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vgg16

Pretrained VGG-16 convolutional neural network

Syntax

net = vgg16

Description

example

net = vgg16 returns a pretrained VGG-16 model. This model is trained on a subset of the ImageNet database [1], which is used in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) [2]. VGG-16 is trained on more than a million images and can classify images into 1000 object categories. For example, keyboard, mouse, pencil, and many animals. As a result, the model has learned rich feature representations for a wide range of images.

This function requires Neural Network Toolbox™ Model for VGG-16 Network support package. If this support package is not installed, then the function provides a download link.

Examples

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Download and install Neural Network Toolbox Model for VGG-16 Network support package.

Type vgg16 at the command line.

vgg16

If Neural Network Toolbox Model for VGG-16 Network support package is not installed, then the function provides a link to the required support package in the Add-On Explorer. To install the support package, click the link, and then click Install. Check that the installation is successful by typing vgg16 at the command line.

vgg16
ans = 

  SeriesNetwork with properties:

    Layers: [41×1 nnet.cnn.layer.Layer]

Load a pretrained VGG-16 convolutional neural network and examine the layers and classes.

Use vgg16 to load the pretrained VGG-16 network. The output net is a SeriesNetwork object.

net = vgg16
net = 

  SeriesNetwork with properties:

    Layers: [41×1 nnet.cnn.layer.Layer]

View the network architecture using the Layers property. The network has 41 layers. There are 16 layers with learnable weights: 13 convolutional layers, and 3 fully connected layers.

net.Layers
ans = 

  41x1 Layer array with layers:

     1   'input'     Image Input             224x224x3 images with 'zerocenter' normalization
     2   'conv1_1'   Convolution             64 3x3x3 convolutions with stride [1  1] and padding [1  1]
     3   'relu1_1'   ReLU                    ReLU
     4   'conv1_2'   Convolution             64 3x3x64 convolutions with stride [1  1] and padding [1  1]
     5   'relu1_2'   ReLU                    ReLU
     6   'pool1'     Max Pooling             2x2 max pooling with stride [2  2] and padding [0  0]
     7   'conv2_1'   Convolution             128 3x3x64 convolutions with stride [1  1] and padding [1  1]
     8   'relu2_1'   ReLU                    ReLU
     9   'conv2_2'   Convolution             128 3x3x128 convolutions with stride [1  1] and padding [1  1]
    10   'relu2_2'   ReLU                    ReLU
    11   'pool2'     Max Pooling             2x2 max pooling with stride [2  2] and padding [0  0]
    12   'conv3_1'   Convolution             256 3x3x128 convolutions with stride [1  1] and padding [1  1]
    13   'relu3_1'   ReLU                    ReLU
    14   'conv3_2'   Convolution             256 3x3x256 convolutions with stride [1  1] and padding [1  1]
    15   'relu3_2'   ReLU                    ReLU
    16   'conv3_3'   Convolution             256 3x3x256 convolutions with stride [1  1] and padding [1  1]
    17   'relu3_3'   ReLU                    ReLU
    18   'pool3'     Max Pooling             2x2 max pooling with stride [2  2] and padding [0  0]
    19   'conv4_1'   Convolution             512 3x3x256 convolutions with stride [1  1] and padding [1  1]
    20   'relu4_1'   ReLU                    ReLU
    21   'conv4_2'   Convolution             512 3x3x512 convolutions with stride [1  1] and padding [1  1]
    22   'relu4_2'   ReLU                    ReLU
    23   'conv4_3'   Convolution             512 3x3x512 convolutions with stride [1  1] and padding [1  1]
    24   'relu4_3'   ReLU                    ReLU
    25   'pool4'     Max Pooling             2x2 max pooling with stride [2  2] and padding [0  0]
    26   'conv5_1'   Convolution             512 3x3x512 convolutions with stride [1  1] and padding [1  1]
    27   'relu5_1'   ReLU                    ReLU
    28   'conv5_2'   Convolution             512 3x3x512 convolutions with stride [1  1] and padding [1  1]
    29   'relu5_2'   ReLU                    ReLU
    30   'conv5_3'   Convolution             512 3x3x512 convolutions with stride [1  1] and padding [1  1]
    31   'relu5_3'   ReLU                    ReLU
    32   'pool5'     Max Pooling             2x2 max pooling with stride [2  2] and padding [0  0]
    33   'fc6'       Fully Connected         4096 fully connected layer
    34   'relu6'     ReLU                    ReLU
    35   'drop6'     Dropout                 50% dropout
    36   'fc7'       Fully Connected         4096 fully connected layer
    37   'relu7'     ReLU                    ReLU
    38   'drop7'     Dropout                 50% dropout
    39   'fc8'       Fully Connected         1000 fully connected layer
    40   'prob'      Softmax                 softmax
    41   'output'    Classification Output   crossentropyex with 'tench', 'goldfish', and 998 other classes

To view the names of the classes learned by the network, you can view the ClassNames property of the classification output layer (the final layer). View the first 10 classes by specifying the first 10 elements.

net.Layers(end).ClassNames(1:10)
ans =

  10×1 cell array

    'tench'
    'goldfish'
    'great white shark'
    'tiger shark'
    'hammerhead'
    'electric ray'
    'stingray'
    'cock'
    'hen'
    'ostrich'

Output Arguments

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Pretrained VGG-16 convolutional neural network returned as a SeriesNetwork object.

References

[1] ImageNet. http://www.image-net.org

[2] Russakovsky, O., Deng, J., Su, H., et al. "ImageNet Large Scale Visual Recognition Challenge." International Journal of Computer Vision (IJCV). Vol 115, Issue 3, 2015, pp. 211–252

[3] Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).

[4] Very Deep Convolutional Networks for Large-Scale Visual Recognition http://www.robots.ox.ac.uk/~vgg/research/very_deep/

Introduced in R2017a

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