I created a CNN model that takes as input a 3x512x512 image, and reduces it, through convolutions and maxpoolings, to a 1024x32x32 cube. In this cube I am using a 1x1 kernel conv layer to make a binary classification and a (x, y) regression for each of the 32x32 cells.
So I have a Conv2d later like this:
predConv = nn.Conv2d(1024, 3, kernel_size=1, padding=0)

My question is: there is any difference between using a single conv layer with 3 kernels (the first for classification, and the last two for regression), and two conv layers, one with 1 kernel (for classification) and another with 2 kernels (for regression), like this:
predClass = nn.Conv2d(1024, 1, kernel_size=1, padding=0)
predXYCoord = nn.Conv2D(1024, 2, kernel_size=2, padding=0)

I think that the answer is that there is no difference, but my classification is getting a good accuracy (0.85) and the regression is outputting nonsense values. I am using BCEWithlogitsloss and MSELoss as loss functions.
Thank you very much, and sorry for the silly question.