Demo: Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling

The Lightning Pose App is a comprehensive, user-friendly platform that streamlines the entire animal pose estimation workflow. Built with JavaScript and Python, this interface guides users through every stage of the process: from annotating data and training models to running video inference and visualizing diagnostic results.

Accessibility and Deployment. The app can run both locally and in the cloud, making animal pose estimation accessible to users without coding expertise. All they need is a computer with internet access. This flexibility ensures researchers can work in their preferred environment while maintaining full functionality.

Advanced Technology. At its core, the app leverages the Lightning Pose software package, which combines semi-supervised learning with a novel Bayesian ensembling technique for post-processing. This innovative approach delivers improved accuracy and more reliable uncertainty estimates while requiring fewer manually labeled frames, a significant advantage for researchers working with limited annotated data.

Interactive Demonstration. Our demo will showcase both operational modes by running on a local laptop instance and a cloud instance simultaneously. Using an existing dataset, participants will experience the complete workflow: extracting and labeling new frames, retraining models, and exploring output visualizations. This hands-on experience will highlight how the tool seamlessly integrates multiple functions into a cohesive platform designed for practitioners.

This demonstration directly supports the workshop's focus on large-scale video analysis, as pose estimation serves as a foundational technique in this rapidly growing field.