Warm regards to all!
I am currently trying to predict x and y locations (landmarks) from a 512x512 image for a circle detection problem. I have a segmented image which shows circles as black and background as white, for each image I have the x and y coordinate of the centroid for each circle with a total of 435 points to predict (so 870 total values x and y). I had previously worked in a facial landmark detection where I was only predicting 138 values instead of 870. However, I am not successful at the moment at predicting the correct values for the centroids and the main question I have is regarding normalization. I have followed the following procedure for normalizing to [-1 1]:
Y_C /= 512
Y_C = 2*Y_C - 1
I am using SmoothL1Loss, and after training it turns out the predicted values are outside of the range [-1 1]. Is there a reason for this I am missing? Also if you have some advise for predicting a large number of landmarks for this type of problem I would really appreciate the help as I am currently not successful.