Coral USBをどうすれば・・・

とりあえず、Coral USBをどうすれば・・・





そんなこんなで、Coral USBをどうすれば・・・良いのか。



AI at the edge
AI is pervasive today, from consumer to enterprise applications. With the explosive growth of connected devices, combined with a demand for privacy/confidentiality, low latency and bandwidth constraints, AI models trained in the cloud increasingly need to be run at the edge. Edge TPU is Google’s purpose-built ASIC designed to run AI at the edge. It delivers high performance in a small physical and power footprint, enabling the deployment of high-accuracy AI at the edge.

An open, end-to-end infrastructure for deploying AI solutions
Edge TPU enables the deployment of high-quality ML inference at the edge. It augments Google’s Cloud TPU and Cloud IoT to provide an end-to-end (cloud-to-edge, hardware + software) infrastructure to facilitate the deployment of customers’ AI-based solutions. In addition to its open-source TensorFlow Lite programming environment, Edge TPU will initially be deployed with several Google AI models, combining Google’s expertise in both AI and hardware.

Edge TPU complements CPUs, GPUs, FPGAs, and other ASIC solutions for running AI at the edge, which will be supported by Cloud IoT Edge.

API demos
Try the following scripts that demonstrate how to use ClassificationEngine and DetectionEngine. Then inspect the source code for each to learn more about the Edge TPU APIs.

First you must navigate to the directory with the demos:

# If you're on the Dev Board:
cd /usr/lib/python3/dist-packages/edgetpu/

# If you're using the USB Accelerator:
cd python-tflite-source/edgetpu/
This script performs image classification with ClassificationEngine, using the classification model, labels file, and image you give it.

python3 demo/ \
--model test_data/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite \
--label test_data/inat_bird_labels.txt \
--image test_data/parrot.jpg

You should see some results like this:

Ara macao (Scarlet Macaw)
Score : 0.613281
Platycercus elegans (Crimson Rosella)
Score : 0.152344

Raspberry Piの画面インチが小さすぎたので、それも少し変更しなければ・・・
THE MNIST DATABASE of handwritten digits