A quick post, because I looked into this, and decided it wasn’t a viable option. We’re using RPi Zero W for the simplest robot, and I was thinking that with object detection, and ultrasound sensors for depth, one could approximate the far more complicated Realsense on Jetson option.
QEngineering managed to get 11FPS on classification, on the RPi.
But the simplest object detection, MobileNet SSD on Tensorflow 2 Lite, (supposedly faster than Tiny-YOLO3), appears to be narrowly possible, but it is limited to running inference on a picture, in about 6 or 7 seconds.
There is a Tensorflow Lite Micro, and some people have ported it for RPi Zero, (eg. tflite_micro_runtime) but I wasn’t able to install the pip wheel, and gave up.
This guy may have got it working, though it’s hard to tell. I followed the method for installing tensorflow 2 lite, and managed to corrupt my SD card, with “Structure needs cleaning” errors.
So maybe I try again some day, but it doesn’t look like a good option. The RPi 3 or 4 is a better bet. Some pages mentioned NNPack, which allows the use of multiple cores, for NNs. But since the RPi Zero has a single core, it’s likely that if I got it working, it would only achieve inference on a single image frame in 7 seconds, which isn’t going to cut it.