Coral USB Accelerator
In order to continue laying the ground work to merge my developing underwater sensor platform and fish id model I’ve finally started up my new Google USB Coral Accelerator1 and have gone through Google’s available demos2 for it. In this post I’ll cover the process and any hurdles along the way to successfully running the Google Coral USB Accelerator1.
To test it, I used my Raspberry Pi 3 Model B+ and followed Google’s tutorial3.
Because my version of Raspbian was 4.9, I didn’t have a version of Python above 3.4.
The tutorial3 calls for Python 3.5 or higher, so I attempted to install Python 3.5, 3.6 and 3.7 using some well documented instructions4 5 6. Unfortunately I was met with Python related issues either at runtime7 of the tutorial’s3 model inference invocation or during the tutorial’s3
./install.sh execution that involved what were likely
After searching around, I stumbled across a few others who had successfully used the accelerator and found a commonality among them all; all successful projects using the accelerator used a fresh and current Raspbian install8.
So I backed up9 my PI’s SD card using my MacBook Pro, erased and then formatted the SD card using
gparted10 on my linux machine (with
boot flag set to true11 to avoid an annoying MBR related issue12) and then installed the latest Raspbian release using NOOBS13
I noticed that the power consumption while running the
classify_capture.py demo2 using the
pi cam v214 with only a terminal open on the PI, peaks at about
Furthermore, the classification speed of the accelerator was approximately
11fps while running
classify_capture.py, which meets my target
fps for my underwater sensor platform and fish id model