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# vit-large-binary-isic-patch-16

This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the ahishamm/isic_binary_augmented dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2719
- Accuracy: 0.8742
- Recall: 0.8742
- F1: 0.8742
- Precision: 0.8742

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.5596        | 0.09  | 100  | 0.4728          | 0.7781   | 0.7781 | 0.7781 | 0.7781    |
| 0.3373        | 0.19  | 200  | 0.3594          | 0.8266   | 0.8266 | 0.8266 | 0.8266    |
| 0.397         | 0.28  | 300  | 0.5284          | 0.7695   | 0.7695 | 0.7695 | 0.7695    |
| 0.3913        | 0.37  | 400  | 0.3315          | 0.8384   | 0.8384 | 0.8384 | 0.8384    |
| 0.3147        | 0.46  | 500  | 0.4425          | 0.7778   | 0.7778 | 0.7778 | 0.7778    |
| 0.2709        | 0.56  | 600  | 0.3787          | 0.8352   | 0.8352 | 0.8352 | 0.8352    |
| 0.4062        | 0.65  | 700  | 0.3613          | 0.8193   | 0.8193 | 0.8193 | 0.8193    |
| 0.3047        | 0.74  | 800  | 0.3086          | 0.8480   | 0.8480 | 0.8480 | 0.8480    |
| 0.3542        | 0.84  | 900  | 0.3232          | 0.8620   | 0.8620 | 0.8620 | 0.8620    |
| 0.2096        | 0.93  | 1000 | 0.2981          | 0.8734   | 0.8734 | 0.8734 | 0.8734    |
| 0.2214        | 1.02  | 1100 | 0.3148          | 0.8623   | 0.8623 | 0.8623 | 0.8623    |
| 0.2646        | 1.12  | 1200 | 0.3193          | 0.8592   | 0.8592 | 0.8592 | 0.8592    |
| 0.2464        | 1.21  | 1300 | 0.4324          | 0.8347   | 0.8347 | 0.8347 | 0.8347    |
| 0.2769        | 1.3   | 1400 | 0.2832          | 0.8716   | 0.8716 | 0.8716 | 0.8716    |
| 0.2726        | 1.39  | 1500 | 0.2838          | 0.8705   | 0.8705 | 0.8705 | 0.8705    |
| 0.3334        | 1.49  | 1600 | 0.3292          | 0.8494   | 0.8494 | 0.8494 | 0.8494    |
| 0.2172        | 1.58  | 1700 | 0.3023          | 0.8635   | 0.8635 | 0.8635 | 0.8635    |
| 0.2382        | 1.67  | 1800 | 0.3191          | 0.8514   | 0.8514 | 0.8514 | 0.8514    |
| 0.1616        | 1.77  | 1900 | 0.3044          | 0.8875   | 0.8875 | 0.8875 | 0.8875    |
| 0.1527        | 1.86  | 2000 | 0.2963          | 0.8789   | 0.8789 | 0.8789 | 0.8789    |
| 0.2123        | 1.95  | 2100 | 0.2719          | 0.8742   | 0.8742 | 0.8742 | 0.8742    |
| 0.1489        | 2.04  | 2200 | 0.3445          | 0.8605   | 0.8605 | 0.8605 | 0.8605    |
| 0.2052        | 2.14  | 2300 | 0.3297          | 0.8799   | 0.8799 | 0.8799 | 0.8799    |
| 0.172         | 2.23  | 2400 | 0.3089          | 0.8834   | 0.8834 | 0.8834 | 0.8834    |
| 0.1167        | 2.32  | 2500 | 0.2973          | 0.8763   | 0.8763 | 0.8763 | 0.8763    |
| 0.0705        | 2.42  | 2600 | 0.3585          | 0.8912   | 0.8912 | 0.8912 | 0.8912    |
| 0.212         | 2.51  | 2700 | 0.4051          | 0.8671   | 0.8671 | 0.8671 | 0.8671    |
| 0.2053        | 2.6   | 2800 | 0.3088          | 0.8911   | 0.8911 | 0.8911 | 0.8911    |
| 0.0718        | 2.7   | 2900 | 0.3223          | 0.8894   | 0.8894 | 0.8894 | 0.8894    |
| 0.0648        | 2.79  | 3000 | 0.3427          | 0.8776   | 0.8776 | 0.8776 | 0.8776    |
| 0.0889        | 2.88  | 3100 | 0.3504          | 0.8880   | 0.8880 | 0.8880 | 0.8880    |
| 0.098         | 2.97  | 3200 | 0.3520          | 0.8770   | 0.8770 | 0.8770 | 0.8770    |
| 0.1231        | 3.07  | 3300 | 0.4712          | 0.8799   | 0.8799 | 0.8799 | 0.8799    |
| 0.0598        | 3.16  | 3400 | 0.4759          | 0.8779   | 0.8779 | 0.8779 | 0.8779    |
| 0.0558        | 3.25  | 3500 | 0.4180          | 0.8798   | 0.8798 | 0.8798 | 0.8798    |
| 0.0595        | 3.35  | 3600 | 0.5600          | 0.8865   | 0.8865 | 0.8865 | 0.8865    |
| 0.0796        | 3.44  | 3700 | 0.4691          | 0.8922   | 0.8922 | 0.8922 | 0.8922    |
| 0.0122        | 3.53  | 3800 | 0.4117          | 0.8935   | 0.8935 | 0.8935 | 0.8935    |
| 0.0633        | 3.62  | 3900 | 0.4275          | 0.8957   | 0.8957 | 0.8957 | 0.8957    |
| 0.0659        | 3.72  | 4000 | 0.4218          | 0.8936   | 0.8936 | 0.8936 | 0.8936    |
| 0.0155        | 3.81  | 4100 | 0.4189          | 0.8981   | 0.8981 | 0.8981 | 0.8981    |
| 0.0296        | 3.9   | 4200 | 0.4444          | 0.8974   | 0.8974 | 0.8974 | 0.8974    |
| 0.0703        | 4.0   | 4300 | 0.4499          | 0.8983   | 0.8983 | 0.8983 | 0.8983    |


### Framework versions

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

benign malignant