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# vit-tiny-patch16-224-finetuned-papsmear

This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2121
- Accuracy: 0.9338

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.4005        | 0.9935  | 38   | 1.2214          | 0.5294   |
| 0.8877        | 1.9869  | 76   | 1.0727          | 0.6691   |
| 0.603         | 2.9804  | 114  | 0.6807          | 0.7574   |
| 0.465         | 4.0     | 153  | 0.6485          | 0.7574   |
| 0.432         | 4.9935  | 191  | 0.5024          | 0.8015   |
| 0.2957        | 5.9869  | 229  | 0.4485          | 0.8162   |
| 0.2203        | 6.9804  | 267  | 0.3850          | 0.8529   |
| 0.236         | 8.0     | 306  | 0.3628          | 0.8456   |
| 0.1857        | 8.9935  | 344  | 0.2930          | 0.8824   |
| 0.1907        | 9.9869  | 382  | 0.2121          | 0.9338   |
| 0.1546        | 10.9804 | 420  | 0.2242          | 0.9265   |
| 0.1375        | 12.0    | 459  | 0.1918          | 0.9191   |
| 0.1237        | 12.9935 | 497  | 0.1809          | 0.9338   |
| 0.1637        | 13.9869 | 535  | 0.1774          | 0.9338   |
| 0.0803        | 14.9020 | 570  | 0.1882          | 0.9338   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1

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