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# vit-large-binary-isic-sharpened-patch-32

This model is a fine-tuned version of [google/vit-large-patch32-224-in21k](https://huggingface.co/google/vit-large-patch32-224-in21k) on the ahishamm/isic_binary_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2092
- Accuracy: 0.9202
- Recall: 0.9202
- F1: 0.9202
- Precision: 0.9202

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.3437        | 0.09  | 100  | 0.3367          | 0.8412   | 0.8412 | 0.8412 | 0.8412    |
| 0.3702        | 0.18  | 200  | 0.3094          | 0.8585   | 0.8585 | 0.8585 | 0.8585    |
| 0.2693        | 0.28  | 300  | 0.4361          | 0.8007   | 0.8007 | 0.8007 | 0.8007    |
| 0.3183        | 0.37  | 400  | 0.2955          | 0.8643   | 0.8643 | 0.8643 | 0.8643    |
| 0.2688        | 0.46  | 500  | 0.3064          | 0.8603   | 0.8603 | 0.8603 | 0.8603    |
| 0.2507        | 0.55  | 600  | 0.3556          | 0.8329   | 0.8329 | 0.8329 | 0.8329    |
| 0.203         | 0.65  | 700  | 0.3134          | 0.8433   | 0.8433 | 0.8433 | 0.8433    |
| 0.2315        | 0.74  | 800  | 0.2525          | 0.8856   | 0.8856 | 0.8856 | 0.8856    |
| 0.3527        | 0.83  | 900  | 0.2815          | 0.8731   | 0.8731 | 0.8731 | 0.8731    |
| 0.292         | 0.92  | 1000 | 0.3879          | 0.8534   | 0.8534 | 0.8534 | 0.8534    |
| 0.1342        | 1.02  | 1100 | 0.2927          | 0.8874   | 0.8874 | 0.8874 | 0.8874    |
| 0.1571        | 1.11  | 1200 | 0.2560          | 0.8912   | 0.8912 | 0.8912 | 0.8912    |
| 0.1787        | 1.2   | 1300 | 0.3245          | 0.8789   | 0.8789 | 0.8789 | 0.8789    |
| 0.1757        | 1.29  | 1400 | 0.3308          | 0.8720   | 0.8720 | 0.8720 | 0.8720    |
| 0.1867        | 1.39  | 1500 | 0.2716          | 0.8876   | 0.8876 | 0.8876 | 0.8876    |
| 0.124         | 1.48  | 1600 | 0.3663          | 0.8744   | 0.8744 | 0.8744 | 0.8744    |
| 0.082         | 1.57  | 1700 | 0.2793          | 0.9034   | 0.9034 | 0.9034 | 0.9034    |
| 0.1365        | 1.66  | 1800 | 0.2399          | 0.9077   | 0.9077 | 0.9077 | 0.9077    |
| 0.0998        | 1.76  | 1900 | 0.3361          | 0.8901   | 0.8901 | 0.8901 | 0.8901    |
| 0.0748        | 1.85  | 2000 | 0.3239          | 0.8960   | 0.8960 | 0.8960 | 0.8960    |
| 0.1163        | 1.94  | 2100 | 0.2092          | 0.9202   | 0.9202 | 0.9202 | 0.9202    |
| 0.0604        | 2.03  | 2200 | 0.3056          | 0.9139   | 0.9139 | 0.9139 | 0.9139    |
| 0.0792        | 2.13  | 2300 | 0.2880          | 0.9071   | 0.9071 | 0.9071 | 0.9071    |
| 0.0749        | 2.22  | 2400 | 0.3015          | 0.9070   | 0.9070 | 0.9070 | 0.9070    |
| 0.0032        | 2.31  | 2500 | 0.3685          | 0.9090   | 0.9090 | 0.9090 | 0.9090    |
| 0.1038        | 2.4   | 2600 | 0.3539          | 0.9075   | 0.9075 | 0.9075 | 0.9075    |
| 0.0474        | 2.5   | 2700 | 0.3220          | 0.9152   | 0.9152 | 0.9152 | 0.9152    |
| 0.0376        | 2.59  | 2800 | 0.2926          | 0.9203   | 0.9203 | 0.9203 | 0.9203    |
| 0.0424        | 2.68  | 2900 | 0.3463          | 0.9065   | 0.9065 | 0.9065 | 0.9065    |
| 0.0408        | 2.77  | 3000 | 0.2772          | 0.9263   | 0.9263 | 0.9263 | 0.9263    |
| 0.0467        | 2.87  | 3100 | 0.2963          | 0.9227   | 0.9227 | 0.9227 | 0.9227    |
| 0.0083        | 2.96  | 3200 | 0.2971          | 0.9203   | 0.9203 | 0.9203 | 0.9203    |
| 0.0165        | 3.05  | 3300 | 0.3162          | 0.9257   | 0.9257 | 0.9257 | 0.9257    |
| 0.0023        | 3.14  | 3400 | 0.3147          | 0.9267   | 0.9267 | 0.9267 | 0.9267    |
| 0.0009        | 3.23  | 3500 | 0.3433          | 0.9266   | 0.9266 | 0.9266 | 0.9266    |
| 0.0007        | 3.33  | 3600 | 0.3216          | 0.9312   | 0.9312 | 0.9312 | 0.9312    |
| 0.0011        | 3.42  | 3700 | 0.3209          | 0.9346   | 0.9346 | 0.9346 | 0.9346    |
| 0.0029        | 3.51  | 3800 | 0.3236          | 0.9325   | 0.9325 | 0.9325 | 0.9325    |
| 0.0011        | 3.6   | 3900 | 0.3297          | 0.9302   | 0.9302 | 0.9302 | 0.9302    |
| 0.0225        | 3.7   | 4000 | 0.3263          | 0.9323   | 0.9323 | 0.9323 | 0.9323    |
| 0.0008        | 3.79  | 4100 | 0.3352          | 0.9311   | 0.9311 | 0.9311 | 0.9311    |
| 0.0391        | 3.88  | 4200 | 0.3343          | 0.9282   | 0.9282 | 0.9282 | 0.9282    |
| 0.0019        | 3.97  | 4300 | 0.3319          | 0.9280   | 0.9280 | 0.9280 | 0.9280    |


### Framework versions

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

benign malignant