[jiayq84]

Just to clarify a little bit... "At the time, very few object detection models had public implementations" - this is wrong. Almost all object detection models had public implementations starting from 2014, most notably Detectron (Caffe), GoogleNet/SSD (Tensorflow and matlab). Post 2015 when TensorFlow was released, one can find even more implementations.

Data is the problem. Everyone has the algorithm but not enough people have data (especially labeled ones)

[cjv]

No, I'm not wrong.

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.

[jiayq84]

I don’t want to be mean, but since you mentioned RCNN - no, you are dead wrong. RCNN was open sourced in 2014, check the repo: https://github.com/rbgirshick/rcnn

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.