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Revise README for improved clarity and formatting #9219
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| <p align="center"> | ||
| <img src="https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/MONAI-logo-color.png" width="50%" alt='project-monai'> | ||
| <img src="https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/MONAI-logo-color.png" width="50%" alt="Project MONAI"> | ||
| </p> | ||
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| **M**edical **O**pen **N**etwork for **AI** | ||
| **MONAI: Medical Open Network for AI** | ||
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|  | ||
| [](https://opensource.org/licenses/Apache-2.0) | ||
| [](https://arxiv.org/abs/2211.02701) | ||
| [](https://badge.fury.io/py/monai) | ||
| [](https://hub.docker.com/r/projectmonai/monai) | ||
| [](https://anaconda.org/conda-forge/monai) | ||
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| [](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml) | ||
| [](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev) | ||
| [](https://monai.readthedocs.io/en/latest/) | ||
| [](https://codecov.io/gh/Project-MONAI/MONAI) | ||
| [](https://piptrends.com/package/monai) | ||
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| MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/dev/LICENSE) framework for deep learning in healthcare imaging, part of the [PyTorch Ecosystem](https://pytorch.org/ecosystem/). | ||
| Its ambitions are as follows: | ||
| MONAI is a free and open-source deep learning framework for medical imaging, built on top of [PyTorch](https://pytorch.org/) and listed in the [PyTorch Ecosystem](https://pytorch.org/ecosystem/). The project aims to: | ||
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| - Developing a community of academic, industrial and clinical researchers collaborating on a common foundation; | ||
| - Creating state-of-the-art, end-to-end training workflows for healthcare imaging; | ||
| - Providing researchers with the optimized and standardized way to create and evaluate deep learning models. | ||
| - Bring academic, industry and clinical researchers together around one shared foundation; | ||
| - Offer modern, complete training pipelines for medical imaging tasks; | ||
| - Give researchers a standard, well-optimized way to build and assess deep learning models. | ||
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| ## Features | ||
| ## Key Capabilities | ||
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| > _Please see [the technical highlights](https://monai.readthedocs.io/en/latest/highlights.html) and [What's New](https://monai.readthedocs.io/en/latest/whatsnew.html) of the milestone releases._ | ||
| > For details, see the [technical highlights](https://monai.readthedocs.io/en/latest/highlights.html) and the [release notes](https://monai.readthedocs.io/en/latest/whatsnew.html). | ||
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| - flexible pre-processing for multi-dimensional medical imaging data; | ||
| - compositional & portable APIs for ease of integration in existing workflows; | ||
| - domain-specific implementations for networks, losses, evaluation metrics and more; | ||
| - customizable design for varying user expertise; | ||
| - multi-GPU multi-node data parallelism support. | ||
| - Pre-processing that can be adapted to medical images of any dimensionality; | ||
| - Modular, portable APIs that slot easily into existing code; | ||
| - Networks, loss functions, metrics and other components designed for the medical domain; | ||
| - A design that suits users with different levels of experience; | ||
| - Data-parallel training across several GPUs and several machines. | ||
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| ## Requirements | ||
| ## Dependencies | ||
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| MONAI works with the [currently supported versions of Python](https://devguide.python.org/versions), and depends directly on NumPy and PyTorch with many optional dependencies. | ||
| MONAI runs on the Python versions that are still officially supported. It depends directly on NumPy and PyTorch, plus a number of optional packages. | ||
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| * Major releases of MONAI will have dependency versions stated for them. The current state of the `dev` branch in this repository is the unreleased development version of MONAI which typically will support current versions of dependencies and include updates and bug fixes to do so. | ||
| * PyTorch support covers [the current version](https://github.com/pytorch/pytorch/releases) plus three previous minor versions. If compatibility issues with a PyTorch version and other dependencies arise, support for a version may be delayed until a major release. | ||
| * Our support policy for other dependencies adheres for the most part to [SPEC0](https://scientific-python.org/specs/spec-0000), where dependency versions are supported where possible for up to two years. Discovered vulnerabilities or defects may require certain versions to be explicitly not supported. | ||
| * See the `pyproject.toml` file for dependency version information. | ||
| * Each major release lists its own dependency versions. The `dev` branch is the in-progress version and usually tracks current dependencies, including the fixes needed to keep up with them. | ||
| * Supported PyTorch versions are the newest release and the three minor versions before it. If a PyTorch version clashes with another dependency, support for it may wait until the next major release. | ||
| * For other dependencies the project mostly follows [SPEC0](https://scientific-python.org/specs/spec-0000), supporting versions for up to about two years. A version with a known vulnerability or defect may be explicitly dropped. | ||
| * Exact version requirements are in `pyproject.toml`. | ||
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| ## Installation | ||
| ## How to Install | ||
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| To install [the current release](https://pypi.org/project/monai/), you can simply run: | ||
| The latest [stable release](https://pypi.org/project/monai/) installs with a single command: | ||
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| ```bash | ||
| pip install monai | ||
| ``` | ||
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| Please refer to [the installation guide](https://monai.readthedocs.io/en/latest/installation.html) for other installation options. | ||
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| ## Getting Started | ||
| Other ways to install are covered in the [installation guide](https://monai.readthedocs.io/en/latest/installation.html). | ||
