A Dietary Nutrition-aided Healthcare Platform via Effective Food Recognition on a Localized Singaporean Food Dataset

Localized food datasets have profound meaning in revealing a country's special cuisines to explore people's dietary behaviors, which will shed light on their health conditions and disease development. In this paper, revolving around the demand for accurate food recognition in Singapore, we develop the FoodSG platform to incubate diverse healthcare-oriented applications as a service in Singapore, taking into account their shared requirements. We release a localized Singaporean food dataset FoodSG-233 with a systematic cleaning and curation pipeline for promoting future data management research in food computing. To overcome the hurdle in recognition performance brought by Singaporean multifarious food dishes, we propose to integrate supervised contrastive learning into our food recognition model FoodSG-SCL for the intrinsic capability to mine hard positive/negative samples and therefore boost the accuracy. Through a comprehensive evaluation, we share the insightful experience with practitioners in the data management community regarding food-related data-intensive healthcare applications. The FoodSG-233 dataset can be accessed via: https://foodlg.comp.nus.edu.sg/.