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# finetuned-indian-food

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the indian_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2575
- Accuracy: 0.9320

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.1123        | 0.3003 | 100  | 0.9179          | 0.8799   |
| 0.9547        | 0.6006 | 200  | 0.8116          | 0.8108   |
| 0.8798        | 0.9009 | 300  | 0.5791          | 0.8650   |
| 0.4529        | 1.2012 | 400  | 0.4330          | 0.8990   |
| 0.3011        | 1.5015 | 500  | 0.4332          | 0.8874   |
| 0.4487        | 1.8018 | 600  | 0.3668          | 0.9054   |
| 0.2804        | 2.1021 | 700  | 0.3622          | 0.9086   |
| 0.2845        | 2.4024 | 800  | 0.3347          | 0.9118   |
| 0.2787        | 2.7027 | 900  | 0.3119          | 0.9203   |
| 0.1751        | 3.0030 | 1000 | 0.2879          | 0.9214   |
| 0.1861        | 3.3033 | 1100 | 0.2724          | 0.9309   |
| 0.1015        | 3.6036 | 1200 | 0.2628          | 0.9277   |
| 0.0959        | 3.9039 | 1300 | 0.2575          | 0.9320   |


### 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