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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV49

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: 0.7811
- Accuracy: 0.7386

## 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: 1.5e-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        | 0.9412  | 8    | 1.5166          | 0.4318   |
| 1.5823        | 1.9412  | 16   | 1.4043          | 0.4432   |
| 1.5029        | 2.9412  | 24   | 1.3230          | 0.5      |
| 1.5029        | 3.9412  | 32   | 1.2373          | 0.5795   |
| 1.3569        | 4.9412  | 40   | 1.0701          | 0.6023   |
| 1.1064        | 5.9412  | 48   | 0.9832          | 0.6023   |
| 1.1064        | 6.9412  | 56   | 0.9004          | 0.6705   |
| 0.941         | 7.9412  | 64   | 0.8323          | 0.6591   |
| 0.7975        | 8.9412  | 72   | 0.7830          | 0.6818   |
| 0.7975        | 9.9412  | 80   | 0.7657          | 0.7045   |
| 0.7242        | 10.9412 | 88   | 0.7484          | 0.7386   |
| 0.6308        | 11.9412 | 96   | 0.7143          | 0.7386   |
| 0.6308        | 12.9412 | 104  | 0.6923          | 0.7273   |
| 0.5782        | 13.9412 | 112  | 0.6776          | 0.7386   |
| 0.5333        | 14.9412 | 120  | 0.6889          | 0.7614   |
| 0.5333        | 15.9412 | 128  | 0.6799          | 0.7841   |
| 0.495         | 16.9412 | 136  | 0.6794          | 0.7614   |
| 0.4931        | 17.9412 | 144  | 0.6921          | 0.7614   |
| 0.4931        | 18.9412 | 152  | 0.7162          | 0.7273   |
| 0.435         | 19.9412 | 160  | 0.7128          | 0.7386   |
| 0.4109        | 20.9412 | 168  | 0.7157          | 0.75     |
| 0.4109        | 21.9412 | 176  | 0.7404          | 0.7386   |
| 0.3897        | 22.9412 | 184  | 0.7275          | 0.7386   |
| 0.3718        | 23.9412 | 192  | 0.7492          | 0.7727   |
| 0.3718        | 24.9412 | 200  | 0.7520          | 0.7386   |
| 0.3866        | 25.9412 | 208  | 0.7550          | 0.7273   |
| 0.366         | 26.9412 | 216  | 0.7395          | 0.7386   |
| 0.366         | 27.9412 | 224  | 0.7340          | 0.7386   |
| 0.3454        | 28.9412 | 232  | 0.7578          | 0.7273   |
| 0.346         | 29.9412 | 240  | 0.7679          | 0.7273   |
| 0.346         | 30.9412 | 248  | 0.7546          | 0.75     |
| 0.3325        | 31.9412 | 256  | 0.7600          | 0.75     |
| 0.3117        | 32.9412 | 264  | 0.7798          | 0.7386   |
| 0.3117        | 33.9412 | 272  | 0.7944          | 0.7273   |
| 0.3177        | 34.9412 | 280  | 0.7856          | 0.7386   |
| 0.3263        | 35.9412 | 288  | 0.7813          | 0.7386   |
| 0.3263        | 36.9412 | 296  | 0.7798          | 0.7386   |
| 0.3305        | 37.9412 | 304  | 0.7804          | 0.7386   |
| 0.2999        | 38.9412 | 312  | 0.7810          | 0.7386   |
| 0.2999        | 39.9412 | 320  | 0.7811          | 0.7386   |


### Framework versions

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

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