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# vit-base-HAM-10000-sharpened-large-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_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4582
- Accuracy: 0.8404
- Recall: 0.8404
- F1: 0.8404
- Precision: 0.8404

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.6739        | 0.2   | 100  | 0.7775          | 0.7257   | 0.7257 | 0.7257 | 0.7257    |
| 0.6922        | 0.4   | 200  | 0.6455          | 0.7711   | 0.7711 | 0.7711 | 0.7711    |
| 0.8219        | 0.6   | 300  | 0.7582          | 0.7426   | 0.7426 | 0.7426 | 0.7426    |
| 0.6801        | 0.8   | 400  | 0.6363          | 0.7651   | 0.7651 | 0.7651 | 0.7651    |
| 0.5499        | 1.0   | 500  | 0.6231          | 0.7751   | 0.7751 | 0.7751 | 0.7751    |
| 0.5156        | 1.2   | 600  | 0.6399          | 0.7761   | 0.7761 | 0.7761 | 0.7761    |
| 0.4478        | 1.4   | 700  | 0.5324          | 0.8020   | 0.8020 | 0.8020 | 0.8020    |
| 0.4364        | 1.6   | 800  | 0.5597          | 0.7970   | 0.7970 | 0.7970 | 0.7970    |
| 0.4545        | 1.8   | 900  | 0.5212          | 0.8115   | 0.8115 | 0.8115 | 0.8115    |
| 0.4294        | 2.0   | 1000 | 0.4926          | 0.8264   | 0.8264 | 0.8264 | 0.8264    |
| 0.135         | 2.2   | 1100 | 0.5448          | 0.8204   | 0.8204 | 0.8204 | 0.8204    |
| 0.2628        | 2.4   | 1200 | 0.4916          | 0.8304   | 0.8304 | 0.8304 | 0.8304    |
| 0.2577        | 2.59  | 1300 | 0.4582          | 0.8404   | 0.8404 | 0.8404 | 0.8404    |
| 0.2093        | 2.79  | 1400 | 0.5079          | 0.8344   | 0.8344 | 0.8344 | 0.8344    |
| 0.1415        | 2.99  | 1500 | 0.4760          | 0.8439   | 0.8439 | 0.8439 | 0.8439    |
| 0.0686        | 3.19  | 1600 | 0.5379          | 0.8444   | 0.8444 | 0.8444 | 0.8444    |
| 0.1031        | 3.39  | 1700 | 0.5572          | 0.8384   | 0.8384 | 0.8384 | 0.8384    |
| 0.102         | 3.59  | 1800 | 0.5343          | 0.8464   | 0.8464 | 0.8464 | 0.8464    |
| 0.0531        | 3.79  | 1900 | 0.5482          | 0.8479   | 0.8479 | 0.8479 | 0.8479    |
| 0.0756        | 3.99  | 2000 | 0.5454          | 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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