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# vit-food

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the thini77/data-food-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2856
- Accuracy: 0.9143

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 53   | 0.5583          | 0.9238   |
| 0.8727        | 2.0   | 106  | 0.3149          | 0.9238   |
| 0.8727        | 3.0   | 159  | 0.2508          | 0.9238   |
| 0.306         | 4.0   | 212  | 0.2295          | 0.9238   |
| 0.306         | 5.0   | 265  | 0.2246          | 0.9238   |


### Framework versions

- Transformers 4.52.3
- Pytorch 2.7.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1

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