talmolab/sleap
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|CI| |Coverage| |Documentation| |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=Downloads :target: https://pepy.tech/project/sleap :alt: 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. <<<<<<< HEAD ======= >>>>>>> origin/main **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. .. 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>`_