neon green with gyro, pegs and reinforced chromoly frame. +Gravel adventure with multiple top tube strap points, rugged titanium bolts and a tasteful fade from black to rust. +City step-through with simple hub gear, carrier rack, low pedals and a comfortable upright geometry for errands and commuting. +A gravel bike with hand-brushed metal flake paint, 700x40 tires, and elegant wire brush-logo head badge. +A gravel sled with custom anodized components, dropper post, 2.2-inch tire clearance front and rear, and stealth black anodizing. +Touring steel frame with triple bottle mounts, sturdy rack, wide gearing, and mudguard bosses for expedition reliability. +A commuter with integrated smart-lights, belt drive, low-step frame, and a robust chainstay guard to protect the frame from cargo wear. +A compact kids' cruiser with colorful decals, step-through frame, coaster brake, and a small basket for collecting treasures on short rides. +A low-step electric cargo bike with dual-battery range, hydraulic front brake, and locking cargo box for urban deliveries or family errands. +A lightweight carbon road bicycle with deep-section aero wheels, electronic shifting, and matte race-blue paint. +Fat-bike expedition setup with reinforced fork, low-pressure studded tires, framebag-oriented mounts and heavy-duty drivetrain for snow travel. +Classic city step-through with woven basket, brass bell, and chrome head badge for a nostalgic commute. +Gravel race rig with carbon forks, 700c x 40mm tyres, SRAM Force 1 and a gradient paint fade. +Full-suspension trail bike with progressive geometry, 140mm front travel, tubeless tires and a light 1x drivetrain for happy trails +A low-trail gravel racer with ultra-light alloy frame, 30mm tires, compact cranks, and a minimalistic decal set for uncluttered looks. +Road aero bike with deep-section front wheel, disc rear, and subtle sculpted seat tube for rider comfort. +A classic city roller with wicker basket, hand-painted crest, and a large comfy saddle designed for laid-back errands and coffeeshop stops. +Carbon all-mountain bike with 170mm travel, adjustable geometry, and coil-sprung linkage shock. +A cyclocross frameset with asymmetrical chainstay sculpting, internal mud deflectors, and quick-release thru-axles to speed race prep. +Touring tandem with matching frame proportions, drop bars for both riders, and multiple rack mounting points for long rides. +A gravel racer with an integrated top tube bag, power meter crankset, and progressive geometry for high-speed dirt descents. +Tandem touring bike with pneumatic suspension seatpost, reinforced rims, and dual racks for extensive two-up travel. +A city utility bike with frame-integrated lock, built-in cargo strap anchors, and oversized tires for stability with heavy loads. +Urban utility bike with heavy-duty rear rack, puncture-resistant tires, and reflective sidewall stripes. +A touring gravel bike with three bottle mounts, braid-wrapped handlebars, and durable alloy racks for heavy loads. +A heritage touring bicycle with leather saddle, wooden fenders, brass lamp mounts, and hand-stitched saddlebag for classic touring aesthetics. +A lightweight cyclocross race machine featuring a carbon fork, alloy frame, and a geometry that balances nimbleness with stability in mud. +A kids' city bike with coaster brake, chainguard, colorful frame stickers and a small matching saddle for safe neighborhood runs. +Road time-trial build with full carbon monocoque, integrated hydration and a slender profile optimized for aerodynamics. +A folding cargo bike with a telescoping handlebar, modular trays, and quick-release attachments to switch between passenger and freight modes. +A performance road bike with 28mm tires, semi-aero profile, electronic shifting, and crisp metallic blue paint with thin silver stripes. +Performance track bike with ultra-stiff carbon frame, narrow saddle, and high-gear ratio for sprinting on the velodrome +Road aero bike with integrated stem, full internal routing, and deep-section wheels for high-speed efforts. +A performance cyclocross bike with tubeless-ready rims, SRAM 1x11 drivetrain, and a grippy tread pattern. +Gravel racer with aerodynamic dropped seatpost, narrow aero bars and shallow-section wheels for fast gravel stages. +Road endurance titanium frame with comfortable compliance, subtle logos and a practical disc-brake setup for all-weather riding. +A premium carbon gravel bike with clearance for 2.2-inch tires, SRAM-style wireless shifting and a stealthy matte paint scheme. +A full-suspension trail bike with 150mm travel, broad handlebars, and a reliable SRAM transmission to handle technical singletrack. +Retrofitted vintage roadster with modern disc brakes, updated wheelset, and restored lugs for classic looks with modern safety. +A mountain bike with a mixed-wheel mullet setup offering playful handling without sacrificing roll-over capability in rough sections. +Gravel plus adventure bike with wide tyres, rack mounts, and a stealth matte finish that hides mud and scratches. +Mountain freeride bike with reinforced downtube, replaceable hanger, and stout geometry for aggressive downhill laps. +A gravel-allroad bike with bright orange accents, wide-range cassette, and versatile mounting points for fenders and racks. +Kids' balance bike with bright rainbow paint, aluminum frame and non-slip footrest for toddler stability. +Cargo bicycle with mid-mounted platform, reversible seat, and brass rivet detailing on the wooden deck. +A mountain hardtail with modern progressive slack head angle, short chainstays for pop, and wide 2.35 tires for confidence on rough singletrack. +Off-road plus bike with 2.8-inch tires, stiff alloy rims, and a slack head angle for stability. +High-volume tire mountain plus bike with 2.8" rubber, stiff hubs, and burly rim widths for traction. +City commuter with low-maintenance shaft drive, sealed headset, and full fender protection for rainy commutes. +Vintage steel track frame converted with modern wheelset, polished lugs, and leather saddle for city speed runs. +A bombproof utility cargo bike with a wooden deck, sturdy low step-through frame, and hydraulic brakes to haul heavy loads in the city. +Mountain enduro rig with 170mm travel, progressive leverage curve, and overall balanced geometry for descending and climbing. +Performance road bike with large-diameter downtube, aero-optimized chainstays and a race-ready cockpit for sprints. +A high-volume downhill sled with reinforced swingarm, dual crown fork, and a long dropper post for bike park stomps. +Classic Dutch cargo with weatherproof box, child seat options and sturdy kickstand for family errands. +A retro candy-apple red cruiser bicycle with swept handlebars, wide saddle, balloon tires and coaster brake for relaxed beach rides. +Cyclocross race bike with quick geometry, double-tap shifters, and grippy mud-specific tires for race-day traction. +Modern gravel bike with stealth internal storage, stealthy black-on-black hardware and 700x42 clearance for long adventures. +Vintage BMX collector's bike with chromed frame, old-school sticker set, box-section rims and original seat clamp +A titanium endurance road bicycle with relaxed geometry, vibration-damping seatstays, internal cable routing, and capacity for two bottle cages. +Lightweight carbon endurance bike with compliance zones in the seat tube, internal routing, and a 1x electronic groupset. +MTB trail bike with dropper post, 130mm rear travel, and a progressive geometry for modern singletrack. +A vintage step-through city bicycle with basket, spring saddle, coaster brake, and patinated chrome accents reminiscent of 1950s style. +Lightweight endurance road bike with a plush saddle, tire clearance for 32mm, and aerodynamic fork shaping for stability. +A gravel bike with raised bottom bracket for rough surfaces, tubeless tires, 650b option and desert-oxide paint with salt-themed decals. +Folding electric commuter with step-through frame, compact battery housed in downtube, and anti-pinch hinge mechanism for safe folding +A steel fixed-gear track-style bike with minimal branding, polished lugs, deep gloss black paint and a tight wheelbase for urban agility. +Gravel-focused drop-bar mountain bike with 1x11 drivetrain, rugged tires, and bar-mounted guide for long-day navigation. +A commuter with low-step frame, integrated child seat mount on the rear rack, hub drive and wide platform pedals for family-focused trips. +A cyclocross-specific aluminum frame with oversized down tube, flared handlebars, 1x drivetrain compatibility and durable finish for mud and grime. +A fillet-brazed titanium gravel bike with three bottle mounts, full mudguard clearance, and elegant welds. +Lightweight weekend road bike with aerodynamic tubing, wide-range cassette, and race-ready handling for spirited rides. +A gravel-tuned hardtail with 650b wheels, thick 47mm tires, dropper post and a compact cockpit for off-road climbs. +A cyclocross race bike with minimal paint, exposed carbon weave, 33mm tubular tires and race-ready cantilever brake options for traditionists. +Beach cruiser with pastel sunset gradients, wide swept back bars, oversized seat and smooth-rolling balloon tires. +A utilitarian steel cargo bike with wooden deck, reinforced dropouts, and weatherproof paint for daily heavy hauling. +A restored lugged steel racer with Campagnolo dropouts, waxed leather bar tape, and period-correct steel rims laced with brass nipples. +Aero road bike in white and electric blue with integrated cockpit and rim brakes for classic look. +Touring steel frame with matching steel fenders, leather-wrapped bars, and a deep forest green enamel finish. +A kids' BMX street bike with reinforced gusset plates, 20-inch wheels, pegs and matte midnight-blue paint splattered with neon. +Mountain cross-country race bike with hardtail frame, stiff alloy construction, and a race-ready 1x12 drivetrain for podium chases. +Road aero pro-level with integrated tool storage, full wireless groupset and high-profile carbon wheels for fast weekend races. +A vintage-inspired cruiser with scalloped fenders, brass bell, oversized saddle and a pearl-white gloss finish for nostalgic beach vibes. +A cyclocross-specific alloy frame with high-clearance stays, solid chainstay protector, and a low-slung down tube for easy shoulder carries. +Recreational neighborhood cruiser with bright pastel paint, balloon tires, chrome fenders and a comfortable foam saddle. +A classic step-through Dutch-style bicycle with chaincase, rear hub brake, full chain guard and upright bars for comfortable commuting. +A vintage city bike refurbished into a single-speed with new sealed bearings, subtle pastel paint, and comfortable upright posture for relaxed rides. +Gravel touring bike with discreet wiring, large tire clearance, and carefully placed racks for balanced load carrying. +A performance gravel bike with integrated GPS/phone mount, long chainstay for traction, and a tubeless wheelset ready for rough rides. +Mountain trail hardtail with light alloy frame, efficient pedaling posture and tubeless-ready wheels for mixed terrain. +A cargo e-bike with long low deck, lockable storage, and a low center of gravity that keeps handling predictable while loaded. +Cargo box bike with wooden platform, three-wheel stability, and bright orange powder coat for visibility. +Road endurance machine with relaxed head angle, 28mm tires for comfort, and a carbon seatpost designed to absorb vibration. +A cyclocross pro build with full carbon fork, disc brakes, and race-specific gearing to sprint and dismount across a muddy, twisty course. +Road bike with compact double crank, shallow section wheels and endurance geometry for sportive days. +Road climbing lightweight with compact crank, high-ratio cassette and rim tires shaved for weight savings on climbs. +A carbon monocoque road bike with integrated seatmast, hidden cables, and smooth transitions between downtube and chainstays to reduce turbulence. +A cyclocross rig with mechanical 1x drivetrain, stiff alloy frame, and an emphasis on quick handling through tight, twisty courses. +Urban cargo tricycle with large front bin, electric assist, stable three-wheel layout, and weatherproofing for frequent deliveries. +Gravel bike for bikepacking with heavy-duty racks, reinforced bottle bosses, and large 2.1-inch tires for rough, remote roads +Compact cargo bike with mid-mount electric assist, low center of gravity cargo tray, and twin safety straps for bulky loads. +A city folding bike with internal gear hub, quick-release wheels, and a swiveling stem for compact storage and short hops. +A kids' cruiser with polka-dot paint, training wheels, coaster brake, and a little bell for neighborhood joy. +City cruiser with classic white pinstripe, chrome-plated handlebars, balloon tires and a comfortable ergonomic saddle for casual rides +A titanium gravel bike with slightly slacker head angle, 650b compatibility, and minimalist laser logos to preserve the metal’s natural sheen. +A tandem touring frame with brazed-on racks, long chainstay to accommodate heavy loads, panniers and antique blue finish with cream lining. +A downhill-ready mountain bike with burly headset, double-crown fork option, 7-inch-wide rims and reinforced links that survive big hits. +Folding cargo bike with extended platform, foldable sides and an electric hub motor to ease heavy loads. +Road endurance build with vibration-tuned carbon layup, 30mm tires and a relaxed stack/ reach for multi-hour comfort. +A fixed-gear city bike with narrow profile tires, riser bars, bite-size front brake and matte graphite finish with minimalist logo. +All-terrain fat-tire bike with 5-inch tires, reinforced steel fork, and compatibility for extra-wide fenders on snowy rides. +Folding electric bike with mid-drive motor, 20" wheels and smartphone-integrated display. +Bicycle with cargo trailer and reinforced hitch, long-range panniers, and a low-maintenance gearing setup for family trips. +Lightweight climbing road frame with oversized bottom bracket, thin seatstays and a high-modulus carbon layup. +Mountain enduro bike with progressive geometry, coil shock tuning, 29-inch wheels and aggressive 2.5-inch tires for steep trails +A cyclocross course-ready bike with clever internal mud-shedding features, 1x drivetrain, and a bold white-and-black contrast paint pattern. +A full-carbon road race bike with an internal hydration cavity, aero-optimized downtube, and a glossy paint with subtle matte logos. +A fixed-gear track bike with deep-section rims, narrow tubular tires, and a polished silver finish that gleams under indoor velodrome lights. +A modern mountain bike with 29" wheels, progressive geo, and a suspension platform that climbs efficiently but doesn't compromise descending ability. +A compact city folding bike with low-step frame, 8-speed internal hub, and quick-folding hinge to stash under desks or trains. +A gravel adventure machine with weatherproof frame bag system, extra-large 650b+ rear tire, and a sturdy rack for carrying backcountry supplies. +Vintage-inspired cruiser with swooping fenders, huge cushioned saddle, and a single-speed drivetrain for relaxed coastline rides. +A specialized gravel touring frame with reinforced downtube, full braze-on sets for cargo, and a long-reach geometry to manage heavy packs. +Gravel plus bike with wide rubber, reinforced rims, and frame-mounted shock absorbers for rough singletrack. +A classic steel road bicycle with delicate lug fillets, period-correct quill stem, and a vintage headbadge restored to gleam in the sun. +Gravel endurance alloy bike with wide rims, vibration-damped bar tape and roomy tire clearances for long rides over rough roads. +Vintage roadster step-through with ornate chaincase, wicker basket mount and polished brass lamp for classic urban charm. +Commuter with hub motor rear, built-in rack, and puncture-resistant tires designed for daily reliability. +Youth mountain bike with suspension fork, wide knobby tires, simple grip shifters and bright decals appealing to kids. +Gravel e-bike with long-range battery, bikepacking racks, and wide-volume 650b tires for comfort. +A vintage-inspired roadster with brass headlamp, leather saddle, and full fenders for dignified morning coffee runs. +Mountain trail bike with modern progressive geometry, internal cable routing, and dropper compatibility for technical sections. +Road endurance rig with micro-suspension, compliant seatpost, and generous tire clearance up to 32mm. +Mountain trail full-suspension bike with modern chainstay geometry, tuned rear shock, and supportive mid-stroke for jumps. +A fixed-gear city bike with deep-section wheelset, minimal fenders removed, and a stealth matte black paint job for alleyway speed. +A vintage steel road bike with ornate head badge, chromed fenders, and narrow tubular tires for Thursday night group rides. +Lightweight endurance gravel frame with subtle detailing, multiple mounts, and a supple ride tailored for long days in the saddle. +A retro steel track bicycle with fixed gear, pursuit bars, high-flange hubs, and polished chrome fork crown for velodrome nights. +A city folding cargo bike with an extended front deck, rapid-fold hinges, and electric assist for last-mile commercial runs. +A speedy crit bike with a pro-level aluminum frame, rim brakes for low weight, and a low-profile saddle to optimize aerodynamics while cornering. +Kid's balance bicycle with bright decals, low step height, foam tires and grippy handlebar ends for first rides +A steel touring bike with polished brazing, triple-bolt rack tabs, and a custom paint job that maps routes of a long-distance trip on the top tube. +Time-trial specific bicycle with fully integrated hydration system, aerodynamic fork and a sculpted seat tube. +A titanium commuter with integrated fender mounts, belt drive, and stealth rear rack compatible with quick-release panniers. +Electric commuting bike with theft-deterrent GPS, integrated alarm and a removable battery for safe overnight parking. +Touring tandem in brushed steel with double bottle mounts, low gearing, robust rack mounts, and matching leather saddles. +Retro beach cruiser with surfboard rack, wide beach tires and classic chrome handlebar mirrors for a coastal