Food dataset for "SibNet: Food instance counting and segmentation"

These three datasets cover Western, Chinese and Japanese food used for food instance counting and segmentation evaluation.

This dataset consists of 3 folders, storing images, instance segmentation masks and the splits of training, testing, and validation.

This record includes the data and source code for the paper SibNet: Food instance counting and segmentation. 

Food computing has recently attracted considerable research attention due to its significance for health risk analysis. In the literature, the majority of research efforts are dedicated to food recognition. Relatively few works are conducted for food counting and segmentation, which are essential for portion size estimation. This paper presents a deep neural network, named SibNet, for simultaneous counting and extraction of food instances from an image. The problem is challenging due to varying size and shape of food as well as arbitrary viewing angle of camera, not to mention that food instances often occlude each other. SibNet is novel for proposal of learning seed map to minimize the overlap between instances. The map facilitates counting and can be completed as an instance segmentation map that depicts the arbitrary shape and size of individual instance under occlusion. To this end, a novel sibling relation sub-network is proposed for pixel connectivity analysis. Along with this paper, three new datasets covering Western, Chinese and Japanese food are also constructed for performance evaluation.