how to combine two separately trained semantic segmentation neural networks into one to detect mixtures

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Brian Chesney Quartey 2021-1-27

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评论： Mahesh Taparia 2021-1-31

I'm using machine learning to train a semantic segmentation network model to segments certain objects in image files. I have different objects in my image files that i want my network to be able to segment and differentiate. For now i have been abe to separately train networks for each of them and these networks can segment each of the objects in test images containing the specific objects. However i want t have a network that can detect each object in an image containing all 3 types of objects that i want to differentiate. How do i go about this? Can i combine the 3 separate networks i have individually trained to be able to achieve this? What will be the best way to do this. @MathWorks Support Team

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Mahesh Taparia 2021-1-30

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You can train a single network which can segment the multiple classes in an image. Assign the different label ID of each class and train the network. Then evaluate that trained network on the test dataset. You can refer this documentation of multi class semantic segmentation using deep neural network. Hope it will help!

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Brian Chesney Quartey 2021-1-31

I'm not sure if i understand this

Mahesh Taparia 2021-1-31

In your older comment, you mentioned that you have the labels information of one class in one label image, i.e if the image have 4 classes then you have 4 different label images.

You can combine this label information into 1 image by mapping those label value to 4 different categorical labels (say 1,2,3,4) which is also followed in this documentation. My previous comment was just a sample illustration.

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