
<!-- 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-0.4

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: 0.4924
- Accuracy: 0.8291
- Precision: 0.8250
- Recall: 0.8291
- F1: 0.8244

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.6459        | 1.0   | 321  | 0.8160          | 0.7292   | 0.7119    | 0.7292 | 0.6910 |
| 1.0644        | 2.0   | 642  | 0.6323          | 0.7514   | 0.7687    | 0.7514 | 0.7575 |
| 1.0213        | 3.0   | 963  | 0.6378          | 0.7635   | 0.7718    | 0.7635 | 0.7422 |
| 0.9727        | 4.0   | 1284 | 0.6045          | 0.7438   | 0.7972    | 0.7438 | 0.7594 |
| 0.9062        | 5.0   | 1605 | 0.5953          | 0.7902   | 0.7965    | 0.7902 | 0.7802 |
| 0.8719        | 6.0   | 1926 | 0.6095          | 0.7743   | 0.8084    | 0.7743 | 0.7839 |
| 0.7537        | 7.0   | 2247 | 0.5970          | 0.7639   | 0.8125    | 0.7639 | 0.7778 |
| 0.677         | 8.0   | 2568 | 0.7108          | 0.7074   | 0.8148    | 0.7074 | 0.7301 |
| 0.6638        | 9.0   | 2889 | 0.4924          | 0.8291   | 0.8250    | 0.8291 | 0.8244 |
| 0.5787        | 10.0  | 3210 | 0.5415          | 0.8162   | 0.8406    | 0.8162 | 0.8222 |
| 0.5373        | 11.0  | 3531 | 0.5298          | 0.8103   | 0.8409    | 0.8103 | 0.8189 |
| 0.4923        | 12.0  | 3852 | 0.5428          | 0.8117   | 0.8444    | 0.8117 | 0.8213 |
| 0.3798        | 13.0  | 4173 | 0.4968          | 0.8499   | 0.8470    | 0.8499 | 0.8467 |
| 0.3912        | 14.0  | 4494 | 0.5339          | 0.8443   | 0.8531    | 0.8443 | 0.8460 |
| 0.3002        | 15.0  | 4815 | 0.5219          | 0.8450   | 0.8548    | 0.8450 | 0.8481 |
| 0.2744        | 16.0  | 5136 | 0.6369          | 0.8204   | 0.8482    | 0.8204 | 0.8280 |
| 0.2251        | 17.0  | 5457 | 0.5156          | 0.8571   | 0.8561    | 0.8571 | 0.8556 |
| 0.2187        | 18.0  | 5778 | 0.5825          | 0.8457   | 0.8550    | 0.8457 | 0.8491 |
| 0.1767        | 19.0  | 6099 | 0.5693          | 0.8526   | 0.8605    | 0.8526 | 0.8551 |


### 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