Skip to main content

torchvision

total torchvision downloads documentation

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

Installation

Please refer to the official instructions to install the stable versions of torch and torchvision on your system.

To build source, refer to our contributing page.

The following is the corresponding torchvision versions and supported Python versions.

torch torchvision Python
main / nightly main / nightly >=3.10, <=3.14
2.13 0.28 >=3.10, <=3.14
2.12 0.27 >=3.10, <=3.14
2.11 0.26 >=3.10, <=3.14
2.10 0.25 >=3.10, <=3.14
older versions
torch torchvision Python
2.9 0.24 >=3.10, <=3.14
2.8 0.23 >=3.9, <=3.13
2.7 0.22 >=3.9, <=3.13
2.6 0.21 >=3.9, <=3.12
2.5 0.20 >=3.9, <=3.12
2.4 0.19 >=3.8, <=3.12
2.3 0.18 >=3.8, <=3.12
2.2 0.17 >=3.8, <=3.11
2.1 0.16 >=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
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

Image Backends

Torchvision currently supports the following image backends:

  • torch tensors
  • PIL images:

Read more in in our docs.

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}}
}

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

If you're not sure about the file name format, learn more about wheel file names.

torchvision-0.29.0-cp314-cp314t-win_amd64.whl (1.4 MB view details)

Uploaded CPython 3.14tWindows x86-64

torchvision-0.29.0-cp314-cp314t-manylinux_2_28_x86_64.whl (7.4 MB view details)

Uploaded CPython 3.14tmanylinux: glibc 2.28+ x86-64

torchvision-0.29.0-cp314-cp314t-manylinux_2_28_aarch64.whl (7.5 MB view details)

Uploaded CPython 3.14tmanylinux: glibc 2.28+ ARM64

torchvision-0.29.0-cp314-cp314t-macosx_14_0_arm64.whl (1.8 MB view details)

Uploaded CPython 3.14tmacOS 14.0+ ARM64

torchvision-0.29.0-cp314-cp314-win_amd64.whl (1.4 MB view details)

Uploaded CPython 3.14Windows x86-64

torchvision-0.29.0-cp314-cp314-manylinux_2_28_x86_64.whl (7.4 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

torchvision-0.29.0-cp314-cp314-manylinux_2_28_aarch64.whl (7.5 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

torchvision-0.29.0-cp314-cp314-macosx_14_0_arm64.whl (1.8 MB view details)

Uploaded CPython 3.14macOS 14.0+ ARM64

torchvision-0.29.0-cp313-cp313-win_amd64.whl (1.4 MB view details)

Uploaded CPython 3.13Windows x86-64

torchvision-0.29.0-cp313-cp313-manylinux_2_28_x86_64.whl (7.4 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

torchvision-0.29.0-cp313-cp313-manylinux_2_28_aarch64.whl (7.5 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

torchvision-0.29.0-cp313-cp313-macosx_14_0_arm64.whl (1.8 MB view details)

Uploaded CPython 3.13macOS 14.0+ ARM64

torchvision-0.29.0-cp312-cp312-win_amd64.whl (1.4 MB view details)

Uploaded CPython 3.12Windows x86-64

torchvision-0.29.0-cp312-cp312-manylinux_2_28_x86_64.whl (7.4 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

torchvision-0.29.0-cp312-cp312-manylinux_2_28_aarch64.whl (7.6 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

torchvision-0.29.0-cp312-cp312-macosx_14_0_arm64.whl (1.8 MB view details)

Uploaded CPython 3.12macOS 14.0+ ARM64

torchvision-0.29.0-cp311-cp311-win_amd64.whl (1.4 MB view details)

Uploaded CPython 3.11Windows x86-64

torchvision-0.29.0-cp311-cp311-manylinux_2_28_x86_64.whl (7.4 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

torchvision-0.29.0-cp311-cp311-manylinux_2_28_aarch64.whl (7.6 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

torchvision-0.29.0-cp311-cp311-macosx_14_0_arm64.whl (1.8 MB view details)

Uploaded CPython 3.11macOS 14.0+ ARM64

torchvision-0.29.0-cp310-cp310-win_amd64.whl (1.4 MB view details)

Uploaded CPython 3.10Windows x86-64

torchvision-0.29.0-cp310-cp310-manylinux_2_28_x86_64.whl (7.4 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

torchvision-0.29.0-cp310-cp310-manylinux_2_28_aarch64.whl (7.6 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ ARM64

torchvision-0.29.0-cp310-cp310-macosx_14_0_arm64.whl (1.8 MB view details)

Uploaded CPython 3.10macOS 14.0+ ARM64

File details

Details for the file torchvision-0.29.0-cp314-cp314t-win_amd64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp314-cp314t-win_amd64.whl
Algorithm Hash digest
SHA256 bd12e152640d1024fca15deaf6f39c53702e12eb398db4b2b038a5a97ab85f7f
MD5 7329b63d6f4226f4e6b197c5dc1197ef
BLAKE2b-256 078e0e3682cad7f6b2aba93585bc3b792f8087e4e3fb916c0f6638eacdb3170c

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp314-cp314t-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp314-cp314t-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 2428a13706a354ee3901fd63e3f1ef8c2a59f6e20d24cce13210b4e61be08f7a
MD5 38425a723b378745d64c4bcf06d5a0ee
BLAKE2b-256 84b367692c3785702c24d6c9c95026a558791366f2dc48823aec2321f23a0858

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp314-cp314t-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp314-cp314t-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 9b99a7385da8d706f2dc14cacffb8cbf653d48fc37013a38c41a36659acc3731
MD5 11c7ec566a9f7e5db2e0f0d18f513c5b
BLAKE2b-256 8055b39dba8e3d574428e7f00f212a3ee5a2c2d0ab5a25a95338a7c724a87850

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp314-cp314t-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp314-cp314t-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 80789f9b50f5277302e7020c8128344e0269b12f6e3b5855034e56bb7081b874
MD5 4371f485331cfb5a2e0eee10d56bde05
BLAKE2b-256 7ff0bc564a8ed409db4d7ce72215a676c3cbda9559779a53e8ecc477ad11d2c5

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp314-cp314-win_amd64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp314-cp314-win_amd64.whl
Algorithm Hash digest
SHA256 bb74d08d65785b51c61d4d4ec167bfa50a5ea644b57555a33ba2f4435b0d05dd
MD5 e39b441209ce56648813d3cafa170ce9
BLAKE2b-256 acddf5571d0c2aedc3d363f9b818de8fbb810a0d2b2ab0293de6003f4291ce15

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 e8fdf234d76dca6fc47bc5f4af86ecf1ef57fab6285a7b21681bdcbb494e4c1b
MD5 e5448bdef5ce9f5bc49d29ce173dbd2e
BLAKE2b-256 9790ae1d76e76c5d97b70539cd38df309777d66020d33b346301e79a7a8c132a

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 f9e9627d5036cac8e6de76aeb2f1acc67841ff5ff4b63c4cf78ff954efeb073e
MD5 f15cfe1563b11572037761b4c080a11a
BLAKE2b-256 3a7e3cffb013454af4c126a40e97cff7ac91dff692b749bcf80754ae97ada225

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp314-cp314-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp314-cp314-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 01a029fa1b2eacac27e1e4f9e0dba24d112bcbd523261b294e4b4a1c7f8330bc
MD5 0bcc7574607708b9c6284a6951202580
BLAKE2b-256 af6accdf22f6ee57e862aed41ab70e735f6302b8330bf304d8125f60d84413ba

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 ebf54f6744556ebd8d7b5f9b6515e2060e5d3156723970dc257bf093f63b2170
MD5 23fe16a2410d02f460884229a5ebe24d
BLAKE2b-256 c01e72fa361d60e46b4f003ebdc68508467f46d4ef1011650a80258b40d7cf46

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 942a3b3fb4e981e1abb7846679ee3882dfc733533a30b68df0dadf6c06f238a9
MD5 d712952302a0d2d55e413a0d317ce98c
BLAKE2b-256 3478afa8f85e1f3cb276202ad3af9e36765feee1f0c5ec2942d8522b321e5afc

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 83db0299170502b038640444214c99d7b54bad72db447f0554deb8d2c4f02f94
MD5 df4b9aaf0d47a93f43a792c7125f871f
BLAKE2b-256 d7da1418b42ed642521262d2b276ffb698772f06827202dca9065a66824f69bf

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp313-cp313-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp313-cp313-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 28a3964a9e6db34354d4ca440d2c2038deeef24e7219e8d6f49d9d174f44830c
MD5 572a190028e68cc424680733481e29d8
BLAKE2b-256 81564e122b6b59269cb2e6a8f7ef969cff7b1eca1499c48784805ad156d3720b

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 0e3b99c62f7f095153887330c2c60c9ebc2da84dfbe08eea564a41ec361629cf
MD5 325cd12af542defa8559d10e5dff7617
BLAKE2b-256 efa859152b945c09840f582c40e38aa4128777625af7f2883ce2d77133f96a46

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 0e631a2f8b24732672d35224d2574ff89a78d56bef5364ef6094a625fa5f1771
MD5 d2cc139d8936987fb388f91d68038eb8
BLAKE2b-256 27d73cf1992414fd712af4865ae5f8f5758d9003c1ed82624908d54f7afb4342

