vision

by pytorch

pytorch /vision

Datasets, Transforms and Models specific to Computer Vision

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torchvision

.. image:: https://travis-ci.org/pytorch/vision.svg?branch=master :target: https://travis-ci.org/pytorch/vision

.. image:: https://codecov.io/gh/pytorch/vision/branch/master/graph/badge.svg :target: https://codecov.io/gh/pytorch/vision

.. image:: https://pepy.tech/badge/torchvision :target: https://pepy.tech/project/torchvision

.. image:: https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchvision%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v :target: https://pytorch.org/docs/stable/torchvision/index.html

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:

.. code:: bash

conda install torchvision -c pytorch

pip:

.. code:: bash

pip install torchvision

From source:

.. code:: bash

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.

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 :code:
    torchvision.set_image_backend('accimage')
  • libpng
    _ - can be installed via conda :code:
    conda install libpng
    or any of the package managers for debian-based and RHEL-based Linux distributions.
  • libjpeg
    _ - can be installed via conda :code:
    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.

.. _libpng : http://www.libpng.org/pub/png/libpng.html .. _Pillow : https://python-pillow.org/ .. _Pillow-SIMD : https://github.com/uploadcare/pillow-simd .. _accimage: https://github.com/pytorch/accimage .. _libjpeg: http://ijg.org/ .. _libjpeg-turbo: https://libjpeg-turbo.org/

C++ API

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

Installation From source:

.. code:: bash

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 :code:
TorchVision::TorchVision
target:

.. code:: rest

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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