pytorch tensorboard vit image-classification dataset:imagefolder dataset:rajistics/indian_food_images transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
- rajistics/indian_food_images
metrics:
- accuracy
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: Chapathi
model-index:
- name: finetuned-indian-food
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: indian_food_images
      type: imagefolder
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9521785334750266
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: rajistics/indian_food_images
      type: rajistics/indian_food_images
      config: rajistics--indian_food_images
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8257173219978746
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.8391547623590003
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.8257173219978746
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.8437849242516663
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.8199909093335551
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.8257173219978746
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.8257173219978746
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.8207881196755944
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.8257173219978746
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.8256340007731109
      verified: true
    - name: loss
      type: loss
      value: 0.6241679787635803
      verified: true
---

<!-- 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: 0.2139
- Accuracy: 0.9522

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0846        | 0.3   | 100  | 0.9561          | 0.8555   |
| 0.7894        | 0.6   | 200  | 0.5871          | 0.8927   |
| 0.6233        | 0.9   | 300  | 0.4447          | 0.9107   |
| 0.3619        | 1.2   | 400  | 0.4355          | 0.8937   |
| 0.34          | 1.5   | 500  | 0.3712          | 0.9118   |
| 0.3413        | 1.8   | 600  | 0.4088          | 0.8916   |
| 0.3619        | 2.1   | 700  | 0.3741          | 0.9044   |
| 0.2135        | 2.4   | 800  | 0.3286          | 0.9160   |
| 0.2166        | 2.7   | 900  | 0.2758          | 0.9416   |
| 0.1557        | 3.0   | 1000 | 0.2679          | 0.9330   |
| 0.1115        | 3.3   | 1100 | 0.2529          | 0.9362   |
| 0.1571        | 3.6   | 1200 | 0.2360          | 0.9469   |
| 0.1079        | 3.9   | 1300 | 0.2139          | 0.9522   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
