Social LEAP Estimates Animal Poses (SLEAP)

.. image:: https://sleap.ai/docs/_static/sleap_movie.gif :width: 600px

SLEAP is an open source deep-learning based framework for multi-animal pose tracking (Pereira et al., Nature Methods, 2022) <https://www.nature.com/articles/s41592-022-01426-1>__. It can be used to track any type or number of animals and includes an advanced labeling/training GUI for active learning and proofreading.

Features

Easy, one-line installation with support for all OSes
Purpose-built GUI and human-in-the-loop workflow for rapidly labeling large datasets
Single- and multi-animal pose estimation with top-down and bottom-up training strategies
State-of-the-art pretrained and customizable neural network architectures that deliver accurate predictions with very few labels
Fast training: 15 to 60 mins on a single GPU for a typical dataset
Fast inference: up to 600+ FPS for batch, <10ms latency for realtime
Support for remote training/inference workflow (for using SLEAP without GPUs)
Flexible developer API for building integrated apps and customization

Get some SLEAP

SLEAP is installed as a Python package. We strongly recommend using Miniconda <https://https://docs.conda.io/en/latest/miniconda.html>_ to install SLEAP in its own environment.

You can find the latest version of SLEAP in the Releases <https://github.com/talmolab/sleap/releases>_ page.

Quick install ^^^^^^^^^^^^^ conda (Windows/Linux/GPU):

.. code-block:: bash

conda create -y -n sleap -c conda-forge -c nvidia -c sleap -c anaconda sleap

pip (any OS except Apple silicon):

.. code-block:: bash

pip install sleap[pypi]

See the docs for full installation instructions <https://sleap.ai/installation.html>_.

Learn to SLEAP

Learn step-by-step: Tutorial <https://sleap.ai/tutorials/tutorial.html>_
Learn more advanced usage: Guides <https://sleap.ai/guides/> and Notebooks <https://sleap.ai/notebooks/>
Learn by watching: ABL:AOC 2023 Workshop <https://www.youtube.com/watch?v=BfW-HgeDfMI> and MIT CBMM Tutorial <https://cbmm.mit.edu/video/decoding-animal-behavior-through-pose-tracking>
Learn by reading: Paper (Pereira et al., Nature Methods, 2022) <https://www.nature.com/articles/s41592-022-01426-1>_ and Review on behavioral quantification (Pereira et al., Nature Neuroscience, 2020) <https://rdcu.be/caH3H>
Learn from others: Discussions on Github <https://github.com/talmolab/sleap/discussions>_

References

SLEAP is the successor to the single-animal pose estimation software LEAP <https://github.com/talmo/leap> (Pereira et al., Nature Methods, 2019 <https://www.nature.com/articles/s41592-018-0234-5>).

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 <https://www.nature.com/articles/s41592-022-01426-1>`__. *Nature Methods*, 19(4), 2022

BibTeX:

.. code-block::

@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} } }

Contact

Follow @talmop <https://twitter.com/talmop>_ on Twitter for news and updates!

Technical issue with the software?

Check the Help page <https://sleap.ai/help.html>_.
Ask the community via discussions on Github <https://github.com/talmolab/sleap/discussions>_.
Search the issues on GitHub <https://github.com/talmolab/sleap/issues>_ or open a new one.

General inquiries? Reach out to talmo@salk.edu.

.. _Contributors:

Contributors

Talmo Pereira, Salk Institute for Biological Studies
Liezl Maree, Salk Institute for Biological Studies
Arlo Sheridan, Salk Institute for Biological Studies
Arie Matsliah, Princeton Neuroscience Institute, Princeton University
Nat Tabris, Princeton Neuroscience Institute, Princeton University
David Turner, Research Computing and Princeton Neuroscience Institute, Princeton University
Joshua Shaevitz, Physics and Lewis-Sigler Institute, Princeton University
Mala Murthy, Princeton Neuroscience Institute, Princeton University

SLEAP was created in the Murthy <https://murthylab.princeton.edu> and Shaevitz <https://shaevitzlab.princeton.edu> labs at the Princeton Neuroscience Institute <https://pni.princeton.edu>_ at Princeton University.

SLEAP is currently being developed and maintained in the Talmo Lab <https://talmolab.org> at the Salk Institute for Biological Studies <https://salk.edu>, 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 NS104899
Princeton Innovation Accelerator Fund

License

SLEAP is released under a Clear BSD License <https://raw.githubusercontent.com/talmolab/sleap/main/LICENSE>_ and 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.

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talmolab / sleap

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