Papers › Multi-animal pose estimation, identification and tracking with DeepLabCut

Multi-animal pose estimation, identification and tracking with DeepLabCut

12 Apr 2022Nature Methods 2022 4archive 2025-07-28

Jessy Lauer, Mu Zhou, Shaokai Ye, William Menegas, Steffen Schneider, Tanmay Nath, Mohammed Mostafizur Rahman, Valentina Di Santo, Daniel Soberanes, Guoping Feng, Venkatesh N. Murthy, George Lauder, Catherine Dulac, Mackenzie Weygandt Mathis & Alexander Mathis

Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, an open-source pose estimation toolbox, and provide high-performance animal assembly and tracking—features required for multi-animal scenarios. Furthermore, we integrate the ability to predict an animal’s identity to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.

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Code

deeplabcut/madlc_naturemethods2022 officialmentioned in paper report
DeepLabCut/DeepLabCut mentioned in papertfLGPL-3.0 report

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Tasks

Animal Pose EstimationPose Estimation

Datasets

Introduced by this paper, per the archive.

Fish-100Marmoset-8KTriMouse-161

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Animal Pose Estimation Fish-100 DLCRNet_ms4graph9 mAP 71.6 #4 of 4 Archive leaderboard report
Animal Pose Estimation Marmoset-8K DLCRNet_ms-graph34 mAP 89 #4 of 4 Archive leaderboard report
Animal Pose Estimation TriMouse-161 ResNet50_s4graph11 mAP 93 #4 of 7 Archive leaderboard report
Animal Pose Estimation TriMouse-161 DLCRNet_ms4graph11 mAP 92 #5 of 7 Archive leaderboard report

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