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 >=3.9, <=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
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!
Pre-trained Model LicenseThe 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}}
}
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