Towards Sim-to-Real Industrial Parts Classification with Synthetic
  Dataset
Xiaomeng Zhu Talha Bilal Pär Mårtensson Lars Hanson Mårten Björkman Atsuto Maki
This paper is about effectively utilizing synthetic data for training deep
neural networks for industrial parts classification, in particular, by taking
into account the domain gap against real-world images. To this end, we
introduce a synthetic dataset that may serve as a preliminary testbed for the
Sim-to-Real challenge; it contains 17 objects of six industrial use cases,
including isolated and assembled parts. A few subsets of objects exhibit large
similarities in shape and albedo for reflecting challenging cases of industrial
parts. All the sample images come with and without random backgrounds and
post-processing for evaluating the importance of domain randomization. We call
it Synthetic Industrial Parts dataset (SIP-17). We study the usefulness of
SIP-17 through benchmarking the performance of five state-of-the-art deep
network models, supervised and self-supervised, trained only on the synthetic
data while testing them on real data. By analyzing the results, we deduce some
insights on the feasibility and challenges of using synthetic data for
industrial parts classification and for further developing larger-scale
synthetic datasets. Our dataset and code are publicly available.