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# vit-epsilon-1e-9

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: 0.5143
- Accuracy: 0.8131
- Precision: 0.8359
- Recall: 0.8131
- F1: 0.8206

## 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=(0.9,0.99) and epsilon=1e-09
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1733
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.7127        | 1.0   | 321  | 0.9579          | 0.6987   | 0.6581    | 0.6987 | 0.6331 |
| 1.1596        | 2.0   | 642  | 0.7026          | 0.7299   | 0.7442    | 0.7299 | 0.7299 |
| 1.0337        | 3.0   | 963  | 0.6560          | 0.7549   | 0.7647    | 0.7549 | 0.7356 |
| 0.9695        | 4.0   | 1284 | 0.5708          | 0.7656   | 0.7963    | 0.7656 | 0.7758 |
| 0.9137        | 5.0   | 1605 | 0.6460          | 0.7611   | 0.7828    | 0.7611 | 0.7625 |
| 1.0053        | 6.0   | 1926 | 0.6020          | 0.7673   | 0.8049    | 0.7673 | 0.7797 |
| 0.896         | 7.0   | 2247 | 0.7087          | 0.7271   | 0.8055    | 0.7271 | 0.7477 |
| 0.7646        | 8.0   | 2568 | 0.6615          | 0.7441   | 0.8134    | 0.7441 | 0.7622 |
| 0.7262        | 9.0   | 2889 | 0.5611          | 0.7975   | 0.8190    | 0.7975 | 0.7985 |
| 0.7025        | 10.0  | 3210 | 0.5338          | 0.7975   | 0.8278    | 0.7975 | 0.8058 |
| 0.6138        | 11.0  | 3531 | 0.5143          | 0.8131   | 0.8359    | 0.8131 | 0.8206 |
| 0.5582        | 12.0  | 3852 | 0.6157          | 0.7864   | 0.8253    | 0.7864 | 0.7992 |
| 0.4736        | 13.0  | 4173 | 0.6899          | 0.8117   | 0.8253    | 0.8117 | 0.8007 |
| 0.4581        | 14.0  | 4494 | 0.6062          | 0.8128   | 0.8447    | 0.8128 | 0.8199 |
| 0.407         | 15.0  | 4815 | 0.5317          | 0.8308   | 0.8410    | 0.8308 | 0.8339 |
| 0.369         | 16.0  | 5136 | 0.6475          | 0.8197   | 0.8414    | 0.8197 | 0.8270 |
| 0.2855        | 17.0  | 5457 | 0.5153          | 0.8617   | 0.8572    | 0.8617 | 0.8578 |
| 0.2545        | 18.0  | 5778 | 0.5455          | 0.8436   | 0.8555    | 0.8436 | 0.8473 |
| 0.2221        | 19.0  | 6099 | 0.5955          | 0.8471   | 0.8624    | 0.8471 | 0.8516 |
| 0.2143        | 20.0  | 6420 | 0.5772          | 0.8575   | 0.8604    | 0.8575 | 0.8582 |
| 0.1619        | 21.0  | 6741 | 0.6021          | 0.8627   | 0.8567    | 0.8627 | 0.8572 |


### Framework versions

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

akiec bcc bkl df mel nv vasc