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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= pixelClassificationLayercreates 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 optionalClasses,ClassWeights, andNameproperties 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'. IfClassesis'auto', then the software automatically sets the classes at training time. If you specify the string array or cell array of character vectorsstr, then the software sets the classes of the output layer tocategorical(str,str).

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 inClasses. If you specifyClassWeights, then you must specifyClasses.

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. ForLayerarray input, thetrainNetwork,assembleNetwork,layerGraph, anddlnetworkfunctions automatically assign names to layers with name''.

Data Types:char|string

This property is read-only.

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

Data Types:双

This property is read-only.

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,“填充”,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 pixelLabelDatastore. 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 inimdsand the pixel labeled images inpxds.

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

Tabulate class distribution in dataset.

tbl = countEachLabel(pxds)
tbl=4×3 tableName 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 =4×14.8632 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

Version History

Introduced in R2017b

See Also

Objects

Functions

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