Rxivist logo

Using DeepLabCut for 3D markerless pose estimation across species and behaviors

By Tanmay Nath, Mackenzie Weygandt Mathis, An Chi Chen, Amir Patel, Matthias Bethge

Posted 24 Nov 2018
bioRxiv DOI: 10.1101/476531 (published DOI: 10.1038/s41596-019-0176-0)

Noninvasive behavioral tracking of animals during experiments is crucial to many scientific pursuits. Extracting the poses of animals without using markers is often essential for measuring behavioral effects in biomechanics, genetics, ethology & neuroscience. Yet, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open source toolbox called DeepLabCut that builds on a state-of-the-art human pose estimation algorithm to allow a user to train a deep neural network using limited training data to precisely track user-defined features that matches human labeling accuracy. Here, with this paper we provide an updated toolbox that is self contained within a Python package that includes new features such as graphical user interfaces and active-learning based network refinement. Lastly, we provide a step-by-step guide for using DeepLabCut.

Download data

  • Downloaded 18,548 times
  • Download rankings, all-time:
    • Site-wide: 78 out of 84,201
    • In neuroscience: 9 out of 14,992
  • Year to date:
    • Site-wide: 341 out of 84,201
  • Since beginning of last month:
    • Site-wide: 545 out of 84,201

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News