---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-indian-food
  results: []
---

<!-- 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

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2692
- Accuracy: 0.9341

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3949        | 0.3003 | 100  | 0.6593          | 0.8395   |
| 0.2833        | 0.6006 | 200  | 0.3689          | 0.9001   |
| 0.4671        | 0.9009 | 300  | 0.5113          | 0.8682   |
| 0.1231        | 1.2012 | 400  | 0.3643          | 0.9097   |
| 0.1812        | 1.5015 | 500  | 0.3605          | 0.9033   |
| 0.2414        | 1.8018 | 600  | 0.3426          | 0.9203   |
| 0.0845        | 2.1021 | 700  | 0.3238          | 0.9150   |
| 0.1232        | 2.4024 | 800  | 0.3523          | 0.9129   |
| 0.1553        | 2.7027 | 900  | 0.3726          | 0.9065   |
| 0.1323        | 3.0030 | 1000 | 0.2706          | 0.9352   |
| 0.1057        | 3.3033 | 1100 | 0.2697          | 0.9373   |
| 0.1585        | 3.6036 | 1200 | 0.2695          | 0.9341   |
| 0.0312        | 3.9039 | 1300 | 0.2692          | 0.9341   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0