vibe. +Gravel racer with high-volume tyres, light carbon fork, precise shifting, and a cockpit geared for long distances at speed. +A gravel bike with racy compact crankset, wide-range cassette, flared bar ends, and a paint job that transitions from matte gray to metallic teal. +A Japanese-inspired lugged-steel road bike with minimal graphics, polished chrome, and a compact rear triangle for responsive handling. +A kids' training BMX with lower top tube, padded crossbar, and durable knobby tires for learning jumps and basic tricks safely. +A custom steel gravel grinder with hammered finish, integrated framebag mounts, and a flush-fit two-bottle cage arrangement. +Touring tandem recumbent with dual recumbent seats, heavy-duty frame and pannier racks for long-distance comforts. +Mountain bike with short chainstays, aggressive geometry, 1x11 drivetrain, and race-ready dropper post for podium ambitions. +Steel commuter with internal gear hub, heavy-duty rear rack, full chaincase, and reflective paint for night visibility. +Mountain downhill competitor with reinforced headset, heavy-gauge chainstays, oversized rotors and wide rims for heat management on long runs +Electric folding commuter with a small footprint, torque sensor assist and a modular battery that pops out for charging. +A vintage track sprint frame restored with new paint, tubular wheels, leather saddle and crisp white decals on glossy black. +Mountain freeride bomber with chain guide, burly cranks, and reinforced headset for aggressive trail use. +A European-style city bike with four-speed IGH, chaincase, skirt guard and ivory enamel finish with subtle metallic flakes. +Classic city bicycle with upright geometry, chain guard, skirt guard, and built-in front basket. +A compact folding commuter with small 16-inch wheels, fast fold latch, and an integrated strap to carry the bicycle on the shoulder. +Mountain enduro alloy frame with tuned kinematics, short stem compatibility and long-travel fork for aggressive terrain. +A lightweight trail hardtail with burly wheelset, 120mm fork, stiff frame junctions and agile geometry for tight singletrack and quick climbs. +A titanium cyclocross racer with internal dropper compatibility, disc brakes, and a brushed finish that grows more interesting with every ride. +Mountain bike with mixed wheel sizes (29 front, 27.5 rear), burly tires and strong 12-speed drivetrain for tech trails +Speed-focused track bike with aggressive saddle setback and oversized bottom bracket shell for stiffness. +Urban commuter with integrated frame lock, belt drive, upright handlebars and puncture-resistant tires for daily reliability +A gravel frame with hand-etched headbadge, Smokey gray finish, and ample mudguard clearance for rainy-season adventures. +A gravel touring bicycle with dual-bolt rack mounts, multiple bottle bosses, and stability-oriented geometry ready for loaded adventures. +Trail mountain bike with 140mm front and rear travel, internal cable routing, and stout aluminum chainstays. +Vintage road bike restored with period components, leather handlebars, and classic steel lugs with a gleaming finish. +Compact urban utility bike in matte gray with integrated lock slot, internal gears, and slooping top tube for easy mounting. +Commuter with hub dynamo, integrated front and rear lights, chaincase, and a comfortable upright geometry for city streets. +Classic two-seater tandem with welded-on racks, matching leather saddles, and robust wheels for touring couples. +A mountain hardtail with progressive geometry, 130mm fork travel, 29-inch traction tires and an emphasis on efficient pedal power on rough climbs. +Cyclocross race bike with short wheelbase, powerful hydraulic discs, and clear mud-shedding frame shaping for winter racing. +Vintage road bicycle with brass headbadge, chrome lugs, restored paint and narrow tubular tires for Sunday group rides +A cyclocross pro-level carbon frameset with internal cable routing, mud-optimized geometry, 33mm tread-ready tires and matte slate paint. +A cargo tricycle with locked cargo box, low bed for easy loading, and a step-through design for quick mounting and dismounting. +Classic touring tandem bicycle with steel frame, two sets of drop bars, and matching leather saddles. +Highly durable utility bike with thick steel tubing, heavy-gauge spokes, coaster brake, and cargo mounting points for deliveries. +A lightweight steel road bicycle with long reach calipers, modern geometry, and a classic glossy racing blue finish. +A kids’ balance bike painted candy red with low seat height, no pedals, and lightweight frame to help toddlers learn steering and balance. +A commuter with built-in GPS cradle, integrated rear carrier, and puncture-resistant tires for daily, worry-free city riding. +A boutique titanium frame with custom geometry, engraved head badge, and a hand-filled satin finish that patinas subtly with age. +Gravel bike with electronic Di2 shifting, 700x40 tires, headset stack and top-tube framebag mounts. +Mountain freeride hardtail with 27.5" wheels, thick-walled rims, and chainstay protectors for heavy impacts and drops. +A gravel race rig with electronic drivetrain, aerodynamic seatpost, 700x32 tubeless tires and pearly white paint with chromed accents. +A retro road bike with downtube shifters, quill stem, thin steel tubing, and mustard-yellow paint with hand-lettered decals. +A commuter with hydraulic hub brakes, integrated taillight, anti-theft frame lock and a low-maintenance chain guard. +A trail-oriented full-suspension bike with 140mm travel, adaptable geometry flip-chip, 2.6-inch tires, and a stout chainstay design to prevent squat. +Gravel endurance bike with carbon compliance features, wide tyre clearance, and a comfortable geometry for multi-day events. +A mid-travel full-suspension mountain bike with 140mm travel, modern geometry, and a tuneable shock for balanced climbing and descending. +BMX park bike with tapered short forks, gyro system, strong rims, and low seat for technical tricks and park lines. +Gravel-specific bike with stealthy matte black, clearance for 45mm tires, and an old-school bottle mount on the fork leg. +City commuter with integrated GPS mount, large ergnomic grips and reflective pinstripe for night rides. +A compact city foldable with 20-inch wheels, quick-fold latch, small rear rack and elegant matte crimson paint. +Classic randonneur with low trail geometry, support braze-ons for racks and a brass headlamp for period charm. +A superlight carbon road bike with minimalist seatpost, narrow 25mm tires, and a single-bolt seat clamp to shave grams for climbing. +Mountain enduro full-suspension with adjustable geometry, air shock with volume tokens, and reinforced chainstay protector for rocky trails. +Urban cargo longtail with adjustable passenger backrest, double-kickstand stability, and powder-coated steel for durability. +Urban cargo trike with large front box, weatherproof lining and kid-secure seats for safe family rides. +A gravel e-bike with downtube-mounted battery, 1x wide-range drivetrain, reinforced fork and muted moss-green finish with tan accents. +A minimalist single-speed road bike with a bold frame color, narrow tires, and a lightweight approach to keep the bike nimble on smooth pavement. +A modern track fixed-gear with carbon fork, deep dish front wheel, and minimalistic aesthetics that scream speed on the boards. +Classic Dutch-style step-through bicycle with chainguard, rear roller brake, and whimsical floral bell on the handlebars. +Cyclocross-style commuter with knobby semi-slicks, mudguards removed for weight savings and a lightweight rack +Gravel adventure bike with front lowrider rack mounts, bar bag, 40mm tires, and bombproof steel fork for remote exploration. +Folding utility bike with aluminum frame, quick-release seat clamp, and a compact folded profile for easy storage. +Electric commuter with lightweight frame, mid-drive motor, integrated rear rack and a quiet belt drive for maintenance-free use. +Mountain trail bike with progressive slack head angle, wide handlebars, and dropper post for technical descents. +Electric cargo longtail with stable geometry, passenger footrests, and vibration-damping saddle for comfort. +Gravel-adventure frameset with extra frame bag mounts, stealth integrated lighting, and robust chainstay protection for remote exploration. +Single-speed fixed-gear track-style bike in candy red, deep-profile aero wheels, bullhorn bars, flip-flop hub for fixed or freewheel, and minimalistic frame tubing. +A mountain freeride bike with slack geometry, 170mm front travel, and a burly rotor-friendly braking system to stay controlled on big lines. +A clean commuter with internal gear hub, coaster-compatible pedal option, integrated lights and stone-gray matte finish. +A touring bicycle with reinforced dropouts, Rohloff hub, and stainless-steel spokes to keep loads rolling reliably across continents. +A gravel adventure rig in stone-gray with integrated frame pack, bar-top GPS mount, and external cable guides for field repairs. +Mountain cross-country race bike with lightweight carbon frame, 100mm travel fork and paired lightweight wheels +Road climbing bike with small chainrings, high-cadence gearing, and featherweight wheelset for alpine stages. +A sleek commuter with electronic shifting, bright daytime running lights, and a USB-rechargeable battery tucked into the frame. +A rugged fat-tire bicycle with 4.8-inch tires, band-on fenders, steel frame and single-ring drivetrain built for sand and snow exploration. +Classic commuter with chaincase, full fenders, upright bars, and a comfortable saddle for everyday urban reliability. +Electric mountain bike with robust alloy frame, mid-drive motor, large-cap battery, and low gearing for steep trails. +Performance road bike with progressive geometry, stiffer bottom bracket, aero seatpost and wide rim profile for sprinting. +Cyclocross bike with anodized gold finish, disc brakes, short wheelbase, and clear-coat over exposed carbon weave. +Classic steel road bike with period-correct Campagnolo parts, vintage decals, and gloss deep forest green paint. +A sleek aero road bike with integrated stem and bar, 45mm tubular wheels, wireless groupset and pearlescent graphite finish. +A restored 1980s road bike with period decals, vintage components, tan bar tape and a gleaming chrome fork crown. +A gravel adventure rig with two-bolt rack mounts, top-tube bag loops, and a handpainted gradient inspired by desert sunsets. +Urban shopper with basket, low-step frame, integrated bell, and puncture-resistant tires to shuttle supplies across town. +A gravel mountain crossover with 650b wheels, fat 2.2 tires, and an adjustable offset to fine-tune steering for loaded tours. +Race-ready cyclocross frameset with carbon layup, optimized tire clearance and asymmetric chainstay for power transfer +A kid-sized BMX with small frame geometry, reinforced tires, and bright decals that make it clearly a bicycle meant for playful tricks and learning. +Small-wheel folding bike with compact frame, magnetic latch, and cushioned saddle for door-to-platform storage. +Adventure touring bike with robust steel frame, rack mounts, extra tyre clearance, and a comfortable long-distance geometry. +Performance gravel build with carbon fork, tubeless-ready wheels and a nimble, race-oriented geometry. +A restored Dutch city bicycle with step-through frame, chaincase, comfortable saddle, and integrated rear rack for daily errands. +Gravel plus touring bike with heavy pannier mounts, wide tires for rough tracks and reinforced front forks. +Fat-snow commuter with studded tires, rigid fork, and heated grips for winter reliability on icy streets. +Gravel touring rig with integrated access door in down tube for tools and spares on long rides. +Track pursuit bike with extended chainstays, disc front wheel compatibility, and a super-stiff bottom bracket. +Mountain enduro bike with adjustable geometry, coil shock and 2.5" tires for loose rock conditions. +A barn-fresh vintage touring bicycle with patinaed chrome, original leather saddlebags, and an old-school steel frame. +Folding commuter with internal gear hub, compact frame lock and quick-fold handlebars for transport. +Gravel endurance build with vibration-damping bars, flared drops, and a two-bottle cage setup for long days. +Urban electric step-through bike with comfy cruiser bars, automatic LED lights, and pedal-assist mapping for relaxed city travel. +City folding bicycle designed for commuters with quick-fold latch, integrated basket and sporty color accents. +Touring tandem with synchronized shifters, strong rear axle and additional cargo mounting space. +A gravel explorer with full-length rack mounts, large-volume tires, and a reinforced head tube to carry large loads into the backcountry. +Lightweight endurance road frame with carbon compliance, broader tire clearance, and a geometry designed to reduce fatigue. +A gravel adventure frame with robust steel construction, three bottle mounts, rack mounts and desert-sand paint with tan highlights. +A classic BMX street bike with 20-inch double-wall rims, gyro detangler, and matte black anodized parts. +Electric cargo longtail with passenger footpegs, low step-in height, and a high-torque hub motor for city family transport. +Classic Dutch-style step-through with spring saddle, full chainguard, and elegant riveted fenders. +A buff steel singlespeed commuter with glossy candy-apple red paint, leather grips and narrow tires for stress-free urban speed. +A custom-painted city bicycle with hand-brushed art on the top tube, comfortable swept bars and a matte cream base color. +Compact BMX freestyler with reinforced frame, short chainstay, gyro-equipped bars, and bright anodized components for park work. +A lowrider cruiser with chrome accent trim, chopper-style handlebars, oversized rear fender and deep burgundy sparkle paint. +Compact recreational bike with low gearing, 24-inch wheels, and colorful decals for beginner cyclists. +A vintage-style town bicycle with wicker basket, classic bell, leather saddle and a glossy powder-blue paint finish for scenic routes. +Women's-specific hybrid with lower standover, step-through geometry, and cushioned saddle for comfort. +A lightweight carbon cross-country bike with 100mm travel fork, 29-inch wheels, efficient pedaling geometry and bright coral highlights. +Mountain free-ride alloy bike with reinforced welds, adjustable geometry and micro-suspension seatpost for comfort. +Sleek aero time trial bicycle with integrated hydration, teardrop tube profiles, carbon monocoque frame, and deep tubular wheels. +Urban folding commuter with compact geometry, low step-over, single-speed simplicity and quick fold latch for train storage +A commuter folding e-bike with compact folded size, integrated lights, and a durable hinge capable of daily use in multi-modal commutes. +Retro commuter with enamel finish, fenders, coaster brake, and a leather saddle for comfortable city spins. +A commuter with internal gear hub, sealed cartridge bearings, chain guard, and integrated rear light that charges off a hub dynamo. +A performance e-mountain bike with 160mm travel, adjustable geometry, powerful mid-drive motor, and a burly wheelset for aggressive terrain. +Mountain trail bike with 29er wheels, progressive geometry, and tubeless-ready rims for confident cornering. +Lightweight endurance bike with vibration-damping materials, slightly relaxed geometry, and clearance for tires up to 32mm. +Touring adventure steel bike with extra-long rear stays, triple-chainring, and durable wheels for rugged transcontinental travel. +A light-duty mid-tail cargo e-bike with youth bench, removable canopy, pedal-assist, and sturdy kickstand for short family errands. +Modern mountain hardtail with slack geometry, 130mm fork, and aggressive 2.4-inch tires. +Gravel race frame with asymmetric chainstay design, aero tube shaping, and a translucent pearl finish. +Steel gravel bike with hand-brazed lugs, custom paint, 650b clearance, and a sturdy fork crown for mounting low riders. +A compact kids' foldable bike with 16" wheels, quick-fold frame, simple coaster brake, and bright safety color for car-topping and storage. +Folding cargo bike with extendable deck, electric assist, and sturdy side-stand for stability while loading groceries. +Mountain enduro carbon frame with woven reinforcement, aggressive geometry and reinforced linkage for heavy impacts. +A downhill-ready sled with massive rotor brakes, coil rear shock, and confidence-inspiring geometry for steep-staircase rides. +A gravel racing bicycle with aero-shaped tubing, wirelessly-shifted cogs, and disc brakes for responsive stopping on dirt roads. +A cyclocross-adjacent mountain bike with 650b wheels, 2.1 tires, and a wide, flat handlebar for stability on loose terrain. +A gravel-specific frame with integrated mudguard mounts, room for two bottle cages, and a slender seat tube for comfort-focused compliance. +Vintage track frameset restored with period decals, deep-v rims, leather saddle and classic steel handlebars for nostalgic track meets +Mountain gravity bike with double crown fork, reinforced swingarm and wide knobbed tires for traction on loose lines. +A vintage road bike transformed into a single-speed with polished stem, silk-wrapped bar tape, and slim-profile 700x25 tires for comfortable cruising. +A high-performance time trial bike with full carbon fairing, integrated hydration, aero basebar and luminous metallic gold paint. +Gravel adventure frame with titanium tubing, multiple mounts, and an understated hand-brushed finish for long-term durability. +Urban fixed-gear with deep-dish wheels, single brake, matte black saddle and stealth decals for minimalist commuting. +Retro-styled city bike with modern internals: hub gear, roller brakes, integrated lighting and decorative rivets +A gravel bike with slick aero-mounted downtube storage, integrated rear rack mounts, and 35mm mixed-surface tires for quick touring. +A performance gravel racer with carbon fork, thru-axles, 700x40mm tubeless gravel tires, and a compact double drivetrain for speed on rough roads. +Gravel all-road machine with mixed surface tires, integrated mudguards, and triple-row chainstay protection