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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV52

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: 0.7948
- Accuracy: 0.7841

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 1.0     | 6    | 1.6162          | 0.2045   |
| 1.3978        | 2.0     | 12   | 1.6140          | 0.1818   |
| 1.3978        | 3.0     | 18   | 1.4029          | 0.4318   |
| 1.0539        | 4.0     | 24   | 1.2503          | 0.5455   |
| 1.0539        | 5.0     | 30   | 1.0014          | 0.625    |
| 0.7171        | 6.0     | 36   | 0.9539          | 0.6364   |
| 0.7171        | 7.0     | 42   | 0.9958          | 0.6136   |
| 0.5557        | 8.0     | 48   | 0.8233          | 0.7386   |
| 0.5557        | 9.0     | 54   | 0.8813          | 0.6136   |
| 0.4942        | 10.0    | 60   | 0.8385          | 0.7159   |
| 0.4942        | 11.0    | 66   | 0.7914          | 0.7614   |
| 0.3957        | 12.0    | 72   | 0.7742          | 0.7273   |
| 0.3957        | 13.0    | 78   | 0.8122          | 0.7045   |
| 0.3664        | 14.0    | 84   | 0.7981          | 0.75     |
| 0.3664        | 15.0    | 90   | 0.7852          | 0.7159   |
| 0.3042        | 16.0    | 96   | 0.8829          | 0.7159   |
| 0.3042        | 17.0    | 102  | 0.7630          | 0.7386   |
| 0.2673        | 18.0    | 108  | 0.7936          | 0.75     |
| 0.2673        | 19.0    | 114  | 0.7491          | 0.7727   |
| 0.2308        | 20.0    | 120  | 0.7948          | 0.7841   |
| 0.2308        | 21.0    | 126  | 0.7798          | 0.7841   |
| 0.2113        | 22.0    | 132  | 0.7635          | 0.7614   |
| 0.2113        | 23.0    | 138  | 0.8521          | 0.7159   |
| 0.1852        | 24.0    | 144  | 0.8660          | 0.7386   |
| 0.1852        | 25.0    | 150  | 0.7984          | 0.7386   |
| 0.1765        | 26.0    | 156  | 0.7750          | 0.7614   |
| 0.1765        | 27.0    | 162  | 0.7935          | 0.7386   |
| 0.1969        | 28.0    | 168  | 0.7956          | 0.75     |
| 0.1969        | 29.0    | 174  | 0.7902          | 0.7727   |
| 0.1502        | 30.0    | 180  | 0.7868          | 0.7614   |
| 0.1502        | 31.0    | 186  | 0.7842          | 0.7614   |
| 0.1621        | 32.0    | 192  | 0.7836          | 0.75     |
| 0.1621        | 33.0    | 198  | 0.7837          | 0.75     |
| 0.1621        | 33.3810 | 200  | 0.7838          | 0.75     |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0

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