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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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 12.1 (March 2020) or above

Resource History

• Date Created: 24 April 2020
• Latest Update: 10 October 2022

Reference

• A. T. Tran, T. Hassner, I. Masi, G. Medioni, "Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network," arXiv:1612.04904 (2016)
• Available from: https://github.com/anhttran/3dmm_cnn
• Rights: Model License