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# food_classify_viT

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8683
- Accuracy: 0.8948

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.9970 | 83   | 1.6473          | 0.8236   |
| No log        | 1.9940 | 166  | 1.1061          | 0.8863   |
| No log        | 2.9910 | 249  | 0.9208          | 0.8820   |
| No log        | 3.9880 | 332  | 0.8683          | 0.8948   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3

burger butter_naan chai chapati chole_bhature dal_makhani dhokla fried_rice idli jalebi kaathi_rolls kadai_paneer kulfi masala_dosa momos paani_puri pakode pav_bhaji pizza samosa