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# vit-base-patch16-224-isic248

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4425
- Accuracy: 0.6

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 1
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.4757        | 0.9888  | 22   | 2.2018          | 0.15     |
| 2.0035        | 1.9775  | 44   | 1.9165          | 0.35     |
| 1.5959        | 2.9663  | 66   | 1.6904          | 0.35     |
| 1.1695        | 4.0     | 89   | 1.5546          | 0.45     |
| 0.8869        | 4.9888  | 111  | 1.5021          | 0.45     |
| 0.6398        | 5.9775  | 133  | 1.5211          | 0.5      |
| 0.5405        | 6.9663  | 155  | 1.4225          | 0.55     |
| 0.3626        | 8.0     | 178  | 1.3762          | 0.45     |
| 0.2971        | 8.9888  | 200  | 1.5558          | 0.55     |
| 0.2324        | 9.9775  | 222  | 1.4037          | 0.55     |
| 0.169         | 10.9663 | 244  | 1.4982          | 0.55     |
| 0.1625        | 12.0    | 267  | 1.3437          | 0.55     |
| 0.1088        | 12.9888 | 289  | 1.2965          | 0.55     |
| 0.1924        | 13.9775 | 311  | 1.2558          | 0.5      |
| 0.124         | 14.9663 | 333  | 1.4687          | 0.5      |
| 0.0901        | 16.0    | 356  | 1.5054          | 0.6      |
| 0.0641        | 16.9888 | 378  | 1.4898          | 0.5      |
| 0.1093        | 17.9775 | 400  | 1.3880          | 0.55     |
| 0.1307        | 18.9663 | 422  | 1.4609          | 0.45     |
| 0.1268        | 20.0    | 445  | 1.5605          | 0.6      |
| 0.0416        | 20.9888 | 467  | 1.5644          | 0.55     |
| 0.0538        | 21.9775 | 489  | 1.5292          | 0.6      |
| 0.109         | 22.9663 | 511  | 1.4049          | 0.6      |
| 0.0633        | 24.0    | 534  | 1.5017          | 0.55     |
| 0.031         | 24.9888 | 556  | 1.4835          | 0.55     |
| 0.0926        | 25.9775 | 578  | 1.4584          | 0.6      |
| 0.067         | 26.9663 | 600  | 1.5057          | 0.65     |
| 0.0344        | 28.0    | 623  | 1.4680          | 0.6      |
| 0.0806        | 28.9888 | 645  | 1.4399          | 0.6      |
| 0.0329        | 29.6629 | 660  | 1.4425          | 0.6      |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.0
- Datasets 2.19.2
- Tokenizers 0.19.1

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