Welcome! 👋

Welcome! 👋#

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 DeepLabCut #

Please click the link above for all the information you need to get started! Please note that currently we support only Python 3.10+ (see conda files for guidance).

Developers Stable Release:

Very quick start: You need to have TensorFlow installed (up to v2.10 supported across platforms) pip install "deeplabcut[gui,tf]" that includes all functions plus GUIs, or pip install deeplabcut[tf] (headless version with PyTorch and TensorFlow).

Developers Alpha Release:

We also have an alpha release of PyTorch DeepLabCut available! Please see here for instructions and information.

We recommend using our conda file, see here or the new deeplabcut-docker package.

Documentation: The DeepLabCut Process #

Our 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 at Nature 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.org

DEMO the code #

🐭 pose tracking of single animals demo

🐭🐭🐭 pose tracking of multiple animals demo

See more 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 for trail tracking, reaching in mice and various Drosophila behaviors during egg-laying (see Mathis 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) to rats, humans, various fish species, bacteria, leeches, various robots, cheetahs, mouse whiskers and race 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 (see Pretraining boosts out-of-domain robustness for pose estimation and Lauer 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. In v3.0+ we have changed the backend to support PyTorch. This brings not only an easier installation process for users, but performance gains, developer flexibility, and a lot of new tools! Importantly, the high-level API stays the same, so it will be a seamless transition for users 💜! We currently provide state-of-the-art performance for animal pose estimation and the labs (M. Mathis Lab and A. Mathis Group) have both top journal and computer vision conference papers.

Left: Due to transfer learning it requires little training data for multiple, challenging behaviors (see Mathis et al. 2018 for details). Mid Left: The feature detectors are robust to video compression (see Mathis/Warren for details). Mid Right: It allows 3D pose estimation with a single network and camera (see Mathis/Warren). Right: It allows 3D pose estimation with a single network trained on data from multiple cameras together with standard triangulation methods (see Nath* and Mathis* et al. 2019).

DeepLabCut is 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. See DLC-Utils for some helper code.

Code contributors:#

DLC code was originally developed by Alexander Mathis & Mackenzie Mathis, and was extended in 2.0 with the core dev team consisting of Tanmay Nath (2.0-2.1), and currently (2.1+) with Jessy Lauer and (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 the 100+ contributors. Please see AUTHORS for 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: DeepLabCut	To ask help and support questions👋	Promptly🔥	DLC Team and The DLC Community
GitHub DeepLabCut/Issues	To report bugs and code issues🐛 (we encourage you to search issues first)	2-3 days	DLC Team
	To discuss with other users, share ideas and collaborate💡	2 days	The DLC Community
GitHub DeepLabCut/Contributing	To contribute your expertise and experience🙏💯	Promptly🔥	DLC Team
🚧 GitHub DeepLabCut/Roadmap	To learn more about our journey✈️	N/A	N/A
	To keep up with our latest news and updates 📢	Daily	DLC Team
The DeepLabCut AI Residency Program	To come and work with us next summer👏	Annually	DLC Team

References & Citations:#

Please see our dedicated page on how to cite DeepLabCut 🙏 and our sugestions for your Methods section!

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 see here.

VERSION 3.0: A whole new experience with PyTorch🔥. While the high-level API remains the same, the backend and developer friendliness have greatly improved, along with performance gains!

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 the Python package of DeepLabCut that was originally released in Oct 2018 with our Nature Protocols paper (preprint here). 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 (see panel b).

VERSION 1.0: The initial, Nature Neuroscience version of DeepLabCut can be found in the history of git, or here: DeepLabCut/DeepLabCut

News (and in the news):#

:purple_heart: We released a major update, moving from 2.x –> 3.x with the backend change to PyTorch

:purple_heart: The DeepLabCut Model Zoo launches SuperAnimals, see more here.

: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 the new 2.2+ releases for what’s new & how to install it, please see our new paper, Lauer et al 2022, and the new docs on how to use it!

:purple_heart: We support multi-animal re-identification, see Lauer et al 2022.

:purple_heart: We have a real-time package available! http://DLClive.deeplabcut.org

June 2024: Our second DLC paper ‘Using DeepLabCut for 3D markerless pose estimation across species and behaviors’ in Nature Protocols has surpassed 1,000 Google Scholar citations!
May 2024: DeepLabCut was featured in Nature: ‘DeepLabCut: the motion-tracking tool that went viral’
January 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 release
August 2022: DeepLabCut hit 400,000 downloads
August 2021: 2.2 becomes the new stable release for DeepLabCut.
July 2021: Docs are now at https://deeplabcut.github.io/DeepLabCut and 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 estimation published 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. See DLClive.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 our docs. Let us know what you think!
Feb 2020: Our review 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 the Harvard Gazette, on CZI’s Essential Open Source Software for Science site and in their Medium post
Oct 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 see releases
Sept 2019: We published two preprints. One showing that ImageNet pretraining contributes to robustness and a review on animal pose estimation. Check them out!
Jun 2019: DLC 2.0.7 released with lots of updates. For updates see releases
Feb 2019: DeepLabCut joined twitter
Jan 2019: We hosted workshops for DLC in Warsaw, Munich and Cambridge. The materials are available here
Jan 2019: We joined the Image Source Forum for user help:
Nov 2018: We posted a detailed guide for DeepLabCut 2.0 on BioRxiv. 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 at DLCutils. Feel free to contribute! In particular, there is a script guiding you through importing a project into the new data format for DLC 2.0
Oct 2018: new pre-print on the speed video-compression and robustness of DeepLabCut on BioRxiv
Sept 2018: Nature Lab Animal covers DeepLabCut: Behavior tracking cuts deep
Kunlin Wei & Konrad Kording write a very nice News & Views on our paper: Behavioral Tracking Gets Real
August 2018: Our preprint appeared in Nature Neuroscience
August 2018: NVIDIA AI Developer News: AI Enables Markerless Animal Tracking
July 2018: Ed Yong covered DeepLabCut and interviewed several users for the Atlantic.
April 2018: first DeepLabCut preprint on arXiv.org

Funding

We are grateful for the follow support over the years! This software project was supported in part by the Essential Open Source Software for Science (EOSS) program at Chan Zuckerberg Initiative (cycles 1, 3, 3-DEI, 4), and jointly with the Kavli Foundation for EOSS Cycle 6! We also thank the Rowland Institute at Harvard for funding from 2017-2020, and EPFL from 2020-present.