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# vit-ytr-demo-v3

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the SkinCancerClassification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5923
- Accuracy: 0.8712

## 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: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7103        | 0.31  | 100  | 0.7668          | 0.7464   |
| 0.8059        | 0.62  | 200  | 0.7692          | 0.7374   |
| 0.8991        | 0.93  | 300  | 0.6572          | 0.7639   |
| 0.7064        | 1.25  | 400  | 0.6511          | 0.7718   |
| 0.6537        | 1.56  | 500  | 0.7332          | 0.7609   |
| 0.5387        | 1.87  | 600  | 0.6083          | 0.7743   |
| 0.5508        | 2.18  | 700  | 0.5943          | 0.7938   |
| 0.5509        | 2.49  | 800  | 0.5373          | 0.8118   |
| 0.5104        | 2.8   | 900  | 0.5612          | 0.7973   |
| 0.3535        | 3.12  | 1000 | 0.5531          | 0.8113   |
| 0.2151        | 3.43  | 1100 | 0.6024          | 0.7958   |
| 0.3758        | 3.74  | 1200 | 0.5272          | 0.8198   |
| 0.4745        | 4.05  | 1300 | 0.4738          | 0.8318   |
| 0.4279        | 4.36  | 1400 | 0.4848          | 0.8248   |
| 0.4029        | 4.67  | 1500 | 0.4958          | 0.8243   |
| 0.3744        | 4.98  | 1600 | 0.5503          | 0.8078   |
| 0.4498        | 5.3   | 1700 | 0.5024          | 0.8382   |
| 0.3793        | 5.61  | 1800 | 0.4926          | 0.8283   |
| 0.3461        | 5.92  | 1900 | 0.4819          | 0.8367   |
| 0.3781        | 6.23  | 2000 | 0.5399          | 0.8183   |
| 0.3122        | 6.54  | 2100 | 0.5061          | 0.8397   |
| 0.2431        | 6.85  | 2200 | 0.4794          | 0.8372   |
| 0.2484        | 7.17  | 2300 | 0.5713          | 0.8342   |
| 0.284         | 7.48  | 2400 | 0.5165          | 0.8308   |
| 0.1815        | 7.79  | 2500 | 0.4973          | 0.8407   |
| 0.0996        | 8.1   | 2600 | 0.5248          | 0.8467   |
| 0.2379        | 8.41  | 2700 | 0.5535          | 0.8472   |
| 0.2408        | 8.72  | 2800 | 0.5308          | 0.8362   |
| 0.2151        | 9.03  | 2900 | 0.4568          | 0.8607   |
| 0.1258        | 9.35  | 3000 | 0.5181          | 0.8507   |
| 0.1132        | 9.66  | 3100 | 0.5151          | 0.8477   |
| 0.09          | 9.97  | 3200 | 0.5395          | 0.8532   |
| 0.1324        | 10.28 | 3300 | 0.5641          | 0.8452   |
| 0.0447        | 10.59 | 3400 | 0.5345          | 0.8537   |
| 0.0795        | 10.9  | 3500 | 0.5789          | 0.8577   |
| 0.1806        | 11.21 | 3600 | 0.6749          | 0.8442   |
| 0.0822        | 11.53 | 3700 | 0.5518          | 0.8622   |
| 0.0915        | 11.84 | 3800 | 0.5960          | 0.8607   |
| 0.1171        | 12.15 | 3900 | 0.5547          | 0.8682   |
| 0.1169        | 12.46 | 4000 | 0.5873          | 0.8647   |
| 0.1077        | 12.77 | 4100 | 0.5914          | 0.8652   |
| 0.035         | 13.08 | 4200 | 0.5513          | 0.8642   |
| 0.088         | 13.4  | 4300 | 0.5972          | 0.8627   |
| 0.0419        | 13.71 | 4400 | 0.5934          | 0.8632   |
| 0.0762        | 14.02 | 4500 | 0.5979          | 0.8692   |
| 0.0773        | 14.33 | 4600 | 0.6091          | 0.8652   |
| 0.0611        | 14.64 | 4700 | 0.5911          | 0.8647   |
| 0.0623        | 14.95 | 4800 | 0.5923          | 0.8712   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2

label_0 label_1 label_2 label_3 label_4 label_5 label_6