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In this paper, we systematically analyze unsupervised domain adaptationpipelines for object classification in a challenging industrial setting. Incontrast to standard natural object benchmarks existing in the field, ourresults highlight the most important design choices when only category-labeledCAD models are available but classification needs to be done with real-worldimages. Our domain adaptation pipeline achieves SoTA performance on the VisDAbenchmark, but more importantly, drastically improves recognition performanceon our new open industrial dataset comprised of 102 mechanical parts. Weconclude with a set of guidelines that are relevant for practitioners needingto apply state-of-the-art unsupervised domain adaptation in practice. Our codeis available at https://github.com/dritter-bht/synthnet-transfer-learning.

CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object Classification