A microcontroller board with a camera, mic, and Coral Edge TPU

• edgetpu-yolo

  2 83 3.4 Python

  Minimal-dependency Yolov5 export and inference demonstration for the Google Coral EdgeTPU

I'm on the fence. It's a very nice device if you can get your models working on it - basically untouched at the price/power point. Drivers for me have been OK. I have an M.2 card connected to a Jetson devkit (makes for a nice embedded test bench) and it runs fine, no worse than the NCS for setup anyway. There were a couple of PCI settings to tweak but I documented the setup here [0]. For common use cases it's a decent option, I think. For custom models you really need to know what you're doing.

The main issue I've had is that the compiler behaviour differs between versions (and it's very difficult to find older releases), so where previously you could run a big model and delegate things to the CPU, now it sometimes won't compile at all. There were also problems where we trained a model in AutoML - using free credits but the real cost would have been over $100 - but edgetpu compiled model lost a lot of performance. The developers have been very helpful when I've contacted them, and generally you can get through to real devs (not generic support) who can look at your model for you. Mostly I think you need to take care when training models for these devices, but quantisation-aware training is not trivial to use in Tensorflow and there are only a few off-the-shelf models which are supported in the various toolkits. Model maker looks promising, but it's also finnicky in my experience [1].

I'm not super worried about hardware availability. They're suffering from the chip shortage like everyone else, so it's not surprising that lead times are long. I was able to buy my device in late 2020 without any trouble.

[0] https://github.com/jveitchmichaelis/edgetpu-yolo/blob/main/h...

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