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I am Prathamesh, co-founder of https://nanonets.com https://nanonets.com We were working on a project to detect objects using deep learning with raspberry pi a

I am Prathamesh, co-founder of https://nanonets.com https://nanonets.com We were working on a project to detect objects using deep learning with raspberry pi and we have benchmarked various deep learning architectures on pi. With ~100-200 images, you can create a detector of your own with this method. In this post, we have detected vehicles in Indian traffic using pi and also added github links to code to train the model on your own dataset and then script to get inference on pi. Hope this helps!

thedirt0115 9y ago
Hi, I have a question about the performance benchmarks section. The best performaing model from your benchmarks, ssd_mobilenet_v1, has a prediction time in seconds of 0.72 -- Is that the total runtime of the script? I'm wondering if I could achieve ~1 FPS running in realtime (basically looping as fast as possible against the camera input), or is there more overhead? (edit -- made question more specific)
- prats226 9y ago
  Yeah its total runtime of script. However you can get upto 3-4 FPS with more optimizations. We are going to try more quantization options soon with release of tensorflow 1.7 and will report our findings (Will post updates in blog). Also pi camera code needs to be optimized which will further increase FPS. One big advantage of this method is just by collecting ~200 images for any use case, you can have detector ready in couple of hours. One advantage of using API based approach is you can get much higher FPS without compromising accuracy and is also independent of pi CPU power and heating etc.
  - jd20 9y ago
    Curious, have you looked at any other embedded platforms besides the Pi? The Tegra might be an interesting comparison point, I wonder what kind of FPS the onboard GPU would buy you.
    - prats226 9y ago
      Hey, good suggestion to benchmark against other SOC's as well. I heard raspberry pi recently added support for external graphics card. Haven't tried yet.

thedirt0115 9y ago

Hi, I have a question about the performance benchmarks section. The best performaing model from your benchmarks, ssd_mobilenet_v1, has a prediction time in seconds of 0.72 -- Is that the total runtime of the script? I'm wondering if I could achieve ~1 FPS running in realtime (basically looping as fast as possible against the camera input), or is there more overhead? (edit -- made question more specific)

prats226 9y ago
Yeah its total runtime of script. However you can get upto 3-4 FPS with more optimizations. We are going to try more quantization options soon with release of tensorflow 1.7 and will report our findings (Will post updates in blog). Also pi camera code needs to be optimized which will further increase FPS. One big advantage of this method is just by collecting ~200 images for any use case, you can have detector ready in couple of hours. One advantage of using API based approach is you can get much higher FPS without compromising accuracy and is also independent of pi CPU power and heating etc.
- jd20 9y ago
  Curious, have you looked at any other embedded platforms besides the Pi? The Tegra might be an interesting comparison point, I wonder what kind of FPS the onboard GPU would buy you.
  - prats226 9y ago
    Hey, good suggestion to benchmark against other SOC's as well. I heard raspberry pi recently added support for external graphics card. Haven't tried yet.