rmations for computer vision.

Package Reference

  * [ torchvision.datasets ](datasets.html)
    * [ MNIST ](datasets.html#mnist)
    * [ Fashion-MNIST ](datasets.html#fashion-mnist)
    * [ KMNIST ](datasets.html#kmnist)
    * [ EMNIST ](datasets.html#emnist)
    * [ QMNIST ](datasets.html#qmnist)
    * [ FakeData ](datasets.html#fakedata)
    * [ COCO ](datasets.html#coco)
    * [ LSUN ](datasets.html#lsun)
    * [ ImageFolder ](datasets.html#imagefolder)
    * [ DatasetFolder ](datasets.html#datasetfolder)
    * [ ImageNet ](datasets.html#imagenet)
    * [ CIFAR ](datasets.html#cifar)
    * [ STL10 ](datasets.html#stl10)
    * [ SVHN ](datasets.html#svhn)
    * [ PhotoTour ](datasets.html#phototour)
    * [ SBU ](datasets.html#sbu)
    * [ Flickr ](datasets.html#flickr)
    * [ VOC ](datasets.html#voc)
    * [ Cityscapes ](datasets.html#cityscapes)
    * [ SBD ](datasets.html#sbd)
    * [ USPS ](datasets.html#usps)
    * [ Kinetics-400 ](datasets.html#kinetics-400)
    * [ HMDB51 ](datasets.html#hmdb51)
    * [ UCF101 ](datasets.html#ucf101)
    * [ CelebA ](datasets.html#celeba)
  * [ torchvision.io ](io.html)
    * [ Video ](io.html#video)
  * [ torchvision.models ](models.html)
    * [ Classification ](models.html#classification)
    * [ Semantic Segmentation ](models.html#semantic-segmentation)
    * [ Object Detection, Instance Segmentation and Person Keypoint Detection ](models.html#object-detection-instance-segmentation-and-person-keypoint-detection)
    * [ Video classification ](models.html#video-classification)
  * [ torchvision.ops ](ops.html)
  * [ torchvision.transforms ](transforms.html)
    * [ Transforms on PIL Image ](transforms.html#transforms-on-pil-image)
    * [ Transforms on torch.*Tensor ](transforms.html#transforms-on-torch-tensor)
    * [ Conversion Transforms ](transforms.html#conversion-transforms)
    * [ Generic Transforms ](transforms.html#generic-transforms)
    * [ Functional Transforms ](transforms.html#functional-transforms)
  * [ torchvision.utils ](utils.html)

` torchvision. ` ` get_image_backend ` (  )  [ [source]
](../_modules/torchvision.html#get_image_backend) ¶

    

Gets the name of the package used to load images

` torchvision. ` ` set_image_backend ` (  _backend_ )  [ [source]
](../_modules/torchvision.html#set_image_backend) ¶

    

Specifies the package used to load images.

Parameters

    

**backend** ( _string_ ) – Name of the image backend. one of {‘PIL’,
‘accimage’}. The ` accimage  ` package uses the Intel IPP library. It is
generally faster than PIL, but does not support as many operations.

` torchvision. ` ` set_video_backend ` (  _backend_ )  [ [source]
](../_modules/torchvision.html#set_video_backend) ¶

    

Specifies the package used to decode videos.

Parameters

    

**backend** ( _string_ ) – Name of the video backend. one of {‘pyav’,
‘video_reader’}. The ` pyav  ` package uses the 3rd party PyAv library. It is
a Pythonic binding for the FFmpeg libraries. The ` video_reader  ` package
includes a native C++ implementation on top of FFMPEG libraries, and a python
API of TorchScript custom operator. It is generally decoding faster than `
pyav  ` , but perhaps is less robust.

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