
<!-- 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-beta1-0.88

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: 0.4922
- Accuracy: 0.8523
- Precision: 0.8564
- Recall: 0.8523
- F1: 0.8539

## 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=(0.88,0.999) and epsilon=1e-08
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.713         | 1.0   | 321  | 0.9541          | 0.6987   | 0.6717    | 0.6987 | 0.6282 |
| 1.1578        | 2.0   | 642  | 0.7858          | 0.6956   | 0.7680    | 0.6956 | 0.7157 |
| 1.035         | 3.0   | 963  | 0.7133          | 0.7282   | 0.7631    | 0.7282 | 0.6990 |
| 0.9749        | 4.0   | 1284 | 0.5555          | 0.7712   | 0.7960    | 0.7712 | 0.7759 |
| 0.9392        | 5.0   | 1605 | 0.6676          | 0.7642   | 0.7943    | 0.7642 | 0.7721 |
| 0.992         | 6.0   | 1926 | 0.6117          | 0.7725   | 0.7958    | 0.7725 | 0.7767 |
| 0.8857        | 7.0   | 2247 | 0.6220          | 0.7562   | 0.7969    | 0.7562 | 0.7664 |
| 0.7885        | 8.0   | 2568 | 0.7200          | 0.7042   | 0.8134    | 0.7042 | 0.7319 |
| 0.7404        | 9.0   | 2889 | 0.5796          | 0.7798   | 0.7973    | 0.7798 | 0.7801 |
| 0.6687        | 10.0  | 3210 | 0.5593          | 0.7757   | 0.8340    | 0.7757 | 0.7910 |
| 0.6173        | 11.0  | 3531 | 0.5573          | 0.8017   | 0.8328    | 0.8017 | 0.8090 |
| 0.5531        | 12.0  | 3852 | 0.5931          | 0.7902   | 0.8270    | 0.7902 | 0.8024 |
| 0.4717        | 13.0  | 4173 | 0.6534          | 0.8221   | 0.8265    | 0.8221 | 0.8126 |
| 0.4553        | 14.0  | 4494 | 0.5501          | 0.8329   | 0.8489    | 0.8329 | 0.8367 |
| 0.3776        | 15.0  | 4815 | 0.6037          | 0.8193   | 0.8392    | 0.8193 | 0.8242 |
| 0.3435        | 16.0  | 5136 | 0.6238          | 0.8093   | 0.8367    | 0.8093 | 0.8176 |
| 0.2983        | 17.0  | 5457 | 0.4922          | 0.8523   | 0.8564    | 0.8523 | 0.8539 |
| 0.259         | 18.0  | 5778 | 0.5820          | 0.8356   | 0.8512    | 0.8356 | 0.8406 |
| 0.2105        | 19.0  | 6099 | 0.6471          | 0.8270   | 0.8536    | 0.8270 | 0.8349 |
| 0.2268        | 20.0  | 6420 | 0.6060          | 0.8519   | 0.8551    | 0.8519 | 0.8527 |
| 0.155         | 21.0  | 6741 | 0.5968          | 0.8693   | 0.8649    | 0.8693 | 0.8663 |
| 0.1226        | 22.0  | 7062 | 0.6047          | 0.8682   | 0.8661    | 0.8682 | 0.8668 |
| 0.1267        | 23.0  | 7383 | 0.6395          | 0.8682   | 0.8617    | 0.8682 | 0.8629 |
| 0.1184        | 24.0  | 7704 | 0.6441          | 0.8693   | 0.8660    | 0.8693 | 0.8660 |
| 0.1034        | 25.0  | 8025 | 0.6301          | 0.8689   | 0.8671    | 0.8689 | 0.8672 |
| 0.0907        | 26.0  | 8346 | 0.6288          | 0.8717   | 0.8674    | 0.8717 | 0.8687 |
| 0.1358        | 27.0  | 8667 | 0.6250          | 0.8727   | 0.8682    | 0.8727 | 0.8698 |


### 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