IntelligentDine: Advanced Food Identification and Caloric Assessment through Deep Learning for Smart Diet Tracking

The IntelligentDine initiative introduces an inventive approach to enhance diet tracking and promote healthy eating habits by applying advanced deep learning techniques. The primary goal is to precisely identify various food items and offer real-time estimates of their calorie content, providing users with intelligent and personalized diet monitoring capabilities. Using the Python programming language, we employed the ResNet150 architecture model for food recognition and calorie estimation, utilizing the extensive Food 101 dataset with 37,046 food images spanning 101 distinct food classes. Following rigorous training and evaluation, our model demonstrated notable success, achieving a training accuracy of 97.30% and a validation accuracy of 98.02%. These compelling results underscore the efficacy of our proposed approach in accurately recognizing and categorizing diverse food items. Smart Bite, a key component of this initiative, harnesses deep learning algorithms to swiftly analyze input images within a web framework, identifying specific food items and estimating their calorie content. This valuable information empowers users to effectively monitor their dietary intake.The intelligent diet monitoring capabilities of Smart Bite enable users to make informed decisions about their food choices. By tracking and analyzing daily food intake, users can gain insights into their nutritional habits, set personalized goals, and make necessary adjustments for a balanced and healthy diet. SmartBite showcases the successful integration of deep learning techniques, specifically employing the ResNet150 architecture for food recognition and calorie estimation. With its impressive accuracy, real-time processing capabilities, and intelligent diet monitoring features, SmartBite has the potential to transform how individuals track and manage their dietary habits, fostering healthier lifestyles.