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ConvNets and sliding windows

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Matt J
Matt J on 18 Jul 2021
Edited: Matt J on 23 Jul 2021
(Rephrased) I am starting to play with the Deep Learning Toolbox and deepNetworkDesigner. An example of what I'd like to be able to do is to take a CNN classifier that has already been trained for 30x30 input images, but now use it to do classification on every 30x30 sub-block of a 400x400 image A.
The naive way to do this would be to loop over the sub-blocks in A and feed them to the CNN one at a time, but that is inefficient. The more efficient technique that I have seen recommended is to convert the CNN to a fully-convolutional network, which means adjusting the final layers as follows:
(1) Converting the fully-connected layer to a convolutional layer where the weights are now that of a 30x30 filter, stride=1, Padding=[0,0].
(2) Applying the softmax operation pixel-wise to the output of (1).
After doing the above, every layer in the network is now a shift-invariant operation, and should be able to process input images of any size. If I input a 400x400 image A, the output of the network should be an N-channel image of size 371x371 where each pixel contains the N class probabilities of a particular 30x30 sub-block.
I am wondering if it possible to make the kind of adjustments described above to an already trained CNN?

Answers (1)

Hrishikesh Borate
Hrishikesh Borate on 23 Jul 2021
Hi,
It's my understanding that you are trying to define a convolutional neural network without predeclaring the input image size. One possible approach is to develop an uninitialized dlnetwork object without an input layer as shown here.
  1 Comment
Matt J
Matt J on 23 Jul 2021
Thank you @Hrishikesh Borate, but I'm not sure that this is what I'm after. What I'd like to be able to do, for example, is take a CNN classifier that has already been trained for 30x30 input images, but now use it to do classification on every 30x30 sub-block of a 400x400 image A.
The naive way to do this would be to loop over the sub-blocks in A and feed them to the CNN one at a time, but that is inefficient. The more efficient technique that I have seen recommended is to convert the CNN to a fully-convolutional network, which means adjusting the final layers as follows:
(1) Converting the fully-connected layer to a convolutional layer where the weights are now that of a 30x30 filter, stride=1, Padding=[0,0].
(2) Applying the softmax operation pixel-wise to the output of (1).
If you do the above, then every layer in the network is now a shift-invariant operation, and should be able to process input images of any size. If I input a 400x400 image A, the output of the network should be an N-channel image of size 371x371 where each pixel contains the N class probabilities of a particular 30x30 sub-block.
Is it possible to make these kind of adjustments to an already trained CNN?

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