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DeepLabCut™ is 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.

Where do you start?

The code is freely available and easy to install in a few clicks with Anaconda. Please see instructions here. We also provide a very easy to use GUI interface, and a step-by-step user guide!

Open Source Code: 
https://github.com/AlexEMG/DeepLabCut
Publications:
Mathis et al, Nature Neuroscience 2018 or free link: rdcu.be/4Rep
Nath*, Mathis* et al, Nature Protocols 2019 or free link: https://rdcu.be/bHpHN

Preprints:

[1] arXiv (April 2018): Markerless tracking of user-defined features with deep learning (published in Nature Neuroscience)

[2] bioRxiv(Oct 2018):On the inference speed and video-compression robustness of DeepLabCut
(presented at COSYNE 2019)

[3] bioRxiv (Nov 2018): Using DeepLabCut for 3D markerless pose estimation across species and behaviors (published in Nature Protocols)

[4] arXiv (Sept 2019): Pretraining boosts out-of-domain robustness for pose estimation

[5] arXiv (Oct 2019): Deep learning tools for the measurement of animal behavior in neuroscience (published in Current Opinion in Neurobiology)

Get Inspired! Check out who is citing us:

Are you using DeepLabCut?

Please give us feedback (or just a shout out that you are using it) to help us support the project. We appreciate it! Join the 200+ Universities that have added their name!


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


For general inquiries: admin@deeplabcut.org

Reach a current main developer:

Alexander Mathis - alexander@deeplabcut.org
Mackenzie Mathis - mackenzie@deeplabcut.org
Jessy Lauer - jessy@deeplabcut.org

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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 to launch your environment with DeepLabCut already installed!

*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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