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# vit-base-skin

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6917
- Accuracy: 0.8549
- F1: 0.8552
- Precision: 0.8560
- Recall: 0.8549

## 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: 0.0002
- 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: linear
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.9322        | 0.16  | 100  | 0.8109          | 0.6943   | 0.6290 | 0.5939    | 0.6943 |
| 0.7518        | 0.32  | 200  | 0.6722          | 0.7409   | 0.6832 | 0.6945    | 0.7409 |
| 0.6616        | 0.48  | 300  | 0.7126          | 0.7358   | 0.7077 | 0.7039    | 0.7358 |
| 0.8264        | 0.64  | 400  | 0.6001          | 0.8135   | 0.8092 | 0.8178    | 0.8135 |
| 0.5767        | 0.8   | 500  | 0.6306          | 0.7772   | 0.7619 | 0.7945    | 0.7772 |
| 0.5939        | 0.96  | 600  | 0.4621          | 0.8290   | 0.7988 | 0.8397    | 0.8290 |
| 0.4351        | 1.12  | 700  | 0.5544          | 0.7979   | 0.7894 | 0.8410    | 0.7979 |
| 0.4737        | 1.28  | 800  | 0.5151          | 0.8238   | 0.8334 | 0.8708    | 0.8238 |
| 0.428         | 1.44  | 900  | 0.4980          | 0.8238   | 0.8170 | 0.8299    | 0.8238 |
| 0.4596        | 1.6   | 1000 | 0.5650          | 0.7927   | 0.8032 | 0.8428    | 0.7927 |
| 0.4096        | 1.76  | 1100 | 0.4544          | 0.8342   | 0.8178 | 0.8567    | 0.8342 |
| 0.4328        | 1.92  | 1200 | 0.4524          | 0.8290   | 0.8294 | 0.8482    | 0.8290 |
| 0.2272        | 2.08  | 1300 | 0.4808          | 0.8290   | 0.8304 | 0.8409    | 0.8290 |
| 0.2415        | 2.24  | 1400 | 0.5585          | 0.7927   | 0.7916 | 0.8057    | 0.7927 |
| 0.2743        | 2.4   | 1500 | 0.4144          | 0.8497   | 0.8484 | 0.8497    | 0.8497 |
| 0.1943        | 2.56  | 1600 | 0.3977          | 0.8705   | 0.8722 | 0.8761    | 0.8705 |
| 0.1839        | 2.72  | 1700 | 0.4784          | 0.8394   | 0.8382 | 0.8517    | 0.8394 |
| 0.1905        | 2.88  | 1800 | 0.4314          | 0.8653   | 0.8669 | 0.8724    | 0.8653 |
| 0.111         | 3.04  | 1900 | 0.5080          | 0.8290   | 0.8309 | 0.8348    | 0.8290 |
| 0.0872        | 3.19  | 2000 | 0.5320          | 0.8549   | 0.8520 | 0.8649    | 0.8549 |
| 0.1169        | 3.35  | 2100 | 0.5110          | 0.8342   | 0.8386 | 0.8477    | 0.8342 |
| 0.1181        | 3.51  | 2200 | 0.4916          | 0.8446   | 0.8482 | 0.8563    | 0.8446 |
| 0.0879        | 3.67  | 2300 | 0.5428          | 0.8601   | 0.8657 | 0.8829    | 0.8601 |
| 0.1896        | 3.83  | 2400 | 0.5534          | 0.8497   | 0.8484 | 0.8536    | 0.8497 |
| 0.0794        | 3.99  | 2500 | 0.6542          | 0.8342   | 0.8259 | 0.8270    | 0.8342 |
| 0.0398        | 4.15  | 2600 | 0.5962          | 0.8187   | 0.8243 | 0.8338    | 0.8187 |
| 0.0512        | 4.31  | 2700 | 0.6286          | 0.8497   | 0.8447 | 0.8457    | 0.8497 |
| 0.0106        | 4.47  | 2800 | 0.6446          | 0.8394   | 0.8372 | 0.8377    | 0.8394 |
| 0.0058        | 4.63  | 2900 | 0.5754          | 0.8653   | 0.8616 | 0.8618    | 0.8653 |
| 0.0268        | 4.79  | 3000 | 0.5966          | 0.8653   | 0.8651 | 0.8658    | 0.8653 |
| 0.0146        | 4.95  | 3100 | 0.6707          | 0.8601   | 0.8535 | 0.8577    | 0.8601 |
| 0.0325        | 5.11  | 3200 | 0.6543          | 0.8549   | 0.8518 | 0.8511    | 0.8549 |
| 0.0063        | 5.27  | 3300 | 0.6780          | 0.8497   | 0.8519 | 0.8583    | 0.8497 |
| 0.003         | 5.43  | 3400 | 0.6675          | 0.8601   | 0.8577 | 0.8562    | 0.8601 |
| 0.0143        | 5.59  | 3500 | 0.6967          | 0.8601   | 0.8554 | 0.8539    | 0.8601 |
| 0.004         | 5.75  | 3600 | 0.6992          | 0.8601   | 0.8573 | 0.8552    | 0.8601 |
| 0.003         | 5.91  | 3700 | 0.6917          | 0.8549   | 0.8552 | 0.8560    | 0.8549 |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.13.1
- Datasets 2.14.5
- Tokenizers 0.13.3

mel nv bcc akiec bkl df vasc