The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.

TorchVision requires PyTorch 1.1 or newer.

Anaconda:

conda install torchvision -c pytorch

pip:

pip install torchvision

From source:

python setup.py install
# or, for OSX
# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install

By default, GPU support is built if CUDA is found and torch.cuda.is_available() is true. It's possible to force building GPU support by setting FORCE_CUDA=1 environment variable, which is useful when building a docker image.

Torchvision currently supports the following image backends:

• Pillow (default)
• Pillow-SIMD - a much faster drop-in replacement for Pillow with SIMD. If installed will be used as the default.
• accimage - if installed can be activated by calling torchvision.set_image_backend('accimage')

TorchVision also offers a C++ API that contains C++ equivalent of python models.

Installation From source:

mkdir build
cd build
cmake ..
make
make install

You can find the API documentation on the pytorch website: http://pytorch.org/docs/master/torchvision/

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.