It depends what and how you would like to normalize the data.
E.g. image tensors can be normalized using transforms.Normalize, which will calculate their z-score using the passed mean and std. This would change the values of the image tensor, such that they have a zero mean and unit variance (if the right stats are passed).
However, the landmarks would represent a pixel position inside the image, if I’m not mistaken.
I.e. how would you like to normalize them?
You could divide the values by their max, such that all landmark values are in the range [0, 1], but this would of course mean that you have to “denormalize” them if you want to plot them into the image and calculate the “real” loss.
Nevertheless, normalizing the target (and denormalizing afterwards) might help as shown in the linked thread.