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awezmm
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Semantic Segmentation - How many layers to replace in transfer learning?

Asked by awezmm
on 1 May 2019
Latest activity Edited by Tohru Kikawada on 11 May 2019
Im doing semantic segmentation using Resnet-18 with Deeplab v3+ (https://www.mathworks.com/help/vision/examples/semantic-segmentation-using-deep-learning.html)
However, I want to retain on progressively harder tasks and want to use transfer learning. How many of the final layers should I be replacing? How do I figure out how many layers I have?
On the analyzeNetwork it says I have 101 but I am using Resnet - 18 which I thought had much less?

  3 Comments

I can not find see helperDeeplabv3PlusResnet18.m file. Where can I find this file? Would you mind to send this file for me?
Hi, I also can not find this file anywhere. Were you able to get it?
Philip, you'll need to try this on R2019a since the example has been revised to use DeepLab v3 instead of SegNet in the latest vresion.

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1 Answer

Answer by Tohru Kikawada on 2 May 2019

Did you see helperDeeplabv3PlusResnet18.m which is attached to the example as a supporting file? The supporting function might be helpful to create your own transfer learning network.

  4 Comments

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Type the following command
openExample('deeplearning_shared/SemanticSegmentationUsingDeepLearningExample')
then the helperDeeplabv3PlusResnet18.m can be found in your current folder.
I have already looked at the helperDeeplabv3PlusResnet18.m and said in my previous comment that is want not helpful...
Sorry for the confusion. Someone asked me where the file can be found.

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