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# vit-lr-0.0001

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: 0.4778
- Accuracy: 0.8311
- Precision: 0.8433
- Recall: 0.8311
- F1: 0.8269

## Training procedure
Early stopping is employed with a patience of 10 and validation loss as the stopping criteria.

### 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: cosine
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6718        | 1.0   | 321  | 0.4934          | 0.8242   | 0.8254    | 0.8242 | 0.8163 |
| 0.4045        | 2.0   | 642  | 0.4778          | 0.8311   | 0.8433    | 0.8311 | 0.8269 |
| 0.2419        | 3.0   | 963  | 0.5133          | 0.8405   | 0.8525    | 0.8405 | 0.8410 |
| 0.1267        | 4.0   | 1284 | 0.6154          | 0.8495   | 0.8491    | 0.8495 | 0.8448 |
| 0.0733        | 5.0   | 1605 | 0.7845          | 0.8422   | 0.8421    | 0.8422 | 0.8361 |
| 0.0446        | 6.0   | 1926 | 0.8767          | 0.8471   | 0.8407    | 0.8471 | 0.8408 |
| 0.0523        | 7.0   | 2247 | 0.8674          | 0.8450   | 0.8492    | 0.8450 | 0.8407 |
| 0.0388        | 8.0   | 2568 | 0.9754          | 0.8398   | 0.8566    | 0.8398 | 0.8387 |
| 0.0402        | 9.0   | 2889 | 0.9370          | 0.8492   | 0.8547    | 0.8492 | 0.8461 |
| 0.0283        | 10.0  | 3210 | 0.9218          | 0.8509   | 0.8496    | 0.8509 | 0.8483 |
| 0.0451        | 11.0  | 3531 | 0.9872          | 0.8474   | 0.8400    | 0.8474 | 0.8401 |
| 0.0549        | 12.0  | 3852 | 1.0203          | 0.8457   | 0.8488    | 0.8457 | 0.8416 |


### Framework versions

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

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