Package: sleap
Social LEAP Estimates Animal Poses (SLEAP)SLEAPis an open source deep-learning based framework for multi-animal pose tracking(Pereira et al., Nature Methods, 2022). It can be used to track any type or number of animals and includes an advanced labeling/training GUI for active learning and proofreading.FeaturesEasy, one-line installation with support for all OSesPurpose-built GUI and human-in-the-loop workflow for rapidly labeling large datasetsSingle- and multi-animal pose estimation withtop-downandbottom-uptraining strategiesState-of-the-art pretrained and customizable neural network architectures that deliveraccurate predictionswithvery fewlabelsFast training: 15 to 60 mins on a single GPU for a typical datasetFast inference: up to 600+ FPS for batch, <10ms latency for realtimeSupport for remote training/inference workflow (for using SLEAP without GPUs)Flexible developer API for building integrated apps and customizationGet some SLEAPSLEAP is installed as a Python package. We strongly recommend usingMinicondato install SLEAP in its own environment.You can find the latest version of SLEAP in theReleasespage.Quick installconda(Windows/Linux/GPU):condacreate-y-nsleap-cconda-forge-cnvidia-csleap-canacondasleappip(any OS except Apple silicon):pipinstallsleap[pypi]See the docs forfull installation instructions.Learn to SLEAPLearn step-by-step:TutorialLearn more advanced usage:GuidesandNotebooksLearn by watching:MIT CBMM TutorialLearn by reading:Paper (Pereira et al., Nature Methods, 2022)andReview on behavioral quantification (Pereira et al., Nature Neuroscience, 2020)Learn from others:Discussions on GithubReferencesSLEAP is the successor to the single-animal pose estimation softwareLEAP(Pereira et al., Nature Methods, 2019).If you use SLEAP in your research, please cite:T.D. Pereira, N. Tabris, A. Matsliah, D. M. Turner, J. Li, S. Ravindranath, E. S. Papadoyannis, E. Normand, D. S. Deutsch, Z. Y. Wang, G. C. McKenzie-Smith, C. C. Mitelut, M. D. Castro, J. D’Uva, M. Kislin, D. H. Sanes, S. D. Kocher, S. S-H, A. L. Falkner, J. W. Shaevitz, and M. Murthy.Sleap: A deep learning system for multi-animal pose tracking.Nature Methods, 19(4), 2022BibTeX:@ARTICLE{Pereira2022sleap,
   title={SLEAP: A deep learning system for multi-animal pose tracking},
   author={Pereira, Talmo D and
      Tabris, Nathaniel and
      Matsliah, Arie and
      Turner, David M and
      Li, Junyu and
      Ravindranath, Shruthi and
      Papadoyannis, Eleni S and
      Normand, Edna and
      Deutsch, David S and
      Wang, Z. Yan and
      McKenzie-Smith, Grace C and
      Mitelut, Catalin C and
      Castro, Marielisa Diez and
      D'Uva, John and
      Kislin, Mikhail and
      Sanes, Dan H and
      Kocher, Sarah D and
      Samuel S-H and
      Falkner, Annegret L and
      Shaevitz, Joshua W and
      Murthy, Mala},
   journal={Nature Methods},
   volume={19},
   number={4},
   year={2022},
   publisher={Nature Publishing Group}
   }
}ContactFollow@talmopon Twitter for news and updates!Technical issue with the software?Check theHelp page.Ask the community viadiscussions on Github.Search theissues on GitHubor open a new one.General inquiries?Reach out totalmo@salk.edu.ContributorsTalmo Pereira, Salk Institute for Biological StudiesLiezl Maree, Salk Institute for Biological StudiesArlo Sheridan, Salk Institute for Biological StudiesArie Matsliah, Princeton Neuroscience Institute, Princeton UniversityNat Tabris, Princeton Neuroscience Institute, Princeton UniversityDavid Turner, Research Computing and Princeton Neuroscience Institute, Princeton UniversityJoshua Shaevitz, Physics and Lewis-Sigler Institute, Princeton UniversityMala Murthy, Princeton Neuroscience Institute, Princeton UniversitySLEAP was created in theMurthyandShaevitzlabs at thePrinceton Neuroscience Instituteat Princeton University.SLEAP is currently being developed and maintained in theTalmo Labat theSalk Institute for Biological Studies, in collaboration with the Murthy and Shaevitz labs at Princeton University.This work was made possible through our funding sources, including:NIH BRAIN Initiative R01 NS104899Princeton Innovation Accelerator FundLicenseSLEAP is released under aClear BSD Licenseand is intended for research/academic use only. For commercial use, please contact: Laurie Tzodikov (Assistant Director, Office of Technology Licensing), Princeton University, 609-258-7256.LinksDocumentation HomepageOverviewInstallationTutorialGuidesNotebooksDeveloper APIHelp