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# vit-base-HAM-10000-sharpened-large-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_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5504
- Accuracy: 0.8075
- Recall: 0.8075
- F1: 0.8075
- Precision: 0.8075

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.9294        | 0.2   | 100  | 1.0377          | 0.6733   | 0.6733 | 0.6733 | 0.6733    |
| 1.0067        | 0.4   | 200  | 0.8976          | 0.6813   | 0.6813 | 0.6813 | 0.6813    |
| 1.0081        | 0.6   | 300  | 0.9345          | 0.6773   | 0.6773 | 0.6773 | 0.6773    |
| 0.9326        | 0.8   | 400  | 0.8494          | 0.6883   | 0.6883 | 0.6883 | 0.6883    |
| 0.8243        | 1.0   | 500  | 0.7481          | 0.7267   | 0.7267 | 0.7267 | 0.7267    |
| 0.7408        | 1.2   | 600  | 0.7277          | 0.7317   | 0.7317 | 0.7317 | 0.7317    |
| 0.6844        | 1.4   | 700  | 0.7114          | 0.7392   | 0.7392 | 0.7392 | 0.7392    |
| 0.7411        | 1.6   | 800  | 0.6772          | 0.7416   | 0.7416 | 0.7416 | 0.7416    |
| 0.7138        | 1.8   | 900  | 0.7136          | 0.7377   | 0.7377 | 0.7377 | 0.7377    |
| 0.5838        | 2.0   | 1000 | 0.6625          | 0.7521   | 0.7521 | 0.7521 | 0.7521    |
| 0.5315        | 2.2   | 1100 | 0.6104          | 0.7776   | 0.7776 | 0.7776 | 0.7776    |
| 0.6391        | 2.4   | 1200 | 0.6317          | 0.7591   | 0.7591 | 0.7591 | 0.7591    |
| 0.6903        | 2.59  | 1300 | 0.6098          | 0.7656   | 0.7656 | 0.7656 | 0.7656    |
| 0.5798        | 2.79  | 1400 | 0.6211          | 0.7751   | 0.7751 | 0.7751 | 0.7751    |
| 0.5448        | 2.99  | 1500 | 0.5824          | 0.7820   | 0.7820 | 0.7820 | 0.7820    |
| 0.4523        | 3.19  | 1600 | 0.5951          | 0.7776   | 0.7776 | 0.7776 | 0.7776    |
| 0.4485        | 3.39  | 1700 | 0.6114          | 0.7815   | 0.7815 | 0.7815 | 0.7815    |
| 0.487         | 3.59  | 1800 | 0.5730          | 0.7950   | 0.7950 | 0.7950 | 0.7950    |
| 0.4104        | 3.79  | 1900 | 0.5597          | 0.7965   | 0.7965 | 0.7965 | 0.7965    |
| 0.4468        | 3.99  | 2000 | 0.5504          | 0.8075   | 0.8075 | 0.8075 | 0.8075    |


### Framework versions

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

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