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I've run yolov2 on my Raspberry Pi 3 but I got 1 frame each 13s (which is pretty bad for live object detection). I've also tried an SSD but the results were terrible too. Is there anything I could do to make it faster?

-I've tryied this library for YOLO https://github.com/allanzelener/YAD2K and a keras implementation of SSD

-My camera is a simple Microsoft webcam medium quality

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    Welcome to the site. It's impossible to know whether your approach could be made faster if we can't see how you did it - can you edit your question (don't use comments) to include the exact command string you're using with yolov2, and how you might have any peripherals (cameras etc.) attached? – goobering Feb 19 '17 at 12:50
  • A sure way to increase your processing speed is to decrease the resolution of the image. – Valentin Brasso May 18 '17 at 12:00
  • Can you please describe the setps that you used. Did u used Raspberian OS? Thanks – victorp Jul 21 '17 at 4:16
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OK after a long search I 've found a reliable version of yolo called YAD2K. I have use tiny-yolo which takes about 1.5s per frame on a raspberry pi 3.

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I managed to had yolo v3 tiny run on my Pi 3 model B+ at 1FPS. I compiled the darknet source code with NNPACK option, which significantly improved the FPS (from ~0.1FPS to 1FPS). Check by blogs for results http://funofdiy.blogspot.com/2018/08/deep-learning-with-raspberry-pi-real.html

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    The native darknet performs pretty bad on CPU. That's why you need NNPACK, which optimizes neural network performance on multi-core CPU. I think Pi 3 Cortex-A53 has four cores so using NNPACK you will be expecting to see 3~4x acceleration. Still, Yolo2 is big and will be slow on RPI. Try yolo tiny version – Xiang Zhai Aug 30 '18 at 15:45
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Running YOLO on the raspberry pi 3 was slow. What i did was use Intel's Movidius NCS it was a little tricky getting it all setup, but that was mainly due to the fact it had just came out and had a few bugs. Got it to work using Stretch OS on the Pi 3. Then was able to run it on the Pi zero. The speed you get with it is wicked quick. https://www.pyimagesearch.com/2018/02/19/real-time-object-detection-on-the-raspberry-pi-with-the-movidius-ncs/

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