Use ROIAlign Layer in a FastRCNN network?
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Hello,
I have defined a Fast RCNN network which I have trained with image data from ~9000 images of 1 class + background, split into train/validate/test. The network completed training with >95% validated accuracy, but I found that the bounding box/ROI was not very accurate.
I have discovered that MATLAB includes the ROIAlign layer which can supposedly produce bounding boxes with much higher positional accuracy and I have attempted to include this in my network with no success.
It seems that the output of the roi-input is not compatible with the input of the ROIAlign layer.
I have posted the code to generate my network:
params = load("params_2023_05_17__16_25_31.mat");
lgraph = layerGraph();
tempLayers = [
imageInputLayer([576 720 3],"Name","imageinput","Normalization","none")
convolution2dLayer([5 5],64,"Name","conv_6","Padding","same")
reluLayer("Name","relu_2")
batchNormalizationLayer("Name","batchnorm_3")
maxPooling2dLayer([3 3],"Name","maxpool_1","Padding","same","Stride",[2 2])
convolution2dLayer([5 5],128,"Name","conv_2","Padding","same")
reluLayer("Name","relu")
batchNormalizationLayer("Name","batchnorm_2")
maxPooling2dLayer([3 3],"Name","maxpool_3","Padding","same","Stride",[2 2])
convolution2dLayer([5 5],256,"Name","conv_1","Padding","same")
reluLayer("Name","relu_3")
batchNormalizationLayer("Name","batchnorm_1")
maxPooling2dLayer([3 3],"Name","maxpool_2","Padding","same","Stride",[2 2])
convolution2dLayer([5 5],512,"Name","conv_3","Padding","same")
reluLayer("Name","relu_1")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = roiInputLayer("Name","roi-input");
lgraph = addLayers(lgraph,tempLayers);
% ROIAlign would go here:
tempLayers = [
roiMaxPooling2dLayer([16 16],"Name","roi-max-pooling")
dropoutLayer(0.5,"Name","dropout")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
fullyConnectedLayer(4,"Name","fc_2")
rcnnBoxRegressionLayer("Name","rcnn-regression")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
fullyConnectedLayer(2,"Name","fc_1")
softmaxLayer("Name","softmax")
classificationLayer("Name","classoutput")];
lgraph = addLayers(lgraph,tempLayers);
% clean up helper variable
clear tempLayers;
lgraph = connectLayers(lgraph,"roi-input","roi-max-pooling/roi");
lgraph = connectLayers(lgraph,"relu_1","roi-max-pooling/in");
lgraph = connectLayers(lgraph,"dropout","fc_2");
lgraph = connectLayers(lgraph,"dropout","fc_1");
plot(lgraph);
The documentation for ROIAlignLayer simply states that it requires an input feature map HWCN, and mine is 72,90,512,1, and a list of ROIs to pool, and that logically seems to mean the ROI input layer in my fastRCNN network, or perhaps the output of a region proposal network? I have actually tried both of these with no success.
The error I receive when I analyse the network is:
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1386094/image.jpeg)
The RPN output was a slightly different size of 1(S) x 5(C) x 1(B), but it also failed.
Any help would be appreciated
Thanks,
2 个评论
Tian,HCong
2024-5-27
Hello, has your problem been resolved? I also encountered a similar problem when applying roialignLayer to FastRCNN network.
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