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# vit-dropout-0.5

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: 0.4899
- Accuracy: 0.8592
- Precision: 0.8593
- Recall: 0.8592
- F1: 0.8581

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.6642        | 1.0   | 321  | 0.8597          | 0.7178   | 0.6898    | 0.7178 | 0.6753 |
| 1.1191        | 2.0   | 642  | 0.6286          | 0.7656   | 0.7678    | 0.7656 | 0.7653 |
| 1.0741        | 3.0   | 963  | 0.6771          | 0.7545   | 0.7795    | 0.7545 | 0.7353 |
| 1.0023        | 4.0   | 1284 | 0.6269          | 0.7708   | 0.7839    | 0.7708 | 0.7727 |
| 0.92          | 5.0   | 1605 | 0.6010          | 0.7816   | 0.7913    | 0.7816 | 0.7811 |
| 0.9155        | 6.0   | 1926 | 0.5399          | 0.7944   | 0.8035    | 0.7944 | 0.7979 |
| 0.7971        | 7.0   | 2247 | 0.7016          | 0.7347   | 0.7903    | 0.7347 | 0.7509 |
| 0.7171        | 8.0   | 2568 | 0.6352          | 0.7396   | 0.8175    | 0.7396 | 0.7601 |
| 0.676         | 9.0   | 2889 | 0.5073          | 0.8072   | 0.8241    | 0.8072 | 0.8120 |
| 0.6312        | 10.0  | 3210 | 0.5552          | 0.7850   | 0.8374    | 0.7850 | 0.7978 |
| 0.5777        | 11.0  | 3531 | 0.5175          | 0.8173   | 0.8428    | 0.8173 | 0.8247 |
| 0.4686        | 12.0  | 3852 | 0.5712          | 0.8062   | 0.8413    | 0.8062 | 0.8172 |
| 0.4134        | 13.0  | 4173 | 0.6331          | 0.8214   | 0.8384    | 0.8214 | 0.8180 |
| 0.3904        | 14.0  | 4494 | 0.6161          | 0.8308   | 0.8546    | 0.8308 | 0.8357 |
| 0.3388        | 15.0  | 4815 | 0.5141          | 0.8384   | 0.8519    | 0.8384 | 0.8425 |
| 0.301         | 16.0  | 5136 | 0.5525          | 0.8360   | 0.8521    | 0.8360 | 0.8416 |
| 0.2516        | 17.0  | 5457 | 0.4899          | 0.8592   | 0.8593    | 0.8592 | 0.8581 |
| 0.2433        | 18.0  | 5778 | 0.5183          | 0.8509   | 0.8565    | 0.8509 | 0.8528 |
| 0.1769        | 19.0  | 6099 | 0.6016          | 0.8426   | 0.8622    | 0.8426 | 0.8487 |
| 0.179         | 20.0  | 6420 | 0.6330          | 0.8551   | 0.8543    | 0.8551 | 0.8534 |
| 0.1401        | 21.0  | 6741 | 0.5884          | 0.8669   | 0.8612    | 0.8669 | 0.8630 |
| 0.1085        | 22.0  | 7062 | 0.6131          | 0.8627   | 0.8591    | 0.8627 | 0.8603 |
| 0.1188        | 23.0  | 7383 | 0.6344          | 0.8648   | 0.8581    | 0.8648 | 0.8596 |
| 0.106         | 24.0  | 7704 | 0.6152          | 0.8731   | 0.8687    | 0.8731 | 0.8696 |
| 0.1029        | 25.0  | 8025 | 0.6209          | 0.8717   | 0.8676    | 0.8717 | 0.8692 |
| 0.0761        | 26.0  | 8346 | 0.6190          | 0.8727   | 0.8679    | 0.8727 | 0.8694 |
| 0.1207        | 27.0  | 8667 | 0.6154          | 0.8721   | 0.8677    | 0.8721 | 0.8693 |


### Framework versions

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

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