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GOOGLENET

GoogLeNet convolutional neural network

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 * 

×







SYNTAX

net = googlenet
net = googlenet('Weights',weights)
lgraph = googlenet('Weights','none')



DESCRIPTION

GoogLeNet is a convolutional neural network that is 22 layers deep. You can load
a pretrained version of the network trained on either the ImageNet [1] or
Places365 [2] [3] data sets. The network trained on ImageNet classifies images
into 1000 object categories, such as keyboard, mouse, pencil, and many animals.
The network trained on Places365 is similar to the network trained on ImageNet,
but classifies images into 365 different place categories, such as field, park,
runway, and lobby. These networks have learned different feature representations
for a wide range of images. The pretrained networks both have an image input
size of 224-by-224. For more pretrained networks in MATLAB®, see Pretrained Deep
Neural Networks.

To classify new images using GoogLeNet, use classify. For an example, see
Classify Image Using GoogLeNet.

You can retrain a GoogLeNet network to perform a new task using transfer
learning. When performing transfer learning, the most common approach is to use
networks pretrained on the ImageNet data set. If the new task is similar to
classifying scenes, then using the network trained on Places-365 can give higher
accuracies. For an example showing how to retrain GoogLeNet on a new
classification task, see Train Deep Learning Network to Classify New Images

example

net = googlenet returns a GoogLeNet network trained on the ImageNet data set.

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

net = googlenet('Weights',weights) returns a GoogLeNet network trained on either
the ImageNet or Places365 data set. The syntax googlenet('Weights','imagenet')
(default) is equivalent to googlenet.

The network trained on ImageNet requires the Deep Learning Toolbox Model for
GoogLeNet Network support package. The network trained on Places365 requires the
Deep Learning Toolbox Model for Places365-GoogLeNet Network support package. If
the required support package is not installed, then the function provides a
download link.

lgraph = googlenet('Weights','none') returns the untrained GoogLeNet network
architecture. The untrained model does not require the support package.




EXAMPLES

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DOWNLOAD GOOGLENET SUPPORT PACKAGE

Download and install the Deep Learning Toolbox Model for GoogLeNet Network
support package.

Type googlenet at the command line.

googlenet

If the Deep Learning Toolbox Model for GoogLeNet 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 googlenet at
the command line. If the required support package is installed, then the
function returns a DAGNetwork object.

googlenet

ans = 

  DAGNetwork with properties:

         Layers: [144×1 nnet.cnn.layer.Layer]
    Connections: [170×2 table]

Visualize the network using Deep Network Designer.

deepNetworkDesigner(googlenet)



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



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


INPUT ARGUMENTS

collapse all


WEIGHTS — SOURCE OF NETWORK PARAMETERS
'IMAGENET' (DEFAULT) | 'PLACES365' | 'NONE'

Source of network parameters, specified as 'imagenet' ,'places365', or 'none'.



 * If weights equals 'imagenet', then the network has weights trained on the
   ImageNet data set.

 * If weights equals 'places365', then the network has weights trained on the
   Places365 data set.

 * If weights equals 'none', then the untrained network architecture is
   returned.



Example: 'places365'


OUTPUT ARGUMENTS

collapse all


NET — PRETRAINED GOOGLENET CONVOLUTIONAL NEURAL NETWORK
DAGNETWORK OBJECT

Pretrained GoogLeNet convolutional neural network, returned as a DAGNetwork
object.


LGRAPH — UNTRAINED GOOGLENET CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE
LAYERGRAPH OBJECT

Untrained GoogLeNet convolutional neural network architecture, returned as a
LayerGraph object.


REFERENCES

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

[2] Zhou, Bolei, Aditya Khosla, Agata Lapedriza, Antonio Torralba, and Aude
Oliva. "Places: An image database for deep scene understanding." arXiv preprint
arXiv:1610.02055 (2016).

[3] Places. http://places2.csail.mit.edu/

[4] Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich.
"Going deeper with convolutions." In Proceedings of the IEEE conference on
computer vision and pattern recognition, pp. 1-9. 2015.

[5] BVLC GoogLeNet Model.
https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet


EXTENDED CAPABILITIES


C/C++ CODE GENERATION
GENERATE C AND C++ CODE USING MATLAB® CODER™.

For code generation, you can load the network by using the syntax net =
googlenet or by passing the googlenet function to coder.loadDeepLearningNetwork
(MATLAB Coder). For example: net = coder.loadDeepLearningNetwork('googlenet')

For more information, see Load Pretrained Networks for Code Generation (MATLAB
Coder).

The syntax googlenet('Weights','none') is not supported for code generation.


GPU CODE GENERATION
GENERATE CUDA® CODE FOR NVIDIA® GPUS USING GPU CODER™.

Usage notes and limitations:



 * For code generation, you can load the network by using the syntax net =
   googlenet or by passing the googlenet function to
   coder.loadDeepLearningNetwork (GPU Coder). For example: net =
   coder.loadDeepLearningNetwork('googlenet').
   
   For more information, see Load Pretrained Networks for Code Generation (GPU
   Coder).

 * The syntax googlenet('Weights','none') is not supported for GPU code
   generation.




VERSION HISTORY

Introduced in R2017b


SEE ALSO

Deep Network Designer | vgg16 | vgg19 | resnet18 | resnet50 | resnet101 |
densenet201 | inceptionresnetv2 | squeezenet | trainNetwork | layerGraph |
inceptionv3 | DAGNetwork


TOPICS

 * Transfer Learning with Deep Network Designer
 * Classify Image Using GoogLeNet
 * Train Deep Learning Network to Classify New Images
 * Deep Learning in MATLAB
 * Pretrained Deep Neural Networks
 * Train Residual Network for Image Classification


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