Upsampling makes results typically worse than downsampling, based on my practical experience. However, since this is just a factor of ~2x, this shouldn’t be a big issue.
To avoid rescaling issues altogether, you could also just choose architectures that are agnostic to the input dimensions, e.g., fully-convolutional networks (avoiding FC layers). Or, just add a spatial pyramid pooling layer before the FC layer(s).