Package: deeplabcut
📚Documentation|🛠️ Installation|🌎 Home Page|🐿🐴🐁🐘🐆 Model Zoo|🚨 News|🪲 Reporting Issues🫶 Getting Assistance|∞ DeepLabCut Online Course|📝 Publications|👩🏾‍💻👨‍💻 DeepLabCut AI ResidencyWelcome! 👋DeepLabCut™️is a toolbox for state-of-the-art markerless pose estimation of animals performing various behaviors. As long as you can see (label) what you want to track, you can use this toolbox, as it is animal and object agnostic.Read a short development and application summary below.Installation: how to install DeepLabCutVery quick start:pip install "deeplabcut[gui,tf]"that includes all functions plus GUIs, orpip install deeplabcut[tf](headless version with PyTorch and TensorFlow).We recommend using our conda file, seehereor the newdeeplabcut-dockerpackage.Documentation: The DeepLabCut ProcessOur docs walk you through using DeepLabCut, and key API points. For an overview of the toolbox and workflow for project management, see our step-by-step atNature Protocols paper.For a deeper understanding and more resources for you to get started with Python and DeepLabCut, please check out our free online course!http://DLCcourse.deeplabcut.orgDEMO the code🐭 demo🐭🐭🐭 demoSeemore demos here. We provide data and several Jupyter Notebooks: one that walks you through a demo dataset to test your installation, and another Notebook to run DeepLabCut from the beginning on your own data. We also show you how to use the code in Docker, and on Google Colab.Why use DeepLabCut?In 2018, we demonstrated the capabilities fortrail tracking,reaching in miceand various Drosophila behaviors during egg-laying (seeMathis et al.for details). There is, however, nothing specific that makes the toolbox only applicable to these tasks and/or species. The toolbox has already been successfully applied (by us and others) torats, humans, various fish species, bacteria, leeches, various robots, cheetahs,mouse whiskersandrace horses. DeepLabCut utilized the feature detectors (ResNets + readout layers) of one of the state-of-the-art algorithms for human pose estimation by Insafutdinov et al., called DeeperCut, which inspired the name for our toolbox (see references below). Since this time, the package has changed substantially.  The code has been re-tooled and re-factored since 2.1+: We have added faster and higher performance variants with MobileNetV2s, EfficientNets, and our own DLCRNet backbones (seePretraining boosts out-of-domain robustness for pose estimationandLauer et al 2022). Additionally, we have improved the inference speed and provided both additional and novel augmentation methods, added real-time, and multi-animal support. We currently provide state-of-the-art performance for animal pose estimation.Left:Due to transfer learning it requireslittle training datafor multiple, challenging behaviors (seeMathis et al. 2018for details).Mid Left:The feature detectors are robust to video compression (seeMathis/Warrenfor details).Mid Right:It allows 3D pose estimation with a single network and camera (seeMathis/Warren).Right:It allows 3D pose estimation with a single network trained on data from multiple cameras together with standard triangulation methods (seeNath* and Mathis* et al. 2019).DeepLabCutis embedding in a larger open-source eco-system, providing behavioral tracking for neuroscience, ecology, medical, and technical applications. Moreover, many new tools are being actively developed. SeeDLC-Utilsfor some helper code.Code contributors:DLC code was originally developed byAlexander Mathis&Mackenzie Mathis, and was extended in 2.0 with the core dev team consisting ofTanmay Nath(2.0-2.1), and currently (2.1+) withJessy Lauerand (2.3+)Niels Poulsen. DeepLabCut is an open-source tool and has benefited from suggestions and edits by many individuals including  Mert Yuksekgonul, Tom Biasi, Richard Warren, Ronny Eichler, Hao Wu, Federico Claudi, Gary Kane and Jonny Saunders as well as the100+ contributors. Please seeAUTHORSfor more details!This is an actively developed package and we welcome community development and involvement.Get Assistance & be part of the DLC Community✨:🚉 Platform🎯 Goal⏱️ Estimated Response Time📢 Support Squad🐭Tag: DeepLabCutTo ask help and support questions👋Promptly🔥DLC Team and The DLC CommunityGitHub DeepLabCut/IssuesTo report bugs and code issues🐛   (we encourage you to search issues first)2-3 daysDLC TeamTo discuss with other users,  share ideas and collaborate💡2 daysThe DLC CommunityGitHub DeepLabCut/ContributingTo contribute your expertise and experience🙏💯Promptly🔥DLC Team🚧 GitHub DeepLabCut/RoadmapTo learn more about our journey✈️N/AN/ATo keep up with our latest news and updates 📢DailyDLC TeamThe DeepLabCutAI Residency ProgramTo come and work with us next summer👏AnnuallyDLC TeamReferences:If you use this code or data we kindly ask that you pleasecite Mathis et al, 2018and, if you use the Python package (DeepLabCut2.x) please also citeNath, Mathis et al, 2019. If you utilize the MobileNetV2s or EfficientNets please citeMathis, Biasi et al. 2021. If you use versions 2.2beta+ or 2.2rc1+, please citeLauer et al. 2022.DOIs (#ProTip, for helping you find citations for software, check outCiteAs.org!):Mathis et al 2018:10.1038/s41593-018-0209-yNath, Mathis et al 2019:10.1038/s41596-019-0176-0Lauer et al 2022:10.1038/s41592-022-01443-0Please check out the following references for more details:@article{Mathisetal2018,
    title = {DeepLabCut: markerless pose estimation of user-defined body parts with deep learning},
    author = {Alexander Mathis and Pranav Mamidanna and Kevin M. Cury and Taiga Abe  and Venkatesh N. Murthy and Mackenzie W. Mathis and Matthias Bethge},
    journal = {Nature Neuroscience},
    year = {2018},
    url = {https://www.nature.com/articles/s41593-018-0209-y}}

