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# vit-base-binary-isic-patch-32

This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the ahishamm/isic_binary_augmented dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2618
- Accuracy: 0.8897
- Recall: 0.8897
- F1: 0.8897
- Precision: 0.8897

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.4617        | 0.09  | 100  | 0.4375          | 0.8083   | 0.8083 | 0.8083 | 0.8083    |
| 0.28          | 0.19  | 200  | 0.3352          | 0.8575   | 0.8575 | 0.8575 | 0.8575    |
| 0.2684        | 0.28  | 300  | 0.3081          | 0.8521   | 0.8521 | 0.8521 | 0.8521    |
| 0.2755        | 0.37  | 400  | 0.3489          | 0.8433   | 0.8433 | 0.8433 | 0.8433    |
| 0.2683        | 0.46  | 500  | 0.3533          | 0.8120   | 0.8120 | 0.8120 | 0.8120    |
| 0.2013        | 0.56  | 600  | 0.3404          | 0.8683   | 0.8683 | 0.8683 | 0.8683    |
| 0.2892        | 0.65  | 700  | 0.2950          | 0.8688   | 0.8688 | 0.8688 | 0.8688    |
| 0.2326        | 0.74  | 800  | 0.2858          | 0.8875   | 0.8875 | 0.8875 | 0.8875    |
| 0.2642        | 0.84  | 900  | 0.3176          | 0.8666   | 0.8666 | 0.8666 | 0.8666    |
| 0.1676        | 0.93  | 1000 | 0.3049          | 0.8726   | 0.8726 | 0.8726 | 0.8726    |
| 0.1002        | 1.02  | 1100 | 0.3512          | 0.8797   | 0.8797 | 0.8797 | 0.8797    |
| 0.1888        | 1.12  | 1200 | 0.3286          | 0.8566   | 0.8566 | 0.8566 | 0.8566    |
| 0.134         | 1.21  | 1300 | 0.3178          | 0.8661   | 0.8661 | 0.8661 | 0.8661    |
| 0.1085        | 1.3   | 1400 | 0.2618          | 0.8897   | 0.8897 | 0.8897 | 0.8897    |
| 0.189         | 1.39  | 1500 | 0.3490          | 0.8737   | 0.8737 | 0.8737 | 0.8737    |
| 0.1781        | 1.49  | 1600 | 0.3033          | 0.8728   | 0.8728 | 0.8728 | 0.8728    |
| 0.0788        | 1.58  | 1700 | 0.3623          | 0.8774   | 0.8774 | 0.8774 | 0.8774    |
| 0.1135        | 1.67  | 1800 | 0.3341          | 0.8688   | 0.8688 | 0.8688 | 0.8688    |
| 0.0607        | 1.77  | 1900 | 0.2954          | 0.8874   | 0.8874 | 0.8874 | 0.8874    |
| 0.058         | 1.86  | 2000 | 0.3921          | 0.8780   | 0.8780 | 0.8780 | 0.8780    |
| 0.0873        | 1.95  | 2100 | 0.3094          | 0.8883   | 0.8883 | 0.8883 | 0.8883    |
| 0.0584        | 2.04  | 2200 | 0.3688          | 0.8892   | 0.8892 | 0.8892 | 0.8892    |
| 0.0978        | 2.14  | 2300 | 0.3874          | 0.8916   | 0.8916 | 0.8916 | 0.8916    |
| 0.0958        | 2.23  | 2400 | 0.3679          | 0.8927   | 0.8927 | 0.8927 | 0.8927    |
| 0.0559        | 2.32  | 2500 | 0.4649          | 0.8874   | 0.8874 | 0.8874 | 0.8874    |
| 0.0125        | 2.42  | 2600 | 0.4350          | 0.8943   | 0.8943 | 0.8943 | 0.8943    |
| 0.0636        | 2.51  | 2700 | 0.4195          | 0.8906   | 0.8906 | 0.8906 | 0.8906    |
| 0.0458        | 2.6   | 2800 | 0.4127          | 0.8980   | 0.8980 | 0.8980 | 0.8980    |
| 0.055         | 2.7   | 2900 | 0.4086          | 0.8993   | 0.8993 | 0.8993 | 0.8993    |
| 0.03          | 2.79  | 3000 | 0.3710          | 0.9013   | 0.9013 | 0.9013 | 0.9013    |
| 0.027         | 2.88  | 3100 | 0.4148          | 0.8940   | 0.8940 | 0.8940 | 0.8940    |
| 0.0384        | 2.97  | 3200 | 0.4036          | 0.8938   | 0.8938 | 0.8938 | 0.8938    |
| 0.0095        | 3.07  | 3300 | 0.4830          | 0.8892   | 0.8892 | 0.8892 | 0.8892    |
| 0.0489        | 3.16  | 3400 | 0.4590          | 0.8945   | 0.8945 | 0.8945 | 0.8945    |
| 0.0198        | 3.25  | 3500 | 0.4732          | 0.8972   | 0.8972 | 0.8972 | 0.8972    |
| 0.0604        | 3.35  | 3600 | 0.5283          | 0.8916   | 0.8916 | 0.8916 | 0.8916    |
| 0.0028        | 3.44  | 3700 | 0.5160          | 0.8931   | 0.8931 | 0.8931 | 0.8931    |
| 0.0018        | 3.53  | 3800 | 0.5038          | 0.8966   | 0.8966 | 0.8966 | 0.8966    |
| 0.0192        | 3.62  | 3900 | 0.5112          | 0.8975   | 0.8975 | 0.8975 | 0.8975    |
| 0.0455        | 3.72  | 4000 | 0.4954          | 0.8985   | 0.8985 | 0.8985 | 0.8985    |
| 0.0014        | 3.81  | 4100 | 0.4984          | 0.8994   | 0.8994 | 0.8994 | 0.8994    |
| 0.0012        | 3.9   | 4200 | 0.4987          | 0.9007   | 0.9007 | 0.9007 | 0.9007    |
| 0.0009        | 4.0   | 4300 | 0.5004          | 0.8998   | 0.8998 | 0.8998 | 0.8998    |


### Framework versions

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

benign malignant