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# vit-beta2-0.99

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: 0.5248
- Accuracy: 0.8623
- Precision: 0.8584
- Recall: 0.8623
- F1: 0.8596

## 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=(0.9,0.99) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1733
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.7217        | 1.0   | 321  | 1.0190          | 0.7035   | 0.6635    | 0.7035 | 0.6527 |
| 1.1622        | 2.0   | 642  | 0.7386          | 0.7056   | 0.7576    | 0.7056 | 0.7192 |
| 1.0368        | 3.0   | 963  | 0.6550          | 0.7517   | 0.7564    | 0.7517 | 0.7214 |
| 0.9653        | 4.0   | 1284 | 0.5641          | 0.7843   | 0.7948    | 0.7843 | 0.7863 |
| 0.9272        | 5.0   | 1605 | 0.7957          | 0.6768   | 0.7959    | 0.6768 | 0.7035 |
| 0.9878        | 6.0   | 1926 | 0.5809          | 0.7871   | 0.8062    | 0.7871 | 0.7904 |
| 0.872         | 7.0   | 2247 | 0.6815          | 0.7216   | 0.8081    | 0.7216 | 0.7442 |
| 0.7998        | 8.0   | 2568 | 0.6104          | 0.7559   | 0.8143    | 0.7559 | 0.7723 |
| 0.733         | 9.0   | 2889 | 0.5296          | 0.8148   | 0.8254    | 0.8148 | 0.8172 |
| 0.6957        | 10.0  | 3210 | 0.5797          | 0.7996   | 0.8322    | 0.7996 | 0.8052 |
| 0.6271        | 11.0  | 3531 | 0.5926          | 0.7933   | 0.8343    | 0.7933 | 0.8058 |
| 0.5614        | 12.0  | 3852 | 0.5879          | 0.7920   | 0.8384    | 0.7920 | 0.8060 |
| 0.4576        | 13.0  | 4173 | 0.6665          | 0.8138   | 0.8312    | 0.8138 | 0.8028 |
| 0.4645        | 14.0  | 4494 | 0.5515          | 0.8294   | 0.8470    | 0.8294 | 0.8329 |
| 0.3913        | 15.0  | 4815 | 0.5474          | 0.8225   | 0.8466    | 0.8225 | 0.8288 |
| 0.3693        | 16.0  | 5136 | 0.5769          | 0.8235   | 0.8464    | 0.8235 | 0.8308 |
| 0.2794        | 17.0  | 5457 | 0.5328          | 0.8509   | 0.8571    | 0.8509 | 0.8516 |
| 0.2677        | 18.0  | 5778 | 0.5248          | 0.8623   | 0.8584    | 0.8623 | 0.8596 |
| 0.2104        | 19.0  | 6099 | 0.6284          | 0.8433   | 0.8572    | 0.8433 | 0.8473 |
| 0.2459        | 20.0  | 6420 | 0.6137          | 0.8544   | 0.8596    | 0.8544 | 0.8555 |
| 0.1769        | 21.0  | 6741 | 0.5960          | 0.8637   | 0.8573    | 0.8637 | 0.8566 |
| 0.1294        | 22.0  | 7062 | 0.5844          | 0.8700   | 0.8687    | 0.8700 | 0.8687 |
| 0.1597        | 23.0  | 7383 | 0.6580          | 0.8665   | 0.8604    | 0.8665 | 0.8589 |
| 0.1227        | 24.0  | 7704 | 0.6226          | 0.8731   | 0.8720    | 0.8731 | 0.8712 |
| 0.1054        | 25.0  | 8025 | 0.6198          | 0.8752   | 0.8728    | 0.8752 | 0.8721 |
| 0.0945        | 26.0  | 8346 | 0.6050          | 0.8793   | 0.8757    | 0.8793 | 0.8764 |
| 0.1242        | 27.0  | 8667 | 0.6078          | 0.8828   | 0.8788    | 0.8828 | 0.8798 |
| 0.0819        | 28.0  | 8988 | 0.6190          | 0.8797   | 0.8748    | 0.8797 | 0.8756 |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

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