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Show HN: Offline Object Detection and Tracking on a Raspberry Pi

106 points| bko | 8 years ago |medium.com | reply

12 comments

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[+] dividuum|8 years ago|reply
Is there a more self-contained way of running trained models on devices like the Pi? Almost all tutorials I've come across always require me to install a lot of Python dependencies. I'd love to have classifiers installed as a single binary somewhere. I guess https://pjreddie.com/darknet/yolo/ is pretty close to that and I would imagine with a custom build wrapper around https://github.com/tensorflow/tensorflow/tree/master/tensorf..., the same could be achieved. Any hints?
[+] Maybestring|8 years ago|reply
You could use tensorflow.js, not self-contained, but it only depends on the browser. And... Is it easy to get openCL working on a Pi? WebGL might be slower, but it ought to just work.
[+] MarkMMullin|8 years ago|reply
You want to proceed carefully with a Pi Zero - the chip is an ARMv6, so you'll end up building more things and you loose some optimization - you might not like the performance :-(
[+] ythn|8 years ago|reply
Why not just install all the python 3 dependencies to a docker image and then run the docker container on the pi?
[+] bko|8 years ago|reply
This was actually my original approach. I meant to mention that. And I got it to work but then I had to figure out how to control the hardware from the pi and I just gave up. To be fair, I didn't try too hard. I'm sure there is a way to communicate with peripherals through
[+] icebraining|8 years ago|reply
Since the Pi is ARM, you'd need to get pip to cross-compile any native extensions in the Python libraries (or use a pre-compiled wheel, if available).