FYI, you are replying to a post that I made which was based on data from late February.
Since then,
@mailseth has been working on the Coral object detection module.
Until last week, I was using the 2.2.0 version of the Coral module, with excellent results. Having seen zero failed inferences on about 100,000 inferences, I wondered if there was a mistake somewhere.
On the CPAI forum I was assured the module had been improved, and the zero failed inferences was correct.
I am on Windows 10
Blue iris the latest stable version, CPAI version 2.6.2 and Coral module 2.2.0 with the YOLOv8 model. I see inference times with the small model of 8 to 12 ms.
My only concern at this point is the use of the full COCO label set, as the module will be identifying trains, toilets, elephants, traffic lights, etc. for that reason, I occasionally will revert to using the YOLOv5 .Net with the ipcam-combined and ipcam-dark models. This works well for me.
BTW, the Windows machine is using a Coral M.2 accelerator.
I have been trying to read up on how to reduce the default model for the Coral module. I would love to see something like ipcam-combined or similar for the Coral module, but I am afraid that is beyond my ability at this point.
Bottom line is the Coral module version 2.2.0 is MUCH better. Thanks to the work of
@mailseth!
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