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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV51

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: 0.6495
- Accuracy: 0.8182

## 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: 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    | 1.5572          | 0.3409   |
| No log        | 2.0   | 10   | 1.3890          | 0.4545   |
| 1.4186        | 3.0   | 15   | 1.2638          | 0.5795   |
| 1.4186        | 4.0   | 20   | 1.0291          | 0.6023   |
| 1.0199        | 5.0   | 25   | 1.0125          | 0.5909   |
| 1.0199        | 6.0   | 30   | 0.8328          | 0.6477   |
| 1.0199        | 7.0   | 35   | 0.8662          | 0.625    |
| 0.7093        | 8.0   | 40   | 0.7048          | 0.7045   |
| 0.7093        | 9.0   | 45   | 0.8032          | 0.6818   |
| 0.576         | 10.0  | 50   | 0.6944          | 0.7273   |
| 0.576         | 11.0  | 55   | 0.7730          | 0.6932   |
| 0.4817        | 12.0  | 60   | 0.6605          | 0.7386   |
| 0.4817        | 13.0  | 65   | 0.7316          | 0.75     |
| 0.4817        | 14.0  | 70   | 0.6380          | 0.7727   |
| 0.413         | 15.0  | 75   | 0.6573          | 0.7727   |
| 0.413         | 16.0  | 80   | 0.6570          | 0.75     |
| 0.3959        | 17.0  | 85   | 0.6173          | 0.7955   |
| 0.3959        | 18.0  | 90   | 0.6293          | 0.7841   |
| 0.3959        | 19.0  | 95   | 0.6491          | 0.7727   |
| 0.3043        | 20.0  | 100  | 0.6382          | 0.7955   |
| 0.3043        | 21.0  | 105  | 0.6272          | 0.7955   |
| 0.295         | 22.0  | 110  | 0.6423          | 0.8068   |
| 0.295         | 23.0  | 115  | 0.6413          | 0.8068   |
| 0.2365        | 24.0  | 120  | 0.6388          | 0.7841   |
| 0.2365        | 25.0  | 125  | 0.6457          | 0.7841   |
| 0.2365        | 26.0  | 130  | 0.6513          | 0.7955   |
| 0.2507        | 27.0  | 135  | 0.6495          | 0.8182   |
| 0.2507        | 28.0  | 140  | 0.6463          | 0.8182   |
| 0.2385        | 29.0  | 145  | 0.6468          | 0.8068   |
| 0.2385        | 30.0  | 150  | 0.6480          | 0.8068   |
| 0.2385        | 31.0  | 155  | 0.6484          | 0.8068   |
| 0.2432        | 32.0  | 160  | 0.6486          | 0.8068   |


### Framework versions

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

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