VGG-19 Trained on ImageNet Competition Data

Identify the main object in an image

Number of layers: 46 | Parameter count: 143,667,240 | Trained size: 575 MB |

Training Set Information

ImageNet Large Scale Visual Recognition Challenge 2012 classification dataset, consisting of 1.2 million training images, with 1,000 classes of objects.

Performance

This model achieves 75.2% top-1 and 92.5% top-5 accuracy on the ImageNet Large Scale Visual Recognition Challenge 2012 dataset.

Examples

Download Example Notebook

Open in Wolfram Cloud

Resource retrieval

Get the pre-trained net:

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Basic usage

Classify an image:

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(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/364521e5-2245-4bc4-a8b0-f19cb8b761b7"]

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The prediction is an Entity object, which can be queried:

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Get a list of available properties of the predicted Entity:

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Obtain the probabilities of the ten most likely entities predicted by the net:

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(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/1a482f67-92cf-4679-8c6a-c111df95e5c8"]

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An object outside the list of the ImageNet classes will be misidentified:

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(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/801c3fac-92ee-4a93-8abc-ed92cb92e888"]

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Obtain the list of names of all available classes:

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Feature extraction

Remove the last three layers of the trained net, so that the net produces a vector representation of an image:

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Get a set of images:

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(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/0d05015e-a98d-4b9a-ad42-68b4645a2d71"]

Visualize the features of a set of images:

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Visualize convolutional weights

Extract the weights of the first convolutional layer in the trained net:

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$weights = NetExtract[ NetModel[ "VGG-19 Trained on ImageNet Competition Data"], {"conv1_1", "Weights"}];$

Visualize the weights as a list of 64 images of size 3x3:

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Transfer learning

Use the pre-trained model to build a classifier for telling apart images of dogs and cats. Create a test set and a training set:

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(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/80ac3f76-9b3c-4c1c-bb1f-f24e3c911197"]

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(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/5638a1be-ae09-40e8-8ddf-a22ff7cc8aaa"]

Remove the linear layer from the pre-trained net:

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Create a new net composed of the pre-trained net followed by a linear layer and a softmax layer:

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newNet = NetChain[<|"pretrainedNet" -> tempNet, "linearNew" -> LinearLayer[], "softmax" -> SoftmaxLayer[]|>, "Output" -> NetDecoder[{"Class", {"cat", "dog"}}]]

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Train on the dataset, freezing all the weights except for those in the "linearNew" layer (use TargetDevice -> "GPU" for training on a GPU):

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$trainedNet = NetTrain[newNet, trainSet, LearningRateMultipliers -> {"linearNew" -> 1, _ -> 0}]$

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Perfect accuracy is obtained on the test set:

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Net information

Inspect the number of parameters of all arrays in the net:

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$NetInformation[ NetModel["VGG-19 Trained on ImageNet Competition Data"], \ "ArraysElementCounts"]$

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Obtain the total number of parameters:

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$NetInformation[ NetModel["VGG-19 Trained on ImageNet Competition Data"], \ "ArraysTotalElementCount"]$

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Obtain the layer type counts:

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$NetInformation[ NetModel["VGG-19 Trained on ImageNet Competition Data"], \ "LayerTypeCounts"]$

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Display the summary graphic:

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$NetInformation[ NetModel["VGG-19 Trained on ImageNet Competition Data"], \ "SummaryGraphic"]$

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Export to MXNet

Export the net into a format that can be opened in MXNet:

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Export also creates a net.params file containing parameters:

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Get the size of the parameter file:

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The size is similar to the byte count of the resource object:

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Represent the MXNet net as a graph:

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Construction Notebook

Download Construction Notebook

Open in Wolfram Cloud

Requirements

Wolfram Language 11.2 (September 2017) or above

Resource History

Date Created: 18 July 2017
Latest Update: 21 June 2018

Reference

K. Simonyan, A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv:1409.1556 (2014)
Available from: http://www.robots.ox.ac.uk/~vgg/research/very_deep
Rights: Creative Commons Attribution 4.0 International (CC BY 4.0)