  In the process of intelligently segmenting foods in images using deep neural
networks for diet management, data collection and labeling for network training
are very important but labor-intensive tasks. In order to solve the
difficulties of data collection and annotations, this paper proposes a food
segmentation method applicable to real-world through synthetic data. To perform
food segmentation on healthcare robot systems, such as meal assistance robot
arm, we generate synthetic data using the open-source 3D graphics software
Blender placing multiple objects on meal plate and train Mask R-CNN for
instance segmentation. Also, we build a data collection system and verify our
segmentation model on real-world food data. As a result, on our real-world
dataset, the model trained only synthetic data is available to segment food
instances that are not trained with 52.2% mask AP@all, and improve performance
by +6.4%p after fine-tuning comparing to the model trained from scratch. In
addition, we also confirm the possibility and performance improvement on the
public dataset for fair analysis. Our code and pre-trained weights are
avaliable online at: https://github.com/gist-ailab/Food-Instance-Segmentation