 @article{NathMathisetal2019,
    title = {Using DeepLabCut for 3D markerless pose estimation across species and behaviors},
    author = {Nath*, Tanmay and Mathis*, Alexander and Chen, An Chi and Patel, Amir and Bethge, Matthias and Mathis, Mackenzie W},
    journal = {Nature Protocols},
    year = {2019},
    url = {https://doi.org/10.1038/s41596-019-0176-0}}
    
@InProceedings{Mathis_2021_WACV,
    author    = {Mathis, Alexander and Biasi, Thomas and Schneider, Steffen and Yuksekgonul, Mert and Rogers, Byron and Bethge, Matthias and Mathis, Mackenzie W.},
    title     = {Pretraining Boosts Out-of-Domain Robustness for Pose Estimation},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2021},
    pages     = {1859-1868}}
    
@article{Lauer2022MultianimalPE,
    title={Multi-animal pose estimation, identification and tracking with DeepLabCut},
    author={Jessy Lauer and Mu Zhou and Shaokai Ye and William Menegas and Steffen Schneider and Tanmay Nath and Mohammed Mostafizur Rahman and     Valentina Di Santo and Daniel Soberanes and Guoping Feng and Venkatesh N. Murthy and George Lauder and Catherine Dulac and M. Mathis and Alexander Mathis},
    journal={Nature Methods},
    year={2022},
    volume={19},
    pages={496 - 504}}

@article{insafutdinov2016eccv,
    title = {DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model},
    author = {Eldar Insafutdinov and Leonid Pishchulin and Bjoern Andres and Mykhaylo Andriluka and Bernt Schiele},
    booktitle = {ECCV'16},
    url = {http://arxiv.org/abs/1605.03170}}Review & Educational articles:@article{Mathis2020DeepLT,
    title={Deep learning tools for the measurement of animal behavior in neuroscience},
    author={Mackenzie W. Mathis and Alexander Mathis},
    journal={Current Opinion in Neurobiology},
    year={2020},
    volume={60},
    pages={1-11}}

