Edge TPU Accelerator / Multi-TPU + MobileNet-SSD v2 + Python + Async + LattePandaAlpha/RaspberryPi3/LaptopPC
MIT License
2.Structure visualization of Tensorflow Lite model files (.tflite)
320x240 about 80 - 90 FPS https://youtu.be/LERXuDXn0kY
640x480 about 60 - 80 FPS https://youtu.be/OFEQHCQ5MsM
320x240 about 150 FPS++ https://youtu.be/_qE9kmk8gUA
$ curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add-
$ echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list
$ sudo apt-get update
$ sudo apt-get upgrade edgetpu
$ wget https://dl.google.com/coral/edgetpu_api/edgetpu_api_latest.tar.gz -O edgetpu_api.tar.gz --trust-server-names
$ tar xzf edgetpu_api.tar.gz
$ cd edgetpu_api
$ bash ./install.sh
MobileNet-SSD-TPU-async.py -> USB camera animation and inference are asynchronous (The frame is slightly off.) MobileNet-SSD-TPU-sync.py -> USB camera animation and inference are synchronous (The frame does not shift greatly.)
If you use USB3.0 USBHub and connect multiple TPUs, it automatically detects multiple TPUs and processes inferences in parallel at high speed.
$ git clone https://github.com/PINTO0309/TPU-MobilenetSSD.git
$ cd TPU-MobilenetSSD
$ python3 MobileNet-SSD-TPU-async.py
usage: MobileNet-SSD-TPU-async.py [-h] [--model MODEL] [--label LABEL]
[--usbcamno USBCAMNO]
optional arguments:
-h, --help show this help message and exit
--model MODEL Path of the detection model.
--label LABEL Path of the labels file.
--usbcamno USBCAMNO USB Camera number.