I have seen the same value use of mean and std for nomalizing image data.
For example in dataloader 2 tutorial following lines:
from torchvision import transforms

data_transform = transforms.Compose([
        transforms.RandomSizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    ])

and
from PyTorch Hub ResNet 1 model:
# sample execution (requires torchvision)
from PIL import Image
from torchvision import transforms
input_image = Image.open(filename)
preprocess = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                         std=[0.229, 0.224, 0.225]),])

I learned from the forum that these are used because it has been computed on imagenet database. My doubt is why can’t we use mean and std per image for nomalization?