for chain slap. +Lightweight cyclocross frameset with minimal weight, disc brakes, and a geometry tuned for rapid accelerations and quick handling. +Compact commuter with rear rack, integrated taillight, puncture-protection tires, and a step-through frame for quick errands. +A commuter with step-through frame, chaincase, dynamo front light and a broad saddle for comfortable short commutes. +A BMX race bike with rigid fork, light alloy frame, and high-grip tread for sprint track starts. +Cyclocross pro-level bike with carbon fork, mud-shedding geometry, and grippy knobby tires ready for short, intense races. +A boutique titanium mountain bike with smooth welds, custom dropouts, single-pivot suspension and bead-blasted finish with faint anodized accents. +Touring tandem with precision-cut chainstays, comfy double saddles, and luggage-friendly geometry for long expeditions. +Gravel bike with dropper post, microSHIFT 12-speed groupset, and burly 40mm tubeless tires for rough terrain. +Modern cyclocross rig with 1x groupset, tubeless tyres, and a stiff BB shell for muddy off-road circuits. +A commuter with low-maintenance shaft drive, sealed bearings, and a modular cargo rack that converts to a child seat platform. +A racing road bicycle with slender tubes, shallow rims, and a race-oriented fit for crits and short explosive efforts. +A stripped-down racing bicycle with narrow profile, tubular tires, and classic steel tubing optimized for a smooth road feel. +A speedy gravel race build with electronic 12-speed shifting, aerodynamic bar-stack, 38mm tire setup and a stealth matte finish for competition. +Mountain trail all-rounder with 140mm travel, 29-inch wheels, and a balanced geometry suitable for mixed singletrack. +Kids' mountain bike with scaled brake levers, modest suspension, and tough steel rims for confident trail learning. +A versatile gravel bike featuring 650b wheel compatibility, clearance for 47mm tires, wide drop bars and a sand-blasted matte finish. +Track-inspired sprint bike with steel frame, glued tubular tires, and stripped-down aesthetic for club nights. +Minimal commuter with single-speed hub, flip-flop rear hub, fenders and rack mounting points. +Gravel touring bike with mixed cages, durable racks, and wide-range gearing for loaded ascents. +Lightweight steel cyclocross bike with slender top tube, mud-shedding paint and subtle branding. +Electric urban commuter with discreet battery, quiet hub motor, integrated lights and simple on-board display for daily rides. +Steel touring bike with reinforced triangles, triple bottle bosses, and durable all-weather paint for long rides. +A modern carbon gravel bike with flared bars, tubeless 700x45 tires, and plenty of braze-ons for robust expedition capability. +Kids' BMX with flame decals, extra-thick frame tubing and reinforced forks for trick practice. +Compact folding commuter with 18-inch wheels, rigid fork, and a secure folding latch for frequent transit portability. +A commuter with a built-in top tube bag, integrated lights, puncture-proof tires and an upright, relaxed geometry for daily rides. +Mountain cross-country race bike with lightweight alloy frame, fast-rolling tires and rigid seatpost. +A lightweight carbon road bicycle painted in pearl white, with disc brakes and 28mm tires for fast, refined club rides. +Aero road bike with wing-shaped seat tube, integrated brake calipers, and electronic groupset for sprinter-friendly sprints. +Mountain hardtail with forged dropouts, tapered headtube and lightweight 29-inch wheel build for responsive climbs. +A gravel-friendly adventure bicycle with dynamo hub, bar-mounted navigation, and a light, stiff, versatile frame for exploratory rides. +Mountain trail alloy with progressive geometry, tubeless-ready rims, and a competent dropper post for variable terrain confidence. +A touring tandem designed for two up front and one child seat at the rear, with triple-ring gearing and heavy-duty wheelsets. +Folding electric commuter with rear hub motor, compact battery in frame tube, and quick fold for stowing under desks on arrival +A compact e-bike with center-fold hinge, lightweight motor, and simple paint finish to keep weight down for frequent lifting. +A children's BMX with low seat, sealed bearings, small pegs, and toughened steel frame to keep pace with first tricks and jumps. +Urban fixed gear with wooden fenders, monochrome paint and rear coaster hub for carefree cruising. +BMX street park setup with reinforced top tube gussets, slim profile saddle, and 20" wheels for grinds and manuals. +All-day touring bike with triple chainring, 36-spoke wheels, full pannier racks and leather handlebar grips. +Road time trial bicycle with integrated storage, narrow fairings and a focused geometry for solitary speed efforts. +A low-step commuter bike with step-through aluminum frame, basket, hub dynamo lighting, and a comfortable gel saddle for daily errands. +Mountain trail bike with 140mm front travel, 130mm rear travel and modern slack geometry for stability. +A classic step-through stepper with wicker basket, bell, coaster brake, upright bars and a glossy cherry-red frame for city charm. +Vintage-inspired town bike with chrome fenders, leather grips, and a retro headlamp mounted on the front rack. +Road racing frame with asymmetric stays, disc brakes, integrated mount for a computer and a glossy deep red. +A custom-painted steel touring bike with hand-applied scallops, braze-ons for lowrider racks, and robust double-butted tubing. +Racing mountain bike with lightweight carbon frame, 120mm front travel, and dropper post for aggressive climbing. +A cyclocross full-carbon build with cantilever bosses removed, disc brakes, and a sensitive frame layup tuned for quick accelerations. +A gravel endurance bike with wider tires, a more relaxed head angle, and battery storage integrated into the downtube for extended range. +Bright red BMX bike with gyro detangler, 20-inch wheels, pegs and reinforced chromoly frame for tricks. +Race-focused road frame with refined carbon layup, lowered seatstays, and aerodynamic cockpit integration for high-speed efforts. +Retro-inspired single-speed cruiser with swept-back handlebars, wide saddle, and coaster brake. +Mountain trail 29er with dropper post, 140mm front travel and slimline tubeless-friendly rims. +A gravel touring frame with welded-on tool mounts, deep-set rack points, and a paint finish inspired by old exploration maps. +Gravel endurance bike with longer wheelbase, comfortable cockpit, and clearance for 45mm tires for versatile touring. +Chopper-style cruiser with elongated top tube, springer front end, banana seat and chrome fenders. +Kids' balance bike with low saddle, rubber-coated handle grips and bright, child-friendly decals. +Gravel racer with aerodynamic profile, wide tyre clearance, 1x drivetrain, and a cockpit designed for hours of steady speed. +A gravel bike with extra-low bottom bracket, integrated chainstay guard, and a practical triple-bolt stem for accessories. +A kids' balance bicycle with rounded wooden frame, low center of gravity, durable finish and colorful decals for playful learning. +Road touring bike with extra-strong rims, triple chainset, long cage derailleurs and dual racks for global travel readiness +Track fixed-gear with a polished aluminum frame, single chainring and minimal branding for a clean, fast look. +Cruiser with swept handlebars, soft cushioned saddle, two-speed internal hub, and retro candy apple red paint. +Road aero frame with full internal routing, integrated seat clamp and a glossy black finish with subtle accent stripes. +Gravel race machine with steep seat tube angle, compact cockpit and 700x35 tubeless tires for fast transitions. +Gravel bike with two-tone enamel paint, flared handlebars, and semi-integrated cables for a clean look. +Kids' trail bike with colorful accents, internal gear hub, and training-friendly gearing for confidence. +A modern gravel e-bike with torque-sensing motor, long-range battery, 2.6-inch tires, and reinforced frame mounts for bikepacking gear. +A commuter with automatic shifting, internal hub gear, and an e-brake backup system for convenience and safety in stop-and-go traffic. +A commuter with low-maintenance shaft drive, sealed headset, and a modular rack system designed for daily urban challenges. +Mountain freeride with replaceable shock linkages, reinforced head tube, and durable powder coat for frequent use. +Mountain trail hardtail with 130mm fork, dropper post, 29" wheels, and a geometry tuned for fast technical climbs and playful descents. +Classic mixte with enamel headbadge, swept upright bars, chrome chainring guard and wicker baskets for picturesque city riding. +A modern trail hardtail with tapered head tube, boost hub spacing, 29x2.2 tires and a neutral but confident geometry for technical climbing and descents. +Mountain downhill sled with reinforced seat tube area, heavy-duty linkage and an aggressive colorway for race visibility. +Beach cruiser with coral pink enamel, whitewall tires, swept bars and a spring-loaded leather saddle. +Vintage lightweight racing bicycle restored with cotton brake cables, 8-speed downtube shifters, and tubular clincher rims. +Touring steel frame with reinforced braze-ons, long-chainstay for heavy loads and dependable low-tech parts. +A classic tandem with matching leather saddles, synchronized brakes, and a finely balanced frame for long paired touring days. +A pro-level mountain bike with electronic suspension control, carbon wheels, and discreet graphic language indicating team spec. +A race-ready gravel machine with integrated top tube bag mounts, 1x drivetrain, and a satin black finish with reflective pinstriping. +Gravel titanium touring bike with elegant joints, multiple rack mounts and vibration-damping comfort for multi-day trips. +Kid's BMX freestyle bike with pegs, gyro cable, reinforced top tube, and colorful anodized parts for trick sessions. +City commute e-bike with pedal assist, ergonomic saddle, integrated taillight, and reflective accents for visibility in traffic. +A commuter with throttle e-assist kit, rear fender with integrated light, and a capacity rack for grocery bags and work briefcases. +Road aero frame with integrated braking fairings, hidden cables, and an ultra-clean cockpit for time-sensitive events. +A commuter with hidden lights in the frame, belt drive, internal hub and soft pearl gray powdercoat with subtle reflective seams. +Downhill gravity sled with heavy-duty linkage, 200mm twin-crown fork, and reinforced headtube for extreme trails. +Classic city bike with coaster brake, chaincase, wicker basket and retro chrome accents for short errands. +A lightweight gravel frame with integrated storage, a low center of gravity, and a geometry that smooths the rough pavement sections. +Gravel commuter with discreet mudguards, wide comfortable saddle, and puncture-resistant tires for daily dirt-road commutes. +A matte black carbon road bike with aerodynamic tube shapes, integrated cockpit, 50/34 compact crankset, and 28mm tires for fast, everyday rides. +Electric urban commuter with fender-integrated lighting and smartphone app-controlled motor modes. +Adventure gravel bike with extra-wide clearance, multiple frame mounts, and a long wheelbase for loaded stability. +BMX park bike with nylon pegs, mid BB height, and solid welded gussets where the top tube meets the head tube to withstand tricks. +A performance aero road bike with full carbon fairings, integrated hydration, aero cockpit and liquid black gloss finish with thin silver stripes. +A touring frame built for endurance with triple racks, long-chainstay for loaded stability, full fenders and deep navy lacquer with cream pinstriping. +Mini-velo urban bike with small 20-inch wheels, compact frame, upright bars, and a step-through option for easy mounting. +A cyclocross race frame with short stem and flared bars, integrated mud sheds, and a paint scheme with purposeful contrasts. +Gravel-focused e-bike with pedal-assist, torque sensor, and a robust battery concealed in the downtube for streamlined looks. +A vintage-inspired roadster with swept-back bars, leather saddle, spring-mounted seat and soft pastel teal enamel with white trim. +Fatbike with oversized tires, rigid frame, wide rims, and a relaxed geometry for stability on soft surfaces like sand and snow. +A gravel racer with fully integrated cockpit, electronic 1x drivetrain, wide tire clearance and a custom carbon layup for comfort on rough roads. +A commuter with built-in pannier hooks, integrated lockable top tube bag, and a reflective paint accent for low-light safety. +Electric cargo longtail with dual batteries, child safety harnesses, modular benches and reinforced frame for urban family logistics. +Cyclocross training bike with alloy tubed frame, clearance for oversize tires, and a mud-friendly chainstay protector. +A robust cargo bike with long-tail frame, secure child seats, and an easy-to-use pedal-assist system for family urban transport. +A kids' balance bicycle with lightweight alloy frame, foam tires, and low stand-over height to teach steering and balance safely. +A gravel rig with flared drop bars, integrated top cap storage, wide 700x45 tubeless tires, and a low-rise stem for control. +Gravel race rig with electronic shifting, bladed aero seatmast, stealth finish and tubular-compatible rims for fast stage miles. +Touring tandem with long wheelbase, synchronized shifting options, reinforced racks and comfortable saddles for comfortable two-up travel. +Road endurance bike with comfortable reach, compliance-enhanced seat tube, and modest yet tasteful livery. +Enduro mountain bike with 170mm travel, adjustable geometry, coil-sprung rear shock and reinforced links for heavy-duty descending +Retro-inspired step-through city bike in powder blue with cane-style handlebars, fender skirt guard, chaincase, and a comfy sprung saddle for relaxed rides. +Commuter with triple-reflector lights, fenders, and a quiet belt-drive for a near-silent city trip every day. +Gravel bike with carbon layup optimized for compliance, tubeless tires set up with sealant and a stealthy matte finish. +Commuter single-speed with powder-coated frame, narrow tires for speed, and a small rear rack for lightweight transport. +Commuter e-bike with step-through frame, rear hub motor, integrated rear rack and smartphone-connected display. +Downhill race bike with external coil shock reservoir, massive 36mm stanchions and an ultra-wide 2.5-inch rear tire for traction. +A beach cruiser with dual-tone paint, brushed chrome accents, wicker basket and wide puncture-resistant tires for comfortable strolls. +Touring gravel bike with triple-rack readiness, mudguard eyelets and wide-range gearing for loaded ascents. +High-trail mountain hardtail with slack geometry, wide rims, 2.6-inch tires, and chain guide for aggressive singletrack. +A modern trail bike with 150mm travel, refined shock tune, and asymmetric frame shaping to maintain tire clearance at speed. +Road endurance bike with compliance-focused seatpost, endurance geometry, and endurance-styled handlebars for comfort over hours +Touring bike with chromed steel forks, long wheelbase for stable steering and triple bottle cage capacity for long days. +Road endurance machine with disc brakes, shock-absorbing seatpost and 28mm supple tires for comfort. +A lightweight road race bike with disc brakes, aero-optimized tubing, and a 1x compatible chainline for simplicity under racing stress. +Carbon gravel bike with dropper post compatibility, stealth matte paint, and modular mounting points for racks and bags. +Urban electric folding cargo bike with dual-battery option, wide platform deck and modular accessories for last-mile delivery. +Touring bike set up for bikepacking with three framebags, roll-top drybags and mud-proof fenders. +A mountain enduro bike with adjustable geometry, 170mm fork travel, reliable hydraulic brakes and a supportive saddle for long runs. +Track sprint frame with chromed dropouts, narrow handlebars and a lustrous pearlescent finish that catches the lights. +Mountain enduro rig with 165mm travel, adjustable geometry, and a tough matte finish that hides scuffs from trail use. +A mountain bike designed for bikepark laps featuring coil shock, long travel fork, and 27.5" wheels tuned for jump-oriented stability. +Gravel endurance bike with 700x40 clearance, carbon fork, flared bars and vibration-absorbing seatpost for long days on mixed surfaces. +City commuter with front basket, chaincase, rear hub dynamo, and ergonomic grips for everyday practicality +A cargo bike with electric assist and large front box, two passenger capacity, hydraulic brakes and sturdy wooden slats. +Urban cruiser with soft gel saddle, swept bars, coaster brake, and two-tone soft-pastel paint for comfortable Sunday rides. +A city commuter refined for short trips with integrated kickstand, basket, coaster brake and dove-gray powdercoat with white accents. +A gravel-touring tandem with twin racks, long chainline tensioners, and robust wheelsets for carrying two riders’ kit on long journeys. +A modern gravel bike with micro-suspension seatpost, flexing chainstays for comfort, and a stout front end for power transfer on rough climbs. +Folding folding urban bike with low center hinge, compact folded footprint and magnetic clasp to lock folded state. +Recumbent tricycle with reclining seat, aerodynamic fairing, and upright steering for long-distance comfort. +A cargo e-bike with dual-battery setup, hydraulic disc brakes, low-step frame, and lockable cargo box for urban deliveries. +A lightweight trail hardtail with thin wall alloy tubing, 120mm fork, and a predictable handling package for fast trail navigation. +Performance alloy road bike with tapered headtube, ceramic bearings, carbon fork and crisp shifting for criterium racing. +A gravel adventure tandem with reinforced frame, extra bottle bosses, and a low gear range for loaded touring. +Classic steel randonneur with full-length mudguards, leather saddle, and period-accurate brass lamp mount. +Mountain trail bike with modern dropper-integrated seatpost, aggressive tires, and a compact 1x chainring for easy shifting +A commuter with integrated rain cover