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https://github.com/pytorch/vision

Datasets, Transforms and Models specific to Computer Vision
https://github.com/pytorch/vision

computer-vision machine-learning

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Datasets, Transforms and Models specific to Computer Vision

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# torchvision



[![total torchvision downloads](https://pepy.tech/badge/torchvision)](https://pepy.tech/project/torchvision)

[![documentation](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchvision%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pytorch.org/vision/stable/index.html)



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

vision.



## Installation



Please refer to the [official

instructions](https://pytorch.org/get-started/locally/) to install the stable

versions of `torch` and `torchvision` on your system.



To build source, refer to our [contributing

page](https://github.com/pytorch/vision/blob/main/CONTRIBUTING.md#development-installation).



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

versions.



| `torch`            | `torchvision`      | Python              |

| ------------------ | ------------------ | ------------------- |

| `main` / `nightly` | `main` / `nightly` | `>=3.8`, `<=3.11`   |

| `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:

    - [Pillow](https://python-pillow.org/)

    - [Pillow-SIMD](https://github.com/uploadcare/pillow-simd) - a **much faster** drop-in replacement for Pillow with SIMD.



Read more in in our [docs](https://pytorch.org/vision/stable/transforms.html).



## [UNSTABLE] Video Backend



Torchvision currently supports the following video backends:



- [pyav](https://github.com/PyAV-Org/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++



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

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 ` in your project.



## Documentation



You can find the API documentation on the pytorch website: 



## Contributing



See the [CONTRIBUTING](CONTRIBUTING.md) 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](https://github.com/facebookresearch/SWAG/blob/main/LICENSE) for additional details.



## Citing TorchVision



If you find TorchVision useful in your work, please consider citing the following BibTeX entry:



```bibtex

@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}}

}

```