  The Smart Dietary Assistant utilizes Machine Learning to provide personalized
dietary advice, focusing on users with conditions like diabetes. This app
leverages the Grounding DINO model, which combines a text encoder and image
backbone to enhance food item detection without requiring a labeled dataset.
With an AP score of 52.5 on the COCO dataset, the model demonstrates high
accuracy in real-world scenarios, utilizing attention mechanisms to precisely
recognize objects based on user-provided labels and images. Developed using
React Native and TypeScript, the app operates seamlessly across multiple
platforms and integrates a self-hosted PostgreSQL database, ensuring data
integrity and enhancing user privacy. Key functionalities include personalized
nutrition profiles, real-time food scanning, and health insights, facilitating
informed dietary choices for health management and lifestyle optimization.
Future developments aim to integrate wearable technologies for more tailored
health recommendations. Keywords: Food Image Recognition, Machine Learning in
Nutrition, Zero-Shot Object Detection
