Metadata-Version: 2.4
Name: lightning-pose
Version: 2.0.9
Summary: Semi-supervised pose estimation using pytorch lightning
License-Expression: MIT
License-File: LICENSE
Keywords: machine learning,deep learning,computer_vision
Author: Dan Biderman
Author-email: danbider@gmail.com
Maintainer: Karan Sikka
Maintainer-email: karanssikka@gmail.com
Requires-Python: >=3.10
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Visualization
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Project-URL: Documentation, https://lightning-pose.readthedocs.io/
Project-URL: Repository, https://github.com/paninski-lab/lightning-pose
Description-Content-Type: text/markdown

![](https://github.com/paninski-lab/lightning-pose/raw/main/docs/images/LightningPose_horizontal_light.png)

[![Discord](https://img.shields.io/discord/1103381776895856720)](https://discord.gg/tDUPdRj4BM)
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Lightning Pose is an end-to-end toolkit designed for robust multi-view and single-view animal
pose estimation using advanced transformer architectures. It leverages Multi-View Transformers
and patch-masking training to learn geometric relationships between views,
resulting in strong performance on occlusions 
[Aharon, Whiteway et al. 2026](https://www.biorxiv.org/content/10.64898/2026.04.20.719731v1).
For single-view datasets it leverages temporal context and learned plausibility constraints for 
strong performance in challenging scenarios [Biderman, Whiteway et al. 2024, Nature Methods](https://rdcu.be/dLP3z).
It has a rich GUI that supports the end-to-end workflow: labeling, model management, and evaluation.


## Installation

Lightning-pose requires a Linux or WSL environment with an NVIDIA GPU.

For users without access to a local NVIDIA GPU, it is highly recommended to
use the [Lightning AI](https://lightning.ai/) cloud environment, which provides
persistent, browser-based "Studios" with on-demand access to powerful GPUs
and pre-configured CUDA environments.

Install dependencies:

```shell
sudo apt install ffmpeg

# Verify nvidia-driver with CUDA 12+
nvidia-smi
```

In a clean python virtual environment (conda or other virtual environment manager), run:

```shell
pip install lightning-pose lightning-pose-app
```

That's it! To run the app:

```shell
litpose run_app
```

Please see the [installation guide](https://lightning-pose.readthedocs.io/en/latest/source/installation_guide.html)
for more detailed instructions, and feel free to reach out to us on [Discord](https://discord.gg/tDUPdRj4BM)
in case of any hiccups.

## Getting Started

To get started with Lightning Pose, follow the guides on our documentation:
* [Create your first project](https://lightning-pose.readthedocs.io/en/latest/source/create_first_project.html) using the app
* or follow the CLI User Guides ([Singleview](https://lightning-pose.readthedocs.io/en/latest/source/user_guide_singleview/index.html), [Multiview](https://lightning-pose.readthedocs.io/en/latest/source/user_guide_multiview/index.html)).

## Community

The Lightning Pose team also actively develops the 
[Ensemble Kalman Smoother (EKS)](https://github.com/paninski-lab/eks), 
a simple and performant post-processor that works with any pose estimation package including 
Lightning Pose, DeepLabCut, and SLEAP.

Lightning Pose is primarily maintained by 
[Karan Sikka](https://github.com/ksikka) (Columbia University) and
[Matt Whiteway](https://themattinthehatt.github.io) (Columbia University). 
 
Lightning Pose is under active development and we welcome community contributions.
Whether you want to implement some of your own ideas or help out with our [development roadmap](docs/roadmap.md), please get in touch with us on Discord (see contributing guidelines [here](CONTRIBUTING.md)).

## Funding

We are grateful for support from the following:
* Gatsby Charitable Foundation GAT3708
* [NIH R50NS145433](https://reporter.nih.gov/search/Hmj4KMmLv0evcYPlPEDa-Q/project-details/11240675)
* [NIH U19NS123716](https://reporter.nih.gov/search/Hmj4KMmLv0evcYPlPEDa-Q/project-details/11141703)
* [NSF 1707398](https://ui.adsabs.harvard.edu/abs/2017nsf....1707398A/abstract)
* [The NSF AI Institute for Artificial and Natural Intelligence](https://ui.adsabs.harvard.edu/abs/2023nsf....2229929Z/abstract)
* Simons Foundation
* Wellcome Trust 216324
* Zuckerman Institute (Columbia University) Team Science

