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# vit-class-weight

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: 0.4472
- Accuracy: 0.8478
- Precision: 0.8582
- Recall: 0.8478
- F1: 0.8483

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.5485        | 1.0   | 321  | 0.8743          | 0.6813   | 0.7810    | 0.6813 | 0.7087 |
| 0.9628        | 2.0   | 642  | 0.7893          | 0.6907   | 0.7945    | 0.6907 | 0.7178 |
| 0.8902        | 3.0   | 963  | 0.5577          | 0.7926   | 0.7956    | 0.7926 | 0.7835 |
| 0.8477        | 4.0   | 1284 | 0.5734          | 0.7611   | 0.8190    | 0.7611 | 0.7770 |
| 0.7773        | 5.0   | 1605 | 0.6590          | 0.7431   | 0.8052    | 0.7431 | 0.7590 |
| 0.6953        | 6.0   | 1926 | 0.5321          | 0.8100   | 0.8298    | 0.8100 | 0.8167 |
| 0.6122        | 7.0   | 2247 | 0.5331          | 0.8044   | 0.8280    | 0.8044 | 0.8093 |
| 0.5548        | 8.0   | 2568 | 0.6589          | 0.7649   | 0.8313    | 0.7649 | 0.7832 |
| 0.512         | 9.0   | 2889 | 0.4548          | 0.8395   | 0.8445    | 0.8395 | 0.8402 |
| 0.449         | 10.0  | 3210 | 0.4472          | 0.8478   | 0.8582    | 0.8478 | 0.8483 |
| 0.4012        | 11.0  | 3531 | 0.5304          | 0.8287   | 0.8509    | 0.8287 | 0.8353 |
| 0.3584        | 12.0  | 3852 | 0.5620          | 0.8454   | 0.8576    | 0.8454 | 0.8468 |
| 0.2829        | 13.0  | 4173 | 0.6837          | 0.8436   | 0.8490    | 0.8436 | 0.8359 |
| 0.2761        | 14.0  | 4494 | 0.6061          | 0.8509   | 0.8643    | 0.8509 | 0.8541 |
| 0.2192        | 15.0  | 4815 | 0.5223          | 0.8637   | 0.8662    | 0.8637 | 0.8639 |
| 0.1755        | 16.0  | 5136 | 0.5640          | 0.8558   | 0.8684    | 0.8558 | 0.8591 |
| 0.1568        | 17.0  | 5457 | 0.5585          | 0.8682   | 0.8736    | 0.8682 | 0.8695 |
| 0.1674        | 18.0  | 5778 | 0.5645          | 0.8724   | 0.8735    | 0.8724 | 0.8707 |
| 0.1022        | 19.0  | 6099 | 0.5931          | 0.8745   | 0.8740    | 0.8745 | 0.8737 |
| 0.1487        | 20.0  | 6420 | 0.6107          | 0.8717   | 0.8736    | 0.8717 | 0.8722 |


### Framework versions

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

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