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# vit-base-HAM-10000-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/HAM_db dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4333
- Accuracy: 0.8469
- Recall: 0.8469
- F1: 0.8469
- Precision: 0.8469

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.718         | 0.2   | 100  | 0.7957          | 0.7471   | 0.7471 | 0.7471 | 0.7471    |
| 0.7946        | 0.4   | 200  | 0.7330          | 0.7451   | 0.7451 | 0.7451 | 0.7451    |
| 0.6182        | 0.6   | 300  | 0.6105          | 0.7810   | 0.7810 | 0.7810 | 0.7810    |
| 0.7459        | 0.8   | 400  | 0.6417          | 0.7691   | 0.7691 | 0.7691 | 0.7691    |
| 0.6626        | 1.0   | 500  | 0.6412          | 0.7676   | 0.7676 | 0.7676 | 0.7676    |
| 0.4497        | 1.2   | 600  | 0.5908          | 0.7885   | 0.7885 | 0.7885 | 0.7885    |
| 0.4141        | 1.4   | 700  | 0.5313          | 0.8145   | 0.8145 | 0.8145 | 0.8145    |
| 0.5624        | 1.6   | 800  | 0.5296          | 0.8209   | 0.8209 | 0.8209 | 0.8209    |
| 0.4138        | 1.8   | 900  | 0.5069          | 0.8150   | 0.8150 | 0.8150 | 0.8150    |
| 0.3866        | 2.0   | 1000 | 0.4462          | 0.8374   | 0.8374 | 0.8374 | 0.8374    |
| 0.2498        | 2.2   | 1100 | 0.5281          | 0.8389   | 0.8389 | 0.8389 | 0.8389    |
| 0.289         | 2.4   | 1200 | 0.4333          | 0.8469   | 0.8469 | 0.8469 | 0.8469    |
| 0.23          | 2.59  | 1300 | 0.5256          | 0.8185   | 0.8185 | 0.8185 | 0.8185    |
| 0.2089        | 2.79  | 1400 | 0.5041          | 0.8394   | 0.8394 | 0.8394 | 0.8394    |
| 0.1201        | 2.99  | 1500 | 0.5277          | 0.8474   | 0.8474 | 0.8474 | 0.8474    |
| 0.0851        | 3.19  | 1600 | 0.5087          | 0.8613   | 0.8613 | 0.8613 | 0.8613    |
| 0.0446        | 3.39  | 1700 | 0.5383          | 0.8579   | 0.8579 | 0.8579 | 0.8579    |
| 0.0576        | 3.59  | 1800 | 0.5151          | 0.8673   | 0.8673 | 0.8673 | 0.8673    |
| 0.0691        | 3.79  | 1900 | 0.5331          | 0.8579   | 0.8579 | 0.8579 | 0.8579    |
| 0.0284        | 3.99  | 2000 | 0.5157          | 0.8628   | 0.8628 | 0.8628 | 0.8628    |


### Framework versions

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

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