Pose estimation models implemented in Pytorch Lightning, supporting massively accelerated training on unlabeled videos using NVIDIA DALI. Models can be evaluated with TensorBoard, FiftyOne, and Streamlit.
As of June 2024, Lightning Pose is now published in Nature Methods!
Train a network on an example dataset and visualize the results in Google Colab.
Please see the Lightning Pose documentation for installation instructions and user guides. Note that the Lightning Pose package provides tools for training and evaluating models on already labeled data and unlabeled video clips.
We also offer a browser-based application that supports the full life cycle of a pose estimation project, from data annotation to model training to diagnostic visualizations.
The Lightning Pose team also actively develops the Ensemble Kalman Smoother (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 (Columbia University), Matt Whiteway (Columbia University), and Dan Biderman (Stanford 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, please get in touch with us on Discord (see contributing guidelines here).
FAQs
Semi-supervised pose estimation using pytorch lightning
We found that lightning-pose demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
npm now supports Trusted Publishing with OIDC, enabling secure package publishing directly from CI/CD workflows without relying on long-lived tokens.
Research
/Security News
A RubyGems malware campaign used 60 malicious packages posing as automation tools to steal credentials from social media and marketing tool users.
Security News
The CNA Scorecard ranks CVE issuers by data completeness, revealing major gaps in patch info and software identifiers across thousands of vulnerabilities.
