Relationship between input range and channel means, standard deviations for CNNs So, I'm using a pretrained pnasnet5large model to do some image classification (https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py)

In the file, it says that the input range is in [0,1] (i'm assuming pixel values of input images). The images I have are already in this range.
The channel means and standard deviation for RGB channels are stated as [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] respectively.
Now when I use the torchvision.transforms.Normalize(https://pytorch.org/docs/stable/torchvision/transforms.html#torchvision.transforms.Normalize) to normalize the images using the stated means and standard deviations, the pixel values get to the range [-1,1].

The code I wrote for normalization:

transforms.Normalize([0.5, 0.5, 0.5],[0.5, 0.5, 0.5])

I believe I'm missing something fundamental. Should I normalize the images or should I not? Thanks!
Standardization of the pixel values would bring it mean close to 0 and standard deviation as 1.
Normalization will squeeze the values between 0 and 1.
For RGB images, divide each channel i.e each pixel value by 255.
For reference regarding Standardization see here and for normalization see here.