Released in 2016, these models tackle the problem of the pose and viewpoint variations in facial recognition systems. Unlike other models that attempt to transform different poses and viewpoints to a canonical frontal pose, this set of models provides multiple pose-specific nets.
Number of models: 10
Examples
Resource retrieval
Get the pre-trained net:
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NetModel parameters
This model consists of a family of individual nets, each identified by a specific parameter combination. Inspect the available parameters:
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Pick a non-default net by specifying the parameters:
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Pick a non-default uninitialized net:
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Evaluation function
Create an evaluation function that takes two facial images and outputs True if they belong to the same person and False if not:
Basic usage
Predict whether two facial images belong to the same person or not using the evaluation function:
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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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Export to MXNet
Cell[TextData[{
Cell[BoxData[
ButtonBox["Export",
BaseStyle->"Link",
ButtonData->"ref/Export"]], "InlineFormula"],
" the net into a format that can be opened in MXNet:"
}], "Text",
CellChangeTimes->{{3.7124979354007673`*^9, 3.712498017180984*^9}, {3.7125008536150703`*^9,
3.712500913877118*^9}, 3.72443039120368*^9}]
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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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Requirements
Wolfram Language 12.0
(April 2019) or above
Resource History
Reference