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vgg16

VGG-16 convolutional neural network

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  • VGG-16 network architecture

Syntax

net = vgg16
net = vgg16('Weights','imagenet')
layers = vgg16('Weights','none')

Description

VGG-16 is a convolutional neural network that is 16 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database [1]. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich feature representations for a wide range of images. The network has an image input size of 224-by-224. For more pretrained networks in MATLAB®, see Pretrained Deep Neural Networks.

You can use classify to classify new images using the VGG-16 network. Follow the steps of Classify Image Using GoogLeNet and replace GoogLeNet with VGG-16.

To retrain the network on a new classification task, follow the steps of Train Deep Learning Network to Classify New Images and load VGG-16 instead of GoogLeNet.

example

net = vgg16 returns a VGG-16 network trained on the ImageNet data set.

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

net = vgg16('Weights','imagenet') returns a VGG-16 network trained on the ImageNet data set. This syntax is equivalent to net = vgg16.

layers = vgg16('Weights','none') returns the untrained VGG-16 network architecture. The untrained model does not require the support package.

Examples

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

Type vgg16 at the command line.

vgg16

If Deep Learning 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]

Visualize the network using Deep Network Designer. 

deepNetworkDesigner(vgg16)

Explore other pretrained neural networks in Deep Network Designer by clicking New.

Deep Network Designer start page showing available pretrained neural networks

If you need to download a neural network, pause on the desired neural network and click Install to open the Add-On Explorer.

Open Live Script

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  1  1]
     3   'relu1_1'   ReLU                    ReLU
     4   'conv1_2'   Convolution             64 3x3x64 convolutions with stride [1  1] and padding [1  1  1  1]
     5   'relu1_2'   ReLU                    ReLU
     6   'pool1'     Max Pooling             2x2 max pooling with stride [2  2] and padding [0  0  0  0]
     7   'conv2_1'   Convolution             128 3x3x64 convolutions with stride [1  1] and padding [1  1  1  1]
     8   'relu2_1'   ReLU                    ReLU
     9   'conv2_2'   Convolution             128 3x3x128 convolutions with stride [1  1] and padding [1  1  1  1]
    10   'relu2_2'   ReLU                    ReLU
    11   'pool2'     Max Pooling             2x2 max pooling with stride [2  2] and padding [0  0  0  0]
    12   'conv3_1'   Convolution             256 3x3x128 convolutions with stride [1  1] and padding [1  1  1  1]
    13   'relu3_1'   ReLU                    ReLU
    14   'conv3_2'   Convolution             256 3x3x256 convolutions with stride [1  1] and padding [1  1  1  1]
    15   'relu3_2'   ReLU                    ReLU
    16   'conv3_3'   Convolution             256 3x3x256 convolutions with stride [1  1] and padding [1  1  1  1]
    17   'relu3_3'   ReLU                    ReLU
    18   'pool3'     Max Pooling             2x2 max pooling with stride [2  2] and padding [0  0  0  0]
    19   'conv4_1'   Convolution             512 3x3x256 convolutions with stride [1  1] and padding [1  1  1  1]
    20   'relu4_1'   ReLU                    ReLU
    21   'conv4_2'   Convolution             512 3x3x512 convolutions with stride [1  1] and padding [1  1  1  1]
    22   'relu4_2'   ReLU                    ReLU
    23   'conv4_3'   Convolution             512 3x3x512 convolutions with stride [1  1] and padding [1  1  1  1]
    24   'relu4_3'   ReLU                    ReLU
    25   'pool4'     Max Pooling             2x2 max pooling with stride [2  2] and padding [0  0  0  0]
    26   'conv5_1'   Convolution             512 3x3x512 convolutions with stride [1  1] and padding [1  1  1  1]
    27   'relu5_1'   ReLU                    ReLU
    28   'conv5_2'   Convolution             512 3x3x512 convolutions with stride [1  1] and padding [1  1  1  1]
    29   'relu5_2'   ReLU                    ReLU
    30   'conv5_3'   Convolution             512 3x3x512 convolutions with stride [1  1] and padding [1  1  1  1]
    31   'relu5_3'   ReLU                    ReLU
    32   'pool5'     Max Pooling             2x2 max pooling with stride [2  2] and padding [0  0  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' and 999 other classes

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

net.Layers(end).Classes(1:10)
ans = 10×1 categorical 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.

Untrained VGG-16 convolutional neural network architecture, returned as a Layer array.

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/

Extended Capabilities

Version History

Introduced in R2017a

See Also

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