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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV50

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: 0.8354
- Accuracy: 0.7273

## 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        | 1.0   | 5    | 1.5451          | 0.3864   |
| No log        | 2.0   | 10   | 1.5220          | 0.3864   |
| 1.4177        | 3.0   | 15   | 1.4938          | 0.4205   |
| 1.4177        | 4.0   | 20   | 1.4111          | 0.4432   |
| 1.2671        | 5.0   | 25   | 1.2941          | 0.4545   |
| 1.2671        | 6.0   | 30   | 1.2036          | 0.4545   |
| 1.2671        | 7.0   | 35   | 1.0816          | 0.5114   |
| 0.9869        | 8.0   | 40   | 1.0452          | 0.5795   |
| 0.9869        | 9.0   | 45   | 0.9876          | 0.625    |
| 0.8456        | 10.0  | 50   | 0.9791          | 0.5909   |
| 0.8456        | 11.0  | 55   | 0.9662          | 0.6023   |
| 0.7126        | 12.0  | 60   | 0.9302          | 0.6364   |
| 0.7126        | 13.0  | 65   | 0.9379          | 0.625    |
| 0.7126        | 14.0  | 70   | 0.9036          | 0.6705   |
| 0.6561        | 15.0  | 75   | 0.8846          | 0.6591   |
| 0.6561        | 16.0  | 80   | 0.8689          | 0.6591   |
| 0.6367        | 17.0  | 85   | 0.8543          | 0.6591   |
| 0.6367        | 18.0  | 90   | 0.8342          | 0.6932   |
| 0.6367        | 19.0  | 95   | 0.8185          | 0.6705   |
| 0.5463        | 20.0  | 100  | 0.8290          | 0.7159   |
| 0.5463        | 21.0  | 105  | 0.8354          | 0.7273   |
| 0.5504        | 22.0  | 110  | 0.8160          | 0.7159   |
| 0.5504        | 23.0  | 115  | 0.8073          | 0.7159   |
| 0.507         | 24.0  | 120  | 0.8071          | 0.7045   |
| 0.507         | 25.0  | 125  | 0.8071          | 0.6932   |
| 0.507         | 26.0  | 130  | 0.8047          | 0.7045   |
| 0.5226        | 27.0  | 135  | 0.8000          | 0.7045   |
| 0.5226        | 28.0  | 140  | 0.7987          | 0.7159   |
| 0.5144        | 29.0  | 145  | 0.8000          | 0.7159   |
| 0.5144        | 30.0  | 150  | 0.8002          | 0.7159   |
| 0.5144        | 31.0  | 155  | 0.8008          | 0.7159   |
| 0.4862        | 32.0  | 160  | 0.8008          | 0.7159   |


### Framework versions

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

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