SLEAP: A deep learning system for multi-animal pose tracking. Author Talmo Pereira, Nathaniel Tabris, Arie Matsliah, David Turner, Junyu Li, Shruthi Ravindranath, Eleni Papadoyannis, Edna Normand, David Deutsch, Z Yan Wang, Grace McKenzie-Smith, Catalin Mitelut, Marielisa Castro, John D'Uva, Mikhail Kislin, Dan Sanes, Sarah Kocher, Samuel Wang, Annegret Falkner, Joshua Shaevitz, Mala Murthy Publication Year 2022 Type Journal Article Abstract 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 model, a reproducible configuration system, over 30 model architectures, two approaches to part grouping and two approaches to identity tracking. We applied SLEAP to seven datasets across flies, bees, mice and gerbils to systematically evaluate each approach and architecture, and we compare it with other existing approaches. SLEAP achieves greater accuracy and speeds of more than 800 frames per second, with latencies of less than 3.5 ms at full 1,024 × 1,024 image resolution. This makes SLEAP usable for real-time applications, which we demonstrate by controlling the behavior of one animal on the basis of the tracking and detection of social interactions with another animal. Keywords Animals, Mice, Behavior, Animal, Head, Algorithms, Machine Learning, Social Behavior, Deep Learning Journal Nat Methods Volume 19 Issue 4 Pages 486-495 Date Published 2022 Apr ISSN Number 1548-7105 DOI 10.1038/s41592-022-01426-1 Alternate Journal Nat Methods PMCID PMC9007740 PMID 35379947 PubMedPubMed CentralGoogle ScholarBibTeXEndNote X3 XML