
<!-- 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-dropout-0.3

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: 0.4841
- Accuracy: 0.8346
- Precision: 0.8364
- Recall: 0.8346
- F1: 0.8335

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.6246        | 1.0   | 321  | 0.7425          | 0.7583   | 0.7625    | 0.7583 | 0.7517 |
| 1.0305        | 2.0   | 642  | 0.7197          | 0.7195   | 0.7869    | 0.7195 | 0.7389 |
| 0.9668        | 3.0   | 963  | 0.6473          | 0.7587   | 0.7881    | 0.7587 | 0.7537 |
| 0.9294        | 4.0   | 1284 | 0.5399          | 0.7850   | 0.8140    | 0.7850 | 0.7934 |
| 0.8498        | 5.0   | 1605 | 0.6023          | 0.7916   | 0.8132    | 0.7916 | 0.7859 |
| 0.8204        | 6.0   | 1926 | 0.5088          | 0.8083   | 0.8120    | 0.8083 | 0.8094 |
| 0.6877        | 7.0   | 2247 | 0.5728          | 0.7701   | 0.8218    | 0.7701 | 0.7846 |
| 0.6445        | 8.0   | 2568 | 0.6019          | 0.7566   | 0.8286    | 0.7566 | 0.7750 |
| 0.6017        | 9.0   | 2889 | 0.4841          | 0.8346   | 0.8364    | 0.8346 | 0.8335 |
| 0.5321        | 10.0  | 3210 | 0.4849          | 0.8311   | 0.8483    | 0.8311 | 0.8353 |
| 0.4937        | 11.0  | 3531 | 0.5704          | 0.8031   | 0.8494    | 0.8031 | 0.8152 |
| 0.4328        | 12.0  | 3852 | 0.5285          | 0.8280   | 0.8521    | 0.8280 | 0.8361 |
| 0.3348        | 13.0  | 4173 | 0.5173          | 0.8592   | 0.8607    | 0.8592 | 0.8562 |
| 0.3532        | 14.0  | 4494 | 0.5740          | 0.8450   | 0.8658    | 0.8450 | 0.8512 |
| 0.296         | 15.0  | 4815 | 0.5671          | 0.8374   | 0.8510    | 0.8374 | 0.8402 |
| 0.2892        | 16.0  | 5136 | 0.5201          | 0.8551   | 0.8591    | 0.8551 | 0.8553 |
| 0.185         | 17.0  | 5457 | 0.5161          | 0.8637   | 0.8629    | 0.8637 | 0.8619 |
| 0.2052        | 18.0  | 5778 | 0.5338          | 0.8530   | 0.8632    | 0.8530 | 0.8565 |
| 0.1444        | 19.0  | 6099 | 0.5677          | 0.8478   | 0.8655    | 0.8478 | 0.8528 |


### 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