N -> the batch size
C -> Nb of channels
H -> Height
W -> Width
So, to perform inference on a batch of 10 RGB images of size 100 x 200, you’ll have N=10, C=3, H=100, W=200.
Suppose that you performed a 2d-convolution with 128 kernels of size 3x3 and padding=1, you’ll have a feature map of size N=10, C=128, H=100, W=200.