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 |
---|---|---|
main / nightly |
main / nightly |
>=3.8 , <=3.11 |
2.0 |
0.15 |
>=3.8 , <=3.11 |
1.13 |
0.14 |
>=3.7.2 , <=3.10 |
1.12 |
0.13 |
>=3.7 , <=3.10 |
older versions
torch |
torchvision |
Python |
---|---|---|
1.11 |
0.12 |
>=3.7 , <=3.10 |
1.10 |
0.11 |
>=3.6 , <=3.9 |
1.9 |
0.10 |
>=3.6 , <=3.9 |
1.8 |
0.9 |
>=3.6 , <=3.9 |
1.7 |
0.8 |
>=3.6 , <=3.9 |
1.6 |
0.7 |
>=3.6 , <=3.8 |
1.5 |
0.6 |
>=3.5 , <=3.8 |
1.4 |
0.5 |
==2.7 , >=3.5 , <=3.8 |
1.3 |
0.4.2 / 0.4.3 |
==2.7 , >=3.5 , <=3.7 |
1.2 |
0.4.1 |
==2.7 , >=3.5 , <=3.7 |
1.1 |
0.3 |
==2.7 , >=3.5 , <=3.7 |
<=1.0 |
0.2 |
==2.7 , >=3.5 , <=3.7 |
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
We don't officially support building from source using pip
, but if you do, you'll need to use the
--no-build-isolation
flag. In case building TorchVision from source fails, install the nightly version of PyTorch
following the linked guide on the
contributing page and retry the
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.
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 libpng
or any of the package managers for debian-based and RHEL-based Linux distributions. - libjpeg - can be installed via conda
conda install jpeg
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.
Video Backend
Torchvision currently supports the following video backends:
- pyav (default) - Pythonic binding for ffmpeg libraries.
- video_reader - This needs ffmpeg to be installed and torchvision to be built from source. There shouldn't be any conflicting version of ffmpeg installed. Currently, this is only supported on Linux.
conda install -c conda-forge ffmpeg
python setup.py install
Using the models on C++
TorchVision provides an example project for how to use the models on C++ using JIT Script.
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
.
Python linking is disabled by default when compiling TorchVision with CMake, this allows you to run models without any
Python dependency. In some special cases where TorchVision's operators are used from Python code, you may need to link
to Python. This can be done by passing -DUSE_PYTHON=on
to CMake.
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: https://pytorch.org/vision/stable/index.html
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!
Pre-trained Model License
The pre-trained models provided in this library may have their own licenses or terms and conditions derived from the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.
More specifically, SWAG models are released under the CC-BY-NC 4.0 license. See SWAG LICENSE for additional details.
Citing TorchVision
If you find TorchVision useful in your work, please consider citing the following BibTeX entry:
@software{torchvision2016,
title = {TorchVision: PyTorch's Computer Vision library},
author = {TorchVision maintainers and contributors},
year = 2016,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/pytorch/vision}}
}