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DeepLabCut (www.deeplabcut.org) was initially developed by
Alexander & Mackenzie Mathis in collaboration with Matthias Bethge.
It is actively developed by Alexander & Mackenzie Mathis (steering council and owners).
DeepLabCut is an open-source tool and has benefited from suggestions and edits by many
individuals: DeepLabCut/graphs/contributors
############################################################################################################
DeepLabCut 1.0 Toolbox
A Mathis, alexander.mathis@bethgelab.org | https://github.com/DeepLabCut/DeepLabCut
M Mathis, mackenzie@post.harvard.edu | https://github.com/MMathisLab
Specific external contributors:
E Insafutdinov and co-authors of DeeperCut (see README) for feature detectors: https://github.com/eldar
- Thus, code in this subdirectory at the time of April 2018, deeplabcut/pose_estimation_tensorflow
was adapted from: https://github.com/eldar/pose-tensorflow.
Products:
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience, 2018.
https://doi.org/10.1038/s41593-018-0209-y
A. Mathis, P. Mamidanna, K.M. Cury, T. Abe, V.N. Murthy, M.W. Mathis* & M. Bethge*
Contributions:
Conceptualization: A.M., M.W.M. and M.B.
Software: A.M. and M.W.M.
Formal analysis: A.M.
Experiments: A.M. and V.N.M. (trail-tracking), M.W.M. (mouse reaching), K.M.C. (Drosophila).
Image Labeling: P.M., K.M.C., T.A., M.W.M., A.M.
Writing: A.M. and M.W.M. with input from all authors.
These authors jointly directed this work: M. Mathis, M. Bethge
############################################################################################################
DeepLabCut 2.0 Toolbox
A Mathis, alexander.mathis@bethgelab.org | https://github.com/DeepLabCut/DeepLabCut
T Nath, nath@rowland.harvard.edu | https://github.com/meet10may
M Mathis, mackenzie@post.harvard.edu | https://github.com/MMathisLab
Products:
Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nature Protocols, 2019.
https://www.nature.com/articles/s41596-019-0176-0
T. Nath*, A. Mathis*, AC. Chen, A. Patel, M. Bethge, M. Mathis
Contributions:
Conceptualization: AM, TN, MWM.
Software: AM, TN and MWM.
Dataset (cheetah): AP.
Image Labeling: ACC.
Formal analysis: ACC, AM and AP analyzed the cheetah data.
Writing: MWM, AM and TN with inputs from all authors.
############################################################################################################
DeepLabCut 2.1 major additions:
A Mathis, alexander.mathis@bethgelab.org | https://github.com/DeepLabCut/DeepLabCut
T Nath, nath@rowland.harvard.edu | https://github.com/meet10may
M Yüksekgönül, mertyuksekgonul@gmail.com | https://github.com/mertyg
M Mathis, mackenzie@post.harvard.edu | https://github.com/MMathisLab
Specific external contributors:
Tensorpack augmentation: https://github.com/DeepLabCut/DeepLabCut/pull/409 by Katie Rupp
Products:
Pretraining boosts out-of-domain robustness for pose estimation. WACV, 2021.
http://www.mackenziemathislab.org/horse10
A. Mathis, T. Biasi, S. Schneider, M. Yüksekgönül, B. Rogers, M. Bethge, M. Mathis
############################################################################################################
DeepLabCut 2.1 - 2.2 additions:
A Mathis, alexander.mathis@epfl.ch | https://github.com/AlexEMG
J Lauer, jessy@deeplabcut.org | https://github.com/jeylau
M Mathis, mackenzie@post.harvard.edu | https://github.com/MMathisLab
M Zhou, https://github.com/zhoumu53
S Ye, https://github.com/yeshaokai
S Schneider, https://github.com/stes
T Biasi, https://github.com/tbiasi
G Kane, https://github.com/gkane26
M Yüksekgönül, https://github.com/mertyg
T Nath, https://github.com/meet10may
Preprint:
Multi-animal pose estimation and tracking with DeepLabCut
J Lauer, M Zhou, S Ye, W Menegas, S Schneider, T Nath, MM Rahman, V Di Santo,
D Soberanes, G Feng, VN Murthy, G Lauder, C Dulac, M Mathis, A Mathis (2021).
https://www.biorxiv.org/content/10.1101/2021.04.30.442096v1
Publication:
Multi-animal pose estimation, identification and tracking with DeepLabCut
Lauer, J., Zhou, M., Ye, S., Menegas, W., Schneider, S., Nath, T., Rahman, M.M.,
Di Santo, V., Soberanes, D., Feng, G., Murthy, V.N., Lauder, G.V., Dulac, C.,
Mathis, M.W., & Mathis, A. (2022).
Nature Methods, 19, 496 - 504.
Conceptualization was done by A.M. and M.W.M. Formal analysis and code were done by J.L., A.M. and M.W.M.
New deep architectures were designed by M.Z., S.Y. and A.M. GUIs were done by J.L., M.W.M. and T.N.
Benchmark was set by S.S., M.W.M., A.M. and J.L. Marmoset data were gathered by W.M. and G.F.
Marmoset behavioral analysis was carried out by W.M. Parenting data were gathered by M.M.R., A.M. and C.D.
Tri-mouse data were gathered by D.S., A.M. and V.N.M. Fish data were gathered by V.D.S. and G.L.
The article was written by A.M., M.W.M. and J.L. with input from all authors.
M.W.M. and A.M. co-supervised the project.
############################################################################################################
DeepLabCut 2.2 - 3.0 additions:
A Mathis, alexander.mathis@epfl.ch | https://github.com/AlexEMG
M Mathis, mackenzie@post.harvard.edu | https://github.com/MMathisLab
J Lauer, jessy@deeplabcut.org | https://github.com/jeylau
N Poulsen, neils.poulsen@epfl.ch | https://github.com/n-poulsen
S Ye, https://github.com/yeshaokai
Preprint:
Ye, S., Filippova, A., Lauer, J., Vidal, M., Schneider, S., Qiu, T., Mathis, A., & Mathis, M.W. (2022).
SuperAnimal pretrained pose estimation models for behavioral analysis. https://arxiv.org/abs/2203.07436