for panniers, reflective piping, low-step frame and a quiet belt drive for clean, commuter-friendly rides. +Lightweight cross-country race bike with quick steering, tubeless tires, and SRAM Eagle 12-speed. +A commuter with internal-frame battery, motor in rear hub, integrated lights and reflective sidewalls for nighttime visibility. +A gravel endurance frame with vibration-reducing geometry, 700x40 tires, flared drops and soft stone-gray paint with small rust accents for character. +Single-speed commuter painted in candy apple red with coaster brake, fendered tires and chrome accents for a retro vibe. +An e-cargo bike with longtail deck, dual-battery option, integrated child harnesses and hydraulic disc brakes for family-friendly hauling. +A sleek track throwback with polished deep-section rims, tubular tires, and frame tuned specifically for velodrome geometry. +Steel commuter with classic geometry, upright swept bars, internal hub and wide reflective tires. +A restored city cruiser with vintage badges, new sealed hubs, and wide upright bars for slow serene rides through parks. +A folding electric bike with mid-drive motor, compact folded size, and a large spring-release latch for quick folding on train platforms. +Folding commuter with lightweight alloy frame, small wheels and a durable latch system engineered for repeated folds. +Gravel experiment with stepped top tube, full-coverage fender capacity, and an adaptable fork for bikepacking attachments. +Cyclocross training bike with training wheels removed, wide gear range and durable rim protection for wet courses. +A titanium commuter with brushed finish, internal hub, and a ceramic-coated chain for low friction and long life. +A pedal-assist mountain bike with stout alloy frame, 140mm travel, dropper post, and wide 29x2.6 tires to dominate mixed mountain terrain. +Downcountry mountain bike blending light weight and capable suspension, 120mm travel, and quick-rolling 29" wheels. +A vintage-style single-speed cruiser with balloon whitewall tires, swept handlebars, coaster brake, and a long, comfortable saddle. +A tough cargo bike with a welded front tray, sturdy fork, and reinforcing crossbars to shift heavy goods confidently through urban streets. +A gravel-optimized steel bike with classic tubing, modern geometry, and a utilitarian powder coat that hides scuffs from remote riding. +Retro fixed-gear with bullhorn bars, silver anodized components and minimalistic single-chainring aesthetic. +Dirt jumper with short travel fork, robust chromoly frame, single-speed drivetrain, and 26-inch strong rims for park jumps. +A robust mountain trail bike with durable alloy frame, 140–150mm suspension, and robust 2.4-inch trail tires for all-mountain use. +A full-suspension trail bike with 150mm rear travel, 160mm fork, slack head tube, dropper post, and grippy 2.4" tires for rowdy descents. +Fixed-gear urban commuter with flip-flop hub, minimalist brake setup, matte charcoal frame, and platform pedals for confident city riding. +A performance road bike with aerodynamic seatpost, internal cable routing, 1x drivetrain option and a signature cerulean blue gloss finish. +A classic reproduction racer with Campagnolo friction shifters, period-correct decals, skinny tubular tires and glossy cream paint. +Mountain jump bike with reinforced gussets, short chainstays, and thick-walled tubing for repeated impacts. +Classic British roadster with enclosed chaincase, three-speed hub, upright saddle, and lacquered handlebar grips. +A vintage-style road bike with braze-on downtube shifters, polished steel fork crown, thin profile tubular tires and wine-red lacquer. +A pure road racer with polished alloy frame, minimal fenders, shallow-section rims and a race-ready 2x drivetrain. +Track fixed-gear with polished frame, deep rims, and a minimal decal to emphasize a pure track aesthetic. +Off-road fatbike with low-pressure balloon tyres, rigid steel fork, and frame bosses for a rear rack or extra water. +A competitive track sprinter with solid-disc rear, low-slung frame, and a geometry designed to maximize power output on short efforts. +A classic road frame with gleaming chrome lugs, enamel paint, and thin classic rims for that timeless racing silhouette. +Touring bike with fastener-friendly brazed bosses, double-bottle mounts, and a paint job echoing old travel posters. +A mountain enduro rig with adjustable travel shock, tubeless setup, and 2.8” max tire clearance for mixed-terrain aggression. +A gravel e-bike with hushed motor, long-tail rack, and detachable battery for secure overnight bikepacking stops. +Folding city bike with slim frameline, compact folded footprint, lightweight alloy handlebar stem and quick-release wheels for storage ease +Urban folding bike in bright orange with compact triangular frame, single-speed hub, and non-slip grips for daily short trips. +Touring tandem with matching frame geometry, triple racks, and comfortable saddles to ease long-distance two-person trips. +A gravel bike with integrated GPS mount, rubberized chainstay protector, 700x40 tubeless-ready tires and wide handlebars for controlled gravel handling. +Urban cargo trike with weatherproof storage, electric assist, low gearing and sturdy platform for heavy-duty deliveries. +A polished steel cyclocross bike with classic curved top tube, thumb-shifters and tubed tires for vintage-style gravel events. +A steel festival cruiser with chrome racks, wicker basket, retro leather saddle and candy-pink enamel adorned with floral decals. +A city cruiser with lacquered wooden fenders, soft saddle, and chrome accents for elegant Sunday rides along promenades. +A cyclocross frame with internal brake routing, delicate chainstay geometry, and a paint finish that masks mud and stone chips. +Electric cargo longtail with dual passenger benches, modular cargo racks, strong motor and long-range battery for family transport. +Gravel e-bike with dual bottle mounts, integrated fender clips, and a subtle metallic flake paint. +Steel single-speed with matte green finish, polished headset, and subtle logo embossing on the seat tube. +A classic lugged-steel touring bicycle with chromed fenders, leather saddle, and front and rear racks for panniers. +Touring bike with electronic shifting, dynamo-powered lights, triple racks and 700x35 tires for loaded touring. +Urban courier bike with reinforced fork, large front rack and high-volume tires to handle loads. +Recreational hybrid with suspension fork, comfortable upright stem, wide platform pedals, and hydraulic rim-style brakes. +A compact urban cargo bike with folding rear deck, child seat mounts, electric assist and bright safety orange paint for high visibility. +Cruiser with chopper geometry, long wheelbase, banana seat and chrome accents for a distinctive laid-back look +A racing gravel bike with lightweight wheelset, electronic groupset, and a subtle gloss that accentuates every tube junction. +Performance road bike with ultra-stable geometry, tire clearance for 30mm and precision electronic shifting. +A slim-profile urban folding bike with compact handlebars, low weight, and elegant hinge design for neat storage in apartments. +A classic road bike converted to modern 11-speed with discreet cable housings and tubeless-ready rims for a blend of old and new. +Classic city mixte frame bicycle with step-through design, chaincase, roller brakes, and chrome mudguards for urban elegance. +E-MTB with 150mm travel, torque-sensing motor, burly 30mm-wide rims and predictable progressive geometry. +Lowrider cruiser bicycle with stretched frame, springer fork, sissy bar and whitewall tires for show rides +A compact folding e-bike with a low stand-over, small 16" wheels, and a battery that provides a practical range for short city errands. +High-volume gravel tourer with 650b wheels, large-volume tires, beefy racks and integrated frame protection for sustained loads. +Gravel endurance carbon frameset with subtle logos, integrated seatpost clamp, and hidden frame storage for remote routes. +Touring rigid bike with 26-inch wheels, durable steel frame, and multiple mounts for racks and accessories on long trips. +All-mountain trail bike with 150mm travel, balanced chainstays, modern slack angles, and reinforced dropout for abusive trails. +Fatbike with 4.8-inch tires, rigid aluminum frame, and single-speed hub for sand and snow rides. +A cargo e-bike with mid-drive motor, reinforced frame rails, and a modular platform that can convert between goods and child seats. +Folding commuter with 18-inch wheel set, reinforced hinge, and quick-detach front wheel for compact transit. +A commuter with hub motor stealthily tucked in the rear, belt drive for quiet operation, and integrated internal lights for safe night travel. +Cyclocross race rig with race-ready tire choice, short wheelbase, and clear recesses for mud-shedding in wet conditions. +A modern trail bike with 140mm of travel, 29-inch wheels, tubeless setup and fade-to-black paint with neon underlayer. +Track sprint frame with thick-section tubing, beefy bottom bracket and a glossy clear coat revealing brushed metal underlayers. +Restored vintage Schwinn with chrome fenders, chain case, upright bars, and spring saddle for classic weekend rides. +A mountain freeride machine with reinforced headtube gusset, long-travel fork, and tall-rise handlebars for big park jumps and drops. +A classic road bike converted to modern disc brakes with subtle cable housings, steel frame with brushed chrome accents, and 700x28 tires. +Mountain all-mountain bike with 160mm travel, adjustable shocks and chain guide for rowdy trail days. +A kids’ BMX-style freestyle bike, 18-inch wheels, reinforced forks, and anodized purple chainring with funky sticker graphics. +A durable touring frame in Reynolds steel with triple racks, dynamo lighting, Rohloff hub, and large-volume 26-inch tires for remote paths. +A cyclocross bike with carbon fork, wide chainstays, and tape-wrapped flared handlebars for improved control in the dirt. +A kids' balance bike in bright lime with foam tires, low center of gravity and textured grips to aid in first rides. +Steep-stepped step-through city bike with comfy wide saddle, coaster brakes and pastel green enamel. +Lightweight race-ready aluminum road bike with thin-walled tubing, responsive handling, and sub-7kg build potential. +Utility cargo bike with modular side panniers, low step, powerful brakes and reflective wraps for safety in traffic. +A gravel-adventure machine with wide 650b tire capability, handlebar harness, and a robust fork with multiple rack bosses for long trips. +Beach cruiser with pastel pinstripes, spring-loaded saddle, chromed basket bracket and low gear for relaxed coastal days. +Gravel adventurer with large tyre clearance, frame-mounted pump boss, and a matte basalt finish that shrugs off road grime. +Steel cyclocross frame with subtle lug decoration, modern geometry, and room for 40mm tires. +A lightweight cyclocross frame with modern tubing profiles, discreet bolt-on light mounts, and a matte finish in deep forest green. +Gravel adventure with long-range battery assist, integrated lights, and a robust frame for mixed-surface exploration. +Lightweight track bike with polished carbon fork, fixed gearring, high-precision hubs and steep geometry for quick accelerations +A high-clearance cyclocross frame with oversized downtube, stiff bottom bracket, and a geometry tuned to aggressive race lines. +Bright red single-speed fixie with flip-flop hub, deep-section rims, riser bars and minimalist saddle +A high-volume downhill bicycle with chain guide, heavy-duty rims, and oversized hubs for frequent park laps and tough hits. +A cyclocross bike with integrated mudguards, sealed bearing hubs, and a chain-slap guard mounted to the chainstay. +Retro steel road bike with downtube shifters restored to original spec and leather bar tape. +A kids' mini MTB with easy-to-use coaster brake and low gearing to build confidence on trails. +Cyclocross race bike with 700c tubeless tires, disc brakes, and a lightweight carbon fork for fast and nimble race handling. +High-volume fat-bike with studded tires, full fenders, low-pressure setup, and rack mounts for winter touring. +A compact commuter with folding pedals, quick-fold stem, and a seatpost that collapses for compact storage on trains. +A compact travel folder with durable latch design, high-flange hubs, and comfortable upright riding geometry for short trips and train transfers. +A classic racing bicycle with period-correct components, chrome lug detailing, and a thin steel frame tuned for high cadence pedaling. +A bespoke titanium gravel frame with thru-axles, integrated seat clamp, and raw brushed metal aesthetic. +Classic Dutch commuter with glossy black lacquer, brass bell, and a candy-red chain protector. +Cargo trike with reinforced alloy frame, weatherproof box and stable three-wheel platform for reliable urban delivery. +Touring tandem built for reliability with heavy-duty racks, low gearing, and a traditional steel frame that soaks up miles. +Urban folding cargo bike with rear box, bright daytime running lights, robust hinge, and a pedal-assist motor for quick errands +Mountain trail all-rounder with 130mm travel, progressive head angle, wide handlebars and efficient pedaling platform. +A folding commuter with 20-inch wheels, a neat latch system, adjustable handlebar height and a protective frame bag. +A performance track bike with deep rolled aluminum rims, minimal frontal area, stiff bottom bracket and neutral geometry optimized for pursuit racing. +Touring-ready steel frameset with reinforced headtube, triple water cage holes, and smooth heel clearance for loaded climbing. +Mountain downhill bike with long-travel fork, dual-crown shocks, stout chassis, and heavy-duty rims for gravity runs. +A precision-built track pursuit bike with stiff bottom bracket area, aero seat tube, and a matching full disc rear wheel for aerodynamics. +A titanium mountain bike with 120mm rear travel, full internal routing, dropper post, and a subtle blast-finished frame aesthetic. +A stripped alloy track sprint bike with solid disc wheel, fixed gear, low stack height, and crisp, direct power transfer. +Gravel race machine with wide 700c rims, aggressive tread pattern, SRAM Rival AXS, and lightweight carbon fork. +A retro-inspired city bike with chrome fenders, upright swept bars, leather saddle, and matching leather grips for vintage styling. +A classic road bike with 1970s geometry, steel forks, toe-clip pedals, and period-correct decals for retro weekend events. +Folding cargo bike with long deck, small wheels, compact steering column, and lockable cargo box for secure loads +Urban single-speed with bright graffiti-inspired paint, sealed-bearing hubs, platform pedals and reflective sidewall strips for nightlife rides +Electric cargo bike with longtail deck, Bosch mid-drive motor, twin child seats and hydraulic disc brakes +Road lightweight endurance build with carbon seatpost, damped handlebar stem, and 30mm tires for year-round reliability. +Folding commuter with low folding height, ergonomic grips, and an integrated carry strap for transit convenience. +Handmade steel gravel frame with brazed-on bosses, leather-wrapped bars, and distinctive hammered lugwork near the headtube. +A chromoly dirt jumper with short chainstay, thick tubing, and a 26" wheelset designed to take park landings and repeated abuse. +Sleek single-speed with stealth black components, tapered headtube, and riser bar for urban agility. +Performance mountain hardtail with tapered headtube, 100mm fork, and tubeless-ready rims for cross-country racing. +Folding city bike with step-through frame, easy-fold latch, and 16" wheels for compact storage and quick-turn transportation. +Urban single-speed with deep V rims, retro gloss finish, leather saddle and minimal branding for a sleek city look. +Fancy red track-style fixed-gear with deep V carbon rims, ceramic bearings and ultra-clean cabling. +A commuter with integrated bicycle alarm, GPS tracking module, and quick-charge battery for city dwellers who want security and convenience. +A classic cruiser with flared back fender, brass bell, two-tone paintwork and a plush spring saddle for sunny shoreline rides. +A gravel-specific hardtail with wide internal rims, 650b clearance, long wheelbase and a discreet top-tube mount for a GPS unit. +Gravel adventure plus bike with wide tire clearance, frame-bag-friendly top tube, and distinctive hunter green hue. +All-mountain full-suspension with coil shock, reinforced headtube, and generous tire clearance for gnarly trails. +Touring aluminum bike with triple bottle capacity, heavy-duty rim brake options, and an upright geometry for loaded comfort. +Performance-oriented mountain bike with 29" wheels, modern carbon frame, reliable dropper post, and lightweight cockpit. +Folding electric bike with frame-integrated handle, lithium-ion battery accessible from the side, and a single-button power control. +A compact folding cargo bike with 20-inch wheels, long deck for parcels, and a mid-drive motor for assisted inner-city logistics. +Cyclocross frameset with steel tubing selected for balance of stiffness and compliance and subtle screen-printed logos. +A gravel bike with custom anodized headset, 38mm tubeless tires, hidden fender mounts and matte metallic green finish for understated flash. +Lightweight touring titanium frame with triple-bottle compatibility, elegant welds and long-lasting corrosion resistance. +A steel mountain hardtail with 120mm fork, progressive chainstay length, and modern boost spacing for trail versatility. +A mountain trail e-bike with progressive support, a well-placed battery, and a suspension tune focused on trail flow and efficiency. +Mountain downhill sled with coil shock, thick-walled tubing and reinforced linkage for big impacts. +A retro track fixed-gear with classic paint, tubular tires, and exposed saddle rails for vintage aesthetics on the city streets. +A gravel-equipped all-road bike with 42mm tires, wide rims, flared bars and a lightweight frame tuned for stability and speed. +City e-bike with step-through aluminum frame, integrated basket, pedal-assist level display and hydraulic brakes. +Bright red