<commit_before><p align="center">
  <img src="./docs/images/MONAI-logo-color.png" width="50%" alt='project-monai'>
</p>

**M**edical **O**pen **N**etwork for **AI**

[![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
[![CI Build](https://github.com/Project-MONAI/MONAI/workflows/build/badge.svg?branch=master)](https://github.com/Project-MONAI/MONAI/commits/master)
[![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://monai.readthedocs.io/en/latest/?badge=latest)

MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/master/LICENSE) platform for deep learning in healthcare imaging. Its ambitions are:
- 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.


## Features
> _The codebase is currently under active development._

- 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 data parallelism support.

## Installation
To install [the current release](https://pypi.org/project/monai/):<commit_msg>add coverage, PyPI badges (#261)<commit_after><p align="center">
  <img src="./docs/images/MONAI-logo-color.png" width="50%" alt='project-monai'>
</p>

**M**edical **O**pen **N**etwork for **AI**

[![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
[![CI Build](https://github.com/Project-MONAI/MONAI/workflows/build/badge.svg?branch=master)](https://github.com/Project-MONAI/MONAI/commits/master)
[![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://monai.readthedocs.io/en/latest/?badge=latest)
[![codecov](https://codecov.io/gh/Project-MONAI/MONAI/branch/master/graph/badge.svg)](https://codecov.io/gh/Project-MONAI/MONAI)
[![PyPI version](https://badge.fury.io/py/monai.svg)](https://badge.fury.io/py/monai)

MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/master/LICENSE) platform for deep learning in healthcare imaging. Its ambitions are:
- 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.


## Features
> _The codebase is currently under active development._

- 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 data parallelism support.

## Installation
To install [the current release](https://pypi.org/project/monai/):
{'fraction_non_alphanumeric': 0.1436233611442193, 'fraction_numerical': 0.00566150178784267, 'mean_word_length': 5.480694980694981, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 11, 'https://': 22, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '1104754', 'n_tokens_mistral': 1043, 'n_tokens_neox': 992, 'n_words': 244}