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This is sad news.

I worked at Starsky Robotics as a perception team intern after graduating high school. I will always be grateful for the team for the opportunity, it was a fantastic first job and everyone who worked there was very kind (especially Stefan).

Unfortunately, Starsky had effectively had no machine learning in 2017 (when I worked there), using solely classical computer vision techniques. This didn't match the company's ambitions of not using LIDAR and there was a strong stigma against switching to a deep learning approach. At the time, very few object detection models had public implementations and I spent a lot of time trying to get a YOLO9000 and RetinaNet implementations running at real-time speeds. Frustrating, as a small startup the labeling services kept screwing us over by returning poorly annotated images.

I think what I took away from the experience is that deep learning in domains with long tails requires a enormous investment in a labeling pipeline - dwarfing the computational aspect - to get decent results. I don't think any solutions are on the horizon that will allow us to bypass this reality. You don't see improvements between Comma.ai and Tesla because it's about the improvements far out on the tail.






For others like me who were as amused by the name, YOLO9000, it's a real thing, "a state-of-the-art, real-time object detection system that can detect over 9000 object categories".

YOLO9000: Better, Faster, Stronger - https://arxiv.org/abs/1612.08242 (2016)


YOLO = “You Only Look Once”

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)


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.


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.


It doesn't matter which ones had public implementations. If you are stuck to this comment it is obvious you never tried to productize any implementation of whatever



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