Deep Object Recognition-Based Analysis of Diverse Culinary Landscapes

The rapid development of AI offers promising opportunities to promote healthier habits and address critical public health challenges. Advances in computer vision (CV) and the widespread use of smartphones and social media have sparked interest in food computation from visual data as a scalable and efficient solution. However, developing robust CV models for food object detection requires large-scale, high-quality datasets. To meet this demand, we created the Global Gastronomic Culinary Dataset (GGCD), a comprehensive resource for food object detection, building on our prior work with Central Asian Food Scenes Dataset and also annotation and preprocessing of a publicly available dataset of globally consumed food items. To evaluate the quality and utility of the dataset, we conducted comprehensive experiments using state-of-the-art object detection models. Specifically, the YOLOv11x model achieved a mAP50 of 0.711 on our testing set. To validate the generalizability of the trained model, we tested the performance on the East China University of Science and Technology Food Dataset (ECUSTFD) and our model trained on GGCD achieved a mAP of 0.683, demonstrating its effectiveness in food object detection across diverse settings.