transform_normalize: Normalize a tensor image with mean and standard deviation

In torchvision: Models, Datasets and Transformations for Images

Normalize a tensor image with mean and standard deviation

Description

Given mean: ⁠(mean[1],...,mean[n])⁠ and std: ⁠(std[1],..,std[n])⁠ for n channels, this transform will normalize each channel of the input torch_tensor i.e., output[channel] = (input[channel] - mean[channel]) / std[channel]

Usage

transform_normalize(img, mean, std, inplace = FALSE)

Arguments

img	A magick-image, array or torch_tensor.
mean	(sequence): Sequence of means for each channel.
std	(sequence): Sequence of standard deviations for each channel.
inplace	(bool,optional): Bool to make this operation in-place.

Note

This transform acts out of place, i.e., it does not mutate the input tensor.

See Also

Other unitary_transforms: transform_adjust_brightness(), transform_adjust_contrast(), transform_adjust_gamma(), transform_adjust_hue(), transform_adjust_saturation(), transform_affine(), transform_center_crop(), transform_convert_image_dtype(), transform_crop(), transform_grayscale(), transform_hflip(), transform_linear_transformation(), transform_pad(), transform_perspective(), transform_resize(), transform_rgb_to_grayscale(), transform_rotate(), transform_to_tensor(), transform_vflip()

torchvision documentation built on Nov. 6, 2025, 9:07 a.m.