Deep Learning Toolbox Model for SqueezeNet Network

Pretrained SqueezeNet model for image classification

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Actualizado 11 Sep 2019

The SqueezeNet pretrained model for image classification is a part of the Deep Learning Toolbox in R2020a and does not require a separate installation. If you are using the R2020a version of the Deep Learning Toolbox, you can type ‘squeezenet’ in the command line or access the model directly without installation from the Deep Network Designer App.

If you are using R2018a to R2019b, you'll need to download and install this support package.

SqueezeNet 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 squeezenet.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.

Usage Example:

net = squeezenet()

% Read the image to classify
I = imread('peppers.png');

% Crop image to the input size of the network
sz = net.Layers(1).InputSize
I = I(100:sz(1)+99, 100:sz(2)+99, 1:sz(3));

% Classify the image using SqueezeNet
label = classify(net, I)

% Show the image and classification result
text(10, 20, char(label), 'Color', 'white' )

Compatibilidad con la versión de MATLAB
Se creó con R2018a
Compatible con cualquier versión desde R2018a hasta R2019b
Compatibilidad con las plataformas
Windows macOS (Apple silicon) macOS (Intel) Linux
Más información sobre Deep Learning Toolbox en Help Center y MATLAB Answers.

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