---
license: mit
task_categories:
- object-detection
language:
- en
pretty_name: Real Time Pothole Detection System Training Dataset
size_categories:
- n<1K
tags:
- Yolo
- AI/ML
- Pothole
- Ultralytics
- Object Detection
---

# Real Time Pothole Detection System Training Dataset & Model Files

## Model Files

**Primary model:** `pothole-detector.pt` — this is the actual pre-trained YOLOv10b model used for this project.

You can download it directly from the Hugging Face Hub:

- **Direct download link:** [pothole-detector.pt](https://huggingface.co/datasets/Anshulgada/RT-PDS/resolve/main/pothole-detector.pt)

- **Python snippet:**
```python
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="Anshulgada/RT-PDS",
    filename="pothole-detector.pt"
)
```

---

## Other Available Ultralytics Variants

| Model       | Description                    |
| ----------- | ------------------------------ |
| yolov10n.pt | Nano model, smallest & fastest |
| yolov10s.pt | Small model                    |
| yolov10m.pt | Medium model                   |
| yolov10b.pt | Base model                     |
| yolov10l.pt | Large model                    |
| yolov10x.pt | Extra large, highest accuracy  |

By default, these Ultralytics weights are available from:

👉 [https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov10{variant-name\[n,s,m,b,l,x\]}.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov10b.pt)

A backup of these models may also be hosted on Hugging Face Hub.

---

## Dataset Structure

The dataset follows the standard **YOLO format** with separate directories for training, validation, and testing.
Each split contains both **images/** and **labels/** subdirectories with matching filenames.

```
Yolo/
├── Inference Images/       # Example images for quick testing
└── Datasets/
    ├── train/
    │   ├── images/         # ~38k training images
    │   └── labels/         # YOLO-format labels
    ├── valid/
    │   ├── images/         # 6k validation images
    │   └── labels/         
    └── test/
        ├── images/         # 10k test images
        └── labels/         
```

You can download it directly from the Hugging Face Hub:

- **Direct download link:** [Yolo.zip](https://huggingface.co/datasets/Anshulgada/RT-PDS/resolve/main/Yolo.zip)

- **Python snippet:**
```python
from huggingface_hub import hf_hub_download

# Download the zipped YOLO dataset
dataset_path = hf_hub_download(
    repo_id="Anshulgada/RT-PDS",
    filename="Yolo.zip",
    repo_type="dataset"
)

print("Dataset downloaded to:", dataset_path)
```