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# cidaut_version_1

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0103
- Accuracy: 1.0

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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: 8

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.9524 | 5    | 0.5816          | 0.6790   |
| 0.578         | 1.9048 | 10   | 0.2481          | 0.9506   |
| 0.578         | 2.8571 | 15   | 0.0853          | 0.9877   |
| 0.1389        | 4.0    | 21   | 0.0528          | 0.9877   |
| 0.1389        | 4.9524 | 26   | 0.0357          | 0.9877   |
| 0.0891        | 5.9048 | 31   | 0.0606          | 0.9815   |
| 0.0891        | 6.8571 | 36   | 0.0949          | 0.9753   |
| 0.1003        | 7.6190 | 40   | 0.0103          | 1.0      |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3

editada real