The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
TorchVision requires PyTorch 1.1 or newer.
conda install torchvision -c pytorch
pip install torchvision
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 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.