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:
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 https://github.com/HoldenCaulfieldRye/caffe/blob/master/data/ilsvrc12/synset_words.txt
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: https://github.com/opencv/opencv/blob/master/samples/dnn/classification.py
More pre-trained models are available on github in opencv_extra