Data is the problem. Everyone has the algorithm but not enough people have data (especially labeled ones)
Detectron was open sourced in 2018. R-CNN didn't have any public implementations (there was later a Keras implementation that didn't get the same performance as the paper reported). TensorFlow models added some object detection models a few days after I started my internship, but had various issues at the time. SSD and YOLO both had public implementations, YOLO's being in it's own C based framework.
It's a completely different landscape three years later.
Not to mention that nvidia has thrown numerous open source efforts over the years. If SR was under the impression that 2017 was a dry year for open source deep learning vision systems - I can understand why it didn’t do very well technology wise.
Disclaimer: have been doing deep learning open source and research over the years. Have touched all major frameworks in the market.