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Deep Learning Toolbox Model for VGG-16 Network

Pretrained VGG-16 network model for image classification


Updated 12 Sep 2018

VGG-16 is a pretrained Convolutional Neural Network (CNN) that has been trained on approximately 1.2 million images from the ImageNet Dataset ( by the Visual Geometry Group at University of Oxford (
The model has 16 layers and can classify images into 1000 object categories (e.g. keyboard, mouse, coffee mug, pencil).
Opening the vgg16.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 R2017a and beyond.

Usage Example:
% Load the trained model
net = vgg16()

% 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 VGG-16
label = classify(net, I);

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

Comments and Ratings (9)

Shounak Mitra

Hi Taimoor, we are sorry it is not working for you. Are you trying to access the file from the Add-Ons menu? Did you try downloading it from this File Exchange location and then installing it?

Taimoor Tariq

In MATLAB 2018b, it says that this add-on is not supported on my platform. Why is that?

chao liang

It is a perfectly tool to do Deep learning!

Greg Heath

I get 41 layers !



Hans Oeri

So when I run the command: label = classify(net, I);

I get the error message: Can't reload '/Applications/'

My Matlab is up to date, and the network itself loads fine, does anyone know what this is and how to fix it?

Wei-Wen Hsu

Is there an update version that can work on a single GPU when training?
The VGG16 may need about 14 GB for a batch size of 128.
However, it only has about 8 to 12 GB memory size on a single GPU.
It runs out of memory when training. Any solution? Thank you.

MATLAB Release Compatibility
Created with R2017a
Compatible with R2017a to R2018b
Platform Compatibility
Windows macOS Linux

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