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Allergen30

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.

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).

Citation

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

@article{mishra2022allergen30,
  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},
  pages={1--34},
  year={2022},
  publisher={Springer}
}