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# vit-base-binary-isic-sharpened-patch-16

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ahishamm/isic_binary_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2583
- Accuracy: 0.8912
- Recall: 0.8912
- F1: 0.8912
- Precision: 0.8912

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.3281        | 0.09  | 100  | 0.4381          | 0.8183   | 0.8183 | 0.8183 | 0.8183    |
| 0.3212        | 0.18  | 200  | 0.3179          | 0.8503   | 0.8503 | 0.8503 | 0.8503    |
| 0.2864        | 0.28  | 300  | 0.3126          | 0.8655   | 0.8655 | 0.8655 | 0.8655    |
| 0.2692        | 0.37  | 400  | 0.3217          | 0.8599   | 0.8599 | 0.8599 | 0.8599    |
| 0.3195        | 0.46  | 500  | 0.3061          | 0.8694   | 0.8694 | 0.8694 | 0.8694    |
| 0.2095        | 0.55  | 600  | 0.2910          | 0.8669   | 0.8669 | 0.8669 | 0.8669    |
| 0.2168        | 0.65  | 700  | 0.3248          | 0.8730   | 0.8730 | 0.8730 | 0.8730    |
| 0.2288        | 0.74  | 800  | 0.3067          | 0.8553   | 0.8553 | 0.8553 | 0.8553    |
| 0.2521        | 0.83  | 900  | 0.2723          | 0.8689   | 0.8689 | 0.8689 | 0.8689    |
| 0.1953        | 0.92  | 1000 | 0.2729          | 0.8724   | 0.8724 | 0.8724 | 0.8724    |
| 0.2845        | 1.02  | 1100 | 0.4392          | 0.8666   | 0.8666 | 0.8666 | 0.8666    |
| 0.1484        | 1.11  | 1200 | 0.3031          | 0.8884   | 0.8884 | 0.8884 | 0.8884    |
| 0.153         | 1.2   | 1300 | 0.2849          | 0.8992   | 0.8992 | 0.8992 | 0.8992    |
| 0.1648        | 1.29  | 1400 | 0.2583          | 0.8912   | 0.8912 | 0.8912 | 0.8912    |
| 0.1627        | 1.39  | 1500 | 0.2706          | 0.8933   | 0.8933 | 0.8933 | 0.8933    |
| 0.0943        | 1.48  | 1600 | 0.2783          | 0.9034   | 0.9034 | 0.9034 | 0.9034    |
| 0.0624        | 1.57  | 1700 | 0.2921          | 0.8926   | 0.8926 | 0.8926 | 0.8926    |
| 0.12          | 1.66  | 1800 | 0.2915          | 0.9006   | 0.9006 | 0.9006 | 0.9006    |
| 0.0735        | 1.76  | 1900 | 0.3103          | 0.8897   | 0.8897 | 0.8897 | 0.8897    |
| 0.0609        | 1.85  | 2000 | 0.3382          | 0.8971   | 0.8971 | 0.8971 | 0.8971    |
| 0.1645        | 1.94  | 2100 | 0.2675          | 0.8901   | 0.8901 | 0.8901 | 0.8901    |
| 0.0839        | 2.03  | 2200 | 0.3941          | 0.8962   | 0.8962 | 0.8962 | 0.8962    |
| 0.0571        | 2.13  | 2300 | 0.3888          | 0.9047   | 0.9047 | 0.9047 | 0.9047    |
| 0.0929        | 2.22  | 2400 | 0.3773          | 0.9009   | 0.9009 | 0.9009 | 0.9009    |
| 0.0378        | 2.31  | 2500 | 0.4577          | 0.9029   | 0.9029 | 0.9029 | 0.9029    |
| 0.0085        | 2.4   | 2600 | 0.3183          | 0.9203   | 0.9203 | 0.9203 | 0.9203    |
| 0.06          | 2.5   | 2700 | 0.3548          | 0.9126   | 0.9126 | 0.9126 | 0.9126    |
| 0.0139        | 2.59  | 2800 | 0.3213          | 0.9198   | 0.9198 | 0.9198 | 0.9198    |
| 0.056         | 2.68  | 2900 | 0.3558          | 0.9131   | 0.9131 | 0.9131 | 0.9131    |
| 0.0433        | 2.77  | 3000 | 0.3101          | 0.9215   | 0.9215 | 0.9215 | 0.9215    |
| 0.0074        | 2.87  | 3100 | 0.3140          | 0.9176   | 0.9176 | 0.9176 | 0.9176    |
| 0.0216        | 2.96  | 3200 | 0.3657          | 0.9186   | 0.9186 | 0.9186 | 0.9186    |
| 0.0118        | 3.05  | 3300 | 0.3722          | 0.9195   | 0.9195 | 0.9195 | 0.9195    |
| 0.0014        | 3.14  | 3400 | 0.4089          | 0.9141   | 0.9141 | 0.9141 | 0.9141    |
| 0.001         | 3.23  | 3500 | 0.4045          | 0.9189   | 0.9189 | 0.9189 | 0.9189    |
| 0.0009        | 3.33  | 3600 | 0.3932          | 0.9230   | 0.9230 | 0.9230 | 0.9230    |
| 0.0009        | 3.42  | 3700 | 0.4257          | 0.9174   | 0.9174 | 0.9174 | 0.9174    |
| 0.03          | 3.51  | 3800 | 0.3981          | 0.9222   | 0.9222 | 0.9222 | 0.9222    |
| 0.0007        | 3.6   | 3900 | 0.4211          | 0.9189   | 0.9189 | 0.9189 | 0.9189    |
| 0.0494        | 3.7   | 4000 | 0.4029          | 0.9207   | 0.9207 | 0.9207 | 0.9207    |
| 0.0009        | 3.79  | 4100 | 0.3951          | 0.9226   | 0.9226 | 0.9226 | 0.9226    |
| 0.0319        | 3.88  | 4200 | 0.3944          | 0.9221   | 0.9221 | 0.9221 | 0.9221    |
| 0.0013        | 3.97  | 4300 | 0.3894          | 0.9225   | 0.9225 | 0.9225 | 0.9225    |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3

benign malignant