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# vit-base-HAM-10000-patch-32

This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the ahishamm/HAM_db dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5210
- Accuracy: 0.8040
- Recall: 0.8040
- F1: 0.8040
- Precision: 0.8040

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.7           | 0.2   | 100  | 0.7878          | 0.7307   | 0.7307 | 0.7307 | 0.7307    |
| 0.8248        | 0.4   | 200  | 0.7338          | 0.7476   | 0.7476 | 0.7476 | 0.7476    |
| 0.6647        | 0.6   | 300  | 0.6417          | 0.7541   | 0.7541 | 0.7541 | 0.7541    |
| 0.6755        | 0.8   | 400  | 0.6682          | 0.7576   | 0.7576 | 0.7576 | 0.7576    |
| 0.7443        | 1.0   | 500  | 0.6037          | 0.7890   | 0.7890 | 0.7890 | 0.7890    |
| 0.5316        | 1.2   | 600  | 0.5963          | 0.7915   | 0.7915 | 0.7915 | 0.7915    |
| 0.4404        | 1.4   | 700  | 0.5626          | 0.7955   | 0.7955 | 0.7955 | 0.7955    |
| 0.4431        | 1.6   | 800  | 0.5719          | 0.8005   | 0.8005 | 0.8005 | 0.8005    |
| 0.5011        | 1.8   | 900  | 0.5581          | 0.7880   | 0.7880 | 0.7880 | 0.7880    |
| 0.4692        | 2.0   | 1000 | 0.5210          | 0.8040   | 0.8040 | 0.8040 | 0.8040    |
| 0.2648        | 2.2   | 1100 | 0.5776          | 0.8070   | 0.8070 | 0.8070 | 0.8070    |
| 0.2723        | 2.4   | 1200 | 0.5317          | 0.8180   | 0.8180 | 0.8180 | 0.8180    |
| 0.2325        | 2.59  | 1300 | 0.5223          | 0.8170   | 0.8170 | 0.8170 | 0.8170    |
| 0.2547        | 2.79  | 1400 | 0.5314          | 0.8244   | 0.8244 | 0.8244 | 0.8244    |
| 0.146         | 2.99  | 1500 | 0.5583          | 0.8274   | 0.8274 | 0.8274 | 0.8274    |
| 0.1224        | 3.19  | 1600 | 0.5960          | 0.8289   | 0.8289 | 0.8289 | 0.8289    |
| 0.0313        | 3.39  | 1700 | 0.6081          | 0.8304   | 0.8304 | 0.8304 | 0.8304    |
| 0.104         | 3.59  | 1800 | 0.5770          | 0.8339   | 0.8339 | 0.8339 | 0.8339    |
| 0.0538        | 3.79  | 1900 | 0.5364          | 0.8464   | 0.8464 | 0.8464 | 0.8464    |
| 0.0827        | 3.99  | 2000 | 0.5414          | 0.8454   | 0.8454 | 0.8454 | 0.8454    |


### Framework versions

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

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