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

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: 0.9032
- Accuracy: 0.7692

## 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        | 0.8421  | 4    | 1.6342          | 0.1346   |
| No log        | 1.8947  | 9    | 1.5368          | 0.2692   |
| 1.5734        | 2.9474  | 14   | 1.3871          | 0.4808   |
| 1.5734        | 4.0     | 19   | 1.2443          | 0.5192   |
| 1.3253        | 4.8421  | 23   | 1.1185          | 0.5577   |
| 1.3253        | 5.8947  | 28   | 1.0374          | 0.5962   |
| 1.044         | 6.9474  | 33   | 0.9798          | 0.6346   |
| 1.044         | 8.0     | 38   | 0.9702          | 0.6731   |
| 0.8487        | 8.8421  | 42   | 0.9567          | 0.6538   |
| 0.8487        | 9.8947  | 47   | 0.9292          | 0.6538   |
| 0.7359        | 10.9474 | 52   | 0.9042          | 0.6923   |
| 0.7359        | 12.0    | 57   | 0.9032          | 0.7692   |
| 0.6592        | 12.8421 | 61   | 0.9060          | 0.6538   |
| 0.6592        | 13.8947 | 66   | 0.9208          | 0.6538   |
| 0.6257        | 14.9474 | 71   | 0.9272          | 0.6923   |
| 0.6257        | 16.0    | 76   | 1.0044          | 0.6731   |
| 0.5927        | 16.8421 | 80   | 0.9176          | 0.7308   |
| 0.5927        | 17.8947 | 85   | 0.9261          | 0.75     |
| 0.5255        | 18.9474 | 90   | 0.9058          | 0.6538   |
| 0.5255        | 20.0    | 95   | 0.9338          | 0.75     |
| 0.5255        | 20.8421 | 99   | 0.9103          | 0.6923   |
| 0.5098        | 21.8947 | 104  | 0.9329          | 0.75     |
| 0.5098        | 22.9474 | 109  | 0.9886          | 0.75     |
| 0.4347        | 24.0    | 114  | 0.9331          | 0.7308   |
| 0.4347        | 24.8421 | 118  | 1.0086          | 0.6923   |
| 0.4269        | 25.8947 | 123  | 1.0184          | 0.7308   |
| 0.4269        | 26.9474 | 128  | 0.9698          | 0.7115   |
| 0.42          | 28.0    | 133  | 0.9873          | 0.6923   |
| 0.42          | 28.8421 | 137  | 0.9996          | 0.6923   |
| 0.4239        | 29.8947 | 142  | 0.9858          | 0.6923   |
| 0.4239        | 30.9474 | 147  | 0.9964          | 0.7308   |
| 0.3713        | 32.0    | 152  | 1.0338          | 0.7115   |
| 0.3713        | 32.8421 | 156  | 1.0474          | 0.6923   |
| 0.4133        | 33.6842 | 160  | 1.0444          | 0.6923   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1

avanzada avanzada humeda leve moderada no dmae