image and video datasets and models for torch deep learning

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for atalman from gravatar.com atalman Avatar for ezyang from gravatar.com ezyang Avatar for facebook from gravatar.com facebook Avatar for malfet from gravatar.com malfet Avatar for seemethere from gravatar.com seemethere Avatar for soumith from gravatar.com soumith

Unverified details

These details have not been verified by PyPI
Project links
Meta

Project description

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 >=3.9, <=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
older versions
torch torchvision Python
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.

[UNSTABLE] 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<4.3'
python setup.py install

Using the models on C++

Refer to example/cpp.

DISCLAIMER: the libtorchvision library includes the torchvision custom ops as well as most of the C++ torchvision APIs. Those APIs do not come with any backward-compatibility guarantees and may change from one version to the next. Only the Python APIs are stable and with backward-compatibility guarantees. So, if you need stability within a C++ environment, your best bet is to export the Python APIs via torchscript.

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}}
}

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for atalman from gravatar.com atalman Avatar for ezyang from gravatar.com ezyang Avatar for facebook from gravatar.com facebook Avatar for malfet from gravatar.com malfet Avatar for seemethere from gravatar.com seemethere Avatar for soumith from gravatar.com soumith

Unverified details

These details have not been verified by PyPI
Project links
Meta

Release history Release notifications | RSS feed

This version

0.21.0

0.20.1

0.20.0

0.19.1

0.19.0

0.18.1

0.18.0

0.17.2

0.17.1

0.17.0

0.16.2

0.16.1

0.16.0

0.15.2

0.15.1

0.15.0 yanked

Reason this release was yanked:

Contains an incorrect torch dependency

0.14.1

0.14.0

0.13.1

0.13.0

0.12.0

0.11.3

0.11.2

0.11.1

0.11.0 yanked

Reason this release was yanked:

Dependency issue, depends on a version of torch that does not exist on pypi

0.10.1

0.10.0

0.9.1

0.9.0

0.8.2

0.8.1

0.8.0

0.7.0

0.6.1

0.6.0

0.5.0

0.4.2

0.4.1.post2

0.4.1

0.4.0

0.3.0

0.2.2.post3 yanked

Reason this release was yanked:

So that users won't accidentally install this when using python 3.11

0.2.2.post2 yanked

Reason this release was yanked:

So that users won't accidentally install this when using python 3.11

0.2.2 yanked

Reason this release was yanked:

So that users won't accidentally install this when using python 3.11

0.2.1 yanked

Reason this release was yanked:

So that users won't accidentally install this when using python 3.11

0.2.0 yanked

Reason this release was yanked:

So that users won't accidentally install this when using python 3.11

0.1.9 yanked

Reason this release was yanked:

So that users won't accidentally install this when using python 3.11

0.1.8 yanked

Reason this release was yanked:

So that users won't accidentally install this when using python 3.11

0.1.7 yanked

Reason this release was yanked:

So that users won't accidentally install this when using python 3.11

0.1.6 yanked

Reason this release was yanked:

0.1.6 is past it's support date and confuses users on unsupported platforms

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-0.21.0-cp313-cp313-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.13 Windows x86-64

torchvision-0.21.0-cp313-cp313-manylinux_2_28_aarch64.whl (2.3 MB view details)

Uploaded CPython 3.13 manylinux: glibc 2.28+ ARM64

torchvision-0.21.0-cp313-cp313-manylinux1_x86_64.whl (7.2 MB view details)

Uploaded CPython 3.13

torchvision-0.21.0-cp313-cp313-macosx_11_0_arm64.whl (1.8 MB view details)

Uploaded CPython 3.13 macOS 11.0+ ARM64

torchvision-0.21.0-cp312-cp312-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.12 Windows x86-64

torchvision-0.21.0-cp312-cp312-manylinux_2_28_aarch64.whl (14.7 MB view details)

Uploaded CPython 3.12 manylinux: glibc 2.28+ ARM64

torchvision-0.21.0-cp312-cp312-manylinux1_x86_64.whl (7.2 MB view details)

