- LattePanda Alpha (Ubuntu16.04) / RaspberryPi3 (Raspbian) / LaptopPC (Ubuntu16.04)
- Edge TPU Accelerator
- USB Camera (Playstationeye)
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
$ wget http://storage.googleapis.com/cloud-iot-edge-pretrained-models/edgetpu_api.tar.gz
$ tar xzf edgetpu_api.tar.gz
$ cd python-tflite-source
$ 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.)
$ git clone https://github.com/PINTO0309/TPU-MobilenetSSD.git
$ cd TPU-MobilenetSSD
$ python3 MobileNet-SSD-TPU-async.py
- Get started with the USB Accelerator https://coral.withgoogle.com/tutorials/accelerator
- Models https://coral.withgoogle.com/models/
- Edge TPU Model Compiler https://coral.withgoogle.com/web-compiler/
- API demos https://coral.withgoogle.com/tutorials/edgetpu-api/#api-demos
- Edge TPU Benchmark https://coral.withgoogle.com/tutorials/edgetpu-faq/