Image semantic segmentation dimension layer why not match input and output

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Raúl Rivera
Raúl Rivera 2024-2-18
编辑： Matt J 2024-2-18
Ran in:
Hello , I want to apply a semantic segmentation algorithm to a batch of images , to which I want to detect their failures (7 types of them) with a cnn, so I used Image labeler to detect 7 classes of failures for 80 images (dont know if thats enough data, the size of the images is [743 1324 3], but the output does not match. How can I make them match. I attach the code I use and linked the files of images and its labels.Thanks
load pixel_label_training.mat
%[imds,pxdsTruth] = pixelLabelTrainingData(gTruth.mat)
trainingData = combine(imds,pxdsTruth);
inputSize=[743 1324 3]
inputSize = 1×3
743 1324 3
imgLayer = imageInputLayer(inputSize)
imgLayer =
ImageInputLayer with properties: Name: '' InputSize: [743 1324 3] SplitComplexInputs: 0 Hyperparameters DataAugmentation: 'none' Normalization: 'zerocenter' NormalizationDimension: 'auto' Mean: []
filterSize = 4;
numFilters = 30;
conv = convolution2dLayer(filterSize,numFilters,'Padding',1);
relu = reluLayer();
poolSize = 2;
maxPoolDownsample2x = maxPooling2dLayer(poolSize,'Stride',2,'Padding',[1 1]);
downsamplingLayers = [
conv
relu
maxPoolDownsample2x
conv
relu
maxPoolDownsample2x
]
downsamplingLayers =
6×1 Layer array with layers: 1 '' 2-D Convolution 30 4×4 convolutions with stride [1 1] and padding [1 1 1 1] 2 '' ReLU ReLU 3 '' 2-D Max Pooling 2×2 max pooling with stride [2 2] and padding [1 1 1 1] 4 '' 2-D Convolution 30 4×4 convolutions with stride [1 1] and padding [1 1 1 1] 5 '' ReLU ReLU 6 '' 2-D Max Pooling 2×2 max pooling with stride [2 2] and padding [1 1 1 1]
filterSize = 1;
transposedConvUpsample2x = transposedConv2dLayer(filterSize,numFilters,'Stride',2,'Cropping',1)
transposedConvUpsample2x =
TransposedConvolution2DLayer with properties: Name: '' Hyperparameters FilterSize: [1 1] NumChannels: 'auto' NumFilters: 30 Stride: [2 2] CroppingMode: 'manual' CroppingSize: [1 1 1 1] Learnable Parameters Weights: [] Bias: [] Use properties method to see a list of all properties.
upsamplingLayers = [
transposedConvUpsample2x
relu
transposedConvUpsample2x
relu
]
upsamplingLayers =
4×1 Layer array with layers: 1 '' 2-D Transposed Convolution 30 1×1 transposed convolutions with stride [2 2] and cropping [1 1 1 1] 2 '' ReLU ReLU 3 '' 2-D Transposed Convolution 30 1×1 transposed convolutions with stride [2 2] and cropping [1 1 1 1] 4 '' ReLU ReLU
numClasses = 7;
conv1x1 = convolution2dLayer(1,numClasses);
finalLayers = [
conv1x1
softmaxLayer()
pixelClassificationLayer()
]
finalLayers =
3×1 Layer array with layers: 1 '' 2-D Convolution 7 1×1 convolutions with stride [1 1] and padding [0 0 0 0] 2 '' Softmax softmax 3 '' Pixel Classification Layer Cross-entropy loss
net = [
imgLayer
downsamplingLayers
upsamplingLayers
finalLayers
]
net =
14×1 Layer array with layers: 1 '' Image Input 743×1324×3 images with 'zerocenter' normalization 2 '' 2-D Convolution 30 4×4 convolutions with stride [1 1] and padding [1 1 1 1] 3 '' ReLU ReLU 4 '' 2-D Max Pooling 2×2 max pooling with stride [2 2] and padding [1 1 1 1] 5 '' 2-D Convolution 30 4×4 convolutions with stride [1 1] and padding [1 1 1 1] 6 '' ReLU ReLU 7 '' 2-D Max Pooling 2×2 max pooling with stride [2 2] and padding [1 1 1 1] 8 '' 2-D Transposed Convolution 30 1×1 transposed convolutions with stride [2 2] and cropping [1 1 1 1] 9 '' ReLU ReLU 10 '' 2-D Transposed Convolution 30 1×1 transposed convolutions with stride [2 2] and cropping [1 1 1 1] 11 '' ReLU ReLU 12 '' 2-D Convolution 7 1×1 convolutions with stride [1 1] and padding [0 0 0 0] 13 '' Softmax softmax 14 '' Pixel Classification Layer Cross-entropy loss
opts = trainingOptions('sgdm', ...
'InitialLearnRate',1e-3, ...
'MaxEpochs',100, ...
'MiniBatchSize',1);
net = trainNetwork(trainingData,net,opts);
images:Images
labels: pixelLabelData

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采纳的回答

Matt J
Matt J 2024-2-18
编辑：Matt J 2024-2-18
Below is what analyzeNetwork gives. Only you know what you want the dimensions of each of your activation maps to be. Tell us at what point in the network it starts to deviate from what you intended.
analyzeNetwork(net)

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