So here is shown a simple example - 2 floats as data + 1 float as output:
Layer 1: 2 neurons (2 inputs)
Layer 2: 3 neurons (hidden layer)
Layer 3: 3 neurons (hidden layer)
Layer 4: 1 neurons (1 output)
And we create ANs with something like
cvSet1D(&neuralLayers1, 0, cvScalar(2));
cvSet1D(&neuralLayers1, 1, cvScalar(3));
cvSet1D(&neuralLayers1, 2, cvScalar(3));
cvSet1D(&neuralLayers1, 3, cvScalar(1));
And than we just tall openCV to train our network.
I wonder if we had Nx2 floats of data + 1 float as for output and we would want to give first neuron as input first line (N floats) and to second neuron second line (N float data elements) what would we need to add to our code?
Classification using OpenCV DNN and pre-trained DenseNet. OpenCV can be utilised to solve image classification problems. The OpenCV library offers a Deep Neural Network (DNN) module, which facilitates the use of deep learning related functions within OpenCV.
It is deployed for the detection of items, faces, Diseases, lesions, Number plates, and even handwriting in various images and videos. With help of OpenCV in Deep Learning, we deploy vector space and execute mathematical operations on these features to identify visual patterns and their various features.
OpenCV has a bunch of pre-trained classifiers that can be used to identify objects such as trees, number plates, faces, eyes, etc. We can use any of these classifiers to detect the object as per our need.
OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library.
I would definitely use the KNN mentioned.
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