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?