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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV48

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: 0.7283
- Accuracy: 0.75

## 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: 2e-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: reduce_lr_on_plateau
- 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    | 1.4387          | 0.4432   |
| 1.5789        | 1.9412  | 16   | 1.3131          | 0.5568   |
| 1.3907        | 2.9412  | 24   | 1.1805          | 0.5909   |
| 1.3907        | 3.9412  | 32   | 1.0386          | 0.6136   |
| 1.1967        | 4.9412  | 40   | 1.0065          | 0.6136   |
| 1.0098        | 5.9412  | 48   | 0.8786          | 0.6477   |
| 1.0098        | 6.9412  | 56   | 0.8264          | 0.6932   |
| 0.863         | 7.9412  | 64   | 0.8026          | 0.7273   |
| 0.7309        | 8.9412  | 72   | 0.7853          | 0.7159   |
| 0.7309        | 9.9412  | 80   | 0.7649          | 0.7273   |
| 0.6597        | 10.9412 | 88   | 0.7671          | 0.7386   |
| 0.56          | 11.9412 | 96   | 0.7551          | 0.7159   |
| 0.56          | 12.9412 | 104  | 0.7428          | 0.7273   |
| 0.5207        | 13.9412 | 112  | 0.7396          | 0.7273   |
| 0.5108        | 14.9412 | 120  | 0.7368          | 0.7273   |
| 0.5108        | 15.9412 | 128  | 0.7366          | 0.7386   |
| 0.5062        | 16.9412 | 136  | 0.7364          | 0.7273   |
| 0.5069        | 17.9412 | 144  | 0.7329          | 0.7386   |
| 0.5069        | 18.9412 | 152  | 0.7285          | 0.7273   |
| 0.4952        | 19.9412 | 160  | 0.7371          | 0.7386   |
| 0.4979        | 20.9412 | 168  | 0.7436          | 0.7386   |
| 0.4979        | 21.9412 | 176  | 0.7338          | 0.7386   |
| 0.4745        | 22.9412 | 184  | 0.7291          | 0.75     |
| 0.4735        | 23.9412 | 192  | 0.7305          | 0.75     |
| 0.4735        | 24.9412 | 200  | 0.7301          | 0.75     |
| 0.4862        | 25.9412 | 208  | 0.7283          | 0.75     |
| 0.4955        | 26.9412 | 216  | 0.7273          | 0.75     |
| 0.4955        | 27.9412 | 224  | 0.7275          | 0.75     |
| 0.4602        | 28.9412 | 232  | 0.7280          | 0.75     |
| 0.4714        | 29.9412 | 240  | 0.7291          | 0.75     |
| 0.4714        | 30.9412 | 248  | 0.7298          | 0.75     |
| 0.4727        | 31.9412 | 256  | 0.7301          | 0.75     |
| 0.4689        | 32.9412 | 264  | 0.7293          | 0.75     |
| 0.4689        | 33.9412 | 272  | 0.7287          | 0.75     |
| 0.4725        | 34.9412 | 280  | 0.7287          | 0.75     |
| 0.4747        | 35.9412 | 288  | 0.7284          | 0.75     |
| 0.4747        | 36.9412 | 296  | 0.7284          | 0.75     |
| 0.5012        | 37.9412 | 304  | 0.7284          | 0.75     |
| 0.462         | 38.9412 | 312  | 0.7286          | 0.75     |
| 0.462         | 39.9412 | 320  | 0.7283          | 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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