
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-base-16-thesis-demo-ISIC-binary-class

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__enhanced_balanced dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1585
- Accuracy: 0.9458
- Recall: 0.9458
- F1: 0.9458
- Precision: 0.9458

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.4477        | 0.27  | 50   | 0.5296          | 0.7444   | 0.7444 | 0.7444 | 0.7444    |
| 0.3011        | 0.54  | 100  | 0.3341          | 0.8722   | 0.8722 | 0.8722 | 0.8722    |
| 0.3254        | 0.82  | 150  | 0.3987          | 0.7903   | 0.7903 | 0.7903 | 0.7903    |
| 0.2986        | 1.09  | 200  | 0.3169          | 0.8694   | 0.8694 | 0.8694 | 0.8694    |
| 0.3341        | 1.36  | 250  | 0.3114          | 0.8597   | 0.8597 | 0.8597 | 0.8597    |
| 0.2659        | 1.63  | 300  | 0.2607          | 0.8833   | 0.8833 | 0.8833 | 0.8833    |
| 0.1987        | 1.9   | 350  | 0.2100          | 0.9111   | 0.9111 | 0.9111 | 0.9111    |
| 0.3515        | 2.17  | 400  | 0.2369          | 0.9097   | 0.9097 | 0.9097 | 0.9097    |
| 0.1316        | 2.45  | 450  | 0.2714          | 0.9014   | 0.9014 | 0.9014 | 0.9014    |
| 0.2213        | 2.72  | 500  | 0.1714          | 0.9361   | 0.9361 | 0.9361 | 0.9361    |
| 0.1115        | 2.99  | 550  | 0.1585          | 0.9458   | 0.9458 | 0.9458 | 0.9458    |
| 0.0377        | 3.26  | 600  | 0.2058          | 0.9403   | 0.9403 | 0.9403 | 0.9403    |
| 0.0779        | 3.53  | 650  | 0.2187          | 0.9375   | 0.9375 | 0.9375 | 0.9375    |
| 0.0444        | 3.8   | 700  | 0.2140          | 0.9389   | 0.9389 | 0.9389 | 0.9389    |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

benign malignant