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Social LEAP Estimates Animal Poses (SLEAP)

Overview

SLEAP, or Social LEAP Estimates Animal Poses, is an open source deep-learning based framework for multi animal pose tracking. By using neural networks to locate and associate user defined anatomical structures on unmarked animals, it allows the model to predict and track individual body part kinematics.

Availability

Cluster Module/Version
BOSE sleap/1.4.1
BGSC Not Available

Note: You can simply use "module load sleap" to activate the most recently installed version of this software.

How To Access

Access to Sleap is best done by using Desktop Mode in our web-based Open OnDemand platform, which lets you access our computing infrastructure using your web brower.

Note: Ensure that you've selected the 'Yes - Use GPU Card' field to significantly reduce model efficiency.

Desktop Mode

  1. Log into Open OnDemand - https://ondemand.hpc.uwec.edu
  2. Click "Desktop" on the dashboard, or by first clicking "Interactive Apps" in the top bar.
  3. Fill out your required resources to the best of your abilities. Unsure what to use?
  4. Wait for the job to start, then click "Launch Desktop"
  5. Start the terminal by clicking on the black square icon in the top bar, or by going to Applications --> System Tools --> MATE Terminal"
  6. Type: module load sleap
  7. Type: sleap-label to launch the SLEAP UI

Real Example

Has your research group used SLEAP in a project? Contact the HPC Team and we'd be glad to feature your work.

Citation

Please include the following citation in your papers to support continued development of SLEAP.

Pereira, T.D., Tabris, N., Matsliah, A., Turner, D.M., Li, J., Ravindranath, S., Papadoyannis, E.S., Normand, E., Deutsch, D.S., Wang, Z.Y., McKenzie-Smith, G.C., Mitelut, C.C., Diez Castro, M., D'Uva, J., Kislin, M., Sanes, D.H., Kocher, S.D., Samuel, S.-H., Falkner, A.L., Shaevitz, J.W., and Murthy, M., "SLEAP: A deep learning system for multi-animal pose tracking", Nature Methods, 2022, vol. 19, no. 4.

Resources