fixie bike with flip-flop hub, narrow tires, minimalist riser bars, and a single-speed drivetrain. +A high-volume fat bike with studded tires, reinforced rims, and a low gear ratio for effortless rolling on ice and powder. +Cargo e-bike with foldable side rail, weatherproof electrical connectors, and a reinforced fork to handle heavy loads. +Urban folding bike with one-handed fold, magnetic latch, puncture-proof tires and step-through geometry for ease of use +A classic British roadster bicycle with coaster brake, full chainguard, wheel hub dynamo, and large saddle with springs for comfort. +A lightweight carbon road bicycle with aero tube shaping, integrated cockpit, deep-section 50mm carbon wheels, and a Dura-Ace electronic groupset for fast group rides. +A BMX freestyle bike with reinforced head tube, sealed hubs, and barspin-ready gyro system in a bold colorway for street credibility. +Road race frame with full carbon layup, aerodynamic tube sections, and a tuned compliance seatpost for comfort at speed. +Minimalist fixed-gear with single bolt clamp, narrow seat, and a glossy black finish for understated city speed. +A robust commuter with welded aluminum carrier racks, kevlar-bead tires, and a subtle reflective finish for safer nighttime use. +Lightweight road race frame with full carbon layup, aerodynamic tubing and tuned compliance for long climbs. +A modern commuter with integrated taillight in the seatpost, belt drive, and an anthracite frame with reflective side patterns. +Touring steel frame with classic brazed-on pump bosses, long chainstays and extra-durable frame finishes for expedition use. +Cargo longtail electric bike with child seats, extended deck, and robust cargo straps. +Gravel monster with 29x2.6 tires, fortified rim bed, and long-travel fork for tackling trail-like gravel routes and singletrack detours +Beach cruiser with wide balloon tires, pastel turquoise lacquer and chrome springer fork for relaxed rides. +A utilitarian folding cargo bike with reinforced frame, front-loading box, and electric-assist hub for heavy loads. +A gravel-sprinter with rapid-acceleration gearing, shallow rims, 38mm knobbed tires and flaked gunmetal paint with orange highlights. +A dirt-jump frame with reinforced head tube, short chainstays, and a clean powdercoat to survive repeated park sessions with style. +Gravel touring hardtail with 650b compatibility, multiple mounting points, and a comfortable slacker geometry for loaded travel. +Touring-ready steel frameset with full braze-ons, heavy-duty wheels, and a comfortable long-haul seat position for loaded travel. +Urban electric cargo bike with modular shelving units, theft-deterrent lock, and full LED lighting system for night deliveries +Full-suspension trail bike with 150mm travel, progressive geometry, and low-slung chainstay design for corner stability. +City classic roadster with chromed fenders, wooden rack, and a glossy lacquer finish to channel retro charm. +A commuter with electric-assist rear hub, regenerative braking to top off small battery, and a robust rear rack for laptop bags. +Touring steel frameset with triple cage mounts, integrated mudguard bosses, sealed bearings and subtle two-tone paint for endurance reliability. +Road commuter with mudguard clearance, internal routing, and reflective sidewall tires for low-light city starts. +A cargo trike with heavy-duty winch attachment for odd parcels, a wide deck for crates, and a stable frame geometry tailored for business use. +Lightweight carbon touring bike with titanium hardware, slim seatpost, and an understated raw carbon finish for reduced weight. +A classic Dutch cargo bike with long wooden box, chaincase, and an electric hub motor for assisted shopping and school runs. +A titanium time-trial bicycle with integrated hydration systems, aero positioning, and a steep seat tube angle for efficient power transfer. +A vintage rake-fork chopper bicycle with elongated forks, chrome skull tank, and dramatic low-slung stance. +Electric mountain long-travel bike with adjustable motor tuning, high-flow brakes, and reinforced chainstay protection for aggressive riding +Classic cruiser with chrome-plated detailing, leather saddle, wide swept handlebars and pearlescent mint paint for summer days. +A minimalist black folding bicycle with small 16-inch wheels, hinge frame, quick-release clamp and compact folded footprint for train commuters. +Urban single-speed with polished chrome accents, narrow saddle, and a compact frame for nimble city dodging. +A long-distance endurance bicycle with rounded tube profiles, vibration-damping layup, and headset-integrated stem clamp for a tidy cockpit. +A commuter with step-through aluminum frame, integrated plastic mudguards, chaincase and comfort saddle for easy mounting and dismounting. +Steel framed cyclocross bike with classic lug work, modern geometry, and mud-clearance stays. +Sleek electric-assist commuter bike in pearl white with integrated battery, hub motor, fenders and built-in lights. +A cyclocross race rig with mud-shedding frame tubes, SRAM Rival 1x, and 33mm race tires for quick course transitions. +A stripped-down single-speed yard bike with BMX pedals, thick saddle, and fat tires for short neighborhood errands. +A commuter with full-length skirt guard, internal hub gear, dynamo-powered lighting and an elegant cream paint that hides scratches well. +Classic road bike with period-correct Campagnolo group, leather-wrapped bars, steel rims and a subtle patina that shows character. +A modern gravel bike with 1x drivetrain, flared drops, internal cable ports and a satin olive paint that resists dirt. +A titanium stealth commuter with internal cable routing, integrated rack mounts, and a subtle brushed finish that resists corrosion. +A gravel challenge bike with anodized headset, frame-mounted pump bracket, and a stealthy black-and-gold paint theme for subtle flair. +Lightweight aluminum cyclocross rig with flared drops, 33mm cyclocross tires and a race-ready riding position. +A randonneur bicycle with dynamo hub, low-profile fenders, rack and leather grips, built for long-distance unsupported rides. +A custom steel cyclocross bike with splashed paint, reinforced stays, and hand-cut lugs for a unique race machine. +Endurance gravel bike with 650b wheels, 47mm tires, slacker head angle, and a long wheelbase for stability on rough roads. +A children's mini-mountain bike with low-slung frame, easy-shift cassette, front suspension and teal-blue paint with dinosaur decals. +Commuter hybrid with front basket, puncture-proof tires, and swept handlebars for comfortable, practical travel. +A cyclocross machine with ultra-wide clearance, fast-shifting 1x drivetrain, and a colorway that camouflages mud splashes. +A hand-painted town bicycle with subtle metallic flake, leather accents, and ornate head badge for bespoke street cred. +Vintage-inspired road racer with polished stem, leather bar tape, narrow rims and restored chrome-finish lugs for classic style +Electric commuter with torque-sensing motor, hydraulic disc brakes, and an integrated dash for ride metrics and battery status. +Gravel adventure bike with reversible dropouts, alternator hub capability, and wide 650b compatibility. +Commuter folding with compact footprint, integrated carry strap, and a quick-release clamp that locks the folded frame. +A vintage steel road touring frame with triple bottle mounts, polished lugs, and fender-ready clearance for wet weather. +A compact folding e-bike with battery in the rear rack, torque-sensing motor, and a simple one-button fold for commuters on the go. +A trail-ready 29er with efficient suspension linkage, integrated chainstay protection, and light wheels balanced for acceleration and control. +Mountain downhill weapon with dual-crown fork, reinforced chain protection, and long reach for stability. +Gravel touring frame with stainless-steel fasteners, wide tyre clearance, and hand-painted route markings on the downtube. +A mountain enduro rig with mullet wheel setup, long travel fork, and a matte military green paint splattered with protector film. +A retro track bike with high bottom bracket, tight geometry, and glossy black paint trimmed with gold lettering for velodrome elegance. +A trail e-bike with torque-balanced motor, aggressive geometry, and a mid-fat tire setup to boost confidence on rough singletrack. +Touring expedition rig with oversized racks, reinforced frame plates, triple-chainset and long-travel comfort for remote areas. +Commuter with quiet belt drive, integrated tail light, and an alloy rack with spring clip for grocery pops. +Retro step-through cruiser with pastel gradient paint, leather saddle, full chainguard and whitewall balloon tires. +Mountain enduro bike with longer reach, adjustable geometry, coil-compatible shock, and reinforced wheelset for heavy impact. +A commuter with full-chaincase, generator hub powering integrated lights, and Moss green paint with cream piping for classic style. +Urban fixie with narrow leather saddle, chain tensioner, blacked-out components and subtle pinstriping. +A cargo e-trike with long platform, waterproof cargo bay, dual-motor assist and signal-yellow livery for visibility. +A polished steel touring frame with riveted rack mounts, triple-bottle capability, and a geometry tuned for carrying heavy loads over long distances. +Urban single-speed with bullhorn bars, polished frame, whitewall tires and minimal branding for a clean street look. +A modern single-speed commuter with belt drive, deep V rims, and a front basket mount for grocery runs in town. +A commuter with integrated headphone routing, sealed cartridge bearings in the headset, and a lockable rear rack for backpacks. +Lightweight titanium gravel bike with disc brakes, 40mm gravel tires, wide-range 2x11 gearing, and internal cable routing for a clean aesthetic on rough roads. +A beach cruiser with pastel mint paint, wide cruiser handlebars, single-speed coaster hub and oversized cushioned saddle for relaxed shore-side rides. +Time-trial specific machine with slender seat cluster, tri-bar extensions, and front fairing for solo race aerodynamics. +A single-speed commuter with bright neon frame, thick-kneed tires and reliable coaster brake for carefree short trips around town. +A classic road frame with ornate lugged construction, narrow 700x23 tubular tires, and a lustrous maroon finish for vintage race days. +A mountain trail shredder with burly tires, 150mm fork, dropper post and aggressive geometry for tackling technical features. +Cargo e-bike with large rear box, integrated child harness points and low-load platform for safe carrying. +Retro cruiser with swept-back handlebars, wide cushioned seat, balloon tires, and a glossy teal paint job. +Electric mountain e-full with high-capacity battery, refined suspension and reinforced dropouts for rough trail days. +Performance gravel bike with carbon seatstay, 1x setup and stealth logos for understated competition. +A sleek commuter with internal hub gearing, belt drive, and fully integrated fender system for quiet, clean operation. +A sleek gravel grinder with mixed-wheel setup (650b rear, 700c front), tubeless tires, and clearance for fenders and racks. +A touring bike with full luggage capacity, front and rear racks, dynamo lighting and gold-fleck paint showing off a classic style. +A cyclocross pro bike with carbon fenders, lightweight rails, and race-optimized gearing for quick accelerations out of corners. +A vintage road bike lovingly restored with correct period components, thin tires, and a gleaming chrome fender set for classic Saturday rides. +Road lightweight aero with carbon seatpost, integrated head unit mount and a compact cockpit for focused performance. +A touring tandem with steel brazed joints, relaxed geometry, dual water-bottle mounts and classic British green paint with gold pinstriping. +Race-ready gravel selection with 700x40mm tires, electronic 1x shifting, and a double-bottle mount for long events. +Bright orange hardtail mountain bike with 29" wheels, 120mm suspension fork and chunky 2.4" tires for trail riding. +A townie with comfortable upright geometry, spring saddle, wicker basket and pastel mint paint kisses with cream accents. +A cyclocross race rig with extra tire clearance, crisp short chainstays, and a low-profile frame bag mount for race essentials. +Custom-painted single-speed with anodized bolts, polished headset, and ceramic-sealed bearings. +Traditional Dutch-style bicycle with enclosed chaincase, upright handlebars, and an internal hub for low fuss urban riding. +A compact cargo trike with folding ramp, low center of gravity, and a utilitarian matte green paint suited for daily deliveries. +City commuter with internal hub gear, sturdy alloy rack, and a built-in lock integrated into the frame design. +Folding city bike with easy lock system, reflective accents, and a small front basket for daily convenience. +Folding commuter with compact design, quick-release frame hinge, 16-inch wheels and reflective decals for safe night folding +Commuter with hydraulic rim brakes, wide reflective sidewalls, rear light integrated into the rack and comfortable upright posture. +Touring tandem with double racks, reinforced frame, and a comfortable upright seating for extended two-person exploration. +Commuter with front basket, low-step frame, and puncture-resistant tires for reliable city use. +Urban folding bike with 12-inch wheels, integrated front bag, and a compact hinge system for easy public transport. +Mountain downhill carbon with reinforced pivot points, high-volume rims, and a geometry built for canyon descents. +A gravel bike built for racing with 700c x 38 tires, lightweight carbon frameset, electronic shifting, and a fast but comfortable endurance geometry. +Single-speed urban fixed gear with custom anodized chainring, glossy black frame, lightweight saddle and small cargo bag under saddle +Gravel ultra-light carbon race machine with low stack height, aggressive seating position and narrow tires for fast mixed-road racing. +Road time trial bike with horizontal top tube, aero extensions, disc wheel compatibility and hydration bento attachment. +Electric mid-drive mountain bike with torque-sensing motor, long-travel suspension, and trail-specific geometry for powered climbs. +Urban folding single-speed with quickfold hinge, small transport wheels, rugged frame and a bold accent color for storage ease. +Classic Dutch-style city bicycle with upright position, step-through frame, coaster brake and elegant chaincase. +Commuter e-bike with step-through frame, low standover, and torque-sensing assist for hills. +A refined titanium road frame with smooth welded joints, integrated clampless seatpost, and an understated brushed finish to age gracefully. +A commuter with sturdy fenders, soft gel saddle, and a practical rear rack with easy strap points for grocery runs. +Gravel bike with stealth black paint, stealthy saddlebag, internal fender mounts, and wide rims for confident cornering. +Family cargo longtail with removable child seat, extra tie-downs and a reinforced frame for peace of mind. +A full-suspension enduro mountain bike with 170mm travel, coil shock option, slack head angle and reinforced frame for aggressive descending. +Mountain downhill sled with super-long travel, aluminum linkages, coil shock, and 27.5-plus wheels for gnarly courses. +A tandem bicycle built for two with stable long wheelbase, double bottle mounts, synchronized drivetrain, and comfortable touring saddles. +Adventure touring bike with heavy-duty rack mounts, triple water bottle capability, wide tire clearance and protective skid plates for remote travel. +A commuter with integrated battery, walk-assist mode, wide puncture-proof tires and a fold-down rear rack for compact storage. +Electric cargo bike with dual batteries, hydraulic steering dampener, and adjustable child harness for family logistics +Lightweight cross-country frame with efficient suspension kinematics, quick handling geometry, and narrow 29er wheels for speed. +A kids' cruiser with coaster brake, wide tires, and easy-to-reach grips to build early cycling independence and safety. +A commuter with integrated smartphone mount, dynamo charging port, puncture-resistant road tires, and an ergonomic upright riding position. +Race-focused cyclocross frame with short chainstays, wide tyre clearance, and a stiff bottom bracket for power transfer. +City step-through cruiser with comfortable swept-back bars, integrated bell, and a spring-loaded saddle for smooth rides. +City hybrid with front suspension, ergonomic grips, 27.5 wheels, and a versatile rack to carry laptop bags and purchases. +Retro step-through with polished chrome handlebars, soft saddle, and bell for leisurely city spins. +A kids' BMX with small-frame geometry, durable pegs, sealed bearing wheels and a no-fuss single-speed drivetrain for park confidence. +A compact road bike with endurance geometry, disc brakes, 28mm tires and a plush saddle to reduce rider fatigue over long distances. +A vintage ladies' step-through restored with fresh paint, wicker picnic basket, and polished chrome fenders for classic city charm. +Classic road bike with steel tubes, center-pull brakes, and deep brown leather tape for vintage feel. +Mountain freeride with reinforced chainstays, heavy-gauge spokes, dual-crown fork option and a wide handlebars setup for park domination +A gravel-oriented touring frame with integrated top-tube bag mounts, extra-wide tire clearance, and resilient paint to stand up to expedition wear. +Gravel endurance bicycle with carved compliance zones in the seatstays, 700c x 42 tires, and wide handlebars for stability. +A gravel adventure machine with full mounting braze-ons, three-bottle capacity, 650b wheel option and hand-painted desert-tone finish. +A titanium road bike with slender tubes, 28mm tires, discreet integrated cabling, and a satin raw finish that ages with use. +A restored classic roadster with hub dynamo, polished chrome fenders, and a leather tool roll attached beneath the saddle for character and utility. +A lightweight cyclocross frame with tapered head tube, integrated cable routing, and flared brake bridge for mud clearance. +A high-volume