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| [MedNIST demo](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/2d_classification/mednist_tutorial.ipynb) and [MONAI for PyTorch Users](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/modules/developer_guide.ipynb) are available on Colab. | ||
| ## Where to Begin | ||
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| Examples and notebook tutorials are located at [Project-MONAI/tutorials](https://github.com/Project-MONAI/tutorials). | ||
| Two interactive notebooks run directly in Colab: the [MedNIST demo](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/2d_classification/mednist_tutorial.ipynb) and [MONAI for PyTorch Users](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/modules/developer_guide.ipynb). | ||
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| Technical documentation is available at [monai.readthedocs.io](https://monai.readthedocs.io). | ||
| More examples and notebooks live in the [tutorials repository](https://github.com/Project-MONAI/tutorials). Full technical documentation is at [monai.readthedocs.io](https://monai.readthedocs.io). | ||
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| ## Docker | ||
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| The MONAI Docker image is available from [Dockerhub](https://hub.docker.com/r/projectmonai/monai), | ||
| tagged as `latest` for the latest state of `dev` or with a release version. A slimmed down image can also be built | ||
| locally using `Dockerfile.slim`, see that file for instructions. | ||
| A ready-made image is published on [Docker Hub](https://hub.docker.com/r/projectmonai/monai). The `latest` tag follows the current `dev` branch, and release versions have their own tags. If you want a smaller image, you can build one yourself from `Dockerfile.slim`; the instructions are inside that file. | ||
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| To start a container with the newest MONAI and GPU access: | ||
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| To get started with the latest MONAI, use `docker run -ti --rm --gpus all projectmonai/monai:latest /bin/bash`. | ||
| ```bash | ||
| docker run -ti --rm --gpus all projectmonai/monai:latest /bin/bash | ||
| ``` | ||
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| ## Citation | ||
| ## Citing MONAI | ||
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| If you have used MONAI in your research, please cite us! The citation can be exported from: <https://arxiv.org/abs/2211.02701>. | ||
| If MONAI helped with your research, please give credit by citing it. A citation can be exported from <https://arxiv.org/abs/2211.02701>. | ||
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| ## Model Zoo | ||
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| [The MONAI Model Zoo](https://github.com/Project-MONAI/model-zoo) is a place for researchers and data scientists to share the latest and great models from the community. | ||
| Utilizing [the MONAI Bundle format](https://monai.readthedocs.io/en/latest/bundle_intro.html) makes it easy to [get started](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo) building workflows with MONAI. | ||
| The [MONAI Model Zoo](https://github.com/Project-MONAI/model-zoo) is a shared collection where the community publishes strong models. Models use the [MONAI Bundle format](https://monai.readthedocs.io/en/latest/bundle_intro.html), which makes it simple to [start building workflows](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo). | ||
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| ## Contributing | ||
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| For guidance on making a contribution to MONAI, see the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md). | ||
| To learn how to contribute, read the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md). | ||
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| ## Community | ||
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| Join the conversation on Twitter/X [@ProjectMONAI](https://twitter.com/ProjectMONAI), [LinkedIn](https://www.linkedin.com/company/projectmonai), or join our [Slack channel](https://forms.gle/QTxJq3hFictp31UM9). | ||
| Follow or talk to the project on Twitter/X ([@ProjectMONAI](https://twitter.com/ProjectMONAI)) or [LinkedIn](https://www.linkedin.com/company/projectmonai), or join the [Slack channel](https://forms.gle/QTxJq3hFictp31UM9). | ||
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| Ask and answer questions over on [MONAI's GitHub Discussions tab](https://github.com/Project-MONAI/MONAI/discussions). | ||
| Questions and answers are welcome in the [GitHub Discussions](https://github.com/Project-MONAI/MONAI/discussions) section. | ||
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| ## Links | ||
| ## Useful Links | ||
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| - Website: <https://project-monai.github.io/> | ||
| - API documentation (milestone): <https://monai.readthedocs.io/> | ||
| - API documentation (latest dev): <https://monai.readthedocs.io/en/latest/> | ||
| - Code: <https://github.com/Project-MONAI/MONAI> | ||
| - Project tracker: <https://github.com/Project-MONAI/MONAI/projects> | ||
| - Issue tracker: <https://github.com/Project-MONAI/MONAI/issues> | ||
| - Documentation (stable): <https://monai.readthedocs.io/> | ||
| - Documentation (latest dev): <https://monai.readthedocs.io/en/latest/> | ||
| - Source code: <https://github.com/Project-MONAI/MONAI> | ||
| - Project board: <https://github.com/Project-MONAI/MONAI/projects> | ||
| - Issues: <https://github.com/Project-MONAI/MONAI/issues> | ||
| - Wiki: <https://github.com/Project-MONAI/MONAI/wiki> | ||
| - Test status: <https://github.com/Project-MONAI/MONAI/actions> | ||
| - PyPI package: <https://pypi.org/project/monai/> | ||
| - CI status: <https://github.com/Project-MONAI/MONAI/actions> | ||
| - PyPI: <https://pypi.org/project/monai/> | ||
| - conda-forge: <https://anaconda.org/conda-forge/monai> | ||
| - Weekly previews: <https://pypi.org/project/monai-weekly/> | ||
| - Weekly preview builds: <https://pypi.org/project/monai-weekly/> | ||
| - Docker Hub: <https://hub.docker.com/r/projectmonai/monai> | ||
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Link the supported Python versions.
pyproject.tomldeclares onlyrequires-python = ">=3.10", so it does not tell readers which Python versions MONAI currently supports. Link “officially supported” to the Python version table, or state the tested range and keep it synchronized with CI.As per path instructions, documentation must be updated with the latest information.
Source: Path instructions