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

This model is a fine-tuned version of [xlagor/swin-tiny-patch4-window7-224-finetuned-fit](https://huggingface.co/xlagor/swin-tiny-patch4-window7-224-finetuned-fit) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0711
- Accuracy: 0.9772

## 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: 120
- eval_batch_size: 120
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 480
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3727        | 0.99  | 62   | 0.1103          | 0.9680   |
| 0.3551        | 1.99  | 125  | 0.1018          | 0.9701   |
| 0.3258        | 3.0   | 188  | 0.0995          | 0.9706   |
| 0.3008        | 4.0   | 251  | 0.0939          | 0.9712   |
| 0.2896        | 4.99  | 313  | 0.0872          | 0.9730   |
| 0.2612        | 5.99  | 376  | 0.0829          | 0.9739   |
| 0.2275        | 7.0   | 439  | 0.0815          | 0.9748   |
| 0.2358        | 8.0   | 502  | 0.0839          | 0.9739   |
| 0.2191        | 8.99  | 564  | 0.0778          | 0.9775   |
| 0.2096        | 9.99  | 627  | 0.0759          | 0.9769   |
| 0.2063        | 11.0  | 690  | 0.0749          | 0.9778   |
| 0.1916        | 12.0  | 753  | 0.0735          | 0.9775   |
| 0.2002        | 12.99 | 815  | 0.0732          | 0.9781   |
| 0.1905        | 13.99 | 878  | 0.0713          | 0.9784   |
| 0.1835        | 15.0  | 941  | 0.0707          | 0.9784   |
| 0.1949        | 15.81 | 992  | 0.0711          | 0.9772   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

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