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# vit-dropout-0.2

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the skin-cancer dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4888
- Accuracy: 0.8318
- Precision: 0.8450
- Recall: 0.8318
- F1: 0.8340

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1219
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.6129        | 1.0   | 321  | 0.8251          | 0.7223   | 0.7708    | 0.7223 | 0.7354 |
| 0.9753        | 2.0   | 642  | 0.7419          | 0.7115   | 0.7830    | 0.7115 | 0.7320 |
| 0.9289        | 3.0   | 963  | 0.5421          | 0.7968   | 0.7942    | 0.7968 | 0.7901 |
| 0.8978        | 4.0   | 1284 | 0.6563          | 0.7410   | 0.8112    | 0.7410 | 0.7569 |
| 0.8271        | 5.0   | 1605 | 0.5586          | 0.8058   | 0.8090    | 0.8058 | 0.8050 |
| 0.7757        | 6.0   | 1926 | 0.5340          | 0.8065   | 0.8220    | 0.8065 | 0.8113 |
| 0.6981        | 7.0   | 2247 | 0.5943          | 0.7791   | 0.8207    | 0.7791 | 0.7911 |
| 0.5954        | 8.0   | 2568 | 0.5836          | 0.7660   | 0.8270    | 0.7660 | 0.7824 |
| 0.5804        | 9.0   | 2889 | 0.4888          | 0.8318   | 0.8450    | 0.8318 | 0.8340 |
| 0.4894        | 10.0  | 3210 | 0.5404          | 0.8037   | 0.8459    | 0.8037 | 0.8145 |
| 0.4924        | 11.0  | 3531 | 0.4997          | 0.8166   | 0.8530    | 0.8166 | 0.8257 |
| 0.38          | 12.0  | 3852 | 0.5669          | 0.8356   | 0.8487    | 0.8356 | 0.8404 |
| 0.3109        | 13.0  | 4173 | 0.5983          | 0.8467   | 0.8492    | 0.8467 | 0.8415 |
| 0.3445        | 14.0  | 4494 | 0.5060          | 0.8620   | 0.8643    | 0.8620 | 0.8622 |
| 0.2457        | 15.0  | 4815 | 0.5823          | 0.8547   | 0.8564    | 0.8547 | 0.8520 |
| 0.2297        | 16.0  | 5136 | 0.5347          | 0.8620   | 0.8647    | 0.8620 | 0.8628 |
| 0.1701        | 17.0  | 5457 | 0.5561          | 0.8689   | 0.8665    | 0.8689 | 0.8656 |
| 0.186         | 18.0  | 5778 | 0.5319          | 0.8686   | 0.8688    | 0.8686 | 0.8679 |
| 0.1258        | 19.0  | 6099 | 0.6001          | 0.8655   | 0.8655    | 0.8655 | 0.8643 |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

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