169 lines
6.5 KiB
Plaintext
169 lines
6.5 KiB
Plaintext
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Metadata-Version: 2.1
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Name: torchvision
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Version: 0.18.0
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Summary: image and video datasets and models for torch deep learning
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Home-page: https://github.com/pytorch/vision
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Author: PyTorch Core Team
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Author-email: soumith@pytorch.org
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License: BSD
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Requires-Python: >=3.8
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Description-Content-Type: text/markdown
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License-File: LICENSE
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Requires-Dist: numpy
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Requires-Dist: torch (==2.3.0)
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Requires-Dist: pillow (!=8.3.*,>=5.3.0)
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Provides-Extra: scipy
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Requires-Dist: scipy ; extra == 'scipy'
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# torchvision
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[](https://pepy.tech/project/torchvision)
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[](https://pytorch.org/vision/stable/index.html)
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The torchvision package consists of popular datasets, model architectures, and common image transformations for computer
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vision.
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## Installation
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Please refer to the [official
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instructions](https://pytorch.org/get-started/locally/) to install the stable
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versions of `torch` and `torchvision` on your system.
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To build source, refer to our [contributing
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page](https://github.com/pytorch/vision/blob/main/CONTRIBUTING.md#development-installation).
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The following is the corresponding `torchvision` versions and supported Python
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versions.
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| `torch` | `torchvision` | Python |
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| ------------------ | ------------------ | ------------------- |
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| `main` / `nightly` | `main` / `nightly` | `>=3.8`, `<=3.11` |
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| `2.2` | `0.17` | `>=3.8`, `<=3.11` |
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| `2.1` | `0.16` | `>=3.8`, `<=3.11` |
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| `2.0` | `0.15` | `>=3.8`, `<=3.11` |
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<details>
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<summary>older versions</summary>
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| `torch` | `torchvision` | Python |
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|---------|-------------------|---------------------------|
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| `1.13` | `0.14` | `>=3.7.2`, `<=3.10` |
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| `1.12` | `0.13` | `>=3.7`, `<=3.10` |
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| `1.11` | `0.12` | `>=3.7`, `<=3.10` |
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| `1.10` | `0.11` | `>=3.6`, `<=3.9` |
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| `1.9` | `0.10` | `>=3.6`, `<=3.9` |
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| `1.8` | `0.9` | `>=3.6`, `<=3.9` |
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| `1.7` | `0.8` | `>=3.6`, `<=3.9` |
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| `1.6` | `0.7` | `>=3.6`, `<=3.8` |
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| `1.5` | `0.6` | `>=3.5`, `<=3.8` |
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| `1.4` | `0.5` | `==2.7`, `>=3.5`, `<=3.8` |
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| `1.3` | `0.4.2` / `0.4.3` | `==2.7`, `>=3.5`, `<=3.7` |
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| `1.2` | `0.4.1` | `==2.7`, `>=3.5`, `<=3.7` |
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| `1.1` | `0.3` | `==2.7`, `>=3.5`, `<=3.7` |
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| `<=1.0` | `0.2` | `==2.7`, `>=3.5`, `<=3.7` |
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</details>
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## Image Backends
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Torchvision currently supports the following image backends:
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- torch tensors
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- PIL images:
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- [Pillow](https://python-pillow.org/)
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- [Pillow-SIMD](https://github.com/uploadcare/pillow-simd) - a **much faster** drop-in replacement for Pillow with SIMD.
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Read more in in our [docs](https://pytorch.org/vision/stable/transforms.html).
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## [UNSTABLE] Video Backend
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Torchvision currently supports the following video backends:
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- [pyav](https://github.com/PyAV-Org/PyAV) (default) - Pythonic binding for ffmpeg libraries.
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- video_reader - This needs ffmpeg to be installed and torchvision to be built from source. There shouldn't be any
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conflicting version of ffmpeg installed. Currently, this is only supported on Linux.
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```
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conda install -c conda-forge 'ffmpeg<4.3'
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python setup.py install
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```
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# Using the models on C++
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TorchVision provides an example project for how to use the models on C++ using JIT Script.
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Installation From source:
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```
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mkdir build
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cd build
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# Add -DWITH_CUDA=on support for the CUDA if needed
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cmake ..
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make
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make install
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```
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Once installed, the library can be accessed in cmake (after properly configuring `CMAKE_PREFIX_PATH`) via the
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`TorchVision::TorchVision` target:
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```
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find_package(TorchVision REQUIRED)
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target_link_libraries(my-target PUBLIC TorchVision::TorchVision)
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```
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The `TorchVision` package will also automatically look for the `Torch` package and add it as a dependency to
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`my-target`, so make sure that it is also available to cmake via the `CMAKE_PREFIX_PATH`.
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For an example setup, take a look at `examples/cpp/hello_world`.
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Python linking is disabled by default when compiling TorchVision with CMake, this allows you to run models without any
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Python dependency. In some special cases where TorchVision's operators are used from Python code, you may need to link
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to Python. This can be done by passing `-DUSE_PYTHON=on` to CMake.
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### TorchVision Operators
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In order to get the torchvision operators registered with torch (eg. for the JIT), all you need to do is to ensure that
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you `#include <torchvision/vision.h>` in your project.
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## Documentation
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You can find the API documentation on the pytorch website: <https://pytorch.org/vision/stable/index.html>
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## Contributing
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See the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out.
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## Disclaimer on Datasets
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This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets,
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vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to
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determine whether you have permission to use the dataset under the dataset's license.
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If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset
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to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML
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community!
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## Pre-trained Model License
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The pre-trained models provided in this library may have their own licenses or terms and conditions derived from the
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dataset used for training. It is your responsibility to determine whether you have permission to use the models for your
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use case.
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More specifically, SWAG models are released under the CC-BY-NC 4.0 license. See
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[SWAG LICENSE](https://github.com/facebookresearch/SWAG/blob/main/LICENSE) for additional details.
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## Citing TorchVision
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If you find TorchVision useful in your work, please consider citing the following BibTeX entry:
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```bibtex
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@software{torchvision2016,
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title = {TorchVision: PyTorch's Computer Vision library},
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author = {TorchVision maintainers and contributors},
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year = 2016,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/pytorch/vision}}
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}
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```
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