I would appreciate any advice on a CNN architecture for non-square input.
Typically, I’m trying to adapt ResNet50 (coded for 224x224x3 input) to work on smaller and non-square gray images (112x56x1 would be nice).
My inputs are thus 24 times smaller than the original ResNet50 inputs.
I understand the convolution itself can cope with my specific input dimensions. The problem is with the following layers (pooling for example), for which there seems to be only the nOut() method to size the output. This is OK for a square but not for a rectangle, for which width and height values are different.
So, is there any way in Deeplearning4J configurations to explicitly provide layer width and height output values rather than a single nOut value?
I’m still searching, but I did not find any in the zoo models.
Thanks in advance for any advice,