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# vit-lr-cosine-restarts

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: 0.5117
- Accuracy: 0.8221
- Precision: 0.8347
- Recall: 0.8221
- F1: 0.8100

## 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_with_restarts
- lr_scheduler_warmup_steps: 770
- num_epochs: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5999        | 1.0   | 321  | 0.5250          | 0.8141   | 0.8100    | 0.8141 | 0.8011 |
| 0.4483        | 2.0   | 642  | 0.5117          | 0.8221   | 0.8347    | 0.8221 | 0.8100 |
| 0.3425        | 3.0   | 963  | 0.5709          | 0.8270   | 0.8208    | 0.8270 | 0.8107 |
| 0.176         | 4.0   | 1284 | 0.5579          | 0.8575   | 0.8549    | 0.8575 | 0.8544 |
| 0.0183        | 5.0   | 1605 | 0.5555          | 0.8773   | 0.8762    | 0.8773 | 0.8759 |
| 0.0273        | 6.0   | 1926 | 0.8166          | 0.8415   | 0.8369    | 0.8415 | 0.8328 |
| 0.1186        | 7.0   | 2247 | 0.6380          | 0.8617   | 0.8598    | 0.8617 | 0.8591 |
| 0.0166        | 8.0   | 2568 | 0.6608          | 0.8731   | 0.8716    | 0.8731 | 0.8709 |
| 0.0003        | 9.0   | 2889 | 1.0045          | 0.8460   | 0.8438    | 0.8460 | 0.8306 |
| 0.1258        | 10.0  | 3210 | 0.6712          | 0.8499   | 0.8527    | 0.8499 | 0.8490 |
| 0.0137        | 11.0  | 3531 | 0.7952          | 0.8738   | 0.8765    | 0.8738 | 0.8740 |
| 0.0004        | 12.0  | 3852 | 0.7956          | 0.8731   | 0.8708    | 0.8731 | 0.8699 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

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