Food Calorie Tracker: A Deep Learning Based Full-Stack Web Application for Real Time Food Recognition and Calorie Estimation

Authors- Professor Dr. P. Vara Prasad, P. Venkata Vamsi Madhav, Y. Guruprasanth Reddy, P. Subhahan, A. Harikrishna

Abstract-Obesity, a severe and growing chronic disease, has been worsened by the increasing convenience of food delivery services. As access to food has expanded, so too has concern over nutritional habits and health. The main goal of this project is to accurately identify various food items and estimate their calorie content in real-time, offering users smart and personalized diet monitoring capabilities. This project proposes a Deep Learning-based solution as a Full Stack Web Application for food image recognition and calorie estimation. Developed with a React-based frontend and a Python-powered backend, the system allows users to upload images of food items for real-time classification and calorie analysis. By leveraging advanced deep learning architectures such as YOLO (You Only Look Once), the application effectively tackles the challenges of accurate classification and feature extraction in large and diverse datasets, such as the IndianFoodNet-30 dataset. This integration ensures precise recognition and efficient calorie estimation, offering users a seamless and intelligent diet monitoring experience. This full-stack solution demonstrates the integration of modern web technologies with robust deep learning models, delivering a scalable and user-friendly tool for real-time food recognition and calorie monitoring. The frontend offers an intuitive interface for uploading food images and visualizing results, while the backend efficiently handles image processing, deep learning inference, and calorie computation.

DOI: /10.61463/ijset.vol.13.issue2.244