# What transforms do we need to apply to masks of images in segmentation tasks

I am trying a semantic segmentation task for multi-class segmentation. I am wondering what transforms are applied to both images and masks in it. Following are my transforms-

data_transforms = transforms.Compose([transforms.RandomCrop((512,512)),
transforms.Lambda(gaussian_blur),
transforms.Lambda(elastic_transform),
transforms.RandomRotation([+90,+180]),
transforms.RandomRotation([+180,+270]),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=train_mean, std=train_std)
])


Also, when we use transforms.ToTensor(), it convert the image between 0 and 1 but what it means when we say that image is between 0 and 1. Also, converting to 0 and 1 before normalize will have any effect on the final image?