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I try to make it short for now as I spent a very week just before my vacation on trying set up proper benchmarking environments. Generally the overall experience of getting Caffe running on plain vanilla OSX, Ubuntu and RasPi Jessies is horrible. I will publish my really working scripts after I get back.
Putting this aside I wanted to see the numbers for devices seeing a cat in 'cat.jpg' with Squeezenet.
I grabbed the necessary files from
With Caffe on CPU
time ./build/examples/cpp_classification/classification.bin \
gives me following averages:
- 0.15s (Macbook Air 13", early 2015, 1,6 Ghz Intel Core i5, 8Gb RAM running macOS Sierra)
- 0.19s (Lenovo Thinkpad T420S running latest Ubuntu)
- 1.1s (RasPi 3 running RasPi Jessie)
classification_example.py as follows
output, userobj = graph.GetResult()
order = output.argsort()[::-1][:6]
the Movidius stick gives me from an Ubuntu averages of
- 0.307s on
What should I run/test on to get numbers that give me a bigger smile?
PS: does anyone have a working install script for OpenCV on RasPi for Python3 (maybe without virtualenv)? That part is kinda just mentioned in the video, but the Interwebz give the general blob of almost working nothings in the topic -- it's especially painful as compiling OpenCV on a RasPi is far from being instant.