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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV60

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: 0.5934
- Accuracy: 0.8333

## 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.5779        | 0.9630  | 13   | 1.5147          | 0.3509   |
| 1.532         | 1.9630  | 26   | 1.3726          | 0.5395   |
| 1.459         | 2.9630  | 39   | 0.9852          | 0.6184   |
| 1.0357        | 3.9630  | 52   | 0.8558          | 0.6140   |
| 0.9678        | 4.9630  | 65   | 0.7997          | 0.6623   |
| 0.9602        | 5.9630  | 78   | 0.7789          | 0.6711   |
| 0.7664        | 6.9630  | 91   | 0.8890          | 0.6184   |
| 0.8537        | 7.9630  | 104  | 0.6860          | 0.7105   |
| 0.7566        | 8.9630  | 117  | 0.6138          | 0.7588   |
| 0.7025        | 9.9630  | 130  | 0.5207          | 0.7939   |
| 0.6081        | 10.9630 | 143  | 0.5644          | 0.7763   |
| 0.631         | 11.9630 | 156  | 0.5859          | 0.7544   |
| 0.6163        | 12.9630 | 169  | 0.7182          | 0.7105   |
| 0.5743        | 13.9630 | 182  | 0.5643          | 0.7763   |
| 0.5752        | 14.9630 | 195  | 0.5028          | 0.7939   |
| 0.461         | 15.9630 | 208  | 0.5465          | 0.7807   |
| 0.4145        | 16.9630 | 221  | 0.5868          | 0.7719   |
| 0.4065        | 17.9630 | 234  | 0.5470          | 0.7807   |
| 0.501         | 18.9630 | 247  | 0.5406          | 0.7939   |
| 0.4374        | 19.9630 | 260  | 0.5534          | 0.7939   |
| 0.4614        | 20.9630 | 273  | 0.5485          | 0.8158   |
| 0.3836        | 21.9630 | 286  | 0.6217          | 0.7851   |
| 0.4474        | 22.9630 | 299  | 0.6069          | 0.7763   |
| 0.3893        | 23.9630 | 312  | 0.5981          | 0.7939   |
| 0.3548        | 24.9630 | 325  | 0.6003          | 0.7895   |
| 0.3454        | 25.9630 | 338  | 0.5897          | 0.8114   |
| 0.2857        | 26.9630 | 351  | 0.6031          | 0.8158   |
| 0.3282        | 27.9630 | 364  | 0.6140          | 0.7763   |
| 0.3088        | 28.9630 | 377  | 0.5934          | 0.8333   |
| 0.2943        | 29.9630 | 390  | 0.6545          | 0.7895   |
| 0.2857        | 30.9630 | 403  | 0.6423          | 0.7851   |
| 0.2882        | 31.9630 | 416  | 0.6610          | 0.7939   |
| 0.3342        | 32.9630 | 429  | 0.6815          | 0.7807   |
| 0.2679        | 33.9630 | 442  | 0.6481          | 0.8114   |
| 0.2756        | 34.9630 | 455  | 0.6518          | 0.8158   |
| 0.2733        | 35.9630 | 468  | 0.6514          | 0.8026   |
| 0.2749        | 36.9630 | 481  | 0.6525          | 0.8070   |
| 0.2416        | 37.9630 | 494  | 0.6447          | 0.8202   |
| 0.2766        | 38.9630 | 507  | 0.6359          | 0.8202   |
| 0.2528        | 39.9630 | 520  | 0.6481          | 0.8158   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0

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