sleap icon indicating copy to clipboard operation
sleap copied to clipboard

Published 20 hours ago • verified publisher icon talmolab
→

Metadata

A deep learning framework for multi-animal pose tracking.

|CI| |Coverage| |Documentation| |Downloads| |Conda Downloads| |Stable version| |Latest version|

.. |CI| image:: https://github.com/talmolab/sleap/workflows/CI/badge.svg?event=push&branch=develop :target: https://github.com/talmolab/sleap/actions?query=workflow:CI :alt: Continuous integration status

.. |Coverage| image:: https://codecov.io/gh/talmolab/sleap/branch/develop/graph/badge.svg?token=oBmTlGIQRn :target: https://codecov.io/gh/talmolab/sleap :alt: Coverage

.. |Documentation| image:: https://img.shields.io/github/workflow/status/talmolab/sleap/Build%20website?label=Documentation :target: https://sleap.ai :alt: Documentation

.. |Downloads| image:: https://static.pepy.tech/personalized-badge/sleap?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=PyPI%20Downloads :target: https://pepy.tech/project/sleap :alt: Downloads

.. |Conda Downloads| image:: https://img.shields.io/conda/dn/sleap/sleap?label=Conda%20Downloads :target: https://anaconda.org/sleap/sleap :alt: Conda Downloads

.. |Stable version| image:: https://img.shields.io/github/v/release/talmolab/sleap?label=stable :target: https://github.com/talmolab/sleap/releases/ :alt: Stable version

.. |Latest version| image:: https://img.shields.io/github/v/release/talmolab/sleap?include_prereleases&label=latest :target: https://github.com/talmolab/sleap/releases/ :alt: Latest version

.. start-inclusion-marker-do-not-remove

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. 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 sleap -c nvidia -c conda-forge sleap

pip (any OS):

.. code-block:: bash

pip install sleap

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: 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?

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

General inquiries? Reach out to [email protected].

.. _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.

.. end-inclusion-marker-do-not-remove

Links

  • Documentation Homepage <https://sleap.ai>_
  • Overview <https://sleap.ai/overview.html>_
  • Installation <https://sleap.ai/installation.html>_
  • Tutorial <https://sleap.ai/tutorials/tutorial.html>_
  • Guides <https://sleap.ai/guides/index.html>_
  • Notebooks <https://sleap.ai/notebooks/index.html>_
  • Developer API <https://sleap.ai/api.html>_
  • Help <https://sleap.ai/help.html>_

← Metadata

393
Stars
93
Forks
Watchers

Owner

verified publisher icon talmolab

Metadata

A deep learning framework for multi-animal pose tracking.

Back