# Eating Smart: Advancing Health Informatics with the Grounding DINO based Dietary Assistant App

arXiv: https://arxiv.org/abs/2406.00848 · HTML: https://mlsanity.com/paper/2406.00848/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 2 Jun 2024.

## Builds on (most cited first, 1 of 1)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)

## Abstract (arXiv)

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