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# vit-lr-0.01

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: nan
- Accuracy: 0.4997
- Precision: 0.4902
- Recall: 0.4997
- F1: 0.4904

## 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: 0.01
- 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
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.4629        | 1.0   | 321  | nan             | 0.4997   | 0.4902    | 0.4997 | 0.4904 |
| 0.0           | 2.0   | 642  | nan             | 0.4997   | 0.4902    | 0.4997 | 0.4904 |
| 0.0096        | 3.0   | 963  | nan             | 0.0316   | 0.0010    | 0.0316 | 0.0019 |
| 0.0           | 4.0   | 1284 | nan             | 0.0316   | 0.0010    | 0.0316 | 0.0019 |
| 0.0           | 5.0   | 1605 | nan             | 0.0316   | 0.0010    | 0.0316 | 0.0019 |
| 0.0           | 6.0   | 1926 | nan             | 0.0316   | 0.0010    | 0.0316 | 0.0019 |
| 0.0           | 7.0   | 2247 | nan             | 0.0316   | 0.0010    | 0.0316 | 0.0019 |
| 0.0           | 8.0   | 2568 | nan             | 0.0316   | 0.0010    | 0.0316 | 0.0019 |
| 0.0           | 9.0   | 2889 | nan             | 0.0316   | 0.0010    | 0.0316 | 0.0019 |
| 0.0           | 10.0  | 3210 | nan             | 0.0316   | 0.0010    | 0.0316 | 0.0019 |
| 0.0           | 11.0  | 3531 | nan             | 0.0316   | 0.0010    | 0.0316 | 0.0019 |


### Framework versions

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

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