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# swin-tiny-patch4-window7-224-dmae-va-U

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an AMD dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0900
- Accuracy: 0.9725

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.9   | 7    | 1.4643          | 0.2110   |
| 1.4218        | 1.94  | 15   | 1.4070          | 0.3303   |
| 1.3226        | 2.97  | 23   | 1.3454          | 0.3486   |
| 1.1908        | 4.0   | 31   | 1.1438          | 0.4220   |
| 1.1908        | 4.9   | 38   | 0.8730          | 0.5780   |
| 0.9441        | 5.94  | 46   | 0.8100          | 0.6422   |
| 0.7185        | 6.97  | 54   | 0.6099          | 0.7339   |
| 0.6526        | 8.0   | 62   | 0.5096          | 0.7890   |
| 0.6526        | 8.9   | 69   | 0.4925          | 0.8165   |
| 0.5185        | 9.94  | 77   | 0.3989          | 0.8349   |
| 0.4946        | 10.97 | 85   | 0.3276          | 0.8807   |
| 0.4469        | 12.0  | 93   | 0.3023          | 0.8899   |
| 0.376         | 12.9  | 100  | 0.3112          | 0.8991   |
| 0.376         | 13.94 | 108  | 0.2117          | 0.9266   |
| 0.3156        | 14.97 | 116  | 0.2024          | 0.9174   |
| 0.366         | 16.0  | 124  | 0.2065          | 0.9450   |
| 0.2806        | 16.9  | 131  | 0.1942          | 0.9174   |
| 0.2806        | 17.94 | 139  | 0.2393          | 0.9174   |
| 0.2695        | 18.97 | 147  | 0.1498          | 0.9541   |
| 0.2357        | 20.0  | 155  | 0.1465          | 0.9358   |
| 0.2345        | 20.9  | 162  | 0.1522          | 0.9633   |
| 0.2157        | 21.94 | 170  | 0.1403          | 0.9450   |
| 0.2157        | 22.97 | 178  | 0.0999          | 0.9541   |
| 0.1894        | 24.0  | 186  | 0.1427          | 0.9633   |
| 0.2195        | 24.9  | 193  | 0.0949          | 0.9633   |
| 0.1874        | 25.94 | 201  | 0.1152          | 0.9633   |
| 0.1874        | 26.97 | 209  | 0.1226          | 0.9541   |
| 0.1815        | 28.0  | 217  | 0.0964          | 0.9633   |
| 0.1619        | 28.9  | 224  | 0.0912          | 0.9633   |
| 0.201         | 29.94 | 232  | 0.0903          | 0.9633   |
| 0.1659        | 30.97 | 240  | 0.0745          | 0.9633   |
| 0.1659        | 32.0  | 248  | 0.0781          | 0.9633   |
| 0.1459        | 32.9  | 255  | 0.0930          | 0.9633   |
| 0.1459        | 33.94 | 263  | 0.0900          | 0.9725   |
| 0.1487        | 34.97 | 271  | 0.0796          | 0.9725   |
| 0.1487        | 36.0  | 279  | 0.0784          | 0.9725   |
| 0.1504        | 36.13 | 280  | 0.0784          | 0.9725   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
avanzada leve moderada no dmae