# For image preprocessing, is it better to use normalization or standartization?

For a neural network model that classifies images, is it better to use normalization (dividing by 255.0) or using standardization (subtract mean and divide by STD)?

When I started learning convolutional neural networks, I always used normalization because it's simple and effective, but then I started to learn PyTorch and in one of the tutorials https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html they preprocess images like this:

transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,

The transform object is created, which has the NORMALIZE parameter, which in itself has the mean and STD values for each channel.