
<!-- 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-DAV55

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: 0.7678
- Accuracy: 0.8068

## 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_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 1.0     | 6    | 1.7159          | 0.0682   |
| 1.4016        | 2.0     | 12   | 1.6803          | 0.125    |
| 1.4016        | 3.0     | 18   | 1.5242          | 0.2955   |
| 1.105         | 4.0     | 24   | 1.2325          | 0.4318   |
| 1.105         | 5.0     | 30   | 1.2531          | 0.5114   |
| 0.8077        | 6.0     | 36   | 1.1026          | 0.5      |
| 0.8077        | 7.0     | 42   | 0.9852          | 0.5909   |
| 0.6405        | 8.0     | 48   | 0.8993          | 0.7045   |
| 0.6405        | 9.0     | 54   | 0.9155          | 0.6364   |
| 0.5807        | 10.0    | 60   | 1.0444          | 0.5568   |
| 0.5807        | 11.0    | 66   | 0.7696          | 0.7727   |
| 0.511         | 12.0    | 72   | 0.9046          | 0.7159   |
| 0.511         | 13.0    | 78   | 0.7709          | 0.7727   |
| 0.4578        | 14.0    | 84   | 0.8067          | 0.7273   |
| 0.4578        | 15.0    | 90   | 0.8394          | 0.7614   |
| 0.357         | 16.0    | 96   | 0.7473          | 0.7727   |
| 0.357         | 17.0    | 102  | 0.7678          | 0.8068   |
| 0.313         | 18.0    | 108  | 0.7593          | 0.7614   |
| 0.313         | 19.0    | 114  | 0.7597          | 0.7841   |
| 0.2741        | 20.0    | 120  | 0.7861          | 0.75     |
| 0.2741        | 21.0    | 126  | 0.7381          | 0.7386   |
| 0.2508        | 22.0    | 132  | 0.7422          | 0.7955   |
| 0.2508        | 23.0    | 138  | 0.7751          | 0.75     |
| 0.1992        | 24.0    | 144  | 0.7758          | 0.7386   |
| 0.1992        | 25.0    | 150  | 0.7272          | 0.7841   |
| 0.1897        | 26.0    | 156  | 0.7843          | 0.7841   |
| 0.1897        | 27.0    | 162  | 0.7606          | 0.7727   |
| 0.2024        | 28.0    | 168  | 0.7456          | 0.7955   |
| 0.2024        | 29.0    | 174  | 0.7653          | 0.7955   |
| 0.172         | 30.0    | 180  | 0.7677          | 0.7727   |
| 0.172         | 31.0    | 186  | 0.7421          | 0.7614   |
| 0.1561        | 32.0    | 192  | 0.7326          | 0.7614   |
| 0.1561        | 33.0    | 198  | 0.7541          | 0.7614   |
| 0.1472        | 34.0    | 204  | 0.7635          | 0.7727   |
| 0.1472        | 35.0    | 210  | 0.7504          | 0.7727   |
| 0.1402        | 36.0    | 216  | 0.7601          | 0.7727   |
| 0.1402        | 37.0    | 222  | 0.7683          | 0.7841   |
| 0.1414        | 38.0    | 228  | 0.7707          | 0.7841   |
| 0.1414        | 39.0    | 234  | 0.7727          | 0.7727   |
| 0.1344        | 40.0    | 240  | 0.7721          | 0.7727   |
| 0.1344        | 41.0    | 246  | 0.7715          | 0.7727   |
| 0.1344        | 41.7619 | 250  | 0.7712          | 0.7727   |


### 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