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Food recognition is one of the most important components in image-based dietary assessment. However, due to the different complexity level of food images and inter-class similarity of food categories, it is challenging for an image-based food recognition system to achieve high accuracy for a variety of publicly available datasets. In this work, we propose a new two-step food recognition system that includes food localization and hierarchical food classification using Convolutional NeuralarXiv:2012.03368v1 fatcat:v2gfbqrykrgy7po34wubddbxhy