Object Detection

Food_new Computer Vision Project


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About Allergen30

Allergen30 is created by Mayank Mishra, Nikunj Bansal, Tanmay Sarkar and Tanupriya Choudhury with a goal of building a robust detection model that can assist people in avoiding possible allergic reactions.

It contains more than 6,000 images of 30 commonly used food items which can cause an adverse reaction within a human body. This dataset is one of the first research attempts in training a deep learning based computer vision model to detect the presence of such food items from images. It also serves as a benchmark for evaluating the efficacy of object detection methods in learning the otherwise difficult visual cues related to food items.

Description of class labels

There are multiple food items pertaining to specific food intolerances which can trigger an allergic reaction. Such food intolerance primarily include Lactose, Histamine, Gluten, Salicylate, Caffeine and Ovomucoid intolerance. Food intolerance

The following table contains the description relating to the 30 class labels in our dataset.

S. No. Allergen Food label Description
1 Ovomucoid egg Images of egg with yolk (e.g. sunny side up eggs)
2 Ovomucoid whole_egg_boiled Images of soft and hard boiled eggs
3 Lactose/Histamine milk Images of milk in a glass
4 Lactose icecream Images of icecream scoops
5 Lactose cheese Images of swiss cheese
6 Lactose/ Caffeine milk_based_beverage Images of tea/ coffee with milk in a cup/glass
7 Lactose/Caffeine chocolate Images of chocolate bars
8 Caffeine non_milk_based_beverage Images of soft drinks and tea/coffee without milk in a cup/glass
9 Histamine cooked_meat Images of cooked meat
10 Histamine raw_meat Images of raw meat
11 Histamine alcohol Images of alcohol bottles
12 Histamine alcohol_glass Images of wine glasses with alcohol
13 Histamine spinach Images of spinach bundle
14 Histamine avocado Images of avocado sliced in half
15 Histamine eggplant Images of eggplant
16 Salicylate blueberry Images of blueberry
17 Salicylate blackberry Images of blackberry
18 Salicylate strawberry Images of strawberry
19 Salicylate pineapple Images of pineapple
20 Salicylate capsicum Images of bell pepper
21 Salicylate mushroom Images of mushrooms
22 Salicylate dates Images of dates
23 Salicylate almonds Images of almonds
24 Salicylate pistachios Images of pistachios
25 Salicylate tomato Images of tomato and tomato slices
26 Gluten roti Images of roti
27 Gluten pasta Images of one serving of penne pasta
28 Gluten bread Images of bread slices
29 Gluten bread_loaf Images of bread loaf
30 Gluten pizza Images of pizza and pizza slices

Data collection

We used search engines (Google and Bing) to crawl and look for suitable images using JavaScript queries for each food item from the list created. The images with incomplete RGB channels were removed, and the images collected from different search engines were compiled. When downloading images from search engines, many images were irrelevant to the purpose, especially the ones with a lot of text in them. We deployed the EAST text detector to segregate such images. Finally, a comprehensive manual inspection was conducted to ensure the relevancy of images in the dataset.

Fair use

This dataset contains some copyrighted material whose use has not been specifically authorized by the copyright owners. In an effort to advance scientific research, we make this material available for academic research. If you wish to use copyrighted material in our dataset for purposes of your own that go beyond non-commercial research and academic purposes, you must obtain permission directly from the copyright owner. We believe this constitutes a 'fair use' of any such copyrighted material as provided for in section 107 of the US Copyright Law. In accordance with Title 17 U.S.C. Section 107, the material on this site is distributed without profit to those who have expressed a prior interest in receiving the included information for non-commercial research and educational purposes.(adapted from Christopher Thomas).


If you find our dataset useful, please cite us as:

  title={Allergen30: Detecting Food Items with Possible Allergens Using Deep Learning-Based Computer Vision},
  author={Mishra, Mayank and Sarkar, Tanmay and Choudhury, Tanupriya and Bansal, Nikunj and Smaoui, Slim and Rebezov, Maksim and Shariati, Mohammad Ali and Lorenzo, Jose Manuel},
  journal={Food Analytical Methods},

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Last Updated

a year ago

Project Type

Object Detection




alcohol, alcohol_glass, almond, avocado, blackberry, blueberry, bread, bread_loaf, capsicum, cheese, chocolate, cooked_meat, dates, egg, eggplant, icecream, milk, milk_based_beverage, mushroom, non_milk_based_beverage, pasta, pineapple, pistachio, pizza, raw_meat, roti, spinach, strawberry, tomato, whole_egg_boiled

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