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# vit-base-binary-isic-patch-16

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ahishamm/isic_binary_augmented dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2665
- Accuracy: 0.8799
- Recall: 0.8799
- F1: 0.8799
- Precision: 0.8799

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.3887        | 0.09  | 100  | 0.3201          | 0.8539   | 0.8539 | 0.8539 | 0.8539    |
| 0.2725        | 0.19  | 200  | 0.3208          | 0.8526   | 0.8526 | 0.8526 | 0.8526    |
| 0.2653        | 0.28  | 300  | 0.3113          | 0.8532   | 0.8532 | 0.8532 | 0.8532    |
| 0.2439        | 0.37  | 400  | 0.3156          | 0.8749   | 0.8749 | 0.8749 | 0.8749    |
| 0.2183        | 0.46  | 500  | 0.3145          | 0.8391   | 0.8391 | 0.8391 | 0.8391    |
| 0.2233        | 0.56  | 600  | 0.3083          | 0.8543   | 0.8543 | 0.8543 | 0.8543    |
| 0.2234        | 0.65  | 700  | 0.3757          | 0.8598   | 0.8598 | 0.8598 | 0.8598    |
| 0.1825        | 0.74  | 800  | 0.2665          | 0.8799   | 0.8799 | 0.8799 | 0.8799    |
| 0.1244        | 0.84  | 900  | 0.3088          | 0.8886   | 0.8886 | 0.8886 | 0.8886    |
| 0.1454        | 0.93  | 1000 | 0.4594          | 0.8544   | 0.8544 | 0.8544 | 0.8544    |
| 0.043         | 1.02  | 1100 | 0.3424          | 0.8820   | 0.8820 | 0.8820 | 0.8820    |
| 0.1911        | 1.12  | 1200 | 0.4268          | 0.8630   | 0.8630 | 0.8630 | 0.8630    |
| 0.1091        | 1.21  | 1300 | 0.2940          | 0.8779   | 0.8779 | 0.8779 | 0.8779    |
| 0.0426        | 1.3   | 1400 | 0.3423          | 0.8915   | 0.8915 | 0.8915 | 0.8915    |
| 0.0831        | 1.39  | 1500 | 0.4086          | 0.8625   | 0.8625 | 0.8625 | 0.8625    |
| 0.106         | 1.49  | 1600 | 0.3020          | 0.8972   | 0.8972 | 0.8972 | 0.8972    |
| 0.0717        | 1.58  | 1700 | 0.3971          | 0.8875   | 0.8875 | 0.8875 | 0.8875    |
| 0.1134        | 1.67  | 1800 | 0.3530          | 0.8924   | 0.8924 | 0.8924 | 0.8924    |
| 0.0702        | 1.77  | 1900 | 0.3878          | 0.8888   | 0.8888 | 0.8888 | 0.8888    |
| 0.0828        | 1.86  | 2000 | 0.3822          | 0.8852   | 0.8852 | 0.8852 | 0.8852    |
| 0.0497        | 1.95  | 2100 | 0.3364          | 0.8992   | 0.8992 | 0.8992 | 0.8992    |
| 0.0191        | 2.04  | 2200 | 0.4042          | 0.8925   | 0.8925 | 0.8925 | 0.8925    |
| 0.0388        | 2.14  | 2300 | 0.4485          | 0.8970   | 0.8970 | 0.8970 | 0.8970    |
| 0.0273        | 2.23  | 2400 | 0.3862          | 0.8953   | 0.8953 | 0.8953 | 0.8953    |
| 0.0032        | 2.32  | 2500 | 0.4710          | 0.8949   | 0.8949 | 0.8949 | 0.8949    |
| 0.0242        | 2.42  | 2600 | 0.4859          | 0.8938   | 0.8938 | 0.8938 | 0.8938    |
| 0.065         | 2.51  | 2700 | 0.4446          | 0.8983   | 0.8983 | 0.8983 | 0.8983    |
| 0.0258        | 2.6   | 2800 | 0.5354          | 0.8925   | 0.8925 | 0.8925 | 0.8925    |
| 0.001         | 2.7   | 2900 | 0.5201          | 0.8958   | 0.8958 | 0.8958 | 0.8958    |
| 0.0336        | 2.79  | 3000 | 0.5137          | 0.8924   | 0.8924 | 0.8924 | 0.8924    |
| 0.0205        | 2.88  | 3100 | 0.5251          | 0.8972   | 0.8972 | 0.8972 | 0.8972    |
| 0.0033        | 2.97  | 3200 | 0.5083          | 0.8907   | 0.8907 | 0.8907 | 0.8907    |
| 0.0009        | 3.07  | 3300 | 0.4909          | 0.9007   | 0.9007 | 0.9007 | 0.9007    |
| 0.0465        | 3.16  | 3400 | 0.5176          | 0.8984   | 0.8984 | 0.8984 | 0.8984    |
| 0.0007        | 3.25  | 3500 | 0.5411          | 0.8977   | 0.8977 | 0.8977 | 0.8977    |
| 0.0406        | 3.35  | 3600 | 0.4929          | 0.9008   | 0.9008 | 0.9008 | 0.9008    |
| 0.0016        | 3.44  | 3700 | 0.5065          | 0.8993   | 0.8993 | 0.8993 | 0.8993    |
| 0.0006        | 3.53  | 3800 | 0.5403          | 0.8985   | 0.8985 | 0.8985 | 0.8985    |
| 0.0272        | 3.62  | 3900 | 0.5399          | 0.8992   | 0.8992 | 0.8992 | 0.8992    |
| 0.0223        | 3.72  | 4000 | 0.5075          | 0.9044   | 0.9044 | 0.9044 | 0.9044    |
| 0.0006        | 3.81  | 4100 | 0.5432          | 0.8993   | 0.8993 | 0.8993 | 0.8993    |
| 0.0153        | 3.9   | 4200 | 0.5263          | 0.9021   | 0.9021 | 0.9021 | 0.9021    |
| 0.0273        | 4.0   | 4300 | 0.5242          | 0.9029   | 0.9029 | 0.9029 | 0.9029    |


### Framework versions

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

benign malignant