To use pre-trained models it is a preferred practice to normalize the input images with imagenet standards.
mean=[0.485, 0.456, 0.406] and
std=[0.229, 0.224, 0.225].
How are these parameters derived?
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According to the Pytorch's docs, you can calculate
std using this:
import torch from torchvision import datasets, transforms as T transform = T.Compose([T.Resize(256), T.CenterCrop(224), T.ToTensor()]) dataset = datasets.ImageNet(".", split="train", transform=transform) means =  stds =  for img in subset(dataset): means.append(torch.mean(img)) stds.append(torch.std(img)) mean = torch.mean(torch.tensor(means)) std = torch.mean(torch.tensor(stds))