pixelClassificationLayer

Create pixel classification layer for semantic segmentation

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Description

A pixel classification layer provides a categorical label for each image pixel or voxel.

Creation

Syntax

layer = pixelClassificationLayer
layer = pixelClassificationLayer(Name,Value)

Description

example

layer = pixelClassificationLayer creates a pixel classification output layer for semantic image segmentation networks. The layer outputs the categorical label for each image pixel or voxel processed by a CNN. The layer automatically ignores undefined pixel labels during training.

example

layer = pixelClassificationLayer(Name,Value) returns a pixel classification output layer using Name,Value pair arguments to set the optional Classes, ClassWeights, and Name properties by using name-value pairs. You can specify multiple name-value pairs. Enclose each property name in quotes.

For example, pixelClassificationLayer('Name','pixclass') creates a pixel classification layer with the name 'pixclass'.

Properties

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Classes of the output layer, specified as a categorical vector, string array, cell array of character vectors, or 'auto'. If Classes is 'auto', then the software automatically sets the classes at training time. If you specify the string array or cell array of character vectors str, then the software sets the classes of the output layer to categorical(str,str). The default value is 'auto'.

Data Types: char | categorical | string | cell

Class weights, specified as 'none' or as a vector of real scalar. The elements of the vector correspond to the classes in Classes. If you specify ClassWeights, then you must specify Classes.

Use class weighting to balance classes when there are underrepresented classes in the training data.

This property is read-only.

The output size of the layer. The value is 'auto' prior to training, and is specified as a numeric value at training time.

This property is read-only.

Loss function used for training, specified as 'crossentropyex'.

Layer name, specified as a character vector or a string scalar. To include a layer in a layer graph, you must specify a nonempty unique layer name. If you train a series network with the layer and Name is set to '', then the software automatically assigns a name to the layer at training time.

Data Types: char | string

Number of inputs of the layer. This layer accepts a single input only.

Data Types: double

Input names of the layer. This layer accepts a single input only.

Data Types: cell

Examples

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This example uses:

Open Live Script

Predict the categorical label of every pixel in an input image.

layers = [
         imageInputLayer([32 32 3])
         convolution2dLayer(3,16,'Stride',2,'Padding',1)
         reluLayer
         transposedConv2dLayer(3,1,'Stride',2,'Cropping',1)
         softmaxLayer
         pixelClassificationLayer
      ]
layers = 
  6x1 Layer array with layers:

     1   ''   Image Input                  32x32x3 images with 'zerocenter' normalization
     2   ''   Convolution                  16 3x3 convolutions with stride [2  2] and padding [1  1  1  1]
     3   ''   ReLU                         ReLU
     4   ''   Transposed Convolution       1 3x3 transposed convolutions with stride [2  2] and cropping [1  1  1  1]
     5   ''   Softmax                      softmax
     6   ''   Pixel Classification Layer   Cross-entropy loss

This example uses:

Open Live Script

Balance classes using inverse class frequency weighting when some classes are underrepresented in the training data. First, count class frequencies over the training data using pixelLabelImageDatastore. Then, set the 'ClassWeights' in pixelClassificationLayer to the computed inverse class frequencies.

Set the location of image and pixel label data.

  dataDir = fullfile(toolboxdir('vision'),'visiondata');
  imDir = fullfile(dataDir,'building');
  pxDir = fullfile(dataDir,'buildingPixelLabels');

Create a pixel label image datastore using the ground truth images in imds and the pixel labeled images in pxds.

  imds = imageDatastore(imDir);
  classNames = ["sky" "grass" "building" "sidewalk"];
  pixelLabelID = [1 2 3 4];
  pxds = pixelLabelDatastore(pxDir,classNames,pixelLabelID);     
  pximds = pixelLabelImageDatastore(imds,pxds);

Tabulate class distribution in dataset.

  tbl = countEachLabel(pximds)
tbl=4×3 table
        Name        PixelCount    ImagePixelCount
    ____________    __________    _______________

    {'sky'     }    3.1485e+05       1.536e+06   
    {'grass'   }    1.5979e+05       1.536e+06   
    {'building'}    1.0312e+06       1.536e+06   
    {'sidewalk'}         25313       9.216e+05

Calculate inverse frequency class weights.

  totalNumberOfPixels = sum(tbl.PixelCount);
  frequency = tbl.PixelCount / totalNumberOfPixels;
  inverseFrequency = 1./frequency
inverseFrequency = 4×1

    4.8632
    9.5827
    1.4848
   60.4900

Set 'ClassWeights' to the inverse class frequencies.

  layer = pixelClassificationLayer(...
      'Classes',tbl.Name,'ClassWeights',inverseFrequency)
layer = 
  PixelClassificationLayer with properties:

            Name: ''
         Classes: [sky    grass    building    sidewalk]
    ClassWeights: [4x1 double]
      OutputSize: 'auto'

   Hyperparameters
    LossFunction: 'crossentropyex'

Extended Capabilities

See Also

Objects

Functions

Topics

Introduced in R2017b

Computer Vision Toolbox Documentation

Support