Hi everyone!
I’m pretty new to pytorch and interested in Semantic Segmantion.
I know there are severeal pretrained models included in pytorch, but i would like to build one from scratch to really understand what is going on.
What I want is a binary pixelwise prediction that says me for each pixel, if that pixel belongs to a car/human/whatever or whether it is background.
I’ve read the “common” papers like U-Net, FCN, etc. but they usually have more complex skip connections etc… What I would like to try is a very simple Network that receive a: [1,3,28,28] RGB picture and returns a binary pixelwise prediction of the same size. I don’t care if the performance is good or not at the moment.
My main problem is, how to upsample after sending the image through several convolution layers. Obviously the shape is changed due to pooling and the stride size. How do I make sure that the resulting output has the same dimensions like the input image? I’ve read that I can use nn.ConvTranspose2d() to upsample, but I have no idea what values the parameters should take in order to end up with a pixelwise prediction.
Can someone help me out?