Uploaded CPython 3.12

torchvision-0.21.0-cp312-cp312-macosx_11_0_arm64.whl (1.8 MB view details)

Uploaded CPython 3.12 macOS 11.0+ ARM64

torchvision-0.21.0-cp311-cp311-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.11 Windows x86-64

torchvision-0.21.0-cp311-cp311-manylinux_2_28_aarch64.whl (14.7 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.28+ ARM64

torchvision-0.21.0-cp311-cp311-manylinux1_x86_64.whl (7.2 MB view details)

Uploaded CPython 3.11

torchvision-0.21.0-cp311-cp311-macosx_11_0_arm64.whl (1.8 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

torchvision-0.21.0-cp310-cp310-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

torchvision-0.21.0-cp310-cp310-manylinux_2_28_aarch64.whl (14.7 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.28+ ARM64

torchvision-0.21.0-cp310-cp310-manylinux1_x86_64.whl (7.2 MB view details)

Uploaded CPython 3.10

torchvision-0.21.0-cp310-cp310-macosx_11_0_arm64.whl (1.8 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

torchvision-0.21.0-cp39-cp39-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.9 Windows x86-64

torchvision-0.21.0-cp39-cp39-manylinux_2_28_aarch64.whl (2.3 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.28+ ARM64

torchvision-0.21.0-cp39-cp39-manylinux1_x86_64.whl (7.2 MB view details)

Uploaded CPython 3.9

torchvision-0.21.0-cp39-cp39-macosx_11_0_arm64.whl (1.8 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

File details

Details for the file torchvision-0.21.0-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 9147f5e096a9270684e3befdee350f3cacafd48e0c54ab195f45790a9c146d67
MD5 82f6996027b6dcebbd81dd1fc4dfe3fc
BLAKE2b-256 edb4fc60e3bc003879d3de842baea258fffc3586f4b49cd435a5ba1e09c33315

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 5045a3a5f21ec3eea6962fa5f2fa2d4283f854caec25ada493fcf4aab2925467
MD5 62317750fcc5738a5759db63f8d36c51
BLAKE2b-256 0b2d3c3ee10608310a395594aac7da8640372ed79c6585910ccae6919658dcdc

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp313-cp313-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp313-cp313-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 084ac3f5a1f50c70d630a488d19bf62f323018eae1b1c1232f2b7047d3a7b76d
MD5 264997403924e65f6d43aeb981c32821
BLAKE2b-256 cb4c99880813aa50e64447fb1c4c6c804a793d2d78f7f7c53e99ddee7fa175fa

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 659b76c86757cb2ee4ca2db245e0740cfc3081fef46f0f1064d11adb4a8cee31
MD5 8b2cbbaeb15ae79a8d7b2f448ca23bc2
BLAKE2b-256 f95647d456b61c3bbce7bed4af3925c83d405bb87468e659fd3cf3d9840c3b51

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 6eb75d41e3bbfc2f7642d0abba9383cc9ae6c5a4ca8d6b00628c225e1eaa63b3
MD5 c62d69984dd2a44bca11e1a76a423033
BLAKE2b-256 4c6ac7752603060d076dfed95135b78b047dc71792630cbcb022e3693d6f32ef

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 5083a5b1fec2351bf5ea9900a741d54086db75baec4b1d21e39451e00977f1b1
MD5 627e928e82ee44ecc394a2bde9b8a313
BLAKE2b-256 bbea03541ed901cdc30b934f897060d09bbf7a98466a08ad1680320f9ce0cbe0

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp312-cp312-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp312-cp312-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 b578bcad8a4083b40d34f689b19ca9f7c63e511758d806510ea03c29ac568f7b
MD5 c4d125f45c1242bf6f17f894a615203a
BLAKE2b-256 36630722e153fd27d64d5b0af45b5c8cb0e80b35a68cf0130303bc9a8bb095c7

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 97a5814a93c793aaf0179cfc7f916024f4b63218929aee977b645633d074a49f
MD5 b03810c0c53fe4e52e6f4ba3317e11bb
BLAKE2b-256 6e1b28f527b22d5e8800184d0bc847f801ae92c7573a8c15979d92b7091c0751

