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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV37

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: 1.8351
- Accuracy: 0.6538

## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.8   | 3    | 1.6284          | 0.1346   |
| No log        | 1.8   | 6    | 1.5966          | 0.2404   |
| No log        | 2.8   | 9    | 1.5076          | 0.3942   |
| 6.28          | 3.8   | 12   | 1.2912          | 0.4615   |
| 6.28          | 4.8   | 15   | 1.2137          | 0.5096   |
| 6.28          | 5.8   | 18   | 1.1917          | 0.5385   |
| 6.28          | 6.8   | 21   | 1.1498          | 0.5673   |
| 2.9539        | 7.8   | 24   | 1.2026          | 0.5865   |
| 2.9539        | 8.8   | 27   | 1.2711          | 0.5962   |
| 2.9539        | 9.8   | 30   | 1.3534          | 0.625    |
| 2.9539        | 10.8  | 33   | 1.3210          | 0.625    |
| 0.9643        | 11.8  | 36   | 1.3940          | 0.6346   |
| 0.9643        | 12.8  | 39   | 1.4859          | 0.6346   |
| 0.9643        | 13.8  | 42   | 1.4965          | 0.6346   |
| 0.9643        | 14.8  | 45   | 1.5463          | 0.625    |
| 0.3275        | 15.8  | 48   | 1.5885          | 0.6346   |
| 0.3275        | 16.8  | 51   | 1.6466          | 0.6442   |
| 0.3275        | 17.8  | 54   | 1.8351          | 0.6538   |
| 0.3275        | 18.8  | 57   | 1.8326          | 0.6442   |
| 0.1501        | 19.8  | 60   | 1.7521          | 0.6346   |
| 0.1501        | 20.8  | 63   | 1.7806          | 0.6538   |
| 0.1501        | 21.8  | 66   | 1.7669          | 0.6538   |
| 0.1501        | 22.8  | 69   | 1.8874          | 0.6346   |
| 0.09          | 23.8  | 72   | 1.8827          | 0.6538   |
| 0.09          | 24.8  | 75   | 1.8330          | 0.6538   |
| 0.09          | 25.8  | 78   | 1.8331          | 0.6538   |
| 0.09          | 26.8  | 81   | 1.8410          | 0.6538   |
| 0.0595        | 27.8  | 84   | 1.8441          | 0.6442   |
| 0.0595        | 28.8  | 87   | 1.8444          | 0.6538   |
| 0.0595        | 29.8  | 90   | 1.8446          | 0.6538   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0

label_0 label_1 label_2 label_3 label_4