DeepLabCut is an efficient method for 2D and 3D markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results (i.e. you can match human labeling accuracy) with minimal training data (typically 50-200 frames). We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Documentation

GitHub Repo stars for DeepLabCut ../../_images/deeplabcut.gif

DeepLabCut has developed DLC2NWB, a Python package for converting from their native output format to NWB. This library uses the NWB extension ndx-pose, which aims to provide a standardized format for storing pose estimation data in NWB. ndx-pose was developed initially to store the output of DeepLabCut in NWB, but is also designed to store the output of general pose estimation tools.