Mathis et al, Nature Neuroscience 2018 rdcu.be/4Rep

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.

Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results (i.e. you can match human labeling accuracy) with minimal training data (typically 50-200 frames). We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors.

The package is open source, fast, robust, and can be used to compute 3D pose estimates. Please see the original paper and the latest work below.

Open Source Code: https://github.com/AlexEMG/DeepLabCut

Original arXiv preprint (April 2018): https://arxiv.org/abs/1804.03142v1

New preprints!
BioRxiv #1 (Oct 2018): On the inference speed and video-compression robustness of DeepLabCut
BioRxiv #2 (Nov 2018): Using DeepLabCut for 3D markerless pose estimation across species and behaviors

For more information:
Alexander Mathis - alexander.mathis@bethgelab.org
Mackenzie Mathis - mackenzie@post.harvard.edu

Example use cases:
(click on the image to see more details and other use cases!)


Join the user community! https://forum.image.sc/tags/deeplabcut

Please do not take images or videos from this website without providing credit to the authors of the videos!

Easy Install on MacOS, Windows, & Ubuntu!
Simply have Anaconda installed, click to download the appropriate file below and follow these instructions*!

*Launch the program “cmd” (Windows) or “terminal” (Mac) and in the folder where you downloaded the file type:

conda env create -f dlc-macOS-CPU.yaml

conda env create -f dlc-windowsCPU.yaml

conda env create -f dlc-windowsGPU.yaml

conda env create -f dlc-ubuntu-CPU.yaml

conda env create -f dlc-ubuntu-GPU.yaml

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