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README.md

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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
+
+We recommend Anaconda as Python package management system. Please refer to [pytorch.org](https://pytorch.org/) for the
+detail of PyTorch (`torch`) installation. The following is the corresponding `torchvision` versions and supported Python
+versions.
+
+| `torch`            | `torchvision`      | Python              |
+| ------------------ | ------------------ | ------------------- |
+| `main` / `nightly` | `main` / `nightly` | `>=3.8`, `<=3.11`   |
+| `2.0.0`            | `0.15.1`           | `>=3.8`, `<=3.11`   |
+| `1.13.0`           | `0.14.0`           | `>=3.7.2`, `<=3.10` |
+| `1.12.0`           | `0.13.0`           | `>=3.7`, `<=3.10`   |
+| `1.11.0`           | `0.12.0`           | `>=3.7`, `<=3.10`   |
+
+<details>
+    <summary>older versions</summary>
+
+| `torch`            | `torchvision`      | Python                    |
+|--------------------|--------------------|---------------------------|
+| `1.10.2`           | `0.11.3`           | `>=3.6`, `<=3.9`          |
+| `1.10.1`           | `0.11.2`           | `>=3.6`, `<=3.9`          |
+| `1.10.0`           | `0.11.1`           | `>=3.6`, `<=3.9`          |
+| `1.9.1`            | `0.10.1`           | `>=3.6`, `<=3.9`          |
+| `1.9.0`            | `0.10.0`           | `>=3.6`, `<=3.9`          |
+| `1.8.2`            | `0.9.2`            | `>=3.6`, `<=3.9`          |
+| `1.8.1`            | `0.9.1`            | `>=3.6`, `<=3.9`          |
+| `1.8.0`            | `0.9.0`            | `>=3.6`, `<=3.9`          |
+| `1.7.1`            | `0.8.2`            | `>=3.6`, `<=3.9`          |
+| `1.7.0`            | `0.8.1`            | `>=3.6`, `<=3.8`          |
+| `1.7.0`            | `0.8.0`            | `>=3.6`, `<=3.8`          |
+| `1.6.0`            | `0.7.0`            | `>=3.6`, `<=3.8`          |
+| `1.5.1`            | `0.6.1`            | `>=3.5`, `<=3.8`          |
+| `1.5.0`            | `0.6.0`            | `>=3.5`, `<=3.8`          |
+| `1.4.0`            | `0.5.0`            | `==2.7`, `>=3.5`, `<=3.8` |
+| `1.3.1`            | `0.4.2`            | `==2.7`, `>=3.5`, `<=3.7` |
+| `1.3.0`            | `0.4.1`            | `==2.7`, `>=3.5`, `<=3.7` |
+| `1.2.0`            | `0.4.0`            | `==2.7`, `>=3.5`, `<=3.7` |
+| `1.1.0`            | `0.3.0`            | `==2.7`, `>=3.5`, `<=3.7` |
+| `<=1.0.1`          | `0.2.2`            | `==2.7`, `>=3.5`, `<=3.7` |
+
+</details>
+
+Anaconda:
+
+```
+conda install torchvision -c pytorch
+```
+
+pip:
+
+```
+pip install torchvision
+```
+
+From source:
+
+```
+python setup.py install
+# or, for OSX
+# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install
+```
+
+We don't officially support building from source using `pip`, but _if_ you do, you'll need to use the
+`--no-build-isolation` flag. In case building TorchVision from source fails, install the nightly version of PyTorch
+following the linked guide on the
+[contributing page](https://github.com/pytorch/vision/blob/main/CONTRIBUTING.md#development-installation) and retry the
+install.
+
+By default, GPU support is built if CUDA is found and `torch.cuda.is_available()` is true. It's possible to force
+building GPU support by setting `FORCE_CUDA=1` environment variable, which is useful when building a docker image.
+
+## Image Backend
+
+Torchvision currently supports the following image backends:
+
+- [Pillow](https://python-pillow.org/) (default)
+- [Pillow-SIMD](https://github.com/uploadcare/pillow-simd) - a **much faster** drop-in replacement for Pillow with SIMD.
+  If installed will be used as the default.
+- [accimage](https://github.com/pytorch/accimage) - if installed can be activated by calling
+  `torchvision.set_image_backend('accimage')`
+- [libpng](http://www.libpng.org/pub/png/libpng.html) - can be installed via conda `conda install libpng` or any of the
+  package managers for debian-based and RHEL-based Linux distributions.
+- [libjpeg](http://ijg.org/) - can be installed via conda `conda install jpeg` or any of the package managers for
+  debian-based and RHEL-based Linux distributions. [libjpeg-turbo](https://libjpeg-turbo.org/) can be used as well.
+
+**Notes:** `libpng` and `libjpeg` must be available at compilation time in order to be available. Make sure that it is
+available on the standard library locations, otherwise, add the include and library paths in the environment variables
+`TORCHVISION_INCLUDE` and `TORCHVISION_LIBRARY`, respectively.
+
+## 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
+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 <torchvision/vision.h>` in your project.
+
+## Documentation
+
+You can find the API documentation on the pytorch website: <https://pytorch.org/vision/stable/index.html>
+
+## 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}}
+}
+```