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# vit-base-nationality

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2289
- Precision: 0.5992
- Recall: 0.6005
- Accuracy: 0.6005
- F1: 0.5861
- Score: 0.6005

## 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: 0.001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- 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
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1     | Score  |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|:------:|
| 1.2527        | 1.0   | 105  | 1.2744          | 0.5925    | 0.5820 | 0.5820   | 0.5631 | 0.5820 |


### Framework versions

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

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