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# swinv2-tiny-patch4-window8-256-DMAE-da2-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: 0.9368
- Accuracy: 0.7609

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.4149        | 0.9565  | 11   | 1.3905          | 0.2174   |
| 1.3431        | 1.9348  | 22   | 1.3828          | 0.3043   |
| 1.2396        | 2.9130  | 33   | 1.2675          | 0.4348   |
| 1.1377        | 3.9783  | 45   | 1.2067          | 0.3478   |
| 1.0144        | 4.9565  | 56   | 0.9060          | 0.6087   |
| 0.9016        | 5.9348  | 67   | 0.8025          | 0.6739   |
| 0.7941        | 6.9130  | 78   | 0.7812          | 0.6957   |
| 0.6986        | 7.9783  | 90   | 0.9441          | 0.5870   |
| 0.6245        | 8.9565  | 101  | 0.8641          | 0.6957   |
| 0.6044        | 9.9348  | 112  | 0.8648          | 0.6087   |
| 0.536         | 10.9130 | 123  | 0.8800          | 0.5870   |
| 0.4825        | 11.9783 | 135  | 0.8388          | 0.7391   |
| 0.4972        | 12.9565 | 146  | 0.8763          | 0.7174   |
| 0.4284        | 13.9348 | 157  | 0.8228          | 0.6957   |
| 0.3961        | 14.9130 | 168  | 0.8260          | 0.7174   |
| 0.3877        | 15.9783 | 180  | 0.9368          | 0.7609   |
| 0.3744        | 16.9565 | 191  | 1.1221          | 0.6304   |
| 0.3266        | 17.9348 | 202  | 1.0177          | 0.6739   |
| 0.3257        | 18.9130 | 213  | 1.0300          | 0.6957   |
| 0.3164        | 19.9783 | 225  | 1.1344          | 0.6957   |
| 0.2965        | 20.9565 | 236  | 0.9283          | 0.7391   |
| 0.293         | 21.9348 | 247  | 1.0128          | 0.6957   |
| 0.2929        | 22.9130 | 258  | 1.0450          | 0.7609   |
| 0.2878        | 23.9783 | 270  | 1.1482          | 0.7174   |
| 0.2447        | 24.9565 | 281  | 1.0716          | 0.7174   |
| 0.2601        | 25.9348 | 292  | 1.0770          | 0.6957   |
| 0.2299        | 26.9130 | 303  | 1.1769          | 0.7391   |
| 0.2401        | 27.9783 | 315  | 1.1407          | 0.7174   |
| 0.2347        | 28.9565 | 326  | 1.1929          | 0.6957   |
| 0.2584        | 29.9348 | 337  | 1.0957          | 0.6739   |
| 0.2204        | 30.9130 | 348  | 1.1721          | 0.6739   |
| 0.2031        | 31.9783 | 360  | 1.0843          | 0.6739   |
| 0.2241        | 32.9565 | 371  | 1.1350          | 0.6957   |
| 0.1798        | 33.9348 | 382  | 1.2419          | 0.6957   |
| 0.2435        | 34.9130 | 393  | 1.1522          | 0.6957   |
| 0.1857        | 35.9783 | 405  | 1.1207          | 0.6957   |
| 0.1889        | 36.9565 | 416  | 1.1711          | 0.6957   |
| 0.2043        | 37.9348 | 427  | 1.1978          | 0.6957   |
| 0.1951        | 38.9130 | 438  | 1.2107          | 0.7174   |
| 0.1901        | 39.1087 | 440  | 1.2108          | 0.7174   |


### Framework versions

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

avanzada leve moderada no dmae