Snack N’ Track Dataset

Snack N’ Track is an Egyptian food dataset that encompasses 59 traditional Egyptian categories across multiple food items, including main courses, appetizers, side dishes, and soups. 15000 images were collected from multiple sources, including search engines, social media platforms, restaurant websites, Arabic and English food blogs, and personal contributions from culinary practitioners. Subsequently, a subset of a publicly available dataset "https://universe.roboflow.com/capstone-o9k9w/pig_recipe-ynuxu," consisting of 48 classes of fruits and vegetables commonly used in Egyptian cuisine preparation, was integrated. These classes were selected for their nutritional value and frequent use in Egyptian dishes. After the dataset preprocessing, each category has an average of 500 images, with a total of approximately 35,000 annotated images, presenting a balanced dataset. The dataset is designed to assist various artificial intelligence applications, such as food classification, nutritional prediction, and dietary recommendations.