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# pre_CIDAUTv4

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: 0.0177
- Accuracy: 0.9918

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.8889 | 4    | 0.6742          | 0.5679   |
| No log        | 2.0    | 9    | 0.3347          | 0.9218   |
| 0.6003        | 2.8889 | 13   | 0.1238          | 0.9753   |
| 0.6003        | 4.0    | 18   | 0.1298          | 0.9465   |
| 0.199         | 4.8889 | 22   | 0.0360          | 0.9877   |
| 0.199         | 6.0    | 27   | 0.1049          | 0.9671   |
| 0.0832        | 6.8889 | 31   | 0.0058          | 1.0      |
| 0.0832        | 8.0    | 36   | 0.0138          | 0.9918   |
| 0.0438        | 8.8889 | 40   | 0.0177          | 0.9918   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0

editada real