Originally released in 2015 as a pre-trained model for the launch of the IMDB-WIKI dataset by the Computer Vision Lab at ETH Zurich, this model is based on the VGG-16 architecture and is designed to run on cropped images of faces only. The model was then fine-tuned on the dataset for the 2015 Looking At People Age Estimation Challenge. An ensemble of these models won first place at the challenge.
Number of layers: 40 |
Parameter count: 134,674,341 |
Trained size: 540 MB
Examples
Resource retrieval
Get the pre-trained net:
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Basic usage
Guess the age of a person from a photograph. First, obtain the trained net:
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Obtain the probability distribution over all possible ages:
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Plot the probability distribution over possible ages:
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The recommended estimator of the age is the mean of the probability mass function:
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By default, the mode is used as the estimator:
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This net is designed to work with cropped images of faces only. If the photograph is not a facial image, the results may be unexpected:
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Crop the photograph:
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Guess the age of a person from the cropped image:
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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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Represent the MXNet net as a graph:
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Requirements
Wolfram Language 11.2
(September 2017) or above
Resource History
Reference