NutriCal : Sri Lankan Food Recognition and Calorie Estimation System

Abstract

"Existing food recognition systems often struggle with the cultural complexities and regional variations inherent to Sri Lankan cuisine. This results in inaccurate calorie estimations, hindering individuals' efforts to maintain healthy dietary habits. This research addresses this challenge by proposing a novel Sri Lankan food recognition model based on the YOLO8 algorithm. A custom dataset of labelled Sri Lankan food images was created to train the model. This dataset incorporates diverse dish, ensuring robust recognition capabilities. Furthermore, the model undergoes fine-tuning and hyperparameter tuning, optimizing its ability to distinguish between visually similar dishes. Initial results demonstrate promising performance. The model exhibits high overall accuracy with specific misclassifications primarily occurring between visually similar curries. Analysis through Confusion metrics further validates its effectiveness in identifying various curries. This translates to accurate calorie estimation, empowering individuals to make informed dietary choices and promote healthier lifestyles."