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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV10

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: 1.0099
- Accuracy: 0.6731

## 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        | 0.8421  | 4    | 1.5825          | 0.2885   |
| No log        | 1.8421  | 8    | 1.4864          | 0.5385   |
| No log        | 2.8421  | 12   | 1.3187          | 0.5577   |
| No log        | 3.8421  | 16   | 1.0843          | 0.6731   |
| No log        | 4.8421  | 20   | 1.0044          | 0.5962   |
| No log        | 5.8421  | 24   | 0.8914          | 0.6346   |
| No log        | 6.8421  | 28   | 0.9119          | 0.5769   |
| No log        | 7.8421  | 32   | 0.9379          | 0.6538   |
| No log        | 8.8421  | 36   | 0.9471          | 0.6154   |
| No log        | 9.8421  | 40   | 0.9565          | 0.6538   |
| No log        | 10.8421 | 44   | 0.9581          | 0.6923   |
| No log        | 11.8421 | 48   | 0.9655          | 0.6923   |
| 4.2193        | 12.8421 | 52   | 0.9880          | 0.7115   |
| 4.2193        | 13.8421 | 56   | 0.9557          | 0.6923   |
| 4.2193        | 14.8421 | 60   | 0.9275          | 0.6538   |
| 4.2193        | 15.8421 | 64   | 1.0216          | 0.5769   |
| 4.2193        | 16.8421 | 68   | 0.9646          | 0.6346   |
| 4.2193        | 17.8421 | 72   | 0.9957          | 0.6731   |
| 4.2193        | 18.8421 | 76   | 1.0366          | 0.6346   |
| 4.2193        | 19.8421 | 80   | 0.9978          | 0.6538   |
| 4.2193        | 20.8421 | 84   | 0.9941          | 0.6731   |
| 4.2193        | 21.8421 | 88   | 1.0063          | 0.6923   |
| 4.2193        | 22.8421 | 92   | 1.0114          | 0.6731   |
| 4.2193        | 23.8421 | 96   | 1.0134          | 0.6731   |
| 1.6367        | 24.8421 | 100  | 1.0097          | 0.6731   |
| 1.6367        | 25.8421 | 104  | 1.0084          | 0.6731   |
| 1.6367        | 26.8421 | 108  | 1.0097          | 0.6731   |
| 1.6367        | 27.8421 | 112  | 1.0102          | 0.6731   |
| 1.6367        | 28.8421 | 116  | 1.0100          | 0.6731   |
| 1.6367        | 29.8421 | 120  | 1.0099          | 0.6731   |


### 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