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# vit-lr-exponential

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the skin-cancer dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1693
- Accuracy: 0.6689
- Precision: 0.5100
- Recall: 0.6689
- F1: 0.5368

## 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: 0.0001
- 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: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.1467        | 1.0   | 321  | 1.1693          | 0.6689   | 0.5100    | 0.6689 | 0.5368 |
| 1.1338        | 2.0   | 642  | 1.1693          | 0.6689   | 0.5100    | 0.6689 | 0.5368 |
| 1.1328        | 3.0   | 963  | 1.1693          | 0.6689   | 0.5100    | 0.6689 | 0.5368 |
| 1.1331        | 4.0   | 1284 | 1.1693          | 0.6689   | 0.5100    | 0.6689 | 0.5368 |
| 1.1325        | 5.0   | 1605 | 1.1693          | 0.6689   | 0.5100    | 0.6689 | 0.5368 |
| 1.1333        | 6.0   | 1926 | 1.1693          | 0.6689   | 0.5100    | 0.6689 | 0.5368 |
| 1.1332        | 7.0   | 2247 | 1.1693          | 0.6689   | 0.5100    | 0.6689 | 0.5368 |
| 1.1327        | 8.0   | 2568 | 1.1693          | 0.6689   | 0.5100    | 0.6689 | 0.5368 |
| 1.1341        | 9.0   | 2889 | 1.1693          | 0.6689   | 0.5100    | 0.6689 | 0.5368 |
| 1.1328        | 10.0  | 3210 | 1.1693          | 0.6689   | 0.5100    | 0.6689 | 0.5368 |
| 1.1328        | 11.0  | 3531 | 1.1693          | 0.6689   | 0.5100    | 0.6689 | 0.5368 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

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