sleap
pypi i sleap

sleap

A deep learning framework for multi-animal pose tracking.

by talmolab

1.2.6 (see all)License:BSD 3-Clause License
pypi i sleap
Readme

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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. DUva, 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 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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  • 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>_

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