
<!-- 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-DAV40

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: 0.7428
- Accuracy: 0.7614

## 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: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 4    | 1.5349          | 0.3409   |
| No log        | 2.0   | 8    | 1.3213          | 0.4432   |
| 4.7629        | 3.0   | 12   | 1.2541          | 0.4432   |
| 4.7629        | 4.0   | 16   | 1.2072          | 0.6023   |
| 4.7629        | 5.0   | 20   | 1.1313          | 0.6364   |
| 3.7987        | 6.0   | 24   | 1.0712          | 0.6477   |
| 3.7987        | 7.0   | 28   | 0.9677          | 0.6591   |
| 3.7987        | 8.0   | 32   | 0.8655          | 0.7159   |
| 3.0437        | 9.0   | 36   | 0.8564          | 0.6818   |
| 3.0437        | 10.0  | 40   | 0.8003          | 0.6818   |
| 3.0437        | 11.0  | 44   | 0.7987          | 0.7386   |
| 2.4867        | 12.0  | 48   | 0.7619          | 0.7159   |
| 2.4867        | 13.0  | 52   | 0.7426          | 0.7386   |
| 2.4867        | 14.0  | 56   | 0.7492          | 0.6932   |
| 2.147         | 15.0  | 60   | 0.7827          | 0.7159   |
| 2.147         | 16.0  | 64   | 0.7509          | 0.7045   |
| 2.147         | 17.0  | 68   | 0.7364          | 0.7386   |
| 1.8443        | 18.0  | 72   | 0.7705          | 0.7159   |
| 1.8443        | 19.0  | 76   | 0.7515          | 0.7273   |
| 1.8443        | 20.0  | 80   | 0.7470          | 0.7386   |
| 1.659         | 21.0  | 84   | 0.7495          | 0.75     |
| 1.659         | 22.0  | 88   | 0.7237          | 0.75     |
| 1.659         | 23.0  | 92   | 0.7440          | 0.75     |
| 1.5303        | 24.0  | 96   | 0.7367          | 0.75     |
| 1.5303        | 25.0  | 100  | 0.7428          | 0.7614   |
| 1.5303        | 26.0  | 104  | 0.7407          | 0.75     |
| 1.4305        | 27.0  | 108  | 0.7406          | 0.75     |
| 1.4305        | 28.0  | 112  | 0.7423          | 0.75     |
| 1.4305        | 29.0  | 116  | 0.7427          | 0.75     |
| 1.3529        | 30.0  | 120  | 0.7428          | 0.75     |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0

avanzada avanzada humeda leve moderada no dmae