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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV31

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0993
- Accuracy: 0.5902

## 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: 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 5    | 1.5163          | 0.4590   |
| No log        | 2.0   | 10   | 1.3238          | 0.4590   |
| 5.237         | 3.0   | 15   | 1.2645          | 0.5082   |
| 5.237         | 4.0   | 20   | 1.1982          | 0.5082   |
| 3.9571        | 5.0   | 25   | 1.1256          | 0.5246   |
| 3.9571        | 6.0   | 30   | 1.2419          | 0.5902   |
| 3.9571        | 7.0   | 35   | 1.1806          | 0.4918   |
| 2.5954        | 8.0   | 40   | 1.3698          | 0.5738   |
| 2.5954        | 9.0   | 45   | 1.3558          | 0.5574   |
| 1.7082        | 10.0  | 50   | 1.5148          | 0.5902   |
| 1.7082        | 11.0  | 55   | 1.6398          | 0.5574   |
| 0.7716        | 12.0  | 60   | 1.6954          | 0.6066   |
| 0.7716        | 13.0  | 65   | 1.8204          | 0.6066   |
| 0.7716        | 14.0  | 70   | 1.8661          | 0.6066   |
| 0.3952        | 15.0  | 75   | 1.9419          | 0.6066   |
| 0.3952        | 16.0  | 80   | 2.1287          | 0.5902   |
| 0.144         | 17.0  | 85   | 2.4729          | 0.5738   |
| 0.144         | 18.0  | 90   | 2.8243          | 0.5410   |
| 0.144         | 19.0  | 95   | 2.6554          | 0.5902   |
| 0.0821        | 20.0  | 100  | 2.5922          | 0.5574   |
| 0.0821        | 21.0  | 105  | 2.9639          | 0.4918   |
| 0.0948        | 22.0  | 110  | 2.8189          | 0.5738   |
| 0.0948        | 23.0  | 115  | 2.8026          | 0.5410   |
| 0.0809        | 24.0  | 120  | 2.6857          | 0.6066   |
| 0.0809        | 25.0  | 125  | 2.8069          | 0.5574   |
| 0.0809        | 26.0  | 130  | 2.7649          | 0.5902   |
| 0.0309        | 27.0  | 135  | 2.8005          | 0.5902   |
| 0.0309        | 28.0  | 140  | 2.8427          | 0.6066   |
| 0.0269        | 29.0  | 145  | 2.9175          | 0.6066   |
| 0.0269        | 30.0  | 150  | 3.0034          | 0.5738   |
| 0.0269        | 31.0  | 155  | 3.0874          | 0.5902   |
| 0.0279        | 32.0  | 160  | 3.0993          | 0.5902   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0

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