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

We turn natural language descriptions of behaviors into machine-executable code

SDK for running DeepLabCut on a live video stream

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.

Various scripts to support deeplabcut and what to do afterwards!

Workshop material for using DeepLabCut

a module for kinematic analysis of deeplabcut outputs

[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

a napari plugin for labeling and refining keypoint data within DeepLabCut projects

Closed-loop behavioral experiment toolkit using pose estimation of body parts.

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)

DLC2Action is an action segmentation package that makes running and tracking of machine learning experiments easy.

A Primer on Motion Capture with Deep Learning:Principles, Pitfalls and Perspectives

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).