Problem
Normalizing dataset is a common component of machine learning before doing any downstream task. However, when I learn the tutorial 3 in PyTorch, the author used mysterious mean and variance value for different channels of RGB images, i.e. 0.485, 0.456 and 0.406 for mean and 0.229, 0.224, 0.225 for variance (see code below).
I am not sure how those somehow magic numbers come about. Do they come from some statistics of the training dataset which is not disclosed in tutorial. Or maybe they are heuristics that prove to be effective in computer vision literature?
# Data augmentation and normalization for training
# Just normalization for validation
data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}