gravel tourer with three-bottle cage mounts, reinforced head tube, and 700x50c clearance to carry expedition gear. +Lightweight gravel race bike with seamless internal routing, wide rims, and a balanced geometry for aggressive mixed-surface pacing. +A folding city bicycle with quick-release latch, small 20" wheels, internal hub gear and minimal chain area for tidy commuting storage. +A gravel race bike with aero-optimized frameset, narrow profile seat tube, and a gloss finish that hides micro-scratches. +A commuter with upright stance, enclosed chaincase, hub dynamo for lights, and reflective side panels for safe night riding. +A kids' BMX with reinforced top tube, sealed hubs, bash guard and a thrill-seeking bright orange finish perfect for skate park use. +A metal-flake purple cruiser with swept handlebars, coaster brake, and low-pressure balloon tires for soft rides on boardwalks. +A classic children’s tricycle with steel frame, fun red paint, and wide stable base that keeps toddlers secure while learning to pedal. +A gravel racer with flared bars, 700x38 tires, 1x12 drivetrain and forest-green paint with metallic flecks. +City commuter with fold-down pedals, quiet belt drive, internal lights and minimal maintenance design for everyday use. +Electric folding commuter with mid-mounted hinge, compact handlebar folding, and 5-speed hub gearing. +Folding cargo bike with wooden deck, electric-assist option, and quick-release connectors for modular setups. +Carbon gravel bike with full internal cable routing, stealth graphics and clearance for large-volume tires. +Simple commuter with upright posture, bell, front basket and colorful hand-knitted handlebar cozy for charm. +A kids' BMX bike with 20-inch wheels, sturdy chromoly frame, pegs on both wheels and a bright lime-green paint job. +Road aero frame with integrated cockpit, shallow-section wheelset and aerodynamic tubing for sustained high-speed efforts. +Leisure beach cruiser bicycle with pastel mint paint, single-speed coaster brake, and oversized saddle for chill rides. +A wrought-iron style cruiser with heavy steel tubing, wide balloon tires, ornate filigree and a deep wine-red enamel finish. +Compact kids' balance bike in bright yellow with no pedals, low saddle height and rubber grip handles for beginners. +Urban kids' bike with coaster brake, simple frame, and bright fun graphics to entice short neighborhood rides. +Single-speed beach cruiser with glossy turquoise paint, wide balloon tires, coaster brake, and comfy seat for seaside promenades. +A gravel race build with carbon fiber seatpost, 38mm tires, electronic shifting, and aerodynamic bottle placement for fast stage performances. +Mountain freeride alloy with reinforced dropouts, large-volume tires and a short travel rear setup for playful lines. +Mountain hardtail with lightweight alloy frame, tubeless setup, and matte orange accents for trailway recognition. +A commuter with puncture-proof tires, belt drive, integrated frame lock and reflective side stripes for safe and low-maintenance rides. +High-volume gravel with 650b x 2.1 tires, slack geometry, and reinforced spokes for carrying heavier loads off-road. +Classic touring bicycle with leather saddle, full mudguards, brass fittings, and a well-balanced gear range for centuries. +A racing track pursuit bike with aggressive geometry, ultra-aero cockpit, and tubular carbon wheels optimized for velodrome laps. +A classic city bicycle with step-through frame, chaincase, classic round headlamp, comfortable saddle and brass accents for nostalgic commutes. +Racing road bicycle with electronic shifting, ceramic bearings, and a full carbon aero wheelset. +A mountain trail full-susser with adjustable geometry, air shock, 29-inch wheels and a gradient from charcoal to teal along the top tube. +A cargo longtail with electric-assist hub, foldable passenger bench, and integrated tie-downs for carrying crates or kids securely. +BMX street bike with gyro, reinforced top tube gussets, and cool metallic flake paint with contrasting grips. +A mountain bike with long-travel front suspension, adjustable shock tune, and a wide handlebar for confident control on fast descents. +A gravel-alloy frameset with sloping top tube, resilient paint, and a wider BB shell for compatibility with a range of drives. +Time-trial bike with super-aero tubing, tri-bars, integrated hydration system and tubular tires. +A steel lugged city bike with double-butted tubes, Brooks leather saddle, and a classic headbadge harking to old manufacturers. +A folding commuter with compact dual-hinge, 20-inch wheels, easy fold latches and a soft-grip handle for carrying on public transport. +Cyclocross competitor with plenty of tire clearance, tubeless-capable rims and a matt finish that hides dried mud. +A gravel endurance build with vibration-damping features, 700x42 tires, comfortable geometry and deep stone-gray paint with speckled highlights. +A classic steel city racer with chrome details, 32mm tires for a comfortable ride, and a graceful head badge that tells its heritage. +Mountain trail bike with modern geometry, 150mm travel, dropper post, durable rims, and grippy tires for confident handling. +Mountain bike with adjustable travel fork, progressive head angle and reinforced wheelset for aggressive enduro racing. +Mountain e-bike with dual-battery option, motor-tuned for trail feel, and rugged alloy wheels for impact resistance. +A stripped commuter with internal hub gear, matte finish, and a single front light powered by a quiet dynamo hub for sustainable downtown commuting. +Vintage single-speed with restored enamel paint, waxed chain, polished spokes and an upright handlebar for smooth urban cruises +A compact folding commuter with secure hinge, walk mode, and quick-release seatpost that adapts to multi-person households. +Folding commuter with sturdy hinge, compact folded pack, and a simple frame design that requires minimal upkeep. +Gravel racer with modern aero fork, flared drop bars, 700x38c tires and a 1x drivetrain tuned for sharp accelerations on rough roads +A compact folding e-bike with 16-inch wheels, upper frame latch, and integrated headlight for commuters who combine train and pedal. +Modern utility bike with belt drive, low-maintenance hub gears, integrated fenders, and a spring-loaded cargo rack for quick stops. +A retro city coaster bike with painted fenders, chaincase, and wide saddle adorned with vintage stitching. +Trail hardtail with modern geometry, 130mm fork, dropper post and tubeless 29x2.35 tires for fast singletrack +Cargo bike with dual-wheelbase options, modular wooden deck, and quick-release child seat mounts for flexible hauling. +A mountain hardtail with 29+ wheel option, slack head tube, and reinforced hubs for carrying big loads across rough trails. +A modern steel cyclocross bike with through-axles, fender mounts, and resilient paint for muddy race conditions. +Commuter hybrid bicycle with suspension seatpost, puncture-resistant tires, rear light and kickstand. +Electric folding cargo bike with reinforced rear rack, step-through frame, and a wide deck for groceries and gear. +Premium carbon mountain bike with boutique suspension, 160mm travel and refined finishing kit. +Fat-tire e-bike with low-slung battery, extra torque motor and snow-mode for icy commutes. +A compact urban folding electric bike with torque-sensing assist, lockable fold latch, and puncture-resistant tires for the last-mile commuter. +A modern cyclocross bike with flat mount disc brakes, short chainstays, and a balanced feel for quick shouldering and running. +A gravel endurance bike with a long wheelbase, vibration-damping handlebar, and 2.0-inch semi-slick tires for mixed conditions. +A city cruiser with large basket, low gearing, coaster brake, and a soft wide saddle comfortable for casual rides. +Handmade lugged steel road frameset with tasteful enamel stripes, classic geometry and a polished chrome fork crown for timeless vibes. +Lightweight aluminum touring bike with modern clearcoat, braze-on rack mounts and reliable mechanical disc brakes. +A commuter ebike with cargo platform, rear passenger seat mounts, step-through frame and throttle assist for heavy loads and kids. +Race-ready TT bike with integrated wiring, deep-section carbon wheels and a long, aggressive stem. +A downhill race bike with slacker-than-average geometry, coil-sprung shock, and optimized chainstay length for high-speed stability. +A family tandem with child-seat compatibility, adjustable stems for riders of various heights, and long wheelbase stability for comfortable cruising. +A folding cargo bike with reinforced hinge, long rear deck, electric assist and bright safety orange powdercoat for delivery use. +Folding cargo bike with a reinforced hinge, detachable side rails, and a fold that reduces height for storage. +A commuter with integrated rear light cluster, chaincase, and low stand-over for easy mounting while running errands. +Gravel racing machine with pro-level disc brakes, aero seatpost, and deep-section wheels for sustained high-speed efficiency. +A folding electric bicycle with 16-inch wheels, hinge lock, small integrated battery, and torque sensor for easy commuting and storage. +Steel lugged road bicycle in British racing green with leather Brooks saddle and downtube shifters for a timeless look. +Commuter bicycle with child trailer attachment point, reflective paint, integrated lock, and wide ergonomic grips. +Retro step-through with wicker basket, gravy beige paint, and chrome chain guard for city errands. +Kids' balance bike painted sky blue with anti-tip geometry and grippy rubber saddle. +Retro city cruiser with chopped fenders, white-wall tires, banana saddle, and breezy turquoise paint for sunny spins. +Gravel endurance bike with vibration-damping inserts in the fork, comfortable geometry and cassette clearance for rough courses. +A cyclocross race frame with full-length chainstay protection, quick-release thru-axles, and a stealthy matte-black finish to conceal dirt. +Gravel plus touring bike with extra tire clearance, sturdy rack mounts, and a low gearing for loaded off-pavement travel. +Classic city step-through with wicker basket, spring-loaded saddle, and English three-speed internal hub for easy urban shifts. +Compact folding cargo bike with reinforced frame, quick-release wheel system, and a low folded volume for apartment living. +Folding commuter bike with magnetic closure, small wheel size, internal gearing and reflective frame accents for evening rides +Road endurance frame with disc brakes, generous tire clearance to 32mm, and internal routing for a clean look. +Fixed-gear track-style commuter with polished chainring, flush chainstay protector, and a subtle metallic flake paint finish. +Gravel all-road machine with 37mm tubeless tires, compact chainset, and a neat top tube strap for snacks and tools. +Gravel plus adventure bike with 27.5+ tires, protection film on downtube, and strong racks for long off-road expeditions +Classic city cruiser with swept bars, sprung saddle, and pastel green enamel paint with pinstriping. +Fixed-gear track-style bike with high-polish chrome, single-tooth chainring, and a small headlamp for early morning loop rides. +A fixed-gear track bike with wooden rim decals, bonded tubulars, and aggressive forward-leaning geometry. +Cargo tricycle with large front platform, reinforced frame, hydraulic disc brakes, and electric assist for bulky loads. +Lightweight carbon road frameset with integrated cable routing, tapered steerer, and subtle metallic fleck in the paint. +City cargo bike with dual front carriers, adjustable steering, and bright safety paint for early morning pickups +A gravel-bike conversion designed from a road frame with added clearance, low gearing, and thicker tires to tackle muddy farm roads. +Enduro mountain bike with 170mm travel, slack head tube angle, dropper seatpost and 38mm fork stanchions +A cyclocross frame with forged dropouts, molded tire clearance, and a graceful curve to the seatstays to aid mud removal. +A mountain enduro rig with variable geometry, 160mm travel, coil-sprung shock and matte army green with camo detail. +Cargo trike with electric assist, low loading deck, and adjustable suspension to protect fragile deliveries. +Steel mountain hardtail with slack head angle, wide bars and aggressive trail geometry for rowdy singletrack. +Fixed-gear city bike with polished aluminum frame, deep rims, color-matched chain, and an understated aesthetic for downtown cruising. +Cyclocross tubular-equipped race bike with stiff carbon layup, short wheelbase and race-focused clearance. +Compact urban e-bike with folding pedals, removable battery, integrated lights, and a low-maintenance hub gear for short trips. +Track-inspired urban fixed with bold color-blocking, single cog, and matching bar tape for visual cohesion. +Commuter folding bike with easy step-through, 20-inch wheels, internal hub gear and a carrying strap for multi-modal commuters +Lightweight titanium gravel bike with 700x40c tires, clearance for fenders, and multiple bottle mounts. +A high-performance cyclocross bike with quick-release thru-axles, lightweight carbon fork, and an aggressive 2x drivetrain for race-day versatility. +A gravel racing bicycle with narrow aero tubing, disc brakes, and 40mm high-traction tires for mixed-surface speed and endurance. +Gravel light frameset with carbon layup tuned for comfort, discreet head tube badge, and internal fender mounts. +A track pursuit bicycle with massive TT bars, long wheelbase, and stiff frame sections tuned for sustained high speeds on boards. +Commuter city bike with integrated front rack, dynamo lights, and a low-maintenance internal gear hub for dependable travel. +A cyclocross bicycle with clearance for mud, cantilever-style brakes, knobby 35mm tires and a robust steel frame designed for winter cyclocross races. +Cyclocross race bike with disc brakes, knobby tyres and a protective framecoat to guard against stone chips. +Mountain bike with mixed wheel sizes—29-inch front, 27.5-inch rear—plus aggressive geometry and fast-rolling front end. +Vintage-inspired fixed gear with burgundy paint, leather saddle, chrome accents and a simple reliable drivetrain. +City utility bike with heavy-duty frame, spring-loaded front rack, and reflective sidewall tires for increased visibility. +A posh steel cyclocross bike with custom fillet-brazed chainstays, reinforced bottom bracket, and elegant pinstriping down the fork. +A custom hand-painted steel road frame with unique gradient fade, detailed headbadge, and expedition-ready braze-ons for attachments. +Performance gravel bike with dropped chainstays, thru-axles, 2x electronic groupset, and integrated frame storage compartment. +A gravel race machine with slackened head angle, long rake fork, and a paint fade that shifts from orange to burgundy. +Mountain enduro build with burly drivetrain, dropper post, and high-volume tires for control on steep rough descents. +Gravel drop-bar bike with stealthy matte charcoal finish and tire clearance up to 45mm. +A modern trail bike with 150mm travel, 29" wheels, coil option available, and a stout rear end to hold up against aggressive stone gardens. +Touring tandem designed for long days with a beefy frame, twin bottle mounts and a pair of matching leather saddles. +A hardtail mountain bike with 130mm fork, aggressive geometry, and light wheels designed to sprint up fire roads and rip fast descents. +A full-suspension enduro race bike with adjustable leverage ratio, coil-compatible shock, and reinforced pivot hardware. +Classic club road bike with elegant geometry, polished chrome fork crown, downtube shifters, and tan sidewall tires. +City commuter with upright bars, chaincase, low-gear internal hub and integrated rear reflectors for practical daily riding. +Mountain bike with mixed-wheel setup: 29-inch front and 27.5-inch rear for maneuverability and rollover, plus 140mm travel. +A drop-bar gravel bike with two bottle cages on the down tube, mudguard clearance and a gravel-specific wheelset. +A gravel race bike with 700c tubeless wheels, aggressive tread tires under 40mm, and electronic groupset for crisp shifting on rough roads. +Race-ready carbon road bike with lightweight 50mm carbon rims, electronic 2x drivetrain, and a compact crankset for climbing. +Fixed-gear urban style bike with color-blocked rims, polished headset, and a taut chainline for minimal mechanical fuss. +A gravel e-bike with torque-sensing assist, rugged tires, and a reinforced rear triangle for carrying bikepacking gear without flex. +A commuter with low-slung frame, integrated rear reflector, wide saddle and puncture-resistant casings for daily use. +A beach cruiser with chrome accents, two-tone enamel, oversized grips and a low center of gravity for stable show riding. +A mountain enduro rig with a progressive leverage curve, burly chainstay guards, and heavy-duty bearings for all-day endurance on rough trails. +Classic urban cruiser with swept-back bars, wide saddle, and a paint gradient that fades from coral to cream. +Urban commuter with comfortable swept handlebars, integrated lights, puncture-resistant tires, and a rear rack for everyday hauling. +A heritage-inspired touring bicycle with ornate head badge, leather accessories, and triple chainring options for varying mountainous terrain. +A cargo bike with longtail platform, passenger footrests, robust aluminum frame and a bench-style rear seat for kids. +A track sprint bike with ultra-stiff BB, pursuit-oriented geometry, and a simple yet optimized single chainring for sprint power. +Electric mountain bike with full suspension, torque-sensing motor, 750W peak power and reinforced drive-side chainstay. +Steel randonneur bicycle with Porteur bars, low-trail geometry, dynamo hub and large mudguards for wet-weather reliability. +Cargo longtail with adjustable deck height, reinforced rear triangle, and handy cargo tie-down rails along the sides. +Gravel bike with stealth matte olive paint, 38mm gravel tires, and a carbon fork with rack mounts. +A gravel e-bike with mid-drive motor, long-range battery, wide rubber and army-olive paint with tan decal accents suitable for multi-day rides. +Road aero endurance frame