DeepLabCut 3.0: Efficient deep learning for single and multi-animal pose tracking and identification
Niels Poulsen, Anastasiia Filipova, Shaokai Ye, Lucas Stoffl, Mu Zhou, Quentin Mace, Konrad Danielewski, Anna Teruel-Sanchis, Riza Rae Pineda, Jessy Lauer, Timokleia Kousi, Alexander Mathis, Mackenzie Weygandt Mathis · FENS Forum 2024
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. DeepLabCut was the first tool to innovate in this space, and since the initial publication (Mathis et al. Nature Neuroscience 2018) has been downloaded more than half a million times. While the code-base was actively developed, after 6 years it is time for a major release. Here we present DeepLabCut 3.0, which builds on
ePoster · Neuroscience