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AUTHORS
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DeepLabCut (www.deeplabcut.org) was initially developed by
Alexander & Mackenzie Mathis in collaboration with Matthias Bethge.
DeepLabCut is an open-source tool and has benefited from suggestions and edits by many
individuals:
https://github.com/AlexEMG/DeepLabCut/graphs/contributors
############################################################################################################
DeepLabCut 1.0 Toolbox
A Mathis, [email protected] | https://github.com/AlexEMG/DeepLabCut
M Mathis, [email protected] | 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 https://github.com/AlexEMG/DeepLabCut/tree/master/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, [email protected] | https://github.com/AlexEMG/DeepLabCut
T Nath, [email protected] | https://github.com/meet10may
M Mathis, [email protected] | 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 additions
A Mathis, [email protected] | https://github.com/AlexEMG/DeepLabCut
T Nath, [email protected] | https://github.com/meet10may
M Yüksekgönül, [email protected] | https://github.com/mertyg
M Mathis, [email protected] | https://github.com/MMathisLab
Preprint:
Pretraining boosts out-of-domain robustness for pose estimation
A. Mathis, M. Yüksekgönül, B. Rogers, M. Bethge, M. Mathis
############################################################################################################
DeepLabCut 2.1 - 2.2 additions (currently unpublished)
A Mathis, [email protected] | https://github.com/AlexEMG/DeepLabCut
J Lauer, [email protected] | https://github.com/jeylau
T Biasi, [email protected] | https://github.com/tbiasi
G Kane, [email protected] | https://github.com/gkane26
M Yüksekgönül, [email protected] | https://github.com/mertyg
T Nath, [email protected] | https://github.com/meet10may
M Mathis, [email protected] | https://github.com/MMathisLab