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# vit-huge-HAM-10000-patch-14

This model is a fine-tuned version of [google/vit-huge-patch14-224-in21k](https://huggingface.co/google/vit-huge-patch14-224-in21k) on the ahishamm/HAM_db dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3807
- Accuracy: 0.8653
- Recall: 0.8653
- F1: 0.8653
- Precision: 0.8653

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.7133        | 0.2   | 100  | 0.7307          | 0.7551   | 0.7551 | 0.7551 | 0.7551    |
| 0.7015        | 0.4   | 200  | 0.6770          | 0.7546   | 0.7546 | 0.7546 | 0.7546    |
| 0.5847        | 0.6   | 300  | 0.6005          | 0.7890   | 0.7890 | 0.7890 | 0.7890    |
| 0.6016        | 0.8   | 400  | 0.5909          | 0.7810   | 0.7810 | 0.7810 | 0.7810    |
| 0.585         | 1.0   | 500  | 0.4994          | 0.8175   | 0.8175 | 0.8175 | 0.8175    |
| 0.3114        | 1.2   | 600  | 0.4799          | 0.8354   | 0.8354 | 0.8354 | 0.8354    |
| 0.2868        | 1.4   | 700  | 0.5035          | 0.8140   | 0.8140 | 0.8140 | 0.8140    |
| 0.3178        | 1.6   | 800  | 0.4345          | 0.8544   | 0.8544 | 0.8544 | 0.8544    |
| 0.344         | 1.8   | 900  | 0.4539          | 0.8374   | 0.8374 | 0.8374 | 0.8374    |
| 0.3273        | 2.0   | 1000 | 0.3807          | 0.8653   | 0.8653 | 0.8653 | 0.8653    |
| 0.0903        | 2.2   | 1100 | 0.4843          | 0.8574   | 0.8574 | 0.8574 | 0.8574    |
| 0.1105        | 2.4   | 1200 | 0.4116          | 0.8788   | 0.8788 | 0.8788 | 0.8788    |
| 0.1551        | 2.59  | 1300 | 0.4446          | 0.8534   | 0.8534 | 0.8534 | 0.8534    |
| 0.0804        | 2.79  | 1400 | 0.4129          | 0.8778   | 0.8778 | 0.8778 | 0.8778    |
| 0.0811        | 2.99  | 1500 | 0.4459          | 0.8738   | 0.8738 | 0.8738 | 0.8738    |
| 0.0391        | 3.19  | 1600 | 0.4409          | 0.8878   | 0.8878 | 0.8878 | 0.8878    |
| 0.0075        | 3.39  | 1700 | 0.4671          | 0.8888   | 0.8888 | 0.8888 | 0.8888    |
| 0.0113        | 3.59  | 1800 | 0.4591          | 0.8788   | 0.8788 | 0.8788 | 0.8788    |
| 0.0079        | 3.79  | 1900 | 0.4695          | 0.8858   | 0.8858 | 0.8858 | 0.8858    |
| 0.021         | 3.99  | 2000 | 0.4705          | 0.8893   | 0.8893 | 0.8893 | 0.8893    |


### Framework versions

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

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