Mathis et al, Nature Neuroscience 2018 rdcu.be/4Rep
Nath*, Mathis* et al, Nature Protocols 2019 https://rdcu.be/bHpHN
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 3D 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
#1 arXiv preprint (April 2018): https://arxiv.org/abs/1804.03142v1 (Now published in Nature Neuroscience)
#2 bioRxiv #1 (Oct 2018): On the inference speed and video-compression robustness of DeepLabCut
#3 bioRxiv #2 (Nov 2018): Using DeepLabCut for 3D markerless pose estimation across species and behaviors (now published in Nature Protocols)
For more information:
Alexander Mathis - email@example.com
Mackenzie Mathis - firstname.lastname@example.org
Example use cases:
(click on the image to see more details and other use cases!)
watch tutorials here!
*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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