Examples

Resource retrieval

Get the pre-trained network:

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Basic usage

Compute a feature vector for a given image:

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Get the length of the feature vector:

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Use a batch of face images:

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Compute the feature vectors for the batch of images, and obtain the dimensions of the features:

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Reduce the feature vectors to two dimensions with t-SNE:

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Visualize the results:

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Obtain the five closest faces to a given one:

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Net information

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

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Obtain the total number of parameters:

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Obtain the layer type counts:

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Display the summary graphic:

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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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Requirements

Wolfram Language 11.2 (September 2017) or above

Resource History

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

Reference

• I. Masi, A. T. Tran, T. Hassner, J. T. Leksut, G. Medioni, "Do We Really Need to Collect Millions of Faces for Effective Face Recognition?," European Conference on Computer Vision (2016)
• Available from: http://www.openu.ac.il/home/hassner/projects/augmented_faces
• Rights: Unrestricted use