We apply RandomVerticalFlip() transform to flip an image vertically at a random angle with a given probability. It's one of the transforms provided by the torchvision.transforms module. This module contains many important transformations that can be used to perform different types of manipulations on the image data.RandomVerticalFlip() accepts both PIL and tensor images. A tensor image is a torch Tensor with shape [C, H, W], where C is the number of channels, H is the image height, and W is the image width.Syntaxtorchvision.transforms.RandomVerticalFlip(p)(img)If p = 1, it returns the vertically flipped image.If p = 0, It returns the original image.If ... Read More

RandomRotation() rotates an image by a random angle. The chosen random angle is from a given range of angles in degree. RandomRotation() is one of the many important transforms provided by the torchvision.transforms module. RandomRotation() transform accepts both PIL and tensor images.A tensor image is a Torch tensor with shape [C, H, W], where C is the number of channels, H is the image height, and W is the image width. If the image is neither a PIL image nor a tensor image, then we first convert it to a tensor image and then apply the transform.Syntaxtorchvision.transforms.RandomRotation(degree)(img)Where degree is the ... Read More

RandomResizedCrop() transform crops a random area of the original input image. This crop size is randomly selected and finally the cropped image is resized to the given size. RandomResizedCrop() transform is one of the transforms provided by the torchvision.transforms module. This module contains many important transforms that can be used to perform different types of manipulations on the image data.RandomResizedCrop() accepts both PIL and tensor images. A tensor image is a PyTorch tensor with shape [..., H, W], where ... means a number of dimensions, H is the image height, and W is the image width. If the image is ... Read More

To flip an image horizontally in a random fashion with a given probability, we apply RandomHorizontalFlip() transform. It's one of the transforms provided by the torchvision.transforms module. This module contains many important transformations that can be used to perform different types of manipulations on the image data.RandomHorizontalFlip() accepts both PIL and tensor images. A tensor image is a PyTorch Tensor with shape [C, H, W], where C is the number channels, H is the image height, and W is the image width.Syntaxtorchvision.transforms.RandomHorizontalFlip(p)(img)If p = 1, it returns a horizontally flipped image.If p = 0, It returns the original image.If p ... Read More

To randomly convert an image to grayscale with a probability, we apply RandomGrayscale() transformation. It's one of the transforms provided by the torchvision.transforms module. This module contains many important transformations that can be used to perform different manipulations on the image data.RandomGrayscale() accepts both PIL and tensor images or a batch of tensor images. A tensor image is a PyTorch Tensor with shape [3, H, W], where H is the image height and W is the image width. A batch of tensor images is also a torch tensor with [B, 3, H, W]. B is the number of images in the batch.Syntaxtorchvision.transforms.RandomGrayscale(p)(img)If ... Read More

To crop an image at a random location, we apply RandomCrop() transformation. It's one of the many important transforms provided by the torchvision.transforms module.The RandomCrop() transformation accepts both PIL and tensor images. A tensor image is a torch tensor with shape [C, H, W], where C is the number of channels, H is the image height and W is the image width.If the image is neither a PIL image nor tensor image, then we first convert it to a tensor image and then apply RandomCrop().Syntaxtorchvision.transforms.RandomCrop(size)(img)where size is the desired crop size. size is a sequence like (h, w), where h ... Read More

To pad an image on all sides, we can apply Pad() transform provided by the torchvision.transforms module. This module contains many important transformations that can be used to perform different types of manipulations on the image data.Pad() transformation accepts both PIL and tensor images or a batch of tensor images. A tensor image is a torch Tensor with shape [C, H, W], where C is the number of channels, H is the image height, and W is the image width.A batch of tensor images is also a torch tensor with shape [B, C, H, W]. B is the number of ... Read More

To convert an image to grayscale, we apply Grayscale() transformation. It's one of the transforms provided by the torchvision.transforms module. This module contains many important transformations that can be used to perform different types manipulations on the image data.Grayscale() transformation accepts both PIL and tensor images or a batch of tensor images. A tensor image is a PyTorch Tensor with shape [3, H, W], where H is the image height and W is the image width. A batch of tensor images is also a torch tensor with [B, 3, H, W]. B is the number of images in the batch.Syntaxtorchvision.transforms.Grayscale()(img)It ... Read More

To crop a given image into four corners and the central crop, we apply FiveCrop() transformation. It's one of the transformations provided by the torchvision.transforms module. This module contains many important transformations that can be used to perform different types of manipulations on the image data.FiveCrop() transformation accepts both PIL and tensor images. A tensor image is a torch Tensor with shape [C, H, W], where C is the number of channels, H is the image height, and W is the image width. If the image is neither a PIL image nor a tensor image, then we first convert it ... Read More

To randomly change the brightness, contrast, saturation and hue of an image, we apply ColorJitter(). It's one of the transforms provided by the torchvision.transforms module. This module contains many important transformations that can be used to manipulate the image data.ColorJitter() transformation accepts both PIL and tensor images. A tensor image is a PyTorch tensor with shape [C, H, W], where C is the number of channels, H is the image height, and W is the image width.This transform also accepts a batch of tensor images. A batch of tensor images is a tensor with [B, C, H, W]. B is ... Read More