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# vit-base-HAM-10000-sharpened

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/HAM_db_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4392
- Accuracy: 0.8529
- Recall: 0.8529
- F1: 0.8529
- Precision: 0.8529

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.7303        | 0.2   | 100  | 0.7828          | 0.7197   | 0.7197 | 0.7197 | 0.7197    |
| 0.7198        | 0.4   | 200  | 0.7519          | 0.7377   | 0.7377 | 0.7377 | 0.7377    |
| 0.7519        | 0.6   | 300  | 0.7125          | 0.7541   | 0.7541 | 0.7541 | 0.7541    |
| 0.6657        | 0.8   | 400  | 0.6623          | 0.7571   | 0.7571 | 0.7571 | 0.7571    |
| 0.5896        | 1.0   | 500  | 0.5964          | 0.7835   | 0.7835 | 0.7835 | 0.7835    |
| 0.515         | 1.2   | 600  | 0.5745          | 0.8015   | 0.8015 | 0.8015 | 0.8015    |
| 0.4318        | 1.4   | 700  | 0.5061          | 0.8200   | 0.8200 | 0.8200 | 0.8200    |
| 0.4299        | 1.6   | 800  | 0.5239          | 0.8075   | 0.8075 | 0.8075 | 0.8075    |
| 0.4793        | 1.8   | 900  | 0.5366          | 0.8125   | 0.8125 | 0.8125 | 0.8125    |
| 0.4202        | 2.0   | 1000 | 0.4882          | 0.8244   | 0.8244 | 0.8244 | 0.8244    |
| 0.2105        | 2.2   | 1100 | 0.5330          | 0.8234   | 0.8234 | 0.8234 | 0.8234    |
| 0.2597        | 2.4   | 1200 | 0.4604          | 0.8369   | 0.8369 | 0.8369 | 0.8369    |
| 0.2261        | 2.59  | 1300 | 0.4893          | 0.8409   | 0.8409 | 0.8409 | 0.8409    |
| 0.1853        | 2.79  | 1400 | 0.4793          | 0.8494   | 0.8494 | 0.8494 | 0.8494    |
| 0.1739        | 2.99  | 1500 | 0.4392          | 0.8529   | 0.8529 | 0.8529 | 0.8529    |
| 0.0629        | 3.19  | 1600 | 0.4941          | 0.8584   | 0.8584 | 0.8584 | 0.8584    |
| 0.0802        | 3.39  | 1700 | 0.4974          | 0.8613   | 0.8613 | 0.8613 | 0.8613    |
| 0.0712        | 3.59  | 1800 | 0.5416          | 0.8594   | 0.8594 | 0.8594 | 0.8594    |
| 0.0365        | 3.79  | 1900 | 0.5318          | 0.8574   | 0.8574 | 0.8574 | 0.8574    |
| 0.0591        | 3.99  | 2000 | 0.5344          | 0.8574   | 0.8574 | 0.8574 | 0.8574    |


### Framework versions

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

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