with integrated front light, hidden cables, and medium-depth rims for a balance of aero and comfort. +Performance carbon road bike with lightweight rim set, energy-absorbing seatpost, and aerodynamic tube profiles for race days. +A gravel all-road bike with durable alloy tubing, removable fender mounts, and a cozy cockpit for endurance events. +Folding electric cargo bike with large-capacity battery, torque sensor, sturdy hinge and modular cargo accessories for versatile city tasks. +Classic rehabilitation tricycle with large rear basket, step-through chassis, and upright steering for stability and comfort. +A commuter with full-length integrated lighting, low-step frame geometry, and a discreet internal battery in the downtube. +Commuter with belt drive, internal 8-speed hub, puncture-resistant tires, and a comfortable upright geometry. +A handbuilt steel gravel frameset with fillet brazing, subtly tapered top tube, and vintage badgework that nods to classic craft. +A mountain enduro machine with short stem, low bottom bracket, and grippy tires for fast, confident cornering on rough descents. +A time trial machine with integrated cockpit, rear wheel cover, and alien-smooth aerodynamics in glossy pearl white. +Compact folding bike with reinforced hinge, low center of gravity, 16-inch wheels and a matte finish for public transit commuting +A modern gravel adventure bike with integrated frame protection, cable guides for multiple setups, and a gravel-friendly headset. +Time-trial specialized bicycle with integrated hydration sleeves, fully faired cockpit and a geometry tuned for sole effort against wind. +A modern track sprinter built from high-modulus carbon with stiff bottom bracket, minimal compliance and a textured matte finish. +Performance road bike with endurance geometry, aero tubing, and vibration-damping seatpost for long criteriums and classics. +A modern road bike with disc brakes, tubular carbon wheels, SRAM Red eTap AXS, and ultra-stiff bottom bracket area for sprinting. +Urban single-speed with flip-flop hub, riser bars and a phosphorescent paint that glows under streetlight. +A single-speed beach cruiser with turquoise enamel paint, chrome fenders, coaster brake and a woven front basket for coastal afternoons. +Gravel-friendly adventure bike with stainless steel bottle mounts, oversized downtube, and a boxy top tube for gear mounting. +Mountain freeride hardtail with stout chainstays, rigid fork, platform pedals, and desert-tested finishing for jump terrain. +Classic step-through city bike with leather saddle, wicker front basket and elegant pastel frame for market runs. +Performance e-road bike with integrated battery, aerodynamic tubing and torque-sensing motor assistance under load. +All-mountain full-suspension with 150mm travel, aggressive tire choice, stout frame, and a one-by drivetrain for simplicity. +Gravel endurance bike with endurance geometry, 45mm tires, dropper post and low gearing for steep climbs. +An electric cargo trike with three-wheel stability, large deck for goods, hydraulic brakes, and throttle plus pedal-assist modes for delivery. +Touring bike with two-tone enamel, hand-applied pinstriping, and brazed-on front braze-ons for a lowrider rack. +High-performance mountain bike with carbon frame, 160mm fork, robust linkages and geometry tuned for high-speed stability. +Mountain trail hardtail with modest travel, steel dropouts, comfort-focused saddle and tubeless wheel setup for weekend trails +A modern cyclocross bike with electronic shifting, stealth cabling, and a tuned compliance foreseen for muddy and icy conditions alike. +Performance road frameset with thin-walled tubing, liftoff paint, and integrated headset for clean aesthetics. +A BMX park setup with low-slung top tube, reinforced seat clamp, and micro-adjustable headset for precise bar spins and flip tricks. +Performance cyclocross bike with short chainstays, low bottom bracket and quick handling for tight off-road courses. +Handmade lugged steel commuter with classic cable routing, polished fenders and tasteful pinstriping. +Gravel plus adventure frame with super-wide clearance, adjustable dropouts and reinforced fork to handle heavy loads. +A mountain cross-country frame built for ultralight racing with minimalistic cable routing, tapered headtube, and 2.2-inch tire capability. +Fat-tyre commuter with 4" tires, rigid fork, and powerful hydraulic disc brakes for stable winter rides on slush and snow. +A commuter with built-in rear light that pulses when braking, tube liners for puncture resistance, and a torsion-resistant rack for heavy loads. +Compact kids' BMX with colorful decals, sturdy chromoly frame, small pegs, and reinforced forks for progressing tricks. +A rigid single-speed mountain bike with 29-inch wheels, wide 2.35-inch tires, steel frame and raw metallic flake paint. +Gravel explorer with titanium frame, minimalist braze-ons, 650b adaptability, and a raw metal finish for understated durability. +A bright red aluminum hardtail mountain bike with 120mm front suspension, 29er wheels, 1x12 drivetrain, and tubeless-ready rims for cross-country trails. +Youth trail bike with easy-shifting, low-slung frame, front suspension and grippy tires for growing trail skills. +A cyclocross training bike with mechanical disc brakes, cyclocross-specific geometry, and tubular tires glued to agile rims for muddy seasons. +A classic city cruiser with broad swept bars, leatherette saddle, basket and soft pastel paint for easy neighborhood excursions. +Commuter with belt drive, integrated locks, and modular rack system for cargo flexibility. +A kids' pedal bike with training wheels, bright yellow frame, chain guard, and cartoon decal graphics to make learning fun. +A gravel bike with integrated top-tube pocket, stealth mount for a GPS, and an understated matte graphite finish. +Touring bike with welded low-rider mounts, three-bottle capability, and leather-wrapped handlebars. +Fat-tire cruiser with balloon wheels, single-speed drivetrain, and varnished wooden fenders for beachside style +Gravel light race-ready setup with minimal decals, tubeless 38mm tires, and an underslung framebag-ready top tube. +A compact cargo trike with fold-down ramp and removable panel sides to tailor the loadbed to specific delivery needs. +Electric cargo bike with twin motors, weatherproofed electronics, adjustable suspension and foldable child seat for flexible family use. +A more upright city bicycle with 8-speed internal hub, chain guard, wide platform pedals and a stable double-leg kickstand for grocery runs. +Steel touring bicycle with classic pinstriping, welded eyelets, and a large-capacity rear rack for heavy loads. +Electric city cargo bike with fold-away child seat, modular storage cubes and soft-start throttle for safety. +A commuter with cargo basket, step-through frame, upright seating geometry, generator lights and integrated lock mount for convenience. +Handbuilt steel randonneur with low gearing, leather bar wrap and integrated dynamo for long nights. +A hybrid adventure bicycle with Shimano clutch-style derailleur, 650b wheels, 47mm tires and multiple frame mounts for accessories. +Folding commuter with sleek latch system, mid-mounted battery for balance, and a folding stem for quick stowage under desks. +Vintage city bicycle with painted steel frame, leather saddle, and ornate headbadge for slow, dignified neighborhood rides. +Fat-tyred snow bike with rigid fork, tapered head tube, and wide handlebar for stable winter rides across packed snow. +All-terrain cargo bike with front-loading cargo box, hydraulic disc brakes, reinforced spokes and child harness attachments. +A refurbished vintage city bicycle with re-chromed fenders, replaced brake pads, and a new leather saddle that preserves period feel. +A touring bike with robust welded forks, heavy-gauge spokes, and reinforced dropouts for multi-continental expeditions. +Classic road racer with reconditioned steel frame, polished hubs, period-correct components and narrow 25mm tires for retro events +A lugged chromoly frame with classic brazing, leather saddle, 8-speed downtube shifters, and retro block-pattern paint for an old-school vibe. +A track pursuit frameset with an extended fork rake for steady handling at high speeds and a deep-section disc rear wheel for aero gains. +Electric cargo tricycle with three baskets, step-through access, pedal-assist and wide stable footprint for markets +Cyclocross bike with cantilever brakes, mud-shedding frame, and 33mm knobby tires for winter races. +Retro steel city bike with brown leather accents, wicker front basket, coaster brake, and cream-colored balloon tires for leisurely rides. +A purpose-built cyclocross with flared drops, removable mudguard mounts, and machine-braze-ons for race-ready bikepacking. +A gravel e-bike with integrated framebags, torque-sensor assist, and a wide-gear spread for long climbs on mixed-surface adventures. +High-speed gravel bike with aero fork, carbon wheelset, wide cassette range and bold sponsor-style graphics. +Electric commuter with integrated rear pannier rack, belt drive, and single-button display for intuitive use. +Touring gravel tandem with robust wheel builds, framebag loops, and reinforced seat tube clamps. +Gravel adventure setup with welded steel racks, multi-day range gearing and hand-stitched leather saddle. +Mountain downhill bike with massive travel, reinforced linkage, and a protective skid plate on the bottom bracket. +Commuter with internal cable routing, integrated taillight, and a low-maintenance belt drive for daily reliability. +A racing fixed-gear with aerodynamic profile, high-pitched tooth cogset, aero bar mounts removed and steel-gray polished finish. +A vintage-inspired single-speed cruiser with banana seat, high riser bars, and a soft, spring-loaded saddle for leisurely neighborhood rides. +Gravel bike with double bottle mounts, titanium bolts, and a hand-painted mountain silhouette on the downtube. +Urban folding cargo bike with adjustable deck height, swapped double-jointed hinge and a modular canopy for rain protection. +Classic British road bicycle with hub dynamo, leather saddle, chrome mudguards, and a polite, dependable presence for city travel. +Urban commuter with puncture-resistant tyres, secure frame lock, and an efficient belt drive for low maintenance. +Commuter city bike with step-through ease, quiet hub drive, and built-in cargo basket for errands. +Adventure touring bike with triple chainring, reinforced dropouts and broad fender mounts for serious exploration. +A long-haul touring tandem bicycle with reinforced frame, dual racks, three chainrings, and comfortable bar positions for multi-day pair riding. +A classic steel road bike rebuilt with period components, thin tubular tires, polished chrome and deep maroon paint with cream striping. +Compact trail hardtail with 27.5-inch wheels, 120mm fork, and a 1x10 narrow-wide chainring for trail shredding. +A commuter with an enclosed belt drive, automatic shifting hub, and a bright LED headlight integrated into the headtube for safety and convenience. +A compact-travel folding bike with robust hinge lock, handlebar clamp, and integrated rear carrier for portable commuting solutions. +Long-haul touring steel bike with triple-bolt fork crown, extra-length chainstay for panniers, and stable geometry for loaded miles. +Kids' balance trike with adjustable seat, wide plastic wheels and colorful frame to introduce stability and steering. +Touring expedition frame built from steel with full braze-on sets, triple-chainset ready, and durable paint for global travel. +A classic British three-speed bicycle restored with fresh chrome, Sturmey-Archer hub, and traditional upright riding geometry. +Urban minimalist fixed gear with polished chrome, thin tires and a whisper-quiet drivetrain for simple city riding. +A cyclocross contender with race geometry, carbon fork, disc brakes and a vibrant anodized paint job that stands out in the mud. +A polished-aluminum commuter with belt drive, internally geared hub, integrated lights, and a quietly efficient maintenance-free drivetrain. +A cyclocross training single-speed with durable hub, steel frame, aggressive tread 33mm tires and a utilitarian paint job for muddy practice. +Chopper-style cruiser with stretched frame, swooping top tube, low seat, and chrome-plated springer fork. +A mountain bike with slacked geometry, 160mm travel, coil shock compatibility and a burly alloy frame for park days and steep lines. +BMX race bike with light aluminum frame, tight geometry, 20-inch wheels, and top-end sealed bearing hubs. +A short-travel trail bike with 130mm fork, forgiving geometry, dropper post and tubeless grippy tires for all-day rides. +Mountain trail full suspension with commuter-friendly fenders and internal cable routing for reliability. +A lightweight titanium gravel bike with integrated GPS mount, long-range capability, and tasteful laser-etched branding on the downtube. +A mountain bike with 29-inch wheels, tapered headtube, and a nuanced gradient paint that hides scratches well. +Classic BMX cruiser with banana seat, sissy bar, coaster brake and chrome fenders for nostalgic cruising. +A mountain trail bike with 29x2.6 tyres, modern slack geometry, and sensitive suspension tuning for long effortless descents and reliable climbs. +Classic touring frame with leather saddle, double-leg stand and brass mudguard stays for period charm. +A carbon fiber road bike with integrated cockpit, hidden cables, and glossy pearlescent white paint. +Classic single-speed beach cruiser with glossy blue frame, whitewall tires, chrome fenders and springer front fork. +Cross-country race hardtail with lightweight alloy frame, 120mm travel fork and tubeless 29-inch wheels for fast climbs +Gravel grinder with dual bottle mounts, 45mm mixed-terrain tires, and mechanical disc brakes for reliability. +Cargo trike with rear passenger bench, fold-down tarp, and electric-assist throttle for hilly routes. +Cyclocross hardtail with steel fork, high-rise handlebars, and a custom gradient paint job reminiscent of autumn leaves. +A classic touring bicycle with full chromed fork, period-correct saddlebag, and triple-boss frame for accommodating camp stoves and spares. +Road time trial bike with pointy nose top tube, integrated aero hydration, and a glossy jet-black finish. +A gravel-explorer with multiple frame-bag mounts, tubeless rims, and a two-bottle capacity even with a top-tube bag in place. +A winter-ready bike with studded tires, steel frame, fender clearance and a wool saddle for cold-weather commuting. +Commuter e-bike with throttle-assist, integrated rear rack, chaincase, and wide cushioned saddle for comfort on city streets. +A road racing bicycle with carbon seatpost, integrated bar plug, and minimal graphics for a stealthy pro-team aesthetic. +Gravel bike with stealthy flat-mount disc brakes, chainstay protector, and integrated frame bag pockets. +Gravel adventure bike with long wheelbase, 650b tire compatibility, and a simple 1x drivetrain for low-maintenance touring. +Compact folding bike with 16-inch wheels, adjustable stem, and a simple latch that folds in under ten seconds. +A gravel bike with 40mm tubeless tires, relaxed geometry, carbon fork with three-pack mounts and an extra-long wheelbase for comfort over rough roads. +Road bike with subtle gradient paint, integrated cockpit, light carbon wheelset and power meter for training consistency +Kids' balance bike in primary colors with a low center of gravity and sturdy aluminum frame for first rides without training wheels. +A custom steel road frame with curved seat tube, hand-filed lugs, leather-wrapped handlebars and lightly polished silver finish. +Lightweight titanium road frame with discreet integrated headtube cable routing and a natural polished finish for understated performance +A fat-tire e-bike designed for winter commutes, with integrated battery, low-step frame, and 26x4-inch tires for traction on snow and ice. +Gravel endurance bike with built-in GPS mount, wide tire clearance, and a supple ride for long remotes. +Belt-drive city bike with internal gearbox, step-through alloy frame, mudguards and easy-to-use kickstand +A gravel explorer with steel frame, three-bottle mounts, 650b wheel option and warm sandstone paint flecked with mica. +Cargo trike with hydraulic disc brakes, wide load platform, and child-safe seatbelts for urban deliveries. +Kids' balance-run hybrid with colorful frame, ergonomic grips and removable training footrests for skill building. +A cyclocross race bike with asymmetric chainstays, disc brake integration, flared handlebar drops and ultralight carbon layup for competitive advantage. +All-mountain full-suspension with adjustable geometry, wide 27.5+ wheels and a stout aluminum frame. +A commuter e-bike with step-through frame, torque-assist motor, and built-in satchel hooks for multimedia commuting. +Mountain downcountry bike with 130mm travel, light alloy frame and 29" wheels for fast singletrack exploits. +A carbon aero time-trial bicycle with integrated hydration, steep seat angle, aerobars, and deep-section tubular wheels built for speed. +A classic roadster with metal fenders, steel rear rack, and an elegant teal enamel coat to stand out on rainy mornings. +A classic city bike with sweeping fork crown, tapered seat tube, and a comfortable leather saddle for relaxed commutes through historical districts. +Lightweight gravel frame with carbon fork, modern clearance, integrated seatpost clamp, and multiple attachment points for bags. +A gravel bike with endurance geometry, carbon fork, and integrated fender mounts that allow year-round exploration regardless of conditions. +Gravel endurance build with cushy saddle, flared drops, 40mm tires, and bar-mounted navigation gear for long-distance exploration. +Handbuilt touring steel with custom geometry, brazed-on bottle bosses, and understated enamel paint. +Gravel endurance bike in subdued gray with lash points, stealth racks and 700x42 tires to handle rough backroads. +A commuter with integrated basket, built-in lock, and weatherproof seat cover specially designed for unpredictable city mornings. +Gravel endurance bicycle with settled geometry, 700c x 40 