  Diet is central to the epidemic of lifestyle disorders. Accurate and
effortless diet logging is one of the significant bottlenecks for effective
diet management and calorie restriction. Dish detection from food platters is a
challenging problem due to a visually complex food layout. We present an
end-to-end computational framework for diet management, from data compilation,
annotation, and state-of-the-art model identification to its mobile app
implementation. As a case study, we implement the framework in the context of
Indian food platters known for their complex presentation that poses a
challenge for the automated detection of dishes. Starting with the 61 most
popular Indian dishes, we identify the state-of-the-art model through a
comparative analysis of deep-learning-based object detection architectures.
Rooted in a meticulous compilation of 68,005 platter images with 134,814 manual
dish annotations, we first compare ten architectures for multi-label
classification to identify ResNet152 (mAP=84.51%) as the best model. YOLOv8x
(mAP=87.70%) emerged as the best model architecture for dish detection among
the eight deep-learning models implemented after a thorough performance
evaluation. By comparing with the state-of-the-art model for the IndianFood10
dataset, we demonstrate the superior object detection performance of YOLOv8x
for this subset and establish Resnet152 as the best architecture for
multi-label classification. The models thus trained on richly annotated data
can be extended to include dishes from across global cuisines. The proposed
framework is demonstrated through a proof-of-concept mobile application with
diverse applications for diet logging, food recommendation systems, nutritional
interventions, and mitigation of lifestyle disorders.
