mean and std of image transform

Hello,
I was wondering how did the mean and std of the transform come from ? I've calculated that of Pittsburg and it seem way different, especially the std !

OpenIBL/ibl/utils/data/__init__.py

Line 37 in 5ab80d6

def get_transformer_test(height, width, tokyo=False):

mean=[0.48501960784313836, 0.4579568627450961, 0.4076039215686255],

std=[0.00392156862745098, 0.00392156862745098, 0.00392156862745098]

The std is way too small, can you please explain how did you get to it ?
Thanks

The mean and std are borrowed from the VGG pre-training weights of MatConvNet (it should be computed on ImageNet), since the official repos of NetVLAD and SARE are based on MatConvNet. To reproduce their results, we need to load the same pretrained weights as well as the same mean/std.