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The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
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 | >=3.8 , <=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 |
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 |
Torchvision currently supports the following image backends:
Read more in in our docs.
Torchvision currently supports the following video backends:
conda install -c conda-forge 'ffmpeg<4.3'
python setup.py install
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.
You can find the API documentation on the pytorch website: https://pytorch.org/vision/stable/index.html
See the CONTRIBUTING file for how to help out.
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!
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.
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}}
}
FAQs
image and video datasets and models for torch deep learning
We found that torchvision demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 6 open source maintainers collaborating on the project.
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Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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