@article{Mathis2020Primer,
    title={A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives},
    author={Alexander Mathis and Steffen Schneider and Jessy Lauer and Mackenzie W. Mathis},
    journal={Neuron},
    year={2020},
    volume={108},
    pages={44-65}}Other open-access pre-prints related to our work on DeepLabCut:@article{MathisWarren2018speed,
    author = {Mathis, Alexander and Warren, Richard A.},
    title = {On the inference speed and video-compression robustness of DeepLabCut},
    year = {2018},
    doi = {10.1101/457242},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2018/10/30/457242},
    eprint = {https://www.biorxiv.org/content/early/2018/10/30/457242.full.pdf},
    journal = {bioRxiv}}License:This project is primarily licensed under the GNU Lesser General Public License v3.0. Note that the software is provided "as is", without warranty of any kind, express or implied. If you use the code or data, please cite us! Note, artwork (DeepLabCut logo) and images are copyrighted; please do not take or use these images without written permission.SuperAnimal models are provided for research use only (non-commercial use).Major Versions:For all versions, please seehere.VERSION 2.3: Model Zoo SuperAnimals, and a whole new GUI experience.VERSION 2.2: Multi-animal pose estimation, identification, and tracking with DeepLabCut is supported (as well as single-animal projects).VERSION 2.0-2.1: This is thePython packageofDeepLabCutthat was originally released in Oct 2018 with ourNature Protocolspaper (preprinthere).
This package includes graphical user interfaces to label your data, and take you from data set creation to automatic behavioral analysis. It also introduces an active learning framework to efficiently use DeepLabCut on large experimental projects, and data augmentation tools that improve network performance, especially in challenging cases (seepanel b).VERSION 1.0: The initial, Nature Neuroscience version ofDeepLabCutcan be found in the history of git, or here:https://github.com/DeepLabCut/DeepLabCut/releases/tag/1.11News (and in the news)::purple_heart: The DeepLabCut Model Zoo launches SuperAnimals, see morehere.:purple_heart:DeepLabCut supports multi-animal pose estimation!maDLC is out of beta/rc mode and beta is deprecated, thanks to the testers out there for feedback! Your labeled data will be backwards compatible, but not all other steps. Please see thenew2.2+releasesfor what's new & how to install it, please see our newpaper, Lauer et al 2022, and thenew docson how to use it!:purple_heart: We support multi-animal re-identification, seeLauer et al 2022.:purple_heart: We have areal-timepackage available!http://DLClive.deeplabcut.orgJanuary 2024: Our original paper'DeepLabCut: markerless pose estimation of user-defined body parts with deep learning'in Nature Neuroscience has surpassed 3,000 Google Scholar citations!December 2023: DeepLabCut hit 600,000 downloads!October 2023: DeepLabCut celebrates a milestone with 4,000 🌟 in Github!July 2023: The user forum is very active with more than 1k questions and answers:May 2023: The Model Zoo is now fully integrated into the DeepLabCut GUI, making it easier than ever to access a variety of pre-trained models. Check out the accompanying paper:SuperAnimal pretrained pose estimation models for behavioral analysis by Ye et al.December 2022: DeepLabCut hits 450,000 downloads and 2.3 is the new stable releaseAugust 2022: DeepLabCut hit 400,000 downloadsAugust 2021: 2.2 becomes the new stable release for DeepLabCut.July 2021: Docs are now athttps://deeplabcut.github.io/DeepLabCutand we now include TensorFlow 2 support!May 2021: DeepLabCut hit 200,000 downloads! Also, Our preprint on 2.2, multi-animal DeepLabCut is released!Jan 2021:Pretraining boosts out-of-domain robustness for pose estimationpublished in the IEEE Winter Conference on Applications of Computer Vision. We also added EfficientNet backbones to DeepLabCut, those are best trained with cosine decay (see paper). To use them, just pass "efficientnet-b0" to "efficientnet-b6" when creating the trainingset!Dec 2020: We released a real-time package that allows for online pose estimation and real-time feedback. SeeDLClive.deeplabcut.org.5/22 2020: We released 2.2beta5. This beta release has some of the features of DeepLabCut 2.2, whose major goal is to integrate multi-animal pose estimation to DeepLabCut.Mar 2020: Inspired by suggestions we heard at this weeks CZI's Essential Open Source Software meeting in Berkeley, CA we updated ourdocs. Let us know what you think!Feb 2020: Ourreview on animal pose estimation is published!Nov 2019: DeepLabCut was recognized by the Chan Zuckerberg Initiative (CZI) with funding to support this project. Read more in theHarvard Gazette, onCZI's Essential Open Source Software for Science siteand in theirMedium postOct 2019: DLC 2.1 released with lots of updates. In particular, a Project Manager GUI, MobileNetsV2, and augmentation packages (Imgaug and Tensorpack). For detailed updates seereleasesSept 2019: We published two preprints. One showing thatImageNet pretraining contributes to robustnessand areview on animal pose estimation. Check them out!Jun 2019: DLC 2.0.7 released with lots of updates. For updates seereleasesFeb 2019: DeepLabCut joinedtwitterJan 2019: We hosted workshops for DLC in Warsaw, Munich and Cambridge. The materials are availablehereJan 2019: We joined the Image Source Forum for user help:Nov 2018: We posted a detailed guide for DeepLabCut 2.0 onBioRxiv. It also contains a case study for 3D pose estimation in cheetahs.Nov 2018: Various (post-hoc) analysis scripts contributed by users (and us) will be gathered atDLCutils. Feel free to contribute! In particular, there is a script guiding you through
importing a project into the new data format for DLC 2.0Oct 2018: new pre-print on the speed video-compression and robustness of DeepLabCut onBioRxivSept 2018: Nature Lab Animal covers DeepLabCut:Behavior tracking cuts deepKunlin Wei & Konrad Kording write a very nice News & Views on our paper:Behavioral Tracking Gets RealAugust 2018: Ourpreprintappeared inNature NeuroscienceAugust 2018: NVIDIA AI Developer News:AI Enables Markerless Animal TrackingJuly 2018: Ed Yong covered DeepLabCut and interviewed several users for theAtlantic.April 2018: first DeepLabCut preprint onarXiv.org