tires, and a comfortable bar reach for all-day rides. +Endurance gravel bike with carbon fork, integrated GPS mount and comfortable saddle for all-day rides. +Mountain enduro bike with adjustable chainstay length, coil-to-air shock tuning, and ultra-wide rims for control on bomb runs. +A steel cyclocross frame with updated geometry, 700x33 clearance, and subtle textured paint designed for durability in mud. +A commuter with integrated rear rack battery, torque-sensing assist, and an anti-theft GPS module discreetly hidden in the seatpost. +A titanium commuter with discreet graphics, sealed-bearing headset, and a precisely weighted ride that blends daily function with metallic elegance. +Road racing frameset with aero tubing, integrated powerlight mount, electronic shifting and race-optimized clearance for 28mm tires. diff --git a/bike_bench_internal/src/resources/misc/default_weights.csv b/bike_bench_internal/src/resources/misc/default_weights.csv new file mode 100644 index 0000000000000000000000000000000000000000..0d2f5116c3a3e912eaea39fc4839e47cf7cdcd45 --- /dev/null +++ b/bike_bench_internal/src/resources/misc/default_weights.csv @@ -0,0 +1,50 @@ +Usability Score,0.5489644 +Drag Force (N),20.91571 +Knee Angle Error (deg.),33.674683 +Hip Angle Error (deg.),12.387023 +Arm Angle Error (deg.),8.151609 +Arm Too Long for Bike,0.6133517 +Saddle Too Far From Handle,0.4385572 +Torso Too Long for Bike,0.819547 +Saddle Too Far From Crank,0.1198426 +Upper Leg Too Long for Bike,1.0907353 +Lower Leg Too Long for Bike,0.68470716 +Cosine Distance to Embedding,0.0023600538 +Mass (kg),4.7396207 +Planar Compliance Score,1.5312384 +Transverse Compliance Score,1.2523615 +Eccentric Compliance Score,1.3660624 +Planar Safety Factor,0.9045352 +Eccentric Safety Factor,0.64230794 +Predicted Frame Validity,0.4971371 +Saddle height too small,589.8568 +Saddle collides with seat tube,141.48203 +Saddle too short,51.280556 +Head angle over limit,108.246956 +Seat angle over limit,107.03927 +Seat post too short,98.02847 +Seat post too long,420.34003 +Rear Wheel inner diameter too small,389.07608 +Front Wheel inner diameter too small,388.56998 +Seat tube extension longer than seat tube,458.33722 +Head tube upper extension and lower extension overlap,63.33588 +Seat stay Z longer than seat tube,501.12625 +Non-negative parameter is negative,1.3917778 +Chain stay smaller than rear wheel radius,85.56158 +Chain stay shorter than BB drop,370.6811 +Seat stay smaller than rear wheel radius,144.05183 +Seat Tube Intersects Rear Wheel2,29.69933 +Down tube can't reach head tube,166.61848 +Rear wheel cutout severs seat tube,923341300.0 +Foot intersects front wheel,33586.05 +Crank hits ground in lowest position,97.96187 +RGB value greater than 255,406.95776 +Chain stays intersect,9.152262 +Tube wall thickness exceeds radius,1.8774078 +Seat tube inner diameter thinner than seat post outer diameter,3.862171 +Down tube improperly joins head tube,13.428996 +Top tube improperly joins head tube,13.770946 +Top tube improperly joins seat tube,29.337496 +Down tube intersects front wheel,70.381096 +Saddle hits top tube,0.88172686 +Saddle hits head tube,0.94234717 diff --git a/bike_bench_internal/src/resources/misc/ref_point.csv b/bike_bench_internal/src/resources/misc/ref_point.csv new file mode 100644 index 0000000000000000000000000000000000000000..152905992c276da068d4a37782f66d749f569bfa --- /dev/null +++ b/bike_bench_internal/src/resources/misc/ref_point.csv @@ -0,0 +1,50 @@ +Usability Score,0.96583045 +Drag Force (N),26.949783 +Knee Angle Error (deg.),121.69888 +Hip Angle Error (deg.),45.15921 +Arm Angle Error (deg.),51.60288 +Arm Too Long for Bike,-0.2584031 +Saddle Too Far From Handle,-0.0924381 +Torso Too Long for Bike,-0.40180784 +Saddle Too Far From Crank,0.18204135 +Upper Leg Too Long for Bike,-0.6047877 +Lower Leg Too Long for Bike,-0.18946144 +Cosine Distance to Embedding,0.3990872 +Mass (kg),13.66151 +Planar Compliance Score,9.490826 +Transverse Compliance Score,8.403987 +Eccentric Compliance Score,8.238781 +Planar Safety Factor,1.0 +Eccentric Safety Factor,1.0 +Predicted Frame Validity,0.5 +Saddle height too small,-130.0 +Saddle collides with seat tube,56.299988 +Saddle too short,98.0 +Head angle over limit,-97.0 +Seat angle over limit,-90.0 +Seat post too short,227.887 +Seat post too long,43.014893 +Rear Wheel inner diameter too small,52.0 +Front Wheel inner diameter too small,52.0 +Seat tube extension longer than seat tube,-80.0 +Head tube upper extension and lower extension overlap,67.59999 +Seat stay junction longer than seat tube,-82.0 +Non-negative parameter is negative,210.0 +Chain stay smaller than rear wheel radius,37.0 +Chain stay shorter than BB drop,-230.0 +Seat stay smaller than rear wheel radius,-3.105194 +Seat Tube Intersects Rear Wheel,89.69733 +Down tube can't reach head tube,-81.2265 +Rear wheel cutout severs seat tube,-12.901978 +Foot intersects front wheel,36118.047 +Crank hits ground in lowest position,32.5 +RGB value greater than 255,0.0 +Chain stays intersect,22.625 +Tube wall thickness exceeds radius,3.2742386 +Seat tube inner diameter thinner than seat post outer diameter,29.329865 +Down tube improperly joins head tube,55.09871 +Top tube improperly joins head tube,27.778019 +Top tube improperly joins seat tube,25.929976 +Down tube intersects front wheel,30.511627 +Saddle hits top tube,0.3413951 +Saddle hits head tube,-0.0586164 diff --git a/bike_bench_internal/src/resources/models_and_scalers/CTGAN.pkl b/bike_bench_internal/src/resources/models_and_scalers/CTGAN.pkl new file mode 100644 index 0000000000000000000000000000000000000000..c918441ffa4ff2876a88f9d10ba7bc7eb9b850b3 --- /dev/null +++ b/bike_bench_internal/src/resources/models_and_scalers/CTGAN.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:47899a09dc16d7b1cb3ab934883d2560ba72eb29937a60e54936863a3d1cfc14 +size 4601136 diff --git a/bike_bench_internal/src/resources/models_and_scalers/aero_model_weights.pt b/bike_bench_internal/src/resources/models_and_scalers/aero_model_weights.pt new file mode 100644 index 0000000000000000000000000000000000000000..95fbaec09b6217da06c0686aaf303d872b7665d2 --- /dev/null +++ b/bike_bench_internal/src/resources/models_and_scalers/aero_model_weights.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0410844902cbfe4fd6abd27ce20701f92a755a3b0b8b6a7e8e80cf9cd5f35926 +size 145177 diff --git a/bike_bench_internal/src/resources/models_and_scalers/aero_scaler.pt b/bike_bench_internal/src/resources/models_and_scalers/aero_scaler.pt new file mode 100644 index 0000000000000000000000000000000000000000..d81db007840e1e66352a85e53642c240fe7b27a5 --- /dev/null +++ b/bike_bench_internal/src/resources/models_and_scalers/aero_scaler.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6998477831fad20148c90ea43e927ca21746a9bcf8b711f921b653a0423c3781 +size 2629 diff --git a/bike_bench_internal/src/resources/models_and_scalers/aesthetics_model_weights.pt b/bike_bench_internal/src/resources/models_and_scalers/aesthetics_model_weights.pt new file mode 100644 index 0000000000000000000000000000000000000000..4006d0a3081ec82024d0f879491ae547431167c1 --- /dev/null +++ b/bike_bench_internal/src/resources/models_and_scalers/aesthetics_model_weights.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b7da88a1b263a2e75e1b8d221e63c8e0bb05e4f4f7ac20bd5cd07970f434d16a +size 2212539 diff --git a/bike_bench_internal/src/resources/models_and_scalers/aesthetics_scaler.pt b/bike_bench_internal/src/resources/models_and_scalers/aesthetics_scaler.pt new file mode 100644 index 0000000000000000000000000000000000000000..f0e8f548f4d132265e7b237acbcdcbf428c2c876 --- /dev/null +++ b/bike_bench_internal/src/resources/models_and_scalers/aesthetics_scaler.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26d2cdf2cc89b2ceb1b8a7015bbf53cc74e1448b03f0e616fbf37431f3338019 +size 3189 diff --git a/bike_bench_internal/src/resources/models_and_scalers/structural_model_weights.pt b/bike_bench_internal/src/resources/models_and_scalers/structural_model_weights.pt new file mode 100644 index 0000000000000000000000000000000000000000..407c217bcc4aa9be94c4b4b8e97c5d12c5889948 --- /dev/null +++ b/bike_bench_internal/src/resources/models_and_scalers/structural_model_weights.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b59018deed3ce6af5b44e6e4802aa2f6b56529a24f85f72ad5210683392ea12d +size 162209 diff --git a/bike_bench_internal/src/resources/models_and_scalers/structural_scaler.pt b/bike_bench_internal/src/resources/models_and_scalers/structural_scaler.pt new file mode 100644 index 0000000000000000000000000000000000000000..a9226721ce4819c2212be2817c3da050a510fe1b --- /dev/null +++ b/bike_bench_internal/src/resources/models_and_scalers/structural_scaler.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ce9229a9413d6f222491119c6ddb184bb87ddb720b2da1d792140d0dbc1a8ea +size 2805 diff --git a/bike_bench_internal/src/resources/models_and_scalers/usability_model_weights.pt b/bike_bench_internal/src/resources/models_and_scalers/usability_model_weights.pt new file mode 100644 index 0000000000000000000000000000000000000000..bc9923f346f5eae189556c8eff59ad67dc497517 --- /dev/null +++ b/bike_bench_internal/src/resources/models_and_scalers/usability_model_weights.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:46324675c065b9d3faac66478b28b1afdcc08434a91e4cc61baa899b07ef6615 +size 337813 diff --git a/bike_bench_internal/src/resources/models_and_scalers/usability_scaler.pt b/bike_bench_internal/src/resources/models_and_scalers/usability_scaler.pt new file mode 100644 index 0000000000000000000000000000000000000000..29eeb0104c5c83d8feed1085b5e612f017035e03 --- /dev/null +++ b/bike_bench_internal/src/resources/models_and_scalers/usability_scaler.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e032d93f94ee80f2f3bd120e5b5aa9fc7eeaaa249d5f27f3969f39e7d73bdbd2 +size 2541 diff --git a/bike_bench_internal/src/resources/models_and_scalers/validity_model_weights.pt b/bike_bench_internal/src/resources/models_and_scalers/validity_model_weights.pt new file mode 100644 index 0000000000000000000000000000000000000000..e4b186fe364c4ce899537caa97a174bd6efeb123 --- /dev/null +++ b/bike_bench_internal/src/resources/models_and_scalers/validity_model_weights.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3638cf444e90eabb8e4867c01f4c3cf0bd526aa6eade42f4edd9c0571f53ea75 +size 90505 diff --git a/bike_bench_internal/src/resources/models_and_scalers/validity_scaler.pt b/bike_bench_internal/src/resources/models_and_scalers/validity_scaler.pt new file mode 100644 index 0000000000000000000000000000000000000000..8475f755654d0ea68b2c5fa38076cfe14e82e748 --- /dev/null +++ b/bike_bench_internal/src/resources/models_and_scalers/validity_scaler.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:936e436ed1acfbcfbf67f65e134d42e632e9cd0e6c6e1c34a5311da32c8e18f4 +size 2789 diff --git a/data_repository.py b/data_repository.py index 3f7f254b9d2644a32e77e3e102e3d5dae28b229d..858bf9a2ea13c6540b1c484b49977a35538917c5 100644 --- a/data_repository.py +++ b/data_repository.py @@ -14,23 +14,39 @@ from config import APP_CONFIG @attrs.define class ModelScoringResult: uuid: str - score: float submission_time: datetime.datetime - scoring_time: datetime.datetime + design_quality: float + mean_violations: float + sim_to_data_mmd: float + mean_novelty: float + binary_validity: float + diversity_dpp: float -ORDERED_COLUMNS = [ +ORDERED_SCORES_COLUMNS = [ "uuid", - "score", "submission_time", - "scoring_time", + "design_quality", + "mean_violations", + "sim_to_data_mmd", + "mean_novelty", + "binary_validity", + "diversity_dpp", +] + +ORDERED_APPROVAL_COLUMNS = [ + "model_uuid", + "model_verification_time" ] class PandasModelScoresRepository(metaclass=abc.ABCMeta): + def __init__(self, columns): + self.columns = columns + def get_data_to_display(self): - return pd.DataFrame(self.read_curr_state(), columns=ORDERED_COLUMNS) + return pd.DataFrame(self.read_curr_state(), columns=self.columns) def add_row(self, row: ModelScoringResult): previous_state = self.read_curr_state() @@ -50,11 +66,12 @@ class PandasModelScoresRepository(metaclass=abc.ABCMeta): class LocalPandasModelScoresRepository(PandasModelScoresRepository): - def __init__(self, dummy_file_path=os.path.join(os.path.dirname(__file__), "dummy_data.txt")): + def __init__(self, dummy_file_path: str, columns: List[str]): + super().__init__(columns) self.dummy_file_path = dummy_file_path if not os.path.exists(self.dummy_file_path): with open(self.dummy_file_path, "w") as file: - file.write("uuid,score,submission_time,scoring_time") + file.write(",".join(self.columns)) def read_curr_state(self) -> pd.DataFrame: return pd.read_csv(self.dummy_file_path, index_col=None) @@ -63,12 +80,33 @@ class LocalPandasModelScoresRepository(PandasModelScoresRepository): result.to_csv(self.dummy_file_path, index=False) +@attrs.define(frozen=True) +class DatasetParams: + dataset_url: str + repo_id: str + file_path_in_repo: str + + +model_scores_dataset = DatasetParams( + dataset_url="https://huggingface.co/datasets/yaz23/bike-bench-models/resolve/main/scoring_data.txt", + repo_id="yaz23/bike-bench-models", + file_path_in_repo="scoring_data.txt" +) +approval_dataset = DatasetParams( + dataset_url="https://huggingface.co/datasets/yaz23/bike-bench-models/resolve/main/approval_data.txt", + repo_id="yaz23/bike-bench-models", + file_path_in_repo="approval_data.txt" +) + + class HuggingFaceDatasetModelScoresRepository(PandasModelScoresRepository): - def __init__(self): + def __init__(self, dataset_params: DatasetParams, columns: List[str]): + super().__init__(columns) login(APP_CONFIG.hugging_face_token) + self.dataset_params = dataset_params def read_curr_state(self) -> pd.DataFrame: - return pd.read_csv("https://huggingface.co/datasets/yaz23/bike-bench-models/resolve/main/dummy_data.txt", + return pd.read_csv(self.dataset_params.dataset_url, index_col=None) def save_to_disk(self, result: pd.DataFrame): @@ -76,14 +114,21 @@ class HuggingFaceDatasetModelScoresRepository(PandasModelScoresRepository): csv_buffer = BytesIO(csv_string.encode('utf-8')) upload_file( path_or_fileobj=csv_buffer, - repo_id="yaz23/bike-bench-models", + repo_id=self.dataset_params.repo_id, repo_type="dataset", - path_in_repo="dummy_data.txt" + path_in_repo=self.dataset_params.file_path_in_repo ) -REPOSITORY_INSTANCE: PandasModelScoresRepository +MODELS_REPOSITORY_INSTANCE: PandasModelScoresRepository +APPROVAL_REPOSITORY_INSTANCE: PandasModelScoresRepository if APP_CONFIG.production: - REPOSITORY_INSTANCE = HuggingFaceDatasetModelScoresRepository() + REPOSITORY_INSTANCE = HuggingFaceDatasetModelScoresRepository(model_scores_dataset, ORDERED_SCORES_COLUMNS) + APPROVAL_REPOSITORY_INSTANCE = HuggingFaceDatasetModelScoresRepository(model_scores_dataset, ORDERED_APPROVAL_COLUMNS) else: - REPOSITORY_INSTANCE = LocalPandasModelScoresRepository() + REPOSITORY_INSTANCE = LocalPandasModelScoresRepository(os.path.join(os.path.dirname(__file__), + "local-run-data/model_scores.csv"), + ORDERED_SCORES_COLUMNS) + APPROVAL_REPOSITORY_INSTANCE = LocalPandasModelScoresRepository(os.path.join(os.path.dirname(__file__), + "local-run-data/model_approval.csv"), + ORDERED_APPROVAL_COLUMNS) diff --git a/domain_constants.py b/domain_constants.py new file mode 100644 index 0000000000000000000000000000000000000000..05a41e13036b9e42a12f0a79c58c62de841240b7 --- /dev/null +++ b/domain_constants.py @@ -0,0 +1,37 @@ +USER_GEN_DESIGNS_COLUMNS = ['Seatpost LENGTH', 'CS textfield', 'BB textfield', 'Stack', 'Head angle', + 'Head tube length textfield', 'Seat stay junction0', 'Seat tube length', 'Seat angle', + 'DT Length', 'FORK0R', 'BB diameter', 'ttd', 'dtd', 'csd', 'std', 'htd', 'ssd', + 'Chain stay position on BB', 'SSTopZOFFSET', 'Head tube upper extension2', + 'Seat tube extension2', 'Head tube lower extension2', 'SEATSTAYbrdgshift', + 'CHAINSTAYbrdgshift', 'SEATSTAYbrdgdia1', 'CHAINSTAYbrdgdia1', 'SEATSTAYbrdgCheck', + 'CHAINSTAYbrdgCheck', 'Dropout spacing', 'Wall thickness Bottom Bracket', + 'Wall thickness Top tube', 'Wall thickness Head tube', 'Wall thickness Down tube', + 'Wall thickness Chain stay', 'Wall thickness Seat stay', 'Wall thickness Seat tube', + 'Wheel diameter front', 'RDBSD', 'Wheel diameter rear', 'FDBSD', 'Display AEROBARS', + 'BB length', 'Wheel cut', 'Front Fender include', 'Rear Fender include', 'BELTorCHAIN', + 'Number of cogs', 'Number of chainrings', 'FIRST color R_RGB', 'FIRST color G_RGB', + 'FIRST color B_RGB', 'SPOKES composite front', 'SPOKES composite rear', 'SBLADEW front', + 'SBLADEW rear', 'Saddle length', 'Saddle height', 'Down tube type', + 'MATERIAL OHCLASS: ALUMINIUM', 'MATERIAL OHCLASS: BAMBOO', 'MATERIAL OHCLASS: CARBON', + 'MATERIAL OHCLASS: OTHER', 'MATERIAL OHCLASS: STEEL', 'MATERIAL OHCLASS: TITANIUM', + 'Head tube type OHCLASS: 0', 'Head tube type OHCLASS: 1', 'Head tube type OHCLASS: 2', + 'Head tube type OHCLASS: 3', 'RIM_STYLE front OHCLASS: DISC', + 'RIM_STYLE front OHCLASS: SPOKED', 'RIM_STYLE front OHCLASS: TRISPOKE', + 'RIM_STYLE rear OHCLASS: DISC', 'RIM_STYLE rear OHCLASS: SPOKED', + 'RIM_STYLE rear OHCLASS: TRISPOKE', 'Handlebar style OHCLASS: 0', + 'Handlebar style OHCLASS: 1', 'Handlebar style OHCLASS: 2', 'Stem kind OHCLASS: 0', + 'Stem kind OHCLASS: 1', 'Stem kind OHCLASS: 2', 'Fork type OHCLASS: 0', + 'Fork type OHCLASS: 1', 'Fork type OHCLASS: 2', 'Seat tube type OHCLASS: 0', + 'Seat tube type OHCLASS: 1', 'Seat tube type OHCLASS: 2'] + +RESULT_COLUMNS = ['Design Quality ↑ (HV)', 'Mean Violations ↓', 'Sim. to Data ↓ (MMD)', 'Mean Novelty ↑', + 'Binary Validity ↑', 'Diversity ↓ (DPP)'] + +SCORE_NAMES_MAP = { + "design_quality": "Design Quality ↑ (HV)", + "mean_violations": "Mean Violations ↓", + "sim_to_data_mmd": "Sim. to Data ↓ (MMD)", + "mean_novelty": "Mean Novelty ↑", + "binary_validity": "Binary Validity ↑", + "diversity_dpp": "Diversity ↓ (DPP)" +} diff --git a/requirements.txt b/requirements.txt index 55b9bb9f15d9d3e732ec72910bfb9334c7e15cf3..961f8d4768c6d7e09c6a40e80e753372559b2341 100644 --- a/requirements.txt +++ b/requirements.txt @@ -27,7 +27,7 @@ httpcore==1.0.9 httpx==0.28.1 huggingface-hub==0.35.3 idna==3.10 -Jinja2==3.1. 6 +Jinja2==3.1.6 markdown-it-py==4.0.0 MarkupSafe==3.0.3 mdurl==0.1.2 diff --git a/test_scenario.py b/test_scenario.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391