
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# finetuned-indian-food-26

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_26 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3562
- Accuracy: 0.9207

## 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2639        | 0.2874 | 100  | 0.6565          | 0.8372   |
| 0.2524        | 0.5747 | 200  | 0.5063          | 0.8678   |
| 0.4061        | 0.8621 | 300  | 0.4874          | 0.8759   |
| 0.2374        | 1.1494 | 400  | 0.5066          | 0.8769   |
| 0.2202        | 1.4368 | 500  | 0.5157          | 0.8708   |
| 0.2895        | 1.7241 | 600  | 0.5908          | 0.8494   |
| 0.2652        | 2.0115 | 700  | 0.5432          | 0.8688   |
| 0.1772        | 2.2989 | 800  | 0.5406          | 0.8667   |
| 0.165         | 2.5862 | 900  | 0.5264          | 0.8749   |
| 0.358         | 2.8736 | 1000 | 0.5183          | 0.8779   |
| 0.1573        | 3.1609 | 1100 | 0.5022          | 0.8840   |
| 0.2839        | 3.4483 | 1200 | 0.5130          | 0.8881   |
| 0.2695        | 3.7356 | 1300 | 0.4671          | 0.8891   |
| 0.2392        | 4.0230 | 1400 | 0.5282          | 0.8708   |
| 0.2909        | 4.3103 | 1500 | 0.4564          | 0.8932   |
| 0.1485        | 4.5977 | 1600 | 0.4200          | 0.9034   |
| 0.1445        | 4.8851 | 1700 | 0.4566          | 0.8922   |
| 0.1331        | 5.1724 | 1800 | 0.4032          | 0.9044   |
| 0.1931        | 5.4598 | 1900 | 0.4350          | 0.9044   |
| 0.1389        | 5.7471 | 2000 | 0.3991          | 0.9084   |
| 0.0952        | 6.0345 | 2100 | 0.4777          | 0.8881   |
| 0.0906        | 6.3218 | 2200 | 0.3946          | 0.9034   |
| 0.1537        | 6.6092 | 2300 | 0.4495          | 0.8962   |
| 0.1049        | 6.8966 | 2400 | 0.4333          | 0.9044   |
| 0.0593        | 7.1839 | 2500 | 0.4269          | 0.9054   |
| 0.154         | 7.4713 | 2600 | 0.3678          | 0.9135   |
| 0.124         | 7.7586 | 2700 | 0.3875          | 0.9176   |
| 0.1862        | 8.0460 | 2800 | 0.3923          | 0.9105   |
| 0.1579        | 8.3333 | 2900 | 0.3827          | 0.9156   |
| 0.1045        | 8.6207 | 3000 | 0.3829          | 0.9125   |
| 0.0069        | 8.9080 | 3100 | 0.3562          | 0.9207   |
| 0.0407        | 9.1954 | 3200 | 0.3833          | 0.9156   |
| 0.1204        | 9.4828 | 3300 | 0.3766          | 0.9176   |
| 0.1442        | 9.7701 | 3400 | 0.3734          | 0.9207   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0

burger butter_naan idli jalebi kadai_paneer khaman kulfi masala_dosa meduvada mirchi_bajji momos paani_puri chai pakode pizza poha rasgulla samosa sandwitch vadapav chapati chole_bhature dal_makhani frankie fried_rice gajar_halwa gulab_jamun