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# vit-huge-HAM-10000-sharpened-patch-14

This model is a fine-tuned version of [google/vit-huge-patch14-224-in21k](https://huggingface.co/google/vit-huge-patch14-224-in21k) on the ahishamm/HAM_db_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4411
- Accuracy: 0.8554
- Recall: 0.8554
- F1: 0.8554
- Precision: 0.8554

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.6177        | 0.2   | 100  | 0.7082          | 0.7591   | 0.7591 | 0.7591 | 0.7591    |
| 0.6848        | 0.4   | 200  | 0.6570          | 0.7631   | 0.7631 | 0.7631 | 0.7631    |
| 0.622         | 0.6   | 300  | 0.5880          | 0.7920   | 0.7920 | 0.7920 | 0.7920    |
| 0.5887        | 0.8   | 400  | 0.5599          | 0.7965   | 0.7965 | 0.7965 | 0.7965    |
| 0.4812        | 1.0   | 500  | 0.5364          | 0.8010   | 0.8010 | 0.8010 | 0.8010    |
| 0.4013        | 1.2   | 600  | 0.4874          | 0.8249   | 0.8249 | 0.8249 | 0.8249    |
| 0.3987        | 1.4   | 700  | 0.4533          | 0.8354   | 0.8354 | 0.8354 | 0.8354    |
| 0.4118        | 1.6   | 800  | 0.4540          | 0.8424   | 0.8424 | 0.8424 | 0.8424    |
| 0.3272        | 1.8   | 900  | 0.4536          | 0.8254   | 0.8254 | 0.8254 | 0.8254    |
| 0.3318        | 2.0   | 1000 | 0.4411          | 0.8554   | 0.8554 | 0.8554 | 0.8554    |
| 0.0859        | 2.2   | 1100 | 0.4641          | 0.8519   | 0.8519 | 0.8519 | 0.8519    |
| 0.1026        | 2.4   | 1200 | 0.4692          | 0.8554   | 0.8554 | 0.8554 | 0.8554    |
| 0.0934        | 2.59  | 1300 | 0.4555          | 0.8474   | 0.8474 | 0.8474 | 0.8474    |
| 0.1084        | 2.79  | 1400 | 0.5017          | 0.8454   | 0.8454 | 0.8454 | 0.8454    |
| 0.0603        | 2.99  | 1500 | 0.4803          | 0.8599   | 0.8599 | 0.8599 | 0.8599    |
| 0.013         | 3.19  | 1600 | 0.4905          | 0.8633   | 0.8633 | 0.8633 | 0.8633    |
| 0.0585        | 3.39  | 1700 | 0.5305          | 0.8678   | 0.8678 | 0.8678 | 0.8678    |
| 0.0322        | 3.59  | 1800 | 0.5342          | 0.8648   | 0.8648 | 0.8648 | 0.8648    |
| 0.0086        | 3.79  | 1900 | 0.5134          | 0.8668   | 0.8668 | 0.8668 | 0.8668    |
| 0.0275        | 3.99  | 2000 | 0.5136          | 0.8693   | 0.8693 | 0.8693 | 0.8693    |


### Framework versions

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

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