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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: 0.6964
- Accuracy: 0.8228

## 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: 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 3.1649        | 0.1832 | 100  | 2.9846          | 0.5133   |
| 2.5186        | 0.3663 | 200  | 2.2326          | 0.5620   |
| 2.0329        | 0.5495 | 300  | 1.9683          | 0.5737   |
| 1.9823        | 0.7326 | 400  | 1.7542          | 0.6126   |
| 1.7406        | 0.9158 | 500  | 1.5623          | 0.6379   |
| 1.3739        | 1.0989 | 600  | 1.3842          | 0.6989   |
| 1.3276        | 1.2821 | 700  | 1.3007          | 0.6937   |
| 1.0045        | 1.4652 | 800  | 1.1943          | 0.7132   |
| 1.1167        | 1.6484 | 900  | 1.1326          | 0.7502   |
| 1.1052        | 1.8315 | 1000 | 1.0366          | 0.7638   |
| 0.8164        | 2.0147 | 1100 | 1.0313          | 0.7508   |
| 0.7205        | 2.1978 | 1200 | 0.9928          | 0.7547   |
| 0.6983        | 2.3810 | 1300 | 0.9180          | 0.7761   |
| 0.568         | 2.5641 | 1400 | 0.8566          | 0.7930   |
| 0.7619        | 2.7473 | 1500 | 0.8324          | 0.7962   |
| 0.7556        | 2.9304 | 1600 | 0.8008          | 0.8014   |
| 0.5914        | 3.1136 | 1700 | 0.7661          | 0.8138   |
| 0.5826        | 3.2967 | 1800 | 0.7614          | 0.8079   |
| 0.5295        | 3.4799 | 1900 | 0.7281          | 0.8235   |
| 0.398         | 3.6630 | 2000 | 0.7051          | 0.8235   |
| 0.43          | 3.8462 | 2100 | 0.6964          | 0.8228   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1

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