SLEAP (Social LEAP Estimates Animal Poses) is a deep learning framework for animal pose tracking.
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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.
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>_.
Tutorial <https://sleap.ai/tutorials/tutorial.html>_Guides <https://sleap.ai/guides/>__ and Notebooks <https://sleap.ai/notebooks/>__MIT CBMM Tutorial <https://cbmm.mit.edu/video/decoding-animal-behavior-through-pose-tracking>_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>_Discussions on Github <https://github.com/talmolab/sleap/discussions>_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} } }
Follow @talmop <https://twitter.com/talmop>_ on Twitter for news and updates!
Technical issue with the software?
Help page <https://sleap.ai/help.html>_.discussions on Github <https://github.com/talmolab/sleap/discussions>_.issues on GitHub <https://github.com/talmolab/sleap/issues>_ or open a new one.General inquiries?
Reach out to talmo@salk.edu.
.. _Contributors:
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:
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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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>_FAQs
SLEAP (Social LEAP Estimates Animal Poses) is a deep learning framework for animal pose tracking.
We found that sleap demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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