Abstract. We introduce the Densely Segmented Supermarket (D2S)
dataset, a novel benchmark for instance-aware semantic segmentation
in an industrial domain. It contains 21 000 high-resolution images with
pixel-wise labels of all object instances. The objects comprise groceries
and everyday products from 60 categories. The benchmark is designed
such that it resembles the real-world setting of an automatic checkout,
inventory, or warehouse system. The training images only contain objects
of a single class on a homogeneous background, while the validation and
test sets are much more complex and diverse. To further benchmark the
robustness of instance segmentation methods, the scenes are acquired
with different lightings, rotations, and backgrounds. We ensure that there
are no ambiguities in the labels and that every instance is labeled comprehensively. The annotations are pixel-precise and allow using crops of
single instances for articial data augmentation. The dataset covers several challenges highly relevant in the field, such as a limited amount of
training data and a high diversity in the test and validation sets. The
evaluation of state-of-the-art object detection and instance segmentation
methods on D2S reveals significant room for improvement