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

智能饮食助手利用机器学习提供个性化的膳食建议，特别关注患有糖尿病等疾病的用户。该应用程序利用基于文本编码器和图像骨干的Grounding DINO模型，在不需要标记数据集的情况下增强食物识别能力。该模型在COCO数据集上达到52.5的AP得分，展示出在真实场景中的高准确性，利用注意机制根据用户提供的标签和图像精确识别对象。该应用程序使用React Native和TypeScript开发，可以在多个平台上无缝运行，并集成了自托管的PostgreSQL数据库，确保数据完整性和提升用户隐私。关键功能包括个性化营养概况、实时食物扫描和健康洞察，有助于健康管理和生活方式优化的明智膳食选择。未来发展目标是整合可穿戴技术，提供更个性化的健康建议。

智能饮食：基于Grounding DINO的饮食助手应用在健康信息学中的应用