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# deit-tiny-patch16-224-finetuned-papsmear

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4747
- Accuracy: 0.8235

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.5381        | 0.9935  | 38   | 1.4222          | 0.3897   |
| 1.172         | 1.9869  | 76   | 1.1008          | 0.5882   |
| 0.8361        | 2.9804  | 114  | 0.8529          | 0.6618   |
| 0.6869        | 4.0     | 153  | 0.9582          | 0.6324   |
| 0.4995        | 4.9935  | 191  | 0.6926          | 0.7574   |
| 0.4576        | 5.9869  | 229  | 0.4967          | 0.8529   |
| 0.4187        | 6.9804  | 267  | 0.5350          | 0.8162   |
| 0.4075        | 8.0     | 306  | 0.4903          | 0.8088   |
| 0.3585        | 8.9935  | 344  | 0.5252          | 0.7868   |
| 0.3528        | 9.9869  | 382  | 0.5027          | 0.8088   |
| 0.2788        | 10.9804 | 420  | 0.4503          | 0.8456   |
| 0.2419        | 12.0    | 459  | 0.4857          | 0.8309   |
| 0.2544        | 12.9935 | 497  | 0.5543          | 0.7868   |
| 0.2591        | 13.9869 | 535  | 0.4839          | 0.8382   |
| 0.207         | 14.9020 | 570  | 0.4747          | 0.8235   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1

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