How to find the best value for mean and STD of Normalize in torchvision.transforms
I have started working with PyTorch and cannot figure it how I am supposed to find mean and std as the input parameters of normalise.
I have seen this
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) #https://pytorch.org/vision/stable/transforms.html#

and in another example:
transformation = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5,0.5,0.5],std=[0.5,0.5,0.5])
])  #https://github.com/MicrosoftDocs/ml-basics/blob/master/challenges/05%20%20-%20Safari%20CNN%20Solution%20(PyTorch).ipynb

so, how am I supposed to know or get these values if I have a set of images?
Are these three parameters are related to R G B also?

suppose you have already X_train which is a list of numpy matrix eg. 32x32x3:
X_train = X_train / 255 #normalization of pixels
train_mean = X_train.reshape(3,-1).mean(axis=1)
train_std = X_train.reshape(3,-1).std(axis=1)

then you can pass this last 2 variables in your normalizer transformer :
transforms.Normalize(mean = train_mean ,std = train_std)

