Lightning Pose Homepage Lightning Pose Documentation

Lightning Pose Homepage Lightning Pose Documentation To get started, install lightning pose and follow the create your first project tutorial. it covers the end to end workflow of labeling, training, and evaluation. Getting started to get started with lightning pose, follow the guides on our documentation: create your first project using the app or follow the cli user guides (singleview, multiview).

Lightning Pose Homepage Lightning Pose Documentation Lightning pose is a pose estimation framework implemented in pytorch lightning that enables training deep learning models to identify keypoints (body parts) in images and videos of animals or humans. 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). Whether you're new to pose estimation or an experienced researcher, these tutorials will walk you through each step—from logging into the lightning platform and launching the app to training. To improve the robustness and usability of animal pose estimation, we present lightning pose, a solution at three levels: modeling, software and a cloud based application.

Lightning Pose By Llightningcrystal2 On Deviantart Whether you're new to pose estimation or an experienced researcher, these tutorials will walk you through each step—from logging into the lightning platform and launching the app to training. To improve the robustness and usability of animal pose estimation, we present lightning pose, a solution at three levels: modeling, software and a cloud based application. This part of the cli user guide addresses some of the more advanced features of lightning pose. for each feature, we point out necessary modifications and useful information related to the data and config file. Lightning pose is an open source deep learning package for animal pose estimation (biderman, whiteway et al. 2024, nature methods). the framework is based on pytorch lightning and supports accelerated training on unlabeled videos using nvidia dali. Lighting pose supports utilizing camera calibration to make multiview labeling easier. using labels from two views, it can triangulate into 3d space and reproject onto the remaining views.