image and video datasets and models for torch deep learning
Project description
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 pytorch.org for the detail of PyTorch (torch) installation. The following is the corresponding torchvision versions and supported Python versions.
torch |
torchvision |
python |
|---|---|---|
master / nightly |
master / nightly |
>=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 pytorchpip:
pip install torchvisionFrom source:
python setup.py install
# or, for OSX
# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py installBy 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.
Image Backend
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')libpng - can be installed via conda
conda install libpngor any of the package managers for debian-based and RHEL-based Linux distributions.libjpeg - can be installed via conda
conda install jpegor 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 installOnce 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.
Documentation
You can find the API documentation on the pytorch website: https://pytorch.org/docs/stable/torchvision/index.html
Contributing
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.
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!
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