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The method for calculating a running standard deviation is not giving the correct answer (in utils.py)
e.g. for one big batch:
>>> import torch >>> import torchvision >>> import torchvision.transforms as transforms >>> t=transforms.Compose([transforms.ToTensor()]) >>> s=torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=t) >>> d=torch.utils.data.DataLoader(s, batch_size=50000, shuffle=False) >>> for x,y in d: ... for i in range(3): ... print(i, x[:,i,:,:].mean(), x[:,i,:,:].std()) ... 0 tensor(0.4914) tensor(0.2470) 1 tensor(0.4822) tensor(0.2435) 2 tensor(0.4465) tensor(0.2616)
This means the std deviations used to normalize the data set are a little too low:
transform_train = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ])
Thanks for the great set of models for CIFAR10
The text was updated successfully, but these errors were encountered:
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The method for calculating a running standard deviation is not giving the correct answer (in utils.py)
e.g. for one big batch:
This means the std deviations used to normalize the data set are a little too low:
Thanks for the great set of models for CIFAR10
The text was updated successfully, but these errors were encountered: