Skip to main content

image and video datasets and models for torch deep learning

Project description

torchvision

https://travis-ci.org/pytorch/vision.svg?branch=master https://codecov.io/gh/pytorch/vision/branch/master/graph/badge.svg https://pepy.tech/badge/torchvision 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

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

Installation

TorchVision requires PyTorch 1.4 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.

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')

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.

Documentation

You can find the API documentation on the pytorch website: http://pytorch.org/docs/master/torchvision/

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!

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

torchvision-0.6.0-cp38-cp38-manylinux1_x86_64.whl (6.6 MB view details)

Uploaded CPython 3.8

torchvision-0.6.0-cp38-cp38-macosx_10_9_x86_64.whl (436.3 kB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

torchvision-0.6.0-cp37-cp37m-manylinux1_x86_64.whl (6.6 MB view details)

Uploaded CPython 3.7m

torchvision-0.6.0-cp37-cp37m-macosx_10_9_x86_64.whl (436.3 kB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

torchvision-0.6.0-cp36-cp36m-manylinux1_x86_64.whl (6.6 MB view details)

Uploaded CPython 3.6m

torchvision-0.6.0-cp36-cp36m-macosx_10_9_x86_64.whl (436.3 kB view details)

Uploaded CPython 3.6m macOS 10.9+ x86-64

torchvision-0.6.0-cp35-cp35m-manylinux1_x86_64.whl (6.6 MB view details)

Uploaded CPython 3.5m

torchvision-0.6.0-cp35-cp35m-macosx_10_6_x86_64.whl (436.3 kB view details)

Uploaded CPython 3.5m macOS 10.6+ x86-64

File details

Details for the file torchvision-0.6.0-cp38-cp38-manylinux1_x86_64.whl.

File metadata

  • Download URL: torchvision-0.6.0-cp38-cp38-manylinux1_x86_64.whl
  • Upload date:
  • Size: 6.6 MB
  • Tags: CPython 3.8
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/44.0.0.post20200106 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.8.1

File hashes

Hashes for torchvision-0.6.0-cp38-cp38-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 ea39bed9e9497a67c5f66e37d3d5a663a0284868ae8616de81f65c66d9ad802b
MD5 46de551538d995d97d90ce9d8ea1c176
BLAKE2b-256 4597ec9acf1c3fae8d440ca9c786ca44e25534f0e155bac46c0e7dec7763eb1f

See more details on using hashes here.

Provenance

File details

Details for the file torchvision-0.6.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: torchvision-0.6.0-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 436.3 kB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/44.0.0.post20200106 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.8.1

File hashes

Hashes for torchvision-0.6.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 691d68f3726b7392fe37db7184aef8a6b6f7cf6ff38fae769b287b3d6e1eb69a
MD5 4f237e4e99f2302a6b281a5c97ec85dd
BLAKE2b-256 715a389e5c7d8319a29f00167bdf052b69fd536e9ad713f855f165dbf9ac3781

See more details on using hashes here.

Provenance

File details

Details for the file torchvision-0.6.0-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: torchvision-0.6.0-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 6.6 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/44.0.0.post20200106 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.8.1

File hashes

Hashes for torchvision-0.6.0-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 6eb4e0d7dc61030447b98d412162f222a95d848b3b0e484a81282c057af6dd25
MD5 e38c90dbc7a4489382c18c1e451b0cc0
BLAKE2b-256 7beda894f274a7733d6492e438a5831a95b507c5ec777edf6d8c3b97574e08c4

See more details on using hashes here.

Provenance

File details

Details for the file torchvision-0.6.0-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: torchvision-0.6.0-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 436.3 kB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/44.0.0.post20200106 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.8.1

File hashes

Hashes for torchvision-0.6.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 0fa9e4a8381e5e04d0da0acd93f1429347053497ec343fe6d625b1b7fb2ce36e
MD5 88b997d932dadb69bfa142b2e2774ce2
BLAKE2b-256 fb63fbaffd7340c3c324279fb2b51846ba7f4f7705d469f1cb7578c586848016

See more details on using hashes here.

Provenance

File details

Details for the file torchvision-0.6.0-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: torchvision-0.6.0-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 6.6 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/44.0.0.post20200106 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.8.1

File hashes

Hashes for torchvision-0.6.0-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 f43dae3b348afa5778439913ba1f3f176362ffc9e684ef01dc54dae7cf1b82e4
MD5 2358f794edb88103ab21d490dda39488
BLAKE2b-256 6151aa2770a70f612ce9a2fc7da3a1a93f9ecf8746788256fed6b691f9b31ca9

See more details on using hashes here.

Provenance

File details

Details for the file torchvision-0.6.0-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: torchvision-0.6.0-cp36-cp36m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 436.3 kB
  • Tags: CPython 3.6m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/44.0.0.post20200106 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.8.1

File hashes

Hashes for torchvision-0.6.0-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 8992f10a7860e0991766a788b546d5f11e3e7465e87a72eb9c78675dd2616400
MD5 f8940d59a5ebe77fa9fa2b1da980c9de
BLAKE2b-256 6822edc555cc6ad8a5e17d508da8447fe60d1e29207ae63164b35853d072774e

See more details on using hashes here.

Provenance

File details

Details for the file torchvision-0.6.0-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

  • Download URL: torchvision-0.6.0-cp35-cp35m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 6.6 MB
  • Tags: CPython 3.5m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/44.0.0.post20200106 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.8.1

File hashes

Hashes for torchvision-0.6.0-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 a9b08435fdadd89520a78f5a54d196c05878d1a15e37f760d43f72f10bae308f
MD5 fa03292209aa4626eb6666a357cd3f3a
BLAKE2b-256 26bd8be1a9342a9bb7b93f116c59ed8aebcb2eff5677fdc36de1dd3444d66fb3

See more details on using hashes here.

Provenance

File details

Details for the file torchvision-0.6.0-cp35-cp35m-macosx_10_6_x86_64.whl.

File metadata

  • Download URL: torchvision-0.6.0-cp35-cp35m-macosx_10_6_x86_64.whl
  • Upload date:
  • Size: 436.3 kB
  • Tags: CPython 3.5m, macOS 10.6+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/44.0.0.post20200106 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.8.1

File hashes

Hashes for torchvision-0.6.0-cp35-cp35m-macosx_10_6_x86_64.whl
Algorithm Hash digest
SHA256 0ea04a7e0f64599c158d36da01afd0cb3bc49033d2a145be4eb80c17c4c0482b
MD5 c903d7bb67dea500cc5998391d03c9fb
BLAKE2b-256 2472de3dddfcb3e505fd08f99eb767e5091f52c4dd0c274fc2e5941ae6733fc6

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page