Published: 22 August 2022| Version 1 | DOI: 10.17632/9ygs9vhnpw.1


Allergen30 is created with the goal of building a robust detection model that can assist people in avoiding possible allergic reactions. It contains images of 30 commonly used food items that can cause an adverse reaction within a human body. These food items pertain to specific food intolerances which can trigger an allergic reaction. Such food intolerance primarily includes Lactose, Histamine, Gluten, Salicylate, Caffeine, and Ovomucoid intolerance. The 30 food items (labels) in the dataset include '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' and 'whole egg boiled'.


Steps to reproduce

The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 416x416 (Stretch) The following augmentation was applied to create 3 versions of each source image: * Random shear of between -15° to +15° horizontally and -15° to +15° vertically The following transformations were applied to the bounding boxes of each image: * Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down


Object Detection, Allergen, Food Allergy, Food Allergen, Deep Learning, Food Application of Computer Vision