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# vit-beta2-0.995

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: 0.5119
- Accuracy: 0.8076
- Precision: 0.8207
- Recall: 0.8076
- F1: 0.8098

## 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=(0.9,0.995) 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.7709        | 1.0   | 321  | 0.9409          | 0.7039   | 0.6601    | 0.7039 | 0.6317 |
| 1.1633        | 2.0   | 642  | 0.7317          | 0.7372   | 0.7193    | 0.7372 | 0.6970 |
| 1.0429        | 3.0   | 963  | 0.6350          | 0.7625   | 0.7357    | 0.7625 | 0.7240 |
| 0.9649        | 4.0   | 1284 | 0.5760          | 0.7694   | 0.8038    | 0.7694 | 0.7808 |
| 0.9051        | 5.0   | 1605 | 0.6441          | 0.7670   | 0.7941    | 0.7670 | 0.7732 |
| 0.9826        | 6.0   | 1926 | 0.5662          | 0.7850   | 0.7956    | 0.7850 | 0.7892 |
| 0.8855        | 7.0   | 2247 | 0.6882          | 0.7264   | 0.7937    | 0.7264 | 0.7459 |
| 0.789         | 8.0   | 2568 | 0.6491          | 0.7365   | 0.8089    | 0.7365 | 0.7564 |
| 0.7192        | 9.0   | 2889 | 0.5119          | 0.8076   | 0.8207    | 0.8076 | 0.8098 |
| 0.7012        | 10.0  | 3210 | 0.5414          | 0.7975   | 0.8341    | 0.7975 | 0.8077 |
| 0.6376        | 11.0  | 3531 | 0.5712          | 0.7947   | 0.8332    | 0.7947 | 0.8066 |
| 0.5412        | 12.0  | 3852 | 0.5661          | 0.8058   | 0.8328    | 0.8058 | 0.8145 |
| 0.4667        | 13.0  | 4173 | 0.6375          | 0.8190   | 0.8410    | 0.8190 | 0.8179 |
| 0.4766        | 14.0  | 4494 | 0.5736          | 0.8252   | 0.8508    | 0.8252 | 0.8313 |
| 0.384         | 15.0  | 4815 | 0.5305          | 0.8356   | 0.8415    | 0.8356 | 0.8371 |
| 0.37          | 16.0  | 5136 | 0.5531          | 0.8315   | 0.8499    | 0.8315 | 0.8379 |
| 0.2809        | 17.0  | 5457 | 0.5174          | 0.8637   | 0.8630    | 0.8637 | 0.8608 |
| 0.2681        | 18.0  | 5778 | 0.5556          | 0.8478   | 0.8555    | 0.8478 | 0.8504 |
| 0.2139        | 19.0  | 6099 | 0.6291          | 0.8256   | 0.8518    | 0.8256 | 0.8335 |


### Framework versions

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

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