How to train Unet semantic segmentation with only one single class/label?

Hello, I'm currently working on a task to do a semantic segmentation on USG image to locate TMJ. I did my image labelling in Image Labeler App and only did one class so that the class region will be 1 and background is 0. I was about to train my model with unetLayers but it says "The value of 'numClasses' is invalid. Expected numClasses to be a scalar with value > 1."

I'm aware that someone asked this similar question here with an answer, but I want to ask, how to customize specifically unetLayers to accomodate single class? I see that unetLayers also has softmaxLayers but i can't find the pixel layers. Thank you in advance!

Answer 1

[Matt J]

The value of 'numClasses' is invalid. Expected numClasses to be a scalar with value > 1

Because you have two classes (0 and 1). There is no such thing as a single class.

I see that unetLayers also has softmaxLayers but i can't find the pixel layers. Thank you in advance!

Where are you looking? The final layer should be the Pixel Classification Layer, as in the following example,

imageSize = [480 640 3];
numClasses = 5;
encoderDepth = 3;
lgraph = unetLayers(imageSize,numClasses,'EncoderDepth',encoderDepth);

lgraph.Layers(end-5:end)

ans = 
  6x1 Layer array with layers:

     1   'Decoder-Stage-3-ReLU-1'   ReLU                         ReLU
     2   'Decoder-Stage-3-Conv-2'   2-D Convolution              64 3x3 convolutions with stride [1  1] and padding 'same'
     3   'Decoder-Stage-3-ReLU-2'   ReLU                         ReLU
     4   'Final-ConvolutionLayer'   2-D Convolution              5 1x1 convolutions with stride [1  1] and padding 'same'
     5   'Softmax-Layer'            Softmax                      softmax
     6   'Segmentation-Layer'       Pixel Classification Layer   Cross-entropy loss 

[Al]

Thank you so much! I think I was panicking with my assignment so I didn't properly notice the layers

[Matt J]

You're welcome, but please Accept-click the answer to indicate that it resolved your quesiton.