Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
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Updated
Oct 3, 2024 - Python
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
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.
SDK for running DeepLabCut on a live video stream
[ICCV 2023] "Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity"
GUI to run DeepLabCut on live video feed
Closed-loop behavioral experiment toolkit using pose estimation of body parts.
a napari plugin for labeling and refining keypoint data within DeepLabCut projects
replicAnt - generating annotated images of animals in complex environments with Unreal Engine
Toolbox for using multiple cameras from intrinsic calculations to reconstructing kinematics
Headless DeepLabCut (no GUI support)
Deep learning-driven multi animal tracking and pose estimation add-on for Blender
An alpha playground for a web-based labeling tool for DLC
Utilities to convert DeepLabCut (DLC), output to/from Neurodata Without Borders (NWB) format.
Simple vispy-based frame-by-frame behavior annotation GUI. Also can display DeepLabCut poses within videos during annotation.
Fully automated (rodent) limb motion analysis toolbox for behavioral analysis with bodypart coordinate data, building upon markerless pose estimation.
A python toolbox for locating and exporting brain regions from mouse brain images.
DLClibrary is a lightweight library supporting universal functions for the DeepLabCut ecosystem.
Real-time tracking and behavioural reinforcement toolbox for DeepLabCut to accompany Forys, Xiao, Gupta, and Murphy (2020).
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