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# pre_CIDAUTv5

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: 0.0190
- Accuracy: 0.9938

## 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: 8

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.9524 | 5    | 0.6238          | 0.6460   |
| 0.5991        | 1.9048 | 10   | 0.2637          | 0.9814   |
| 0.5991        | 2.8571 | 15   | 0.0767          | 0.9938   |
| 0.1441        | 4.0    | 21   | 0.0365          | 0.9876   |
| 0.1441        | 4.9524 | 26   | 0.0399          | 0.9876   |
| 0.075         | 5.9048 | 31   | 0.0216          | 0.9938   |
| 0.075         | 6.8571 | 36   | 0.0126          | 1.0      |
| 0.0581        | 7.6190 | 40   | 0.0190          | 0.9938   |


### Framework versions

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

editada real