This dataset consists of 101 food categories, with 101'000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('food101', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/food101-2.0.0.png" alt="Visualization" width="500px">

1) Data Introduction ? The Food-101 dataset contains subsets of the original Food-101 data, featuring multiple food categories and intended to serve as a richer alternative to classic image datasets like CIFAR-10 or MNIST.

2) Data Utilization (1) Characteristics of the Food-101 Dataset: ? The dataset consists of 49 food categories, with data files indicating the number of images and their respective resolutions. ? Includes both color (RGB) and grayscale images with labels.

(2) Applications of the Food-101 Dataset: ? Food image classification: Useful for developing and evaluating models that can automatically recognize and classify various food items. ? Model interpretability and explainability: Can be used to study which regions or components of food images are most important for classification decisions. ? Advanced food analysis: Provides opportunities to identify new food types as combinations of existing tags or to build detectors for food items in complex scenes.

101 food categories, with 101,000 images; 250 test images and 750 training images per class. The training images were not cleaned. All images were rescaled to have a maximum side length of 512 pixels.

The Food-101 is a challenging data set of 101 food categories with 101,000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.

🍽️ Food101-RecipeDataset

The Food101-RecipeDataset is a curated collection of food images paired with detailed recipe metadata — including dish names and ingredient breakdowns. It is designed to support machine learning tasks in computer vision, recipe generation, nutrition analysis, and more.

  🗂️ Dataset Overview

Each example in this dataset includes:

Image: A high-quality food image. Food Name: The name of the dish (e.g., "Spaghetti Carbonara"). Ingredients: A list… See the full description on the dataset page: https://huggingface.co/datasets/Moiz2517/Food-101-RecipeDataset.

Dataset

This dataset was created by K. T.

Released under GPL 2

Contents

Dataset

This dataset was created by Sidhant Raj Khati

Contents

Food image dataset used for food image recognition

Dataset Details

  Dataset Description

This dataset is the combination of the Food101, Indian Food Classification and The-massive-Indian-Food-Dataset datasets. This Dataset aims to be a viable dataset for Image Classification of Foods with an added Indian context. This dataset has 121 classes with each class having 800 images in the train split and 200 images in the test split. Maximum resolution of images is 512*512. The Food121-224 dataset has all images downscaled to a… See the full description on the dataset page: https://huggingface.co/datasets/ItsNotRohit/Food121.

Analysis of ‘Indian Food 101’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nehaprabhavalkar/indian-food-101 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

Indian cuisine consists of a variety of regional and traditional cuisines native to the Indian subcontinent. Given the diversity in soil, climate, culture, ethnic groups, and occupations, these cuisines vary substantially and use locally available spices, herbs, vegetables, and fruits. Indian food is also heavily influenced by religion, in particular Hinduism, cultural choices and traditions.

This dataset consists of information about various Indian dishes, their ingredients, their place of origin, etc.

Column Description

name : name of the dish

ingredients : main ingredients used

diet : type of diet - either vegetarian or non vegetarian

prep_time : preparation time

cook_time : cooking time

flavor_profile : flavor profile includes whether the dish is spicy, sweet, bitter, etc

course : course of meal - starter, main course, dessert, etc

state : state where the dish is famous or is originated

region : region where the state belongs

Presence of -1 in any of the columns indicates NaN value.

Acknowledgements

https://www.wikipedia.org/ https://hebbarskitchen.com/ https://www.archanaskitchen.com/

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3885917%2Fa849f4fddf79a836d4ea0539286e3df9%2Fzxl2vnp_1457603881_725x725.jpg?generation=1603611234465596&alt=media" alt="">

--- Original source retains full ownership of the source dataset ---

Dataset Card for Food-102 (Food101+Iraqi-rice-male )

Dataset Name: Food-102 Dataset Summary: Food-102 is an updated version of the Food-101 dataset, now expanded to include 102 food categories. It consists of a total of 102,000 images, with 750 training images and 250 manually reviewed test images provided for each category. The dataset aims to enable food classification tasks and provide a diverse range of food images for research and development purposes. The training images in… See the full description on the dataset page: https://huggingface.co/datasets/Falah/food102-iraqi-rice-meal.

Comparison of the results between the proposed method and the conventional methods on the food-101 dataset.

Food101

## Overview

Food101 is a dataset for object detection tasks - it contains Object annotations for 1,995 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

PFID

This is subset of food101 dataset (https://www.kaggle.com/datasets/dansbecker/food-101) It contains only 4 classes: pizza, risotto, steak, sushi.