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 49bcfad8cfe2c27dee116c45d4f866d7974bcf14a5a9fbef893635deae322f2f
MD5 d99926d02f42115a119f01cd23375f2c
BLAKE2b-256 88534ad334b9b1d8dd99836869fec139cb74a27781298360b91b9506c53f1d10

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 54454923a50104c66a9ab6bd8b73a11c2fc218c964b1006d5d1fe5b442c3dcb6
MD5 796f69e6c3779105a457ffe1f0d5f7e5
BLAKE2b-256 8ca1ee962ef9d0b2bf7a6f8b14cb95acb70e05cd2101af521032a09e43f8582f

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp311-cp311-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp311-cp311-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 3891cd086c5071bda6b4ee9d266bb2ac39c998c045c2ebcd1e818b8316fb5d41
MD5 dc1995a7c54c7b0901b87b062d0c5d94
BLAKE2b-256 bea2b0cedf0a411f1a5d75cfc0b87cde56dd1ddc1878be46a42c905cd8580220

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 110d115333524d60e9e474d53c7d20f096dbd8a080232f88dddb90566f90064c
MD5 c4858c74b62d8c6cb64d37bd03d8cf79
BLAKE2b-256 298800c69db213ee2443ada8886ec60789b227e06bb869d85ee324578221a7f7

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 abbf1d7b9d52c00d2af4afa8dac1fb3e2356f662a4566bd98dfaaa3634f4eb34
MD5 4d2458d2341e8aa37e37afd94cc9190d
BLAKE2b-256 aaf7799ddd538b21017cbf80294c92e9efbf6db08dff6efee37c3be114a81845

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 54815e0a56dde95cc6ec952577f67e0dc151eadd928e8d9f6a7f821d69a4a734
MD5 c59e4662e0cfe1bcaabcf65cb509a3e0
BLAKE2b-256 0e6b4fca9373eda42c1b04096758306b7bd55f7d8f78ba273446490855a0f25d

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp310-cp310-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp310-cp310-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 b0c0b264b89ab572888244f2e0bad5b7eaf5b696068fc0b93e96f7c3c198953f
MD5 ffa0625d58bba8ebefba7f2b2a73e33e
BLAKE2b-256 5e4432e2d2d174391374d5ff3c4691b802e8efda9ae27ab9062eca2255b006af

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 044ea420b8c6c3162a234cada8e2025b9076fa82504758cd11ec5d0f8cd9fa37
MD5 8243c3951263128289fe84342d7e5294
BLAKE2b-256 8e0d143bd264876fad17c82096b6c2d433f1ac9b29cdc69ee45023096976ee3d

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 8c44b6924b530d0702e88ff383b65c4b34a0eaf666e8b399a73245574d546947
MD5 732ff4726173690824554495f2a315e4
BLAKE2b-256 6d348b74ffdcb8e066b9abfc7a59407ae8fa9a67cb1e296f090d71bf4577f6cd

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp39-cp39-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp39-cp39-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 6bdce3890fa949219de129e85e4f6d544598af3c073afe5c44e14aed15bdcbb2
MD5 f246fb16b7fea8d091810825d9cee869
BLAKE2b-256 a8536822bd507088419b44d95d40a165f83d635568044e2e40c8dd5ff48984b2

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp39-cp39-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp39-cp39-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 e6572227228ec521618cea9ac3a368c45b7f96f1f8622dc9f1afe891c044051f
MD5 076decdbb7987a18b25d6ab4008b5a5c
BLAKE2b-256 b11e0c329d94f92c498f1b76eb283b2d26f244ea8631dee37b2566e20ff4724d

See more details on using hashes here.

File details

Details for the file torchvision-0.21.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for torchvision-0.21.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 5c22caeaae8b3c36d93459f1a5294e6f43306cff856ed243189a229331a404b4
MD5 e13e5f7608365b8fe24a47836991e827
BLAKE2b-256 49d5d18c5d89cbe32015b033f1fa06918c7cdd5c0af0c03e55d72a3cc2d768f8

See more details on using hashes here.

Supported by

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