Integrate XMALab and DeepLabCut for high-throughput XROMM. Pipeline created by J.D. Laurence-Chasen.
See (and cite!) our methods paper in the Journal of Experimental Biology.
The default workflow involves training a single DLC network that will analyze videos from both camera planes. Go to the templates folder to find walk-through jupyter notebooks for the whole pipeline. If you want to train seperate networks for each camera plane, use the 2Networks Jupyter Notebook.
Special thanks to Ben Knorlein for XMALab and Mackenzie Mathis, Alexander Mathis, Tanmay Nath, and all other DeepLabCut authors/contributers.