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# swinv2-tiny-patch4-window8-256-DMAE-da-colab2

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: 0.8835
- 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: 1.5e-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
- num_epochs: 40

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.357         | 0.9565  | 11   | 1.3906          | 0.3913   |
| 1.2964        | 2.0     | 23   | 1.2819          | 0.4348   |
| 1.1609        | 2.9565  | 34   | 1.1804          | 0.4783   |
| 1.0747        | 4.0     | 46   | 1.0911          | 0.6087   |
| 1.027         | 4.9565  | 57   | 1.0176          | 0.6304   |
| 0.8985        | 6.0     | 69   | 0.8963          | 0.6739   |
| 0.8031        | 6.9565  | 80   | 0.9867          | 0.6739   |
| 0.7744        | 8.0     | 92   | 0.8710          | 0.6522   |
| 0.7488        | 8.9565  | 103  | 0.8845          | 0.6957   |
| 0.6767        | 10.0    | 115  | 0.8693          | 0.6957   |
| 0.6082        | 10.9565 | 126  | 0.8133          | 0.6739   |
| 0.6354        | 12.0    | 138  | 0.8771          | 0.6739   |
| 0.6422        | 12.9565 | 149  | 0.8137          | 0.7174   |
| 0.584         | 14.0    | 161  | 0.8861          | 0.6522   |
| 0.5763        | 14.9565 | 172  | 0.8459          | 0.7391   |
| 0.5238        | 16.0    | 184  | 0.8590          | 0.7174   |
| 0.528         | 16.9565 | 195  | 0.8705          | 0.7174   |
| 0.5626        | 18.0    | 207  | 0.8636          | 0.7174   |
| 0.5395        | 18.9565 | 218  | 0.8794          | 0.6957   |
| 0.4696        | 20.0    | 230  | 0.8835          | 0.7609   |
| 0.488         | 20.9565 | 241  | 0.8889          | 0.7391   |
| 0.4764        | 22.0    | 253  | 0.9109          | 0.7174   |
| 0.4668        | 22.9565 | 264  | 0.8893          | 0.7391   |
| 0.4676        | 24.0    | 276  | 0.9082          | 0.6957   |
| 0.4619        | 24.9565 | 287  | 0.9353          | 0.7174   |
| 0.4727        | 26.0    | 299  | 0.9331          | 0.7174   |
| 0.4461        | 26.9565 | 310  | 0.8937          | 0.7391   |
| 0.428         | 28.0    | 322  | 0.9175          | 0.7174   |
| 0.4694        | 28.9565 | 333  | 0.9340          | 0.6957   |
| 0.3812        | 30.0    | 345  | 0.9722          | 0.6739   |
| 0.4252        | 30.9565 | 356  | 0.9433          | 0.7174   |
| 0.3883        | 32.0    | 368  | 0.9420          | 0.7391   |
| 0.4228        | 32.9565 | 379  | 0.9483          | 0.6739   |
| 0.4288        | 34.0    | 391  | 0.9529          | 0.7174   |
| 0.3982        | 34.9565 | 402  | 0.9506          | 0.7174   |
| 0.3935        | 36.0    | 414  | 0.9539          | 0.6739   |
| 0.3974        | 36.9565 | 425  | 0.9599          | 0.6957   |
| 0.3893        | 38.0    | 437  | 0.9608          | 0.6957   |
| 0.4201        | 38.2609 | 440  | 0.9608          | 0.6957   |


### Framework versions

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

avanzada leve moderada no dmae