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# swinv2-tiny-patch4-window8-256-DMAE-da-colab

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9394
- Accuracy: 0.7391

## 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use 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: 0.1
- num_epochs: 40

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.3823        | 0.9565  | 11   | 1.4058          | 0.1957   |
| 1.3366        | 2.0     | 23   | 1.4482          | 0.1957   |
| 1.2352        | 2.9565  | 34   | 1.2309          | 0.4565   |
| 1.1374        | 4.0     | 46   | 1.1031          | 0.6087   |
| 1.0344        | 4.9565  | 57   | 1.0230          | 0.5870   |
| 0.8772        | 6.0     | 69   | 0.9115          | 0.6522   |
| 0.7321        | 6.9565  | 80   | 0.8858          | 0.6522   |
| 0.6319        | 8.0     | 92   | 0.8665          | 0.6522   |
| 0.6438        | 8.9565  | 103  | 0.7738          | 0.7174   |
| 0.4714        | 10.0    | 115  | 0.8492          | 0.6304   |
| 0.433         | 10.9565 | 126  | 0.8386          | 0.6957   |
| 0.4793        | 12.0    | 138  | 0.9394          | 0.7391   |
| 0.4769        | 12.9565 | 149  | 0.9471          | 0.6522   |
| 0.3872        | 14.0    | 161  | 1.1526          | 0.6087   |
| 0.3906        | 14.9565 | 172  | 1.0575          | 0.6522   |
| 0.3798        | 16.0    | 184  | 1.0593          | 0.6957   |
| 0.3377        | 16.9565 | 195  | 1.0783          | 0.6087   |
| 0.3919        | 18.0    | 207  | 1.1067          | 0.6522   |
| 0.3631        | 18.9565 | 218  | 1.1018          | 0.6739   |
| 0.2762        | 20.0    | 230  | 1.1479          | 0.6522   |
| 0.2935        | 20.9565 | 241  | 1.1055          | 0.6957   |
| 0.3029        | 22.0    | 253  | 1.1203          | 0.6739   |
| 0.2857        | 22.9565 | 264  | 1.2820          | 0.6304   |
| 0.2603        | 24.0    | 276  | 1.2550          | 0.6304   |
| 0.2162        | 24.9565 | 287  | 1.1655          | 0.6739   |
| 0.2465        | 26.0    | 299  | 1.2511          | 0.6739   |
| 0.2238        | 26.9565 | 310  | 1.3461          | 0.6304   |
| 0.2271        | 28.0    | 322  | 1.3472          | 0.6304   |
| 0.2694        | 28.9565 | 333  | 1.4501          | 0.6304   |
| 0.1903        | 30.0    | 345  | 1.4629          | 0.6304   |
| 0.2054        | 30.9565 | 356  | 1.4672          | 0.6304   |
| 0.199         | 32.0    | 368  | 1.4725          | 0.6304   |
| 0.2034        | 32.9565 | 379  | 1.4507          | 0.6522   |
| 0.2048        | 34.0    | 391  | 1.4330          | 0.6304   |
| 0.1767        | 34.9565 | 402  | 1.4638          | 0.6304   |
| 0.1799        | 36.0    | 414  | 1.4232          | 0.6304   |
| 0.1903        | 36.9565 | 425  | 1.4508          | 0.6304   |
| 0.1864        | 38.0    | 437  | 1.4460          | 0.6304   |
| 0.1818        | 38.2609 | 440  | 1.4456          | 0.6304   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3

avanzada leve moderada no dmae