Social LEAP Estimates Animal Poses (SLEAP)

Type: Software,

Keywords: Academic software, Deep learning Software, Neural network, Anatomical landmark, BRAIN Initiative

Deep learning framework for estimating animal pose, Deep learning software framework for general purpose multi-animal limb tracking from video

SLEAP (Social LEAP Estimates Animal Poses) is a deep learning software framework for general purpose multi-animal limb tracking from video. This software couples a GUI for importing and annotating data with deep neural networks designed for learning to locate and associate user-specified anatomical landmarks on unmarked animals. Use cases range from kinematic studies of animal movement, to quantification of social dynamics via multi-animal part tracking.

* AI-driven multi-animal pose estimation software
* Open source software package. *Python-based
* Tracks individual body part kinematics of single or multiple interacting animals
* Supports multi-animal pose estimation, animal instance tracking
* Labeling/training GUI supports active learning
* Includes a graphical proof-reading tool to quickly assess the accuracy of tracking and correcting problems

* Framework for multi-animal body part position estimation via deep learning
* Tracking of individual body part kinematics of multiple animals
* High throughput analysis of animal behaviors (e.g., drug screens)
* Development of disease diagnostics for animal models
* Wildlife conservation and monitoring of animal well-being (e.g., agrotech)

* Mice, flies, bees (compatible with any animal)

* Learns from few user examples
* Fast training of deep neural networks on consumer hardware
* Highly accurate and robust to variability in imaging conditions
* Single or multiple animal tracking
* No programming skills required

* For research/academic use only

* Compatible with Python versions 3.6 and above, with support for Windows and Linux
* Mac OS X works but without GPU support
* Remote cloud/cluster-based training and inference

* Pereira et al. 2018, Fast animal pose estimation using deep neural networks, Nature Methods 16: 117–125

* Pereira et al. 2020, SLEAP: Multi-animal pose tracking, bioRxiv

* https://sleap.ai
* https://pypi.org/project/sleap/
* https://www.nature.com/articles/s41592-018-0234-5
* https://www.biorxiv.org/content/10.1101/2020.08.31.276246v1.abstract

* Recommended to use the Anaconda Python distribution for Windows
* Pip package available for all systems

CONTACT NAME, POSITION

Talmo Pereira (PhD Candidate)
Mala Murthy
Joshua Shaevitz (Professor)

ORGANIZATION

Princeton

CONTACT INFORMATION

TEAM / COLLABORATOR(S)

Mala Murthy
Talmo Pereira
Nat Tabris
David Turner
Joshua Shaevitz

WEBSITE(S)

FUNDING SOURCE(S)

NIH BRAIN Initative R01 NS104899
Princeton Innovation Accelerator Fund (NSF GRFP, NIH R01, HHMI)