Food Dataset
Food datasets are crucial for developing computer vision algorithms capable of analyzing food images, enabling applications like dietary assessment, food waste reduction, and personalized nutrition recommendations. Current research focuses on creating larger, more diverse datasets encompassing 3D models, nutritional information, and diverse cultural cuisines, alongside developing advanced models like EfficientNets, YOLOv8, and transformers to improve accuracy and speed of food recognition, segmentation, and classification. These advancements are significant for improving the accuracy and accessibility of AI-powered dietary tools and advancing research in food computing and related fields.
Papers
Dish detection in food platters: A framework for automated diet logging and nutrition management
Mansi Goel, Shashank Dargar, Shounak Ghatak, Nidhi Verma, Pratik Chauhan, Anushka Gupta, Nikhila Vishnumolakala, Hareesh Amuru, Ekta Gambhir, Ronak Chhajed, Meenal Jain, Astha Jain, Samiksha Garg, Nitesh Narwade, Nikhilesh Verhwani, Abhuday Tiwari, Kirti Vashishtha, Ganesh Bagler
A Central Asian Food Dataset for Personalized Dietary Interventions, Extended Abstract
Aknur Karabay, Arman Bolatov, Huseyin Atakan Varol, Mei-Yen Chan