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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV36

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6972
- Accuracy: 0.68

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.214         | 1.0   | 22   | 1.4507          | 0.46     |
| 2.298         | 2.0   | 44   | 1.0632          | 0.62     |
| 0.9579        | 3.0   | 66   | 1.1191          | 0.64     |
| 0.4479        | 4.0   | 88   | 0.9825          | 0.6467   |
| 0.1963        | 5.0   | 110  | 1.2844          | 0.6467   |
| 0.1663        | 6.0   | 132  | 1.2373          | 0.6667   |
| 0.1188        | 7.0   | 154  | 1.4338          | 0.6933   |
| 0.0526        | 8.0   | 176  | 1.6726          | 0.7133   |
| 0.01          | 9.0   | 198  | 2.5248          | 0.6267   |
| 0.028         | 10.0  | 220  | 2.6156          | 0.6467   |
| 0.0296        | 11.0  | 242  | 2.8334          | 0.6533   |
| 0.0074        | 12.0  | 264  | 2.2200          | 0.6867   |
| 0.0022        | 13.0  | 286  | 2.2802          | 0.7467   |
| 0.0225        | 14.0  | 308  | 2.1764          | 0.6933   |
| 0.0058        | 15.0  | 330  | 3.0594          | 0.62     |
| 0.0075        | 16.0  | 352  | 3.2166          | 0.6333   |
| 0.0163        | 17.0  | 374  | 2.4014          | 0.6933   |
| 0.0033        | 18.0  | 396  | 2.9112          | 0.6733   |
| 0.0036        | 19.0  | 418  | 2.8147          | 0.6533   |
| 0.0033        | 20.0  | 440  | 2.7731          | 0.6733   |
| 0.0161        | 21.0  | 462  | 2.0340          | 0.7467   |
| 0.0012        | 22.0  | 484  | 2.4596          | 0.6867   |
| 0.0009        | 23.0  | 506  | 2.7352          | 0.6667   |
| 0.0101        | 24.0  | 528  | 2.8204          | 0.6667   |
| 0.0011        | 25.0  | 550  | 2.8091          | 0.6733   |
| 0.0005        | 26.0  | 572  | 2.8126          | 0.6667   |
| 0.0007        | 27.0  | 594  | 2.7742          | 0.6733   |
| 0.0004        | 28.0  | 616  | 2.7208          | 0.6733   |
| 0.0025        | 29.0  | 638  | 2.7022          | 0.6733   |
| 0.0025        | 30.0  | 660  | 2.6972          | 0.68     |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0

label_0 label_1 label_2 label_3 label_4