Deep Learning ToolboxTM Model for ShuffleNet Network
Pretrained ShuffleNet model for image classification
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Mise à jour
20 mars 2024
ShuffleNet 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 shufflenet.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 R2019a and beyond. Use shufflenet instead of imagePretrainedNetwork if using a release prior to R2024a.
Usage Example:
% Access the trained model
[net, classes] = imagePretrainedNetwork("shufflenet");
% See details of the architecture
net.Layers
% 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 ShuffleNet
scores = predict(net, single(I));
label = scores2label(scores, classes)
% Show the image and the classification results
figure
imshow(I)
text(10,20,char(label),'Color','white')
Compatibilité avec les versions de MATLAB
Créé avec
R2019a
Compatible avec les versions R2019a à R2024a
Plateformes compatibles
Windows macOS (Apple Silicon) macOS (Intel) LinuxCatégories
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