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# vit-large-HAM-10000-patch-16

This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the ahishamm/HAM_db dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5464
- Accuracy: 0.8095
- Recall: 0.8095
- F1: 0.8095
- Precision: 0.8095

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.8281        | 0.2   | 100  | 0.9228          | 0.6788   | 0.6788 | 0.6788 | 0.6788    |
| 0.912         | 0.4   | 200  | 0.8353          | 0.7147   | 0.7147 | 0.7147 | 0.7147    |
| 0.6741        | 0.6   | 300  | 0.7841          | 0.7377   | 0.7377 | 0.7377 | 0.7377    |
| 0.8472        | 0.8   | 400  | 0.6710          | 0.7566   | 0.7566 | 0.7566 | 0.7566    |
| 0.7758        | 1.0   | 500  | 0.7587          | 0.7087   | 0.7087 | 0.7087 | 0.7087    |
| 0.5388        | 1.2   | 600  | 0.6607          | 0.7746   | 0.7746 | 0.7746 | 0.7746    |
| 0.5067        | 1.4   | 700  | 0.6133          | 0.7701   | 0.7701 | 0.7701 | 0.7701    |
| 0.4992        | 1.6   | 800  | 0.6075          | 0.7786   | 0.7786 | 0.7786 | 0.7786    |
| 0.5761        | 1.8   | 900  | 0.6286          | 0.7691   | 0.7691 | 0.7691 | 0.7691    |
| 0.5892        | 2.0   | 1000 | 0.5498          | 0.8035   | 0.8035 | 0.8035 | 0.8035    |
| 0.4258        | 2.2   | 1100 | 0.5901          | 0.7940   | 0.7940 | 0.7940 | 0.7940    |
| 0.4066        | 2.4   | 1200 | 0.5553          | 0.8025   | 0.8025 | 0.8025 | 0.8025    |
| 0.3032        | 2.59  | 1300 | 0.5754          | 0.8030   | 0.8030 | 0.8030 | 0.8030    |
| 0.3843        | 2.79  | 1400 | 0.5464          | 0.8095   | 0.8095 | 0.8095 | 0.8095    |
| 0.2679        | 2.99  | 1500 | 0.5683          | 0.8100   | 0.8100 | 0.8100 | 0.8100    |
| 0.1787        | 3.19  | 1600 | 0.5931          | 0.8195   | 0.8195 | 0.8195 | 0.8195    |
| 0.105         | 3.39  | 1700 | 0.6488          | 0.8279   | 0.8279 | 0.8279 | 0.8279    |
| 0.2138        | 3.59  | 1800 | 0.6414          | 0.8130   | 0.8130 | 0.8130 | 0.8130    |
| 0.1336        | 3.79  | 1900 | 0.5920          | 0.8264   | 0.8264 | 0.8264 | 0.8264    |
| 0.1246        | 3.99  | 2000 | 0.5999          | 0.8289   | 0.8289 | 0.8289 | 0.8289    |


### Framework versions

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

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