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# vit-lr-inverse-sqrt

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: 0.4469
- Accuracy: 0.8499
- Precision: 0.8565
- Recall: 0.8499
- F1: 0.8516

## 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: inverse_sqrt
- lr_scheduler_warmup_steps: 80
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6694        | 0.31  | 100  | 0.6511          | 0.7632   | 0.7558    | 0.7632 | 0.7499 |
| 0.5468        | 0.62  | 200  | 0.6537          | 0.7618   | 0.7713    | 0.7618 | 0.7109 |
| 0.6132        | 0.93  | 300  | 0.5132          | 0.8145   | 0.8262    | 0.8145 | 0.8138 |
| 0.3319        | 1.25  | 400  | 0.4706          | 0.8308   | 0.8327    | 0.8308 | 0.8293 |
| 0.2286        | 1.56  | 500  | 0.4952          | 0.8353   | 0.8447    | 0.8353 | 0.8226 |
| 0.2299        | 1.87  | 600  | 0.4696          | 0.8367   | 0.8517    | 0.8367 | 0.8358 |
| 0.0542        | 2.18  | 700  | 0.4469          | 0.8499   | 0.8565    | 0.8499 | 0.8516 |
| 0.198         | 2.49  | 800  | 0.5285          | 0.8225   | 0.8616    | 0.8225 | 0.8323 |
| 0.0311        | 2.8   | 900  | 0.4724          | 0.8651   | 0.8687    | 0.8651 | 0.8662 |
| 0.0543        | 3.12  | 1000 | 0.4949          | 0.8665   | 0.8612    | 0.8665 | 0.8611 |
| 0.0242        | 3.43  | 1100 | 0.6283          | 0.8623   | 0.8661    | 0.8623 | 0.8510 |
| 0.0179        | 3.74  | 1200 | 0.5766          | 0.8724   | 0.8681    | 0.8724 | 0.8675 |
| 0.01          | 4.05  | 1300 | 0.6232          | 0.8596   | 0.8523    | 0.8596 | 0.8535 |
| 0.0018        | 4.36  | 1400 | 0.6013          | 0.8741   | 0.8707    | 0.8741 | 0.8710 |
| 0.0019        | 4.67  | 1500 | 0.6554          | 0.8682   | 0.8689    | 0.8682 | 0.8643 |
| 0.0024        | 4.98  | 1600 | 0.6107          | 0.8714   | 0.8730    | 0.8714 | 0.8719 |
| 0.0006        | 5.3   | 1700 | 0.6353          | 0.8755   | 0.8751    | 0.8755 | 0.8725 |


### Framework versions

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

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