Project
Plugin Development for DeepLabCut
Award
napari Plugin Accelerator
Project Summary
Quantifying behavior is crucial for a wide range of applications such as animal conservation, ethology, genetics, neuroscience, human biomechanics, and medicine. DeepLabCut (DLC) relies on human-annotated frames in order to train deep neural networks to predict customized, user-defined key points. Key points are defined as body parts, a cell body, or objects. This team will develop a new napari plugin for DLC to label key points and objects. The project aims to develop new code that compiles with hook specifications, create a new hook implementation for combined hierarchical keypoint and segmentation mask labeling GUI, and deploy internal testing and community feedback.
Investigators
Principal Investigator
![Mackenzie Mathis, PhD](https://chanzuckerberg.com/wp-content/uploads/2021/11/MWM_headShot2-01-Mackenzie-Mathis.jpeg)
Mackenzie Mathis, PhD
Co-Principal Investigator
![Alexander Mathis, PhD](https://chanzuckerberg.com/wp-content/uploads/2021/11/alexander-mathis-2-Alexander-Mathis.jpeg)
Alexander Mathis, PhD