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# vit-large-binary-isic-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_augmented dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2479
- Accuracy: 0.8913
- Recall: 0.8913
- F1: 0.8913
- Precision: 0.8913

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.3606        | 0.09  | 100  | 0.3225          | 0.8123   | 0.8123 | 0.8123 | 0.8123    |
| 0.2867        | 0.19  | 200  | 0.3654          | 0.8565   | 0.8565 | 0.8565 | 0.8565    |
| 0.3359        | 0.28  | 300  | 0.3051          | 0.8502   | 0.8502 | 0.8502 | 0.8502    |
| 0.2825        | 0.37  | 400  | 0.3576          | 0.8588   | 0.8588 | 0.8588 | 0.8588    |
| 0.2438        | 0.46  | 500  | 0.3043          | 0.8620   | 0.8620 | 0.8620 | 0.8620    |
| 0.1465        | 0.56  | 600  | 0.2791          | 0.8798   | 0.8798 | 0.8798 | 0.8798    |
| 0.2318        | 0.65  | 700  | 0.2816          | 0.8629   | 0.8629 | 0.8629 | 0.8629    |
| 0.1846        | 0.74  | 800  | 0.2479          | 0.8913   | 0.8913 | 0.8913 | 0.8913    |
| 0.1451        | 0.84  | 900  | 0.3232          | 0.8829   | 0.8829 | 0.8829 | 0.8829    |
| 0.1157        | 0.93  | 1000 | 0.2799          | 0.8767   | 0.8767 | 0.8767 | 0.8767    |
| 0.1175        | 1.02  | 1100 | 0.3236          | 0.8697   | 0.8697 | 0.8697 | 0.8697    |
| 0.2015        | 1.12  | 1200 | 0.3056          | 0.8706   | 0.8706 | 0.8706 | 0.8706    |
| 0.0627        | 1.21  | 1300 | 0.3160          | 0.8757   | 0.8757 | 0.8757 | 0.8757    |
| 0.0842        | 1.3   | 1400 | 0.3299          | 0.8908   | 0.8908 | 0.8908 | 0.8908    |
| 0.0617        | 1.39  | 1500 | 0.3353          | 0.8847   | 0.8847 | 0.8847 | 0.8847    |
| 0.1919        | 1.49  | 1600 | 0.3421          | 0.8630   | 0.8630 | 0.8630 | 0.8630    |
| 0.1212        | 1.58  | 1700 | 0.4301          | 0.8693   | 0.8693 | 0.8693 | 0.8693    |
| 0.1012        | 1.67  | 1800 | 0.3352          | 0.8799   | 0.8799 | 0.8799 | 0.8799    |
| 0.0622        | 1.77  | 1900 | 0.3272          | 0.8881   | 0.8881 | 0.8881 | 0.8881    |
| 0.0733        | 1.86  | 2000 | 0.2903          | 0.8867   | 0.8867 | 0.8867 | 0.8867    |
| 0.0293        | 1.95  | 2100 | 0.2772          | 0.9031   | 0.9031 | 0.9031 | 0.9031    |
| 0.0305        | 2.04  | 2200 | 0.3218          | 0.8929   | 0.8929 | 0.8929 | 0.8929    |
| 0.034         | 2.14  | 2300 | 0.4207          | 0.8915   | 0.8915 | 0.8915 | 0.8915    |
| 0.0113        | 2.23  | 2400 | 0.4193          | 0.8948   | 0.8948 | 0.8948 | 0.8948    |
| 0.0071        | 2.32  | 2500 | 0.4372          | 0.8908   | 0.8908 | 0.8908 | 0.8908    |
| 0.0074        | 2.42  | 2600 | 0.4253          | 0.9009   | 0.9009 | 0.9009 | 0.9009    |
| 0.079         | 2.51  | 2700 | 0.3814          | 0.9012   | 0.9012 | 0.9012 | 0.9012    |
| 0.0407        | 2.6   | 2800 | 0.3968          | 0.9071   | 0.9071 | 0.9071 | 0.9071    |
| 0.0096        | 2.7   | 2900 | 0.4318          | 0.9047   | 0.9047 | 0.9047 | 0.9047    |
| 0.052         | 2.79  | 3000 | 0.4112          | 0.8986   | 0.8986 | 0.8986 | 0.8986    |
| 0.0075        | 2.88  | 3100 | 0.4231          | 0.9021   | 0.9021 | 0.9021 | 0.9021    |
| 0.0183        | 2.97  | 3200 | 0.4106          | 0.8963   | 0.8963 | 0.8963 | 0.8963    |
| 0.0134        | 3.07  | 3300 | 0.4210          | 0.9031   | 0.9031 | 0.9031 | 0.9031    |
| 0.0387        | 3.16  | 3400 | 0.4336          | 0.9042   | 0.9042 | 0.9042 | 0.9042    |
| 0.001         | 3.25  | 3500 | 0.4679          | 0.8988   | 0.8988 | 0.8988 | 0.8988    |
| 0.0292        | 3.35  | 3600 | 0.4691          | 0.8976   | 0.8976 | 0.8976 | 0.8976    |
| 0.0109        | 3.44  | 3700 | 0.4713          | 0.9061   | 0.9061 | 0.9061 | 0.9061    |
| 0.0007        | 3.53  | 3800 | 0.4842          | 0.9062   | 0.9062 | 0.9062 | 0.9062    |
| 0.0023        | 3.62  | 3900 | 0.4973          | 0.9042   | 0.9042 | 0.9042 | 0.9042    |
| 0.0039        | 3.72  | 4000 | 0.4994          | 0.9043   | 0.9043 | 0.9043 | 0.9043    |
| 0.0005        | 3.81  | 4100 | 0.4907          | 0.9059   | 0.9059 | 0.9059 | 0.9059    |
| 0.0005        | 3.9   | 4200 | 0.4919          | 0.9058   | 0.9058 | 0.9058 | 0.9058    |
| 0.0432        | 4.0   | 4300 | 0.4923          | 0.9059   | 0.9059 | 0.9059 | 0.9059    |


### Framework versions

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

benign malignant