metadata

license: cc0-1.0
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': biryani
            '1': cholebhature
            '2': dabeli
            '3': dal
            '4': dhokla
            '5': dosa
            '6': jalebi
            '7': kathiroll
            '8': kofta
            '9': naan
            '10': pakora
            '11': paneer
            '12': panipuri
            '13': pavbhaji
            '14': vadapav
  splits:
    - name: train
      num_bytes: 611741947.222
      num_examples: 3809
    - name: test
      num_bytes: 153961285
      num_examples: 961
  download_size: 688922167
  dataset_size: 765703232.222
task_categories:
  - image-classification
  - text-to-image
language:
  - en
pretty_name: indian-foods
size_categories:
  - 1K<n<10K

Dataset Card for Indian Foods Dataset

Dataset Description

Homepage: https://www.kaggle.com/datasets/anshulmehtakaggl/themassiveindianfooddataset
Repository: https://www.kaggle.com/datasets/anshulmehtakaggl/themassiveindianfooddataset
Paper:
Leaderboard:
Point of Contact: https://www.kaggle.com/anshulmehtakaggl

Dataset Summary

This is a multi-category(multi-class classification) related Indian food dataset showcasing The-massive-Indian-Food-Dataset. This card has been generated using this raw template.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

English

Dataset Structure

{
  "image": "Image(decode=True, id=None)",
  "target": "ClassLabel(names=['biryani', 'cholebhature', 'dabeli', 'dal', 'dhokla', 'dosa', 'jalebi', 'kathiroll', 'kofta', 'naan', 'pakora', 'paneer', 'panipuri', 'pavbhaji', 'vadapav'], id=None)"
}

Dataset Splits

This dataset is split into a train and test split. The split sizes are as follows:

Split name	Num samples
train	3809
test	961

Data Instances

Each instance is a picture of the Indian food item, along with the category it belongs to.

Initial Data Collection and Normalization

Collection by Scraping data from Google Images + Leveraging some JS Functions. All the images are resized to (300,300) to maintain size uniformity.