Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
🐜🐀🐒🚶 A toolkit for robust markerless 3D pose estimation
SDK for running DeepLabCut on a live video stream
We turn natural language descriptions of behaviors into machine-executable code
Behavioral segmentation of open field in DeepLabCut, or B-SOID ("B-side"), is a pipeline that pairs unsupervised pattern recognition with supervised classification to achieve fast predictions of behaviors that are not predefined by users.
A monocular 3D pose estimation algorithm for humans and other animals
Various scripts to support deeplabcut and what to do afterwards!
a module for kinematic analysis of deeplabcut outputs
Workshop material for using DeepLabCut
[ICCV 2023] Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity
GUI to run DeepLabCut on live video feed
replicAnt - generating annotated images of animals in complex environments with Unreal Engine
Closed-loop behavioral experiment toolkit using pose estimation of body parts.
a napari plugin for labeling and refining keypoint data within DeepLabCut projects
A Bonsai interface for real-time markerless pose estimation using DeepLabCut
Docker container for running DeepLabCut 2.0, 2.1 (linux support only). Now, DLC main supports 2.2+
Toolbox for using multiple cameras from intrinsic calculations to reconstructing kinematics
Deep learning-driven multi animal tracking and pose estimation add-on for Blender
Headless DeepLabCut (no GUI support)
Trained deep neural-net models for estimating articulatory keypoints from midsagittal ultrasound tongue videos and front-view lip camera videos using DeepLabCut. This research is by Wrench, A. and Balch-Tomes, J. (2022) (https://www.mdpi.com/1424-8220/22/3/1133) (https://doi.org/10.3390/s22031133).
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