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# finetuned-indian-96cls

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: 0.7096
- Accuracy: 0.8254

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 3.1577        | 0.1832 | 100  | 2.9843          | 0.5042   |
| 2.5555        | 0.3663 | 200  | 2.2414          | 0.5633   |
| 2.0015        | 0.5495 | 300  | 1.9346          | 0.5840   |
| 2.0439        | 0.7326 | 400  | 1.7007          | 0.6275   |
| 1.7375        | 0.9158 | 500  | 1.5473          | 0.6502   |
| 1.3248        | 1.0989 | 600  | 1.4405          | 0.6781   |
| 1.3164        | 1.2821 | 700  | 1.2743          | 0.7034   |
| 1.1091        | 1.4652 | 800  | 1.2184          | 0.7099   |
| 1.0913        | 1.6484 | 900  | 1.1246          | 0.7281   |
| 1.037         | 1.8315 | 1000 | 1.0869          | 0.7365   |
| 0.8672        | 2.0147 | 1100 | 1.0038          | 0.7625   |
| 0.7188        | 2.1978 | 1200 | 0.9834          | 0.7515   |
| 0.673         | 2.3810 | 1300 | 0.9177          | 0.7703   |
| 0.5936        | 2.5641 | 1400 | 0.8707          | 0.7800   |
| 0.7091        | 2.7473 | 1500 | 0.8100          | 0.7995   |
| 0.787         | 2.9304 | 1600 | 0.8120          | 0.7995   |
| 0.5979        | 3.1136 | 1700 | 0.7536          | 0.8131   |
| 0.5319        | 3.2967 | 1800 | 0.7447          | 0.8125   |
| 0.5717        | 3.4799 | 1900 | 0.7275          | 0.8235   |
| 0.385         | 3.6630 | 2000 | 0.7247          | 0.8248   |
| 0.4009        | 3.8462 | 2100 | 0.7096          | 0.8254   |


### Framework versions

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

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