You can use it to train your neural networks! Have fun!

Dataset

This dataset was created by Bharat Dhyani

Contents

Dataset Card for "food101-tiny"

More Information needed

Dataset Card for Pizza or Not Pizza?

  Dataset Summary

Who doesn't like pizza? This dataset contains about 1000 images of pizza and 1000 images of dishes other than pizza. It can be used for a simple binary image classification task. All images were rescaled to have a maximum side length of 512 pixels. This is a subset of the Food-101 dataset. Information about the original dataset can be found in the following paper: Bossard, Lukas, Matthieu Guillaumin, and Luc Van Gool.… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/pizza_not_pizza.

Food Classification Dataset

This dataset consists of multiple subsets of food images designed for training and evaluating deep learning models for food classification. It includes full-scale and reduced versions to facilitate experimentation with different data sizes.

  Dataset Overview

File Name Size Description

101_food_classes_10_percent.zip ~1.34GB Contains 10% of the 101_food_classes dataset.

10_food_classes.zip ~393MB Contains images for 10 different… See the full description on the dataset page: https://huggingface.co/datasets/mhamza-007/multi-class-food-dataset.

This dataset is made from the Foodspotting dataset. Actually, Foodspotting dataset is a dataset of food images in color. In the dataset we made, we converted the color images to gray scale, similar to the MNIST dataset. The size of each image is 64 x 64, which we converted linearly. In fact, each image is a 4096 vector. In this dataset, there are both training data with the number of 75750 samples and test data with the number of 25250 samples. The format of the dataset is CSV, like MNIST. The number of classes is 101. You can use this dataset for multi-class classification problems.

{'apple_pie': 0, 'baby_back_ribs': 1, 'baklava': 2, 'beef_carpaccio': 3, 'beef_tartare': 4, 'beet_salad': 5, 'beignets': 6, 'bibimbap': 7, 'bread_pudding': 8, 'breakfast_burrito': 9, 'bruschetta': 10, 'caesar_salad': 11, 'cannoli': 12, 'caprese_salad': 13, 'carrot_cake': 14, 'ceviche': 15, 'cheesecake': 16, 'cheese_plate': 17, 'chicken_curry': 18, 'chicken_quesadilla': 19, 'chicken_wings': 20, 'chocolate_cake': 21, 'chocolate_mousse': 22, 'churros': 23, 'clam_chowder': 24, 'club_sandwich': 25, 'crab_cakes': 26, 'creme_brulee': 27, 'croque_madame': 28, 'cup_cakes': 29, 'deviled_eggs': 30, 'donuts': 31, 'dumplings': 32, 'edamame': 33, 'eggs_benedict': 34, 'escargots': 35, 'falafel': 36, 'filet_mignon': 37, 'fish_and_chips': 38, 'foie_gras': 39, 'french_fries': 40, 'french_onion_soup': 41, 'french_toast': 42, 'fried_calamari': 43, 'fried_rice': 44, 'frozen_yogurt': 45, 'garlic_bread': 46, 'gnocchi': 47, 'greek_salad': 48, 'grilled_cheese_sandwich': 49, 'grilled_salmon': 50, 'guacamole': 51, 'gyoza': 52, 'hamburger': 53, 'hot_and_sour_soup': 54, 'hot_dog': 55, 'huevos_rancheros': 56, 'hummus': 57, 'ice_cream': 58, 'lasagna': 59, 'lobster_bisque': 60, 'lobster_roll_sandwich': 61, 'macaroni_and_cheese': 62, 'macarons': 63, 'miso_soup': 64, 'mussels': 65, 'nachos': 66, 'omelette': 67, 'onion_rings': 68, 'oysters': 69, 'pad_thai': 70, 'paella': 71, 'pancakes': 72, 'panna_cotta': 73, 'peking_duck': 74, 'pho': 75, 'pizza': 76, 'pork_chop': 77, 'poutine': 78, 'prime_rib': 79, 'pulled_pork_sandwich': 80, 'ramen': 81, 'ravioli': 82, 'red_velvet_cake': 83, 'risotto': 84, 'samosa': 85, 'sashimi': 86, 'scallops': 87, 'seaweed_salad': 88, 'shrimp_and_grits': 89, 'spaghetti_bolognese': 90, 'spaghetti_carbonara': 91, 'spring_rolls': 92, 'steak': 93, 'strawberry_shortcake': 94, 'sushi': 95, 'tacos': 96, 'takoyaki': 97, 'tiramisu': 98, 'tuna_tartare': 99, 'waffles': 100}