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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV9

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: 1.2286
- Accuracy: 0.5192

## 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- 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
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 3    | 1.5944          | 0.2308   |
| No log        | 2.0   | 6    | 1.5511          | 0.2692   |
| No log        | 3.0   | 9    | 1.4915          | 0.3462   |
| 6.2974        | 4.0   | 12   | 1.4388          | 0.4615   |
| 6.2974        | 5.0   | 15   | 1.3927          | 0.4615   |
| 6.2974        | 6.0   | 18   | 1.3394          | 0.4423   |
| 5.3611        | 7.0   | 21   | 1.3108          | 0.4423   |
| 5.3611        | 8.0   | 24   | 1.3680          | 0.3462   |
| 5.3611        | 9.0   | 27   | 1.2718          | 0.4038   |
| 3.7205        | 10.0  | 30   | 1.2679          | 0.4231   |
| 3.7205        | 11.0  | 33   | 1.3010          | 0.4038   |
| 3.7205        | 12.0  | 36   | 1.2598          | 0.4231   |
| 3.7205        | 13.0  | 39   | 1.2016          | 0.4231   |
| 2.8178        | 14.0  | 42   | 1.1934          | 0.4423   |
| 2.8178        | 15.0  | 45   | 1.1842          | 0.4808   |
| 2.8178        | 16.0  | 48   | 1.1539          | 0.5      |
| 2.4001        | 17.0  | 51   | 1.1308          | 0.4808   |
| 2.4001        | 18.0  | 54   | 1.2173          | 0.4615   |
| 2.4001        | 19.0  | 57   | 1.1670          | 0.5      |
| 2.081         | 20.0  | 60   | 1.1792          | 0.5      |
| 2.081         | 21.0  | 63   | 1.2286          | 0.5192   |
| 2.081         | 22.0  | 66   | 1.2633          | 0.5      |
| 2.081         | 23.0  | 69   | 1.2380          | 0.5      |
| 1.8588        | 24.0  | 72   | 1.2498          | 0.4808   |
| 1.8588        | 25.0  | 75   | 1.2591          | 0.5      |
| 1.8588        | 26.0  | 78   | 1.2653          | 0.5      |
| 1.7634        | 27.0  | 81   | 1.2599          | 0.5      |
| 1.7634        | 28.0  | 84   | 1.2549          | 0.5      |
| 1.7634        | 29.0  | 87   | 1.2545          | 0.5192   |
| 1.8177        | 30.0  | 90   | 1.2547          | 0.5192   |


### Framework versions

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

avanzada avanzada humeda leve moderada no dmae