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# finetuned-indian-food

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the indian_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2146
- Accuracy: 0.9426

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.0574        | 0.3003 | 100  | 0.9445          | 0.8576   |
| 0.8399        | 0.6006 | 200  | 0.5542          | 0.8863   |
| 0.6418        | 0.9009 | 300  | 0.5741          | 0.8672   |
| 0.3785        | 1.2012 | 400  | 0.4702          | 0.8842   |
| 0.4451        | 1.5015 | 500  | 0.3685          | 0.9118   |
| 0.4535        | 1.8018 | 600  | 0.3781          | 0.9097   |
| 0.4618        | 2.1021 | 700  | 0.3000          | 0.9288   |
| 0.2321        | 2.4024 | 800  | 0.3146          | 0.9182   |
| 0.1816        | 2.7027 | 900  | 0.3045          | 0.9214   |
| 0.2332        | 3.0030 | 1000 | 0.3446          | 0.9044   |
| 0.1173        | 3.3033 | 1100 | 0.2381          | 0.9416   |
| 0.2694        | 3.6036 | 1200 | 0.2146          | 0.9426   |
| 0.1227        | 3.9039 | 1300 | 0.2259          | 0.9490   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1

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