torchvision

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

Installation

We recommend Anaconda as Python package management system. Please refer to torch torchvision python master / nightly master / nightly >=3.6 1.7.1 0.8.2 >=3.6 1.7.0 0.8.1 >=3.6 1.7.0 0.8.0 >=3.6 1.6.0 0.7.0 >=3.6 1.5.1 0.6.1 >=3.5 1.5.0 0.6.0 >=3.5 1.4.0 0.5.0 ==2.7, >=3.5, <=3.8 1.3.1 0.4.2 ==2.7, >=3.5, <=3.7 1.3.0 0.4.1 ==2.7, >=3.5, <=3.7 1.2.0 0.4.0 ==2.7, >=3.5, <=3.7 1.1.0 0.3.0 ==2.7, >=3.5, <=3.7 <=1.0.1 0.2.2 ==2.7, >=3.5, <=3.7

Anaconda:

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

In case building TorchVision from source fails, install the nightly version of PyTorch following the linked guide on the Image Backend

Torchvision currently supports the following image backends:

• Pillow-SIMD - a much faster drop-in replacement for Pillow with SIMD. If installed will be used as the default.
• libpng - can be installed via conda conda install libpng or any of the package managers for debian-based and RHEL-based Linux distributions.
• libjpeg-turbo can be used as well.

Notes: libpng and libjpeg must be available at compilation time in order to be available. Make sure that it is available on the standard library locations, otherwise, add the include and library paths in the environment variables TORCHVISION_INCLUDE and TORCHVISION_LIBRARY, respectively.

C++ API

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

Installation From source:

mkdir build
cd build
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install

Once installed, the library can be accessed in cmake (after properly configuring CMAKE_PREFIX_PATH) via the TorchVision::TorchVision target:

find_package(TorchVision REQUIRED)
target_link_libraries(my-target PUBLIC TorchVision::TorchVision)

The TorchVision package will also automatically look for the Torch package and add it as a dependency to my-target, so make sure that it is also available to cmake via the CMAKE_PREFIX_PATH.

For an example setup, take a look at examples/cpp/hello_world.

TorchVision Operators

In order to get the torchvision operators registered with torch (eg. for the JIT), all you need to do is to ensure that you #include <torchvision/vision.h> in your project.

Documentation

You can find the API documentation on the pytorch website: Contributing

See the CONTRIBUTING file for how to help out.

Disclaimer on Datasets

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!