  Solving complex computer vision tasks by deep learning techniques relies on
large amounts of (supervised) image data, typically unavailable in industrial
environments. The lack of training data starts to impede the successful
transfer of state-of-the-art methods in computer vision to industrial
applications. We introduce BlendTorch, an adaptive Domain Randomization (DR)
library, to help creating infinite streams of synthetic training data.
BlendTorch generates data by massively randomizing low-fidelity simulations and
takes care of distributing artificial training data for model learning in
real-time. We show that models trained with BlendTorch repeatedly perform
better in an industrial object detection task than those trained on real or
photo-realistic datasets.
