Benchmarking the Xnor AI2GO Platform on the Raspberry Pi

Headline results from benchmarking

Part I — Benchmarking

A more detailed analysis of the results

Inferencing speeds in milli-seconds for MobileNet SSD V1 (blue) and MobileNet SSD V2 (green) across all platforms, with the new Xnor binary convolution network performance shown for comparison (red). Lower numbers are good!

Some caveats around the results

Detecting fruit on the workbench in our test image (left) 🍌🍎, using TensorFlow (middle), and Xnor (right).

Summary

Part II — Methodology

Getting the AI2GO SDK and Model Bundle

Downloading the Xnor SDK
Pick your hardware platform
Select an ‘industry’ rather than a specific machine learning model
Selecting a ‘Kitchen Object Detector’ model.
The pre-tuned model.
Other model options.
Download the model bundle.
$ ls
LICENSE.txt libxnornet.so
README.txt xnornet-1.0-cp35-abi3-linux_armv7l.whl

Installing AI2GO on the Raspberry Pi

Everything you need to get started setting up the Raspberry Pi
% ssh pi@raspberrypi.local
$ cd xnor-sdk
$ pip3 install Pillow
$ pip3 install psutil
$ cd ~/kitchen-object-detector-medium-300
$ pip3 install xnornet-1.0-cp35-abi3-linux_armv7l.whl
Processing ./xnornet-1.0-cp35-abi3-linux_armv7l.whl
Installing collected packages: xnornet
Successfully installed xnornet-1.0
$

The benchmarking code

In closing

Links to previous benchmarks

Links to getting started guides

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Scientist, Author, Hacker, Maker, and Journalist.

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Alasdair Allan

Alasdair Allan

Scientist, Author, Hacker, Maker, and Journalist.

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