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

TorchVision requires PyTorch 1.2 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.

This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.

If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!