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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV54

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: 0.7753
- 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- 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: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 1.0     | 3    | 1.5852          | 0.1932   |
| No log        | 2.0     | 6    | 1.5784          | 0.3182   |
| No log        | 3.0     | 9    | 1.5374          | 0.4318   |
| 1.3768        | 4.0     | 12   | 1.4629          | 0.4091   |
| 1.3768        | 5.0     | 15   | 1.2222          | 0.5341   |
| 1.3768        | 6.0     | 18   | 1.2437          | 0.5455   |
| 0.942         | 7.0     | 21   | 1.2428          | 0.5341   |
| 0.942         | 8.0     | 24   | 1.1751          | 0.5341   |
| 0.942         | 9.0     | 27   | 1.1279          | 0.5795   |
| 0.6265        | 10.0    | 30   | 0.9868          | 0.6477   |
| 0.6265        | 11.0    | 33   | 0.9661          | 0.6364   |
| 0.6265        | 12.0    | 36   | 0.9892          | 0.6136   |
| 0.6265        | 13.0    | 39   | 0.8716          | 0.6818   |
| 0.5106        | 14.0    | 42   | 0.8010          | 0.7273   |
| 0.5106        | 15.0    | 45   | 0.8596          | 0.6818   |
| 0.5106        | 16.0    | 48   | 0.8257          | 0.6932   |
| 0.4183        | 17.0    | 51   | 0.8190          | 0.7045   |
| 0.4183        | 18.0    | 54   | 0.7628          | 0.7273   |
| 0.4183        | 19.0    | 57   | 0.7802          | 0.7159   |
| 0.3267        | 20.0    | 60   | 0.7753          | 0.75     |
| 0.3267        | 21.0    | 63   | 0.7771          | 0.7386   |
| 0.3267        | 22.0    | 66   | 0.7770          | 0.75     |
| 0.3267        | 23.0    | 69   | 0.7941          | 0.7273   |
| 0.3008        | 24.0    | 72   | 0.7921          | 0.7273   |
| 0.3008        | 25.0    | 75   | 0.7899          | 0.7386   |
| 0.3008        | 26.0    | 78   | 0.7849          | 0.75     |
| 0.2795        | 27.0    | 81   | 0.7891          | 0.75     |
| 0.2795        | 28.0    | 84   | 0.7973          | 0.7386   |
| 0.2795        | 29.0    | 87   | 0.8068          | 0.7386   |
| 0.2526        | 30.0    | 90   | 0.8088          | 0.7386   |
| 0.2526        | 31.0    | 93   | 0.8098          | 0.7386   |
| 0.2526        | 32.0    | 96   | 0.8096          | 0.7386   |
| 0.2526        | 33.0    | 99   | 0.8095          | 0.7386   |
| 0.2544        | 33.3810 | 100  | 0.8094          | 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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