
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV47

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: 0.9284
- Accuracy: 0.75

## 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: 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_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9412  | 8    | 1.5602          | 0.3409   |
| 1.6237        | 1.9412  | 16   | 1.3767          | 0.4432   |
| 1.4913        | 2.9412  | 24   | 1.3316          | 0.6136   |
| 1.4913        | 3.9412  | 32   | 1.0605          | 0.6591   |
| 1.2218        | 4.9412  | 40   | 0.9235          | 0.6932   |
| 0.9148        | 5.9412  | 48   | 0.8240          | 0.75     |
| 0.9148        | 6.9412  | 56   | 0.7359          | 0.6932   |
| 0.7686        | 7.9412  | 64   | 0.7190          | 0.6932   |
| 0.6291        | 8.9412  | 72   | 0.6824          | 0.7273   |
| 0.6291        | 9.9412  | 80   | 0.7034          | 0.7614   |
| 0.5546        | 10.9412 | 88   | 0.6911          | 0.7727   |
| 0.4494        | 11.9412 | 96   | 0.6893          | 0.75     |
| 0.4494        | 12.9412 | 104  | 0.6927          | 0.7727   |
| 0.3719        | 13.9412 | 112  | 0.7180          | 0.7955   |
| 0.3478        | 14.9412 | 120  | 0.7574          | 0.7159   |
| 0.3478        | 15.9412 | 128  | 0.7665          | 0.7159   |
| 0.3212        | 16.9412 | 136  | 0.8369          | 0.7386   |
| 0.3184        | 17.9412 | 144  | 0.7906          | 0.7159   |
| 0.3184        | 18.9412 | 152  | 0.8438          | 0.7273   |
| 0.2873        | 19.9412 | 160  | 0.8233          | 0.7273   |
| 0.2553        | 20.9412 | 168  | 0.8062          | 0.7386   |
| 0.2553        | 21.9412 | 176  | 0.8711          | 0.7159   |
| 0.2373        | 22.9412 | 184  | 0.8673          | 0.7386   |
| 0.2208        | 23.9412 | 192  | 0.8600          | 0.7273   |
| 0.2208        | 24.9412 | 200  | 0.8984          | 0.7159   |
| 0.2353        | 25.9412 | 208  | 0.8848          | 0.7273   |
| 0.2187        | 26.9412 | 216  | 0.8569          | 0.75     |
| 0.2187        | 27.9412 | 224  | 0.8817          | 0.7386   |
| 0.1943        | 28.9412 | 232  | 0.8949          | 0.75     |
| 0.1926        | 29.9412 | 240  | 0.9077          | 0.7159   |
| 0.1926        | 30.9412 | 248  | 0.9200          | 0.7159   |
| 0.1816        | 31.9412 | 256  | 0.9233          | 0.7386   |
| 0.1744        | 32.9412 | 264  | 0.9231          | 0.7386   |
| 0.1744        | 33.9412 | 272  | 0.9329          | 0.7273   |
| 0.1718        | 34.9412 | 280  | 0.9277          | 0.7386   |
| 0.1701        | 35.9412 | 288  | 0.9258          | 0.75     |
| 0.1701        | 36.9412 | 296  | 0.9262          | 0.75     |
| 0.1921        | 37.9412 | 304  | 0.9274          | 0.75     |
| 0.161         | 38.9412 | 312  | 0.9282          | 0.75     |
| 0.161         | 39.9412 | 320  | 0.9284          | 0.75     |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0

avanzada avanzada humeda leve moderada no dmae