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# swinv2-tiny-patch4-window8-256-DMAE-4e-3

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: 0.7507
- 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: 3e-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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.8571  | 3    | 1.3959          | 0.3043   |
| No log        | 1.7857  | 6    | 1.2662          | 0.3913   |
| No log        | 2.7143  | 9    | 1.1960          | 0.4783   |
| 1.3226        | 3.9286  | 13   | 1.1950          | 0.4565   |
| 1.3226        | 4.8571  | 16   | 1.1891          | 0.4783   |
| 1.3226        | 5.7857  | 19   | 1.1898          | 0.4783   |
| 1.1833        | 6.7143  | 22   | 1.1824          | 0.5435   |
| 1.1833        | 7.9286  | 26   | 1.1618          | 0.5217   |
| 1.1833        | 8.8571  | 29   | 1.1359          | 0.5652   |
| 1.1384        | 9.7857  | 32   | 1.0974          | 0.5870   |
| 1.1384        | 10.7143 | 35   | 1.0524          | 0.5870   |
| 1.1384        | 11.9286 | 39   | 1.0083          | 0.6957   |
| 1.0628        | 12.8571 | 42   | 0.9696          | 0.6739   |
| 1.0628        | 13.7857 | 45   | 0.9369          | 0.6739   |
| 1.0628        | 14.7143 | 48   | 0.8825          | 0.7174   |
| 1.0069        | 15.9286 | 52   | 0.8396          | 0.6957   |
| 1.0069        | 16.8571 | 55   | 0.8267          | 0.7174   |
| 1.0069        | 17.7857 | 58   | 0.8275          | 0.7174   |
| 0.9339        | 18.7143 | 61   | 0.8255          | 0.7174   |
| 0.9339        | 19.9286 | 65   | 0.7899          | 0.7174   |
| 0.9339        | 20.8571 | 68   | 0.7604          | 0.7174   |
| 0.905         | 21.7857 | 71   | 0.7442          | 0.6957   |
| 0.905         | 22.7143 | 74   | 0.7361          | 0.7391   |
| 0.905         | 23.9286 | 78   | 0.7598          | 0.6957   |
| 0.8465        | 24.8571 | 81   | 0.7650          | 0.7174   |
| 0.8465        | 25.7857 | 84   | 0.7631          | 0.7391   |
| 0.8465        | 26.7143 | 87   | 0.7561          | 0.7174   |
| 0.8363        | 27.9286 | 91   | 0.7494          | 0.6957   |
| 0.8363        | 28.8571 | 94   | 0.7539          | 0.7174   |
| 0.8363        | 29.7857 | 97   | 0.7497          | 0.7174   |
| 0.7751        | 30.7143 | 100  | 0.7477          | 0.7174   |
| 0.7751        | 31.9286 | 104  | 0.7463          | 0.7609   |
| 0.7751        | 32.8571 | 107  | 0.7507          | 0.7609   |
| 0.7843        | 33.7857 | 110  | 0.7534          | 0.7391   |
| 0.7843        | 34.7143 | 113  | 0.7542          | 0.7391   |
| 0.7843        | 35.9286 | 117  | 0.7519          | 0.7391   |
| 0.7435        | 36.8571 | 120  | 0.7507          | 0.7391   |


### Framework versions

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

avanzada leve moderada no dmae