Semantic Segmentation of Food through Deep Learning: A Case Study

In this paper, we explore food image semantic segmentation at different levels. We identify two datasets that can be used for training and evaluating models. We also assess the performance of four semantic segmentation models for food segmentation tasks. Our results show that FCN and SegFormer achieve the best overall accuracy at 85.9% and 84.1% when applied to the UECFOODPIXCOMPLETE dataset. The study aims to offer valuable insights and guide future developments in dietary assessment tools that can underpin health management applications.