Singapore has been striving to improve the provision of healthcare services to her people. In this course, the government has taken note of the deficiency in regulating and supervising people's nutrient intake, which is identified as a contributing factor to the development of chronic diseases. Consequently, this issue has garnered significant attention. In this paper, we share our experience in addressing this issue and attaining medical-grade nutrient intake information to benefit Singaporeans in different aspects. To this end, we develop the FoodSG platform to incubate diverse healthcare-oriented applications as a service in Singapore, taking into account their shared requirements. We further identify the profound meaning of localized food datasets and systematically clean and curate a localized Singaporean food dataset FoodSG-233. 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 present performance results of the proposed model and insights on food-related healthcare applications. The FoodSG-233 dataset has been released in https://foodlg.comp.nus.edu.sg/.

翻译：新加坡一直致力于改善其人民的医疗服务供给。在此过程中，政府注意到在调节和监督人们营养素摄入方面存在不足，这被认为是导致慢性疾病发展的一个因素。因此，该问题引起了广泛关注。本文分享了我们在解决这一问题并获取医疗级营养素摄入信息以惠及新加坡人民不同方面的经验。为此，我们开发了FoodSG平台，旨在将多样化面向医疗保健的应用作为一项服务在新加坡进行孵化，同时考虑它们的共同需求。我们进一步认识到本地化食物数据集的深远意义，并系统性地清洗和整理了一个本地化的新加坡食物数据集FoodSG-233。为克服新加坡多样化菜肴带来的识别性能障碍，我们提出将监督对比学习集成到我们的食物识别模型FoodSG-SCL中，利用其内在能力挖掘难正/负样本，从而提升准确性。通过全面评估，我们展示了所提出模型的性能结果以及关于食物相关医疗保健应用的见解。FoodSG-233数据集已在https://foodlg.comp.nus.edu.sg/上发布。