LightningPose
Lightning Pose App is a browser-based GUI for creating, training, and running inference for pose estimation projects in the cloud or locally.
About
Lightning Pose App is a browser-based graphical user interface (GUI) designed to facilitate the development, training, and deployment of pose estimation projects. Built to run both in the cloud using Lightning.ai and on local workstations, Lightning Pose App supports a full workflow: creating projects, extracting frames from videos, labeling keypoints using integrated tools, training various types of models (including supervised, semi-supervised, and context-aware models), and running inference on new videos. The app provides advanced features such as active learning (automatically selecting challenging frames for labeling), ensembling via the Ensemble Kalman Smoother (EKS), and comprehensive diagnostic tools for both labeled and unlabeled data. It supports importing projects from other pose estimation systems like DeepLabCut and SLEAP, making it accessible for users migrating from other platforms. The system is designed primarily for researchers or practitioners working in animal pose estimation and behavior tracking.
Products & Services
- Browser-based pose estimation project manager
- Frame extraction and labeling tools (LabelStudio integration)
- Cloud-based or local training of pose estimation models
- Supervised and semi-supervised model options
- Active learning workflow for selecting frames to label
- Model diagnostics for labeled and unlabeled data
- Inference on large video datasets
- Project importing from DeepLabCut and SLEAP
- Deployment via Lightning Studio with preinstalled environments
- Ensemble model creation and Ensemble Kalman Smoother (EKS) post-processing
Use Cases & Case Studies
- Importing animal pose estimation projects from DLC or SLEAP to Lightning Pose
- Running large-scale inference on high-volume video datasets
- Active learning for efficient manual labeling in pose estimation
- Scientific research requiring robust pose tracking across different experimental setups
Partnerships & Collaborations
- Integration with Lightning.ai
- Built-in LabelStudio annotation tool integration
- Support for FiftyOne diagnostics platform
Awards & Recognition
- 2024 publication in Nature Methods