Examples

Resource retrieval

Get the pre-trained net:

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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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Visualize the features in two and three dimensions:

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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 11.3 (March 2018) or above

Resource History

• Date Created: 23 January 2018
• Latest Update: 21 June 2018

Reference

• F. Schroff, D. Kalenichenko, J. Philbin, "FaceNet: A Unified Embedding for Face Recognition and Clustering," arXiv: 1503.03832 (2015)
• Available from: https://github.com/cmusatyalab/openface
• Rights: Apache 2.0 License