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image and video datasets and models for torch deep learning

Project description


total torchvision downloads documentation

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, <=3.11
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
older versions
torch torchvision Python
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
python install

Using the models on C++

TorchVision provides an example project for how to use the models on C++ using JIT Script.

Installation From source:

mkdir build
cd build
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
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.

Python linking is disabled by default when compiling TorchVision with CMake, this allows you to run models without any Python dependency. In some special cases where TorchVision's operators are used from Python code, you may need to link to Python. This can be done by passing -DUSE_PYTHON=on to CMake.

TorchVision Operators

In order to get the torchvision operators registered with torch (eg. for the JIT), all you need to do is to ensure that you #include <torchvision/vision.h> in your project.


You can find the API documentation on the pytorch website:


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:

    title        = {TorchVision: PyTorch's Computer Vision library},
    author       = {TorchVision maintainers and contributors},
    year         = 2016,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{}}

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