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# swin-tiny-patch4-window7-224-finetuned-omars6

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5625
- Accuracy: 0.8815

## 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: 0.0005
- 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9598        | 0.99  | 92   | 0.7744          | 0.6869   |
| 0.7825        | 2.0   | 185  | 0.7336          | 0.7082   |
| 0.9638        | 2.99  | 277  | 0.8202          | 0.7204   |
| 1.0288        | 4.0   | 370  | 0.8621          | 0.7903   |
| 0.9711        | 4.99  | 462  | 0.8212          | 0.6809   |
| 1.0125        | 6.0   | 555  | 0.8700          | 0.7356   |
| 0.945         | 6.99  | 647  | 0.7959          | 0.7781   |
| 0.9851        | 8.0   | 740  | 0.8755          | 0.6140   |
| 0.8078        | 8.99  | 832  | 0.6970          | 0.7781   |
| 0.7377        | 10.0  | 925  | 0.6063          | 0.7386   |
| 0.7934        | 10.99 | 1017 | 0.6121          | 0.8116   |
| 0.7986        | 12.0  | 1110 | 0.6532          | 0.8116   |
| 0.6129        | 12.99 | 1202 | 0.7250          | 0.8450   |
| 0.7428        | 14.0  | 1295 | 0.6417          | 0.7264   |
| 0.5661        | 14.99 | 1387 | 0.6847          | 0.7964   |
| 0.6631        | 16.0  | 1480 | 0.5470          | 0.8298   |
| 0.5787        | 16.99 | 1572 | 0.5696          | 0.8359   |
| 0.6635        | 18.0  | 1665 | 0.6385          | 0.7872   |
| 0.5251        | 18.99 | 1757 | 0.5842          | 0.8419   |
| 0.6164        | 20.0  | 1850 | 0.5506          | 0.8207   |
| 0.4166        | 20.99 | 1942 | 0.8169          | 0.8055   |
| 0.4189        | 22.0  | 2035 | 0.5882          | 0.8480   |
| 0.699         | 22.99 | 2127 | 0.5767          | 0.8541   |
| 0.6095        | 24.0  | 2220 | 0.6392          | 0.8845   |
| 0.3837        | 24.99 | 2312 | 0.6109          | 0.8723   |
| 0.4916        | 26.0  | 2405 | 0.4862          | 0.8754   |
| 0.4536        | 26.99 | 2497 | 0.5625          | 0.8754   |
| 0.3636        | 28.0  | 2590 | 0.5948          | 0.8663   |
| 0.4004        | 28.99 | 2682 | 0.5735          | 0.8906   |
| 0.4248        | 29.84 | 2760 | 0.5625          | 0.8815   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3

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