SLEAP: A deep learning system for multi-animal pose tracking

The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimation in individual animals, extending these to multiple animals presents unique challenges for studies of social behaviors or animals in their natural environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking. This system enables versatile workflows for data labeling, model training and inference on previously unseen data. SLEAP features an accessible graphical user interface, a standardized data…

• NS
  National Science Foundation

  Awards: 1148900, 1734030, 1754476, PHY-1734030, DGE-1148900
• HH
  Howard Hughes Medical Institute
• NI
  National Institutes of Health

  Awards: NS104899, DC011284, R00 MH109674, R35 NS111580-02, PHY-1734030, R01 DC011284, GM137424-01, R01 NS104899
• NI
  National Institute of Mental Health
• NI
  National Institute on Deafness and Other Communication Disorders

  Award: DC011284
• NI
  National Institute of Neurological Disorders and Stroke

  Award: NS104899
• DO
  Division of Environmental Biology

  Award: 1754476