featurizeImage: Machine Learning Image Featurization Transform

Artikel
02/28/2023

Featurizes an image using a pre-trained deep neural network model.

Usage

  featurizeImage(var, outVar = NULL, dnnModel = "Resnet18")

Arguments

`var`

Input variable containing extracted pixel values.

`outVar`

The prefix of the output variables containing the image features. If null, the input variable name will be used. The default value is NULL.

`dnnModel`

The pre-trained deep neural network. The possible options are:

"resnet18"
"resnet50"
"resnet101"
"alexnet"
The default value is "resnet18". See Deep Residual Learning for Image Recognition for details about ResNet.

Details

featurizeImage featurizes an image using the specified pre-trained deep neural network model. The input variables to this transform must be extracted pixel values.

Value

A maml object defining the transform.

Author(s)

Microsoft Corporation Microsoft Technical Support

Examples


 train <- data.frame(Path = c(system.file("help/figures/RevolutionAnalyticslogo.png", package = "MicrosoftML")), Label = c(TRUE), stringsAsFactors = FALSE)

 # Loads the images from variable Path, resizes the images to 1x1 pixels and trains a neural net.
 model <- rxNeuralNet(
     Label ~ Features,
     data = train,
     mlTransforms = list(
         loadImage(vars = list(Features = "Path")),
         resizeImage(vars = "Features", width = 1, height = 1, resizing = "Aniso"),
         extractPixels(vars = "Features")
         ),
     mlTransformVars = "Path",
     numHiddenNodes = 1,
     numIterations = 1)

 # Featurizes the images from variable Path using the default model, and trains a linear model on the result.
 model <- rxFastLinear(
     Label ~ Features,
     data = train,
     mlTransforms = list(
         loadImage(vars = list(Features = "Path")),
         resizeImage(vars = "Features", width = 224, height = 224), # If dnnModel == "AlexNet", the image has to be resized to 227x227.
         extractPixels(vars = "Features"),
         featurizeImage(var = "Features")
         ),
     mlTransformVars = "Path")

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