Package index • torchvision

Transforms Image transformation functions
`transform_adjust_brightness()`	Adjust the brightness of an image
`transform_adjust_contrast()`	Adjust the contrast of an image
`transform_adjust_gamma()`	Adjust the gamma of an RGB image
`transform_adjust_hue()`	Adjust the hue of an image
`transform_adjust_saturation()`	Adjust the color saturation of an image
`transform_affine()`	Apply affine transformation on an image keeping image center invariant
`transform_center_crop()`	Crops the given image at the center
`transform_color_jitter()`	Randomly change the brightness, contrast and saturation of an image
`transform_convert_image_dtype()`	Convert a tensor image to the given `dtype` and scale the values accordingly
`transform_crop()`	Crop the given image at specified location and output size
`transform_five_crop()`	Crop image into four corners and a central crop
`transform_grayscale()`	Convert image to grayscale
`transform_hflip()`	Horizontally flip a PIL Image or Tensor
`transform_linear_transformation()`	Transform a tensor image with a square transformation matrix and a mean_vector computed offline
`transform_normalize()`	Normalize a tensor image with mean and standard deviation
`transform_pad()`	Pad the given image on all sides with the given "pad" value
`transform_perspective()`	Perspective transformation of an image
`transform_random_affine()`	Random affine transformation of the image keeping center invariant
`transform_random_apply()`	Apply a list of transformations randomly with a given probability
`transform_random_choice()`	Apply single transformation randomly picked from a list
`transform_random_crop()`	Crop the given image at a random location
`transform_random_erasing()`	Randomly selects a rectangular region in an image and erases its pixel values
`transform_random_grayscale()`	Randomly convert image to grayscale with a given probability
`transform_random_horizontal_flip()`	Horizontally flip an image randomly with a given probability
`transform_random_order()`	Apply a list of transformations in a random order
`transform_random_perspective()`	Random perspective transformation of an image with a given probability
`transform_random_resized_crop()`	Crop image to random size and aspect ratio
`transform_random_rotation()`	Rotate the image by angle
`transform_random_vertical_flip()`	Vertically flip an image randomly with a given probability
`transform_resize()`	Resize the input image to the given size
`transform_resized_crop()`	Crop an image and resize it to a desired size
`transform_rgb_to_grayscale()`	Convert RGB Image Tensor to Grayscale
`transform_rotate()`	Angular rotation of an image
`transform_ten_crop()`	Crop an image and the flipped image each into four corners and a central crop
`transform_to_tensor()`	Convert an image to a tensor
`transform_vflip()`	Vertically flip a PIL Image or Tensor
Models Model architectures
Classification models
`model_alexnet()`	AlexNet Model Architecture
`model_inception_v3()`	Inception v3 model
`model_mobilenet_v2()`	Constructs a MobileNetV2 architecture from MobileNetV2: Inverted Residuals and Linear Bottlenecks.
`model_resnet18()` `model_resnet34()` `model_resnet50()` `model_resnet101()` `model_resnet152()` `model_resnext50_32x4d()` `model_resnext101_32x8d()` `model_wide_resnet50_2()` `model_wide_resnet101_2()`	ResNet implementation
`model_vgg11()` `model_vgg11_bn()` `model_vgg13()` `model_vgg13_bn()` `model_vgg16()` `model_vgg16_bn()` `model_vgg19()` `model_vgg19_bn()`	VGG implementation
Datasets Datasets readily available. All have a `x` variable in each item being the input image.
for Image Classification Dataset having items with “y” for target class identifier.
`emnist_dataset()`	EMNIST dataset
`eurosat_dataset()` `eurosat_all_bands_dataset()` `eurosat100_dataset()`	EuroSAT Dataset
`fashion_mnist_dataset()`	Fashion-MNIST dataset
`fgvc_aircraft_dataset()`	FGVC Aircraft dataset
`kmnist_dataset()`	Kuzushiji-MNIST
`mnist_dataset()`	MNIST dataset
`qmnist_dataset()`	QMNIST Dataset
`tiny_imagenet_dataset()`	Tiny ImageNet dataset
`cifar10_dataset()` `cifar100_dataset()`	Cifar datasets
`fer_dataset()`	FER-2013 Facial Expression Dataset
`image_folder_dataset()`	Create an image folder dataset
for Object Detection Dataset having items with “y” as a named list of bounding-box and labels for object detection.
`caltech101_detection_dataset()`	Caltech-101 Dataset
`caltech256_detection_dataset()`	Caltech-256 Object Category Dataset
`coco_detection_dataset()` `coco_caption_dataset()`	COCO Detection Dataset
for Image captionning Dataset having items with “y” as one or multiple captions of the image
`coco_detection_dataset()` `coco_caption_dataset()`	COCO Detection Dataset
Displaying Show images
`draw_bounding_boxes()`	Draws bounding boxes on image.
`draw_keypoints()`	Draws Keypoints
`draw_segmentation_masks()`	Draw segmentation masks
`tensor_image_browse()`	Display image tensor
`tensor_image_display()`	Display image tensor
Misc
`magick_loader()`	Load an Image using ImageMagick
`base_loader()`	Base loader
`vision_make_grid()`	A simplified version of torchvision.utils.make_grid
`batched_nms()`	Batched Non-maximum Suppression (NMS)
`nms()`	Non-maximum Suppression (NMS)
`box_area()`	Box Area
`box_convert()`	Box Convert
`box_cxcywh_to_xyxy()`	box_cxcywh_to_xyxy
`box_iou()`	Box IoU
`box_xywh_to_xyxy()`	box_xywh_to_xyxy
`box_xyxy_to_cxcywh()`	box_xyxy_to_cxcywh
`box_xyxy_to_xywh()`	box_xyxy_to_xywh
`clip_boxes_to_image()`	Clip Boxes to Image
`generalized_box_iou()`	Generalized Box IoU
`remove_small_boxes()`	Remove Small Boxes

Reference

Transforms

Models

Classification models

Datasets

for Image Classification

for Object Detection

for Image captionning

Displaying

Misc