End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: vit-pushup-form-classifier
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9733333333333334
+    - name: F1
+      type: f1
+      value: 0.9733475783475783
+    - name: Precision
+      type: precision
+      value: 0.9737081183656526
+    - name: Recall
+      type: recall
+      value: 0.9733333333333334
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# vit-pushup-form-classifier
+
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0633
+- Accuracy: 0.9733
+- F1: 0.9733
+- Precision: 0.9737
+- Recall: 0.9733
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.2205        | 1.0   | 44   | 0.2602          | 0.8533   | 0.8532 | 0.8534    | 0.8533 |
+| 0.1148        | 2.0   | 88   | 0.2138          | 0.9      | 0.9000 | 0.9       | 0.9    |
+| 0.0869        | 3.0   | 132  | 0.2535          | 0.8933   | 0.8931 | 0.8942    | 0.8933 |
+| 0.0592        | 4.0   | 176  | 0.1646          | 0.9      | 0.8997 | 0.9014    | 0.9    |
+| 0.0559        | 5.0   | 220  | 0.1587          | 0.9267   | 0.9265 | 0.9283    | 0.9267 |
+| 0.034         | 6.0   | 264  | 0.2178          | 0.9133   | 0.9129 | 0.9166    | 0.9133 |
+| 0.0171        | 7.0   | 308  | 0.1712          | 0.9267   | 0.9266 | 0.9272    | 0.9267 |
+| 0.0107        | 8.0   | 352  | 0.1740          | 0.9267   | 0.9265 | 0.9283    | 0.9267 |
+| 0.0139        | 9.0   | 396  | 0.1631          | 0.9333   | 0.9332 | 0.9344    | 0.9333 |
+| 0.0046        | 10.0  | 440  | 0.1692          | 0.9333   | 0.9331 | 0.9359    | 0.9333 |
+
+
+### Framework versions
+
+- Transformers 4.57.1
+- Pytorch 2.8.0+cu126
+- Datasets 4.0.0
+- Tokenizers 0.22.1

config.json

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+{
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "dtype": "float32",
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "correct",
+    "1": "incorrect"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "correct": 0,
+    "incorrect": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "pooler_act": "tanh",
+  "pooler_output_size": 768,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "transformers_version": "4.57.1"
+}

model.safetensors

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+size 343223968

training_args.bin

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