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DeepLabCut

  • Description

    is an open-source Python toolbox designed for high-performance, markerless pose estimation of animals and objects using deep learning. By leveraging transfer learning, the software allows researchers to achieve human-level tracking accuracy with remarkably little training data—typically only 50 to 200 manually labeled frames. It features a user-friendly graphical interface (GUI) and support for both 2D and 3D tracking across various species and behaviors, making it a standard tool in fields like neuroscience, ethology, and biomechanics.

  • Usage

    To list all executables provided by DeepLabCut, run: $ biogrids-list deeplabcut Copy to clipboard

  • Usage Notes

    DeepLabCut in BioGrids provides:

    python.deeplabcut

    ipython.deeplabcut

    jupyter.deeplabcut

  • Installation

    Use the following command to install this title with the CLI client: $ biogrids-cli install deeplabcut Copy to clipboard Available operating systems: Linux 64

  • Primary Citation*

    A. Mathis, P. Mamidanna, K. M. Cury, T. Abe, V. N. Murthy, M. W. Mathis, and M. Bethge. 2018. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nature neuroscience. 21(9): 1281-1289.


    • *Full citation information available through

  • Keywords

    Visualization

  • Default Versions

    Linux 64:  v3.0.0rc10 (11.5 GB)

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