Introduction to DeepLabCut#

DeepLabCut is a toolbox for markerless pose estimation of animals performing various tasks.

Documentation: https://deeplabcut.github.io/DeepLabCut/docs/intro.html
Sourcecode: DeepLabCut/DeepLabCut

DeepLabCut is an open source package for markerless pose estimation based on transfer learning with deep neural networks (Mathis et al., 2018). It uses algorithms from DeeperCut, and an extremely deep neural network pre-trained on a dataset for object detection (ImageNet). DeepLabCut was originally developed for animal pose estimation, which does not exclude humans, see Namba et al. (2021), and can also be used to track inanimate objects.

_images/dlcworkflow.PNG

Fig. 22 DeepLabCut workflow from Nath et al. 2019.#

Installation#

DeepLabCut will need to use python and several python related libraries (i.e, a specific python environment), so the first step should be installing Anaconda.
You can then create a designated environment and install DeepLabCut:

  1. Download this installation file

  2. Open Terminal and execute:

    • cd Downloads

    • conda env create -f DEEPLABCUT.yaml

    • conda activate DEEPLABCUT

    • pip install --upgrade deeplabcut

Documentation#

Check the DeepLabCut documentation for more background and tips on how to get started.