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# vit-large-HAM-10000-patch-32

This model is a fine-tuned version of [google/vit-large-patch32-224-in21k](https://huggingface.co/google/vit-large-patch32-224-in21k) on the ahishamm/HAM_db dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4810
- Accuracy: 0.8364
- Recall: 0.8364
- F1: 0.8364
- Precision: 0.8364

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.6405        | 0.2   | 100  | 0.7318          | 0.7481   | 0.7481 | 0.7481 | 0.7481    |
| 0.7062        | 0.4   | 200  | 0.7735          | 0.7416   | 0.7416 | 0.7416 | 0.7416    |
| 0.6334        | 0.6   | 300  | 0.6075          | 0.7781   | 0.7781 | 0.7781 | 0.7781    |
| 0.7102        | 0.8   | 400  | 0.6618          | 0.7661   | 0.7661 | 0.7661 | 0.7661    |
| 0.6814        | 1.0   | 500  | 0.5717          | 0.7890   | 0.7890 | 0.7890 | 0.7890    |
| 0.4618        | 1.2   | 600  | 0.5624          | 0.8030   | 0.8030 | 0.8030 | 0.8030    |
| 0.3824        | 1.4   | 700  | 0.5987          | 0.7766   | 0.7766 | 0.7766 | 0.7766    |
| 0.4191        | 1.6   | 800  | 0.5145          | 0.8190   | 0.8190 | 0.8190 | 0.8190    |
| 0.3998        | 1.8   | 900  | 0.5226          | 0.8090   | 0.8090 | 0.8090 | 0.8090    |
| 0.4677        | 2.0   | 1000 | 0.4927          | 0.8219   | 0.8219 | 0.8219 | 0.8219    |
| 0.2191        | 2.2   | 1100 | 0.5477          | 0.8284   | 0.8284 | 0.8284 | 0.8284    |
| 0.2302        | 2.4   | 1200 | 0.5018          | 0.8329   | 0.8329 | 0.8329 | 0.8329    |
| 0.191         | 2.59  | 1300 | 0.4810          | 0.8364   | 0.8364 | 0.8364 | 0.8364    |
| 0.1736        | 2.79  | 1400 | 0.5096          | 0.8334   | 0.8334 | 0.8334 | 0.8334    |
| 0.1049        | 2.99  | 1500 | 0.5944          | 0.8364   | 0.8364 | 0.8364 | 0.8364    |
| 0.0612        | 3.19  | 1600 | 0.5552          | 0.8464   | 0.8464 | 0.8464 | 0.8464    |
| 0.0181        | 3.39  | 1700 | 0.6199          | 0.8434   | 0.8434 | 0.8434 | 0.8434    |
| 0.0816        | 3.59  | 1800 | 0.5081          | 0.8534   | 0.8534 | 0.8534 | 0.8534    |
| 0.039         | 3.79  | 1900 | 0.5349          | 0.8544   | 0.8544 | 0.8544 | 0.8544    |
| 0.0208        | 3.99  | 2000 | 0.5445          | 0.8544   | 0.8544 | 0.8544 | 0.8544    |


### Framework versions

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

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