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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV5

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: 1.1815
- Accuracy: 0.6346

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.0169        | 1.0   | 10   | 1.4927          | 0.3654   |
| 2.7431        | 2.0   | 20   | 1.2785          | 0.5577   |
| 2.1791        | 3.0   | 30   | 1.0628          | 0.5769   |
| 1.7293        | 4.0   | 40   | 1.0541          | 0.5577   |
| 1.3855        | 5.0   | 50   | 0.9817          | 0.6346   |
| 1.3507        | 6.0   | 60   | 0.9506          | 0.6154   |
| 1.186         | 7.0   | 70   | 0.9259          | 0.5962   |
| 1.1201        | 8.0   | 80   | 0.8867          | 0.6346   |
| 1.0185        | 9.0   | 90   | 0.9556          | 0.5769   |
| 0.981         | 10.0  | 100  | 0.9069          | 0.6923   |
| 0.8472        | 11.0  | 110  | 0.9413          | 0.6346   |
| 0.7333        | 12.0  | 120  | 0.9594          | 0.6346   |
| 0.7           | 13.0  | 130  | 0.9818          | 0.6538   |
| 0.7062        | 14.0  | 140  | 0.9588          | 0.6154   |
| 0.6666        | 15.0  | 150  | 1.1824          | 0.5769   |
| 0.6756        | 16.0  | 160  | 1.0542          | 0.6538   |
| 0.6148        | 17.0  | 170  | 1.0112          | 0.6346   |
| 0.6123        | 18.0  | 180  | 1.2390          | 0.5769   |
| 0.5931        | 19.0  | 190  | 1.0358          | 0.6923   |
| 0.5242        | 20.0  | 200  | 1.1471          | 0.6346   |
| 0.5499        | 21.0  | 210  | 1.0452          | 0.6731   |
| 0.4806        | 22.0  | 220  | 1.0887          | 0.6346   |
| 0.4294        | 23.0  | 230  | 1.1078          | 0.6346   |
| 0.5176        | 24.0  | 240  | 1.1218          | 0.5769   |
| 0.4051        | 25.0  | 250  | 1.1255          | 0.6731   |
| 0.4486        | 26.0  | 260  | 1.0775          | 0.6346   |
| 0.4262        | 27.0  | 270  | 1.0711          | 0.6731   |
| 0.4717        | 28.0  | 280  | 1.0975          | 0.6346   |
| 0.4067        | 29.0  | 290  | 1.0647          | 0.6731   |
| 0.3691        | 30.0  | 300  | 1.1139          | 0.6346   |
| 0.4446        | 31.0  | 310  | 1.1270          | 0.5962   |
| 0.3399        | 32.0  | 320  | 1.1498          | 0.6346   |
| 0.3449        | 33.0  | 330  | 1.1864          | 0.6346   |
| 0.4118        | 34.0  | 340  | 1.1989          | 0.5962   |
| 0.3945        | 35.0  | 350  | 1.1928          | 0.5962   |
| 0.3609        | 36.0  | 360  | 1.1815          | 0.6346   |


### 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