Py Classification DNN
Object recognition using OpenCV Deep Neural Networks (DNN)
By Laurent Ittiitti@usc.eduhttp://jevois.orgGPL v3
 Language: PythonSupports mappings with USB output: YesSupports mappings with NO USB output: No 
 Video Mapping:   YUYV 320 264 30.0 YUYV 320 240 30.0 JeVois

Module Documentation

This module runs an object classification deep neural network using the OpenCV DNN library. 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 detection networks implemented in TensorFlow, Caffe, Darknet, Torch, ONNX, etc as supported by the OpenCV DNN module.

Included with the standard JeVois distribution are:

  • SqueezeNet v1.1, Caffe model
  • 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 OpenCV code:

More pre-trained models are available on github in opencv_extra

ParameterTypeDescriptionDefaultValid Values
This module exposes no parameter
Detailed docs:PyClassificationDNN
Copyright:Copyright (C) 2018 by Laurent Itti
License:GPL v3
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