# Image Normalization for pretrained network

Hi all,
I’m currently using alexnet pretrained network for my experiments. I need to preprocess the images performing normalization. I have images with values in the range [0,255]. I have divided by 255 and I have performed normalization using `normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])`. However after the normalization the values are not in the range [0,1]. Should I compute for each image the mean and standard deviation and then subtracting the mean and divide by standard deviation?

This is exactly what `torchvision.transforms.Normalize` does.
for example one popular thing to do would be, using `torchvision.transforms.ToTensor` to make your image of range [0,255] to a tensor of range [0,1] and then use `torchvision.transforms.Normalize` with mean and std of 0.5 over all channels. Since (0 - 0.5) / 0.5 = -1 and (1 - 0.5) / 0.5 =1, this would normalize your images in a range of [-1,1].
Which is the one in your code: `mean = [0.485, 0.456, 0.406]`, `stds = [0.229, 0.224, 0.225]`