Changing built-in ResNet50 model to 1 channel images - how to set transforms.Normalize([...])?

[cdahms]

Thanks for the update, it looks like I should follow these posts to calculate stats:

Computing the mean and std of dataset

Hello. So I am trying to compute the mean and the standard deviation per channel of my train dataset (three-channel images of different shapes). For the mean I can do it in two ways, but I get slightly different results. import torch from torchvision import datasets, transforms dataset = datasets.ImageFolder('train', transform=transforms.ToTensor()) First computation: mean = 0.0 for img, _ in dataset: #mean += img.sum([1,2])/torch.numel(img[0]) mean += img.mean([1,2…

Efficient way of calculating mean, std, etc of Subset Dataset

I have a Dataset class that loads two datasets from their respective folders (train and test). I would like to create a validation set from the training set. For this I am using the random_split function. This results in two Subset-Datasets: train_dataset and valid_dataset. For normalization I would like to calculate the mean and std (or min/max) of the training set, but it is not possible to do a simple call of mean() to the dataset, because it is a Subset. What I can do is calculate the mea…