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# swinv2-tiny-patch4-window8-256-DMAE-da2-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: 0.7389
- Accuracy: 0.7174

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.3473        | 0.9565  | 11   | 1.3763          | 0.2609   |
| 1.2385        | 1.9348  | 22   | 1.2596          | 0.5      |
| 1.1683        | 2.9130  | 33   | 1.2476          | 0.5      |
| 1.0809        | 3.9783  | 45   | 1.1625          | 0.5435   |
| 1.0147        | 4.9565  | 56   | 1.0490          | 0.5435   |
| 0.9755        | 5.9348  | 67   | 0.9155          | 0.6739   |
| 0.89          | 6.9130  | 78   | 0.9343          | 0.6304   |
| 0.832         | 7.9783  | 90   | 0.7973          | 0.6522   |
| 0.7846        | 8.9565  | 101  | 0.8325          | 0.6087   |
| 0.74          | 9.9348  | 112  | 0.8328          | 0.6087   |
| 0.679         | 10.9130 | 123  | 0.7195          | 0.7174   |
| 0.6487        | 11.9783 | 135  | 0.6740          | 0.7826   |
| 0.6774        | 12.9565 | 146  | 0.7157          | 0.6957   |
| 0.5727        | 13.9348 | 157  | 0.6934          | 0.7174   |
| 0.5872        | 14.9130 | 168  | 0.7335          | 0.6739   |
| 0.6116        | 15.9783 | 180  | 0.6892          | 0.7609   |
| 0.5783        | 16.9565 | 191  | 0.6796          | 0.7174   |
| 0.5658        | 17.9348 | 202  | 0.6966          | 0.7609   |
| 0.5447        | 18.9130 | 213  | 0.6708          | 0.7174   |
| 0.5452        | 19.9783 | 225  | 0.7297          | 0.6957   |
| 0.509         | 20.9565 | 236  | 0.7137          | 0.6522   |
| 0.527         | 21.9348 | 247  | 0.7047          | 0.7174   |
| 0.5382        | 22.9130 | 258  | 0.7737          | 0.6739   |
| 0.508         | 23.9783 | 270  | 0.7073          | 0.7174   |
| 0.4579        | 24.9565 | 281  | 0.7355          | 0.6739   |
| 0.492         | 25.9348 | 292  | 0.7152          | 0.7174   |
| 0.4273        | 26.9130 | 303  | 0.7243          | 0.7174   |
| 0.4823        | 27.9783 | 315  | 0.7477          | 0.6739   |
| 0.4659        | 28.9565 | 326  | 0.7193          | 0.6957   |
| 0.4401        | 29.9348 | 337  | 0.7509          | 0.6957   |
| 0.4717        | 30.9130 | 348  | 0.7327          | 0.6957   |
| 0.4139        | 31.9783 | 360  | 0.7041          | 0.7174   |
| 0.4365        | 32.9565 | 371  | 0.7286          | 0.6957   |
| 0.4088        | 33.9348 | 382  | 0.7495          | 0.7174   |
| 0.4505        | 34.9130 | 393  | 0.7399          | 0.7174   |
| 0.4143        | 35.9783 | 405  | 0.7160          | 0.7174   |
| 0.4093        | 36.9565 | 416  | 0.7210          | 0.7174   |
| 0.43          | 37.9348 | 427  | 0.7342          | 0.7174   |
| 0.424         | 38.9130 | 438  | 0.7386          | 0.7174   |
| 0.4169        | 39.1087 | 440  | 0.7389          | 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