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# vit-base-patch16-224-RXL1-24

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6158
- Accuracy: 0.8431

## 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: 5.5e-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: 0.05
- num_epochs: 24

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3745        | 0.95  | 13   | 1.3056          | 0.4706   |
| 1.2896        | 1.96  | 27   | 1.1039          | 0.6471   |
| 0.9896        | 2.98  | 41   | 0.9413          | 0.6471   |
| 0.8472        | 4.0   | 55   | 0.9059          | 0.6275   |
| 0.7375        | 4.95  | 68   | 0.6520          | 0.8039   |
| 0.458         | 5.96  | 82   | 0.6754          | 0.8039   |
| 0.3807        | 6.98  | 96   | 0.6158          | 0.8431   |
| 0.3003        | 8.0   | 110  | 0.5666          | 0.8039   |
| 0.2337        | 8.95  | 123  | 0.5409          | 0.8039   |
| 0.2252        | 9.96  | 137  | 0.7382          | 0.7647   |
| 0.1644        | 10.98 | 151  | 0.6363          | 0.8039   |
| 0.1608        | 12.0  | 165  | 0.6941          | 0.8039   |
| 0.1354        | 12.95 | 178  | 0.6985          | 0.7843   |
| 0.1298        | 13.96 | 192  | 0.6610          | 0.8039   |
| 0.1333        | 14.98 | 206  | 0.6751          | 0.8039   |
| 0.1209        | 16.0  | 220  | 0.7723          | 0.7843   |
| 0.1057        | 16.95 | 233  | 0.8038          | 0.7255   |
| 0.0972        | 17.96 | 247  | 0.8375          | 0.7647   |
| 0.0789        | 18.98 | 261  | 0.6971          | 0.8235   |
| 0.0833        | 20.0  | 275  | 0.7507          | 0.7843   |
| 0.0813        | 20.95 | 288  | 0.7085          | 0.7843   |
| 0.0803        | 21.96 | 302  | 0.7566          | 0.7647   |
| 0.0693        | 22.69 | 312  | 0.7772          | 0.7647   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0

avanzada leve moderada no dmae