Py Coral Classify
Object recognition using Coral Edge TPU.
By Laurent Ittiitti@usc.eduhttp://jevois.orgGPL v3
 Language: PythonSupports mappings with USB output: YesSupports mappings with NO USB output: No 
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Module Documentation

This module runs an object classification deep neural network using the Coral TPU library. It only works on JeVois-Pro platform equipped with an Edge TPU add-on card. Classification (recognition) networks analyze a central portion of the whole scene and produce identity labels and confidence scores about what the object in the field of view might be.

This module supports networks implemented in TensorFlow-Lite and ported to Edge TPU/

Included with the standard JeVois distribution are:

  • MobileNetV3
  • more to come, please contribute!

See the module's constructor (init) code and select a value for model to switch network.

Object category names for models trained on ImageNet are at

Sometimes it will make mistakes! The performance of SqueezeNet v1.1 is about 56.1% correct (mean average precision, top-1) on the ImageNet test set.

This module is adapted from the sample code:

More pre-trained models are available at

ParameterTypeDescriptionDefaultValid Values
This module exposes no parameter
Detailed docs:PyCoralClassify
Copyright:Copyright (C) 2020 by Laurent Itti
License:GPL v3
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