DenseNet-201 is a pretrained model that has been trained on a subset of the ImageNet database. The model is trained on more than a million images and can classify images into 1000 object categories (e.g. keyboard, mouse, pencil, and many animals).
Opening the densenet201.mlpkginstall file from your operating system or from within MATLAB will initiate the installation process for the release you have.
This mlpkginstall file is functional for R2018a and beyond.
% Access the trained model
net = densenet201();
% See details of the architecture
% Read the image to classify
I = imread('peppers.png');
% Adjust size of the image
sz = net.Layers(1).InputSize
I = I(1:sz(1),1:sz(2),1:sz(3));
% Classify the image using DenseNet-201
label = classify(net, I)
% Show the image and the classification results
I am a little puzzled about the skip connections used in the densenet of matlab. Is it the same with the original paper? Is the skip connections dense in the matlab version?
Can this model be used with the compiler?
Need an example for image segmentation.
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