---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: finetuned-indian-food
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: indian_food_images
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9330499468650372
widget:
- src: https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/003.jpg
  example_title: fried_rice
- src: https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/126.jpg
  example_title: paani_puri
- src: https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/401.jpg
  example_title: chapati
---

<!-- 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 the indian_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2632
- Accuracy: 0.9330

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1794        | 0.3   | 100  | 0.9208          | 0.8565   |
| 0.6513        | 0.6   | 200  | 0.5410          | 0.8842   |
| 0.5904        | 0.9   | 300  | 0.4978          | 0.8799   |
| 0.4461        | 1.2   | 400  | 0.3669          | 0.9192   |
| 0.5633        | 1.5   | 500  | 0.4340          | 0.8842   |
| 0.2489        | 1.8   | 600  | 0.3355          | 0.9171   |
| 0.3171        | 2.1   | 700  | 0.3286          | 0.9192   |
| 0.3785        | 2.4   | 800  | 0.3232          | 0.9171   |
| 0.2278        | 2.7   | 900  | 0.3338          | 0.9192   |
| 0.0894        | 3.0   | 1000 | 0.2870          | 0.9245   |
| 0.2092        | 3.3   | 1100 | 0.2884          | 0.9288   |
| 0.1466        | 3.6   | 1200 | 0.2673          | 0.9320   |
| 0.1789        | 3.9   | 1300 | 0.2632          | 0.9330   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1