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# vit-large-binary-isic-sharpened-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_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2934
- Accuracy: 0.8585
- Recall: 0.8585
- F1: 0.8585
- Precision: 0.8585

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.5213        | 0.09  | 100  | 0.4459          | 0.7638   | 0.7638 | 0.7638 | 0.7638    |
| 0.4388        | 0.18  | 200  | 0.5329          | 0.7869   | 0.7869 | 0.7869 | 0.7869    |
| 0.4157        | 0.28  | 300  | 0.4438          | 0.7713   | 0.7713 | 0.7713 | 0.7713    |
| 0.4578        | 0.37  | 400  | 0.4327          | 0.7652   | 0.7652 | 0.7652 | 0.7652    |
| 0.4322        | 0.46  | 500  | 0.4179          | 0.7897   | 0.7897 | 0.7897 | 0.7897    |
| 0.4258        | 0.55  | 600  | 0.4319          | 0.7979   | 0.7979 | 0.7979 | 0.7979    |
| 0.3156        | 0.65  | 700  | 0.4470          | 0.7729   | 0.7729 | 0.7729 | 0.7729    |
| 0.449         | 0.74  | 800  | 0.4223          | 0.8036   | 0.8036 | 0.8036 | 0.8036    |
| 0.464         | 0.83  | 900  | 0.4304          | 0.7814   | 0.7814 | 0.7814 | 0.7814    |
| 0.2522        | 0.92  | 1000 | 0.4755          | 0.8069   | 0.8069 | 0.8069 | 0.8069    |
| 0.3268        | 1.02  | 1100 | 0.3678          | 0.8119   | 0.8119 | 0.8119 | 0.8119    |
| 0.3374        | 1.11  | 1200 | 0.3609          | 0.8324   | 0.8324 | 0.8324 | 0.8324    |
| 0.3814        | 1.2   | 1300 | 0.3524          | 0.8393   | 0.8393 | 0.8393 | 0.8393    |
| 0.4162        | 1.29  | 1400 | 0.3600          | 0.8314   | 0.8314 | 0.8314 | 0.8314    |
| 0.3096        | 1.39  | 1500 | 0.3537          | 0.8405   | 0.8405 | 0.8405 | 0.8405    |
| 0.285         | 1.48  | 1600 | 0.3812          | 0.8234   | 0.8234 | 0.8234 | 0.8234    |
| 0.3039        | 1.57  | 1700 | 0.4491          | 0.8259   | 0.8259 | 0.8259 | 0.8259    |
| 0.3026        | 1.66  | 1800 | 0.3793          | 0.8155   | 0.8155 | 0.8155 | 0.8155    |
| 0.2304        | 1.76  | 1900 | 0.3488          | 0.8175   | 0.8175 | 0.8175 | 0.8175    |
| 0.2454        | 1.85  | 2000 | 0.3442          | 0.8357   | 0.8357 | 0.8357 | 0.8357    |
| 0.314         | 1.94  | 2100 | 0.3470          | 0.8370   | 0.8370 | 0.8370 | 0.8370    |
| 0.3015        | 2.03  | 2200 | 0.3263          | 0.8501   | 0.8501 | 0.8501 | 0.8501    |
| 0.2595        | 2.13  | 2300 | 0.3540          | 0.8425   | 0.8425 | 0.8425 | 0.8425    |
| 0.2901        | 2.22  | 2400 | 0.3567          | 0.8578   | 0.8578 | 0.8578 | 0.8578    |
| 0.1825        | 2.31  | 2500 | 0.2934          | 0.8585   | 0.8585 | 0.8585 | 0.8585    |
| 0.2558        | 2.4   | 2600 | 0.3281          | 0.8378   | 0.8378 | 0.8378 | 0.8378    |
| 0.2553        | 2.5   | 2700 | 0.3869          | 0.8306   | 0.8306 | 0.8306 | 0.8306    |
| 0.1911        | 2.59  | 2800 | 0.3586          | 0.8341   | 0.8341 | 0.8341 | 0.8341    |
| 0.1705        | 2.68  | 2900 | 0.3363          | 0.8576   | 0.8576 | 0.8576 | 0.8576    |
| 0.2686        | 2.77  | 3000 | 0.3378          | 0.8535   | 0.8535 | 0.8535 | 0.8535    |
| 0.2136        | 2.87  | 3100 | 0.3312          | 0.8676   | 0.8676 | 0.8676 | 0.8676    |
| 0.1913        | 2.96  | 3200 | 0.3305          | 0.8560   | 0.8560 | 0.8560 | 0.8560    |
| 0.3307        | 3.05  | 3300 | 0.3613          | 0.8675   | 0.8675 | 0.8675 | 0.8675    |
| 0.2204        | 3.14  | 3400 | 0.3567          | 0.8652   | 0.8652 | 0.8652 | 0.8652    |
| 0.2149        | 3.23  | 3500 | 0.3178          | 0.8706   | 0.8706 | 0.8706 | 0.8706    |
| 0.1389        | 3.33  | 3600 | 0.3123          | 0.8706   | 0.8706 | 0.8706 | 0.8706    |
| 0.1567        | 3.42  | 3700 | 0.3374          | 0.8669   | 0.8669 | 0.8669 | 0.8669    |
| 0.1871        | 3.51  | 3800 | 0.3450          | 0.8701   | 0.8701 | 0.8701 | 0.8701    |
| 0.1616        | 3.6   | 3900 | 0.3870          | 0.8608   | 0.8608 | 0.8608 | 0.8608    |
| 0.1582        | 3.7   | 4000 | 0.3490          | 0.8656   | 0.8656 | 0.8656 | 0.8656    |
| 0.1199        | 3.79  | 4100 | 0.3408          | 0.8684   | 0.8684 | 0.8684 | 0.8684    |
| 0.1563        | 3.88  | 4200 | 0.3498          | 0.8669   | 0.8669 | 0.8669 | 0.8669    |
| 0.1544        | 3.97  | 4300 | 0.3398          | 0.8708   | 0.8708 | 0.8708 | 0.8708    |


### Framework versions

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

benign malignant