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 b7a736eaa6b2e22476c95ceb62986d195f3e600cdd7d196d7329aa2dfc951994
MD5 c1af8dc538aebadacfd7aa285cdee504
BLAKE2b-256 4114702cd035fab1b8dfe944d06827cc4628c3b18e5d7ddaa676bf72c9be1c2c

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp312-cp312-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp312-cp312-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 994687b818cac0e6d34cb407a41458e2c3262cb7db927465841c9522ba7ebb92
MD5 e19d0f3e1964ff3f1558c03cd4b044b0
BLAKE2b-256 a7d29daad500db2ab880eca0f0a569afd73e1afdd49f22c94560c85546dd87c2

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 a292ca7044236a2702ec3d4664de135e01fc9fb3ae9422db3fecc819546e831c
MD5 2c0d5cfb5e02dc6edf0705d8a1a22913
BLAKE2b-256 7826dc21cec3eace48d944258a437e293093c69ddb56684e9dd1b5016f7e2154

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 85fa54bec1f7d9227b5e4e201ed4bac92247ee10fb19148d92f9836e3e91d5c3
MD5 c58b3f4c6bff50d3b7a253c6e68a766c
BLAKE2b-256 a62da656bfa09b98d01d4baf30114a5befca1de2f7a16133d94aae01d7cd6942

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 dc5717feef9a0b430b052895db71f1d695939575b74344da981120e9f4da7620
MD5 075f7e22e9d59bdda7782154056654ce
BLAKE2b-256 590a2c5114537cbf4ac374607a41d976911657db9f656bfa42a278470ca8d886

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp311-cp311-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp311-cp311-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 bbe15455b59a6c9d822584fd3faaaac50ee972101d7bb7e2887da456e148a3ea
MD5 23f1f5fe4f6fd9c53e91f326552c4691
BLAKE2b-256 ea476b32740f7ae0ecf8772297f7e61e842b127b11de5c2c07efd9aeeaed0690

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 784c8b8de9e81e02dea093373bc35040791378df2daec0017e657985183bf862
MD5 b128e2d49ba9546961696d7e61bda9d8
BLAKE2b-256 e1eedc1b327b0e48075a355e86577f055f64d90dc07e8ae6029bafb9c47be18c

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 710df2c4f03ab6da161520c10d9467f4c0201ca314fd1432499a48c9d7a5818f
MD5 2000e9bc676dd721761fb5e914029831
BLAKE2b-256 084c5ba83223106dc369e1e71648c8d719da1070d652cfd276254ff11d9251f7

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 add88479361b2bf08790338fce4ba11a11fdc1ae508e80a3a013daf1c0d2893d
MD5 38657fb782a0f7d4e7255cd646df978e
BLAKE2b-256 5a7f9fb0d64fdb490086e7158d225632f298727d55b8a9552d4d3b0d0a7cba2a

See more details on using hashes here.

File details

Details for the file torchvision-0.29.0-cp310-cp310-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for torchvision-0.29.0-cp310-cp310-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 ce3b58631f0b7c8828e8675301bff5cb0623aaed704e3ac91079e89607753205
MD5 435882a38b0aeb32575ce429e66593ce
BLAKE2b-256 0565cc2c08a1b4baa5787e76e887b473f18403c2d943bdd7d1ffa60f41bfae3f

See more details on using hashes here.

Release history Release notifications | RSS feed

This release

0.29.0 This release

24 files

0.28.0

24 files

0.27.1

24 files

0.27.0

28 files

0.26.0

28 files

0.25.0

28 files

0.24.1

28 files

0.24.0

28 files

0.23.0

24 files

0.22.1

24 files

0.22.0

24 files

0.21.0

25 files

0.20.1

16 files

0.20.0

20 files

0.19.1

20 files

0.19.0

25 files

0.18.1

20 files

0.18.0

20 files

0.17.2

25 files

0.17.1

25 files

0.17.0

25 files

0.16.2

20 files

0.16.1

20 files

0.16.0

20 files

0.15.2

20 files

0.15.1

20 files

0.15.0

20 files

0.14.1

19 files

0.14.0

19 files

0.13.1

19 files

0.13.0

19 files

0.12.0

19 files

0.11.3

18 files

0.11.2

18 files

0.11.1

16 files

0.11.0

12 files

0.10.1

12 files

0.10.0

18 files

0.9.1

16 files

0.9.0

12 files

0.8.2

8 files

0.8.1

6 files

0.8.0

6 files

0.7.0

6 files

0.6.1

8 files

0.6.0

8 files

0.5.0

15 files

0.4.2

9 files

0.4.1.post2

4 files

0.4.1

12 files

0.4.0

9 files

0.3.0

12 files

0.2.2.post3

1 file

0.2.2.post2

1 file

0.2.2

1 file

0.2.1

1 file

0.2.0

1 file

0.1.9

1 file

0.1.8

1 file

0.1.7

1 file

0.1.6

3 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page