dannce (3-dimensional aligned neural network for computational ethology)
Creators
- 1. Duke University
- 2. Massachusetts Institute of Technology
- 3. Harvard University
- 4. The Rockefeller University
- 5. Columbia University
Description
DANNCE (3-Dimensional Aligned Neural Network for Computational Ethology) is a convolutional neural network (CNN) that calculates the 3D positions of user-defined anatomical landmarks on behaving animals from videos taken at multiple angles. The key innovation of DANNCE compared to existing approaches for 2D keypoint detection in animals (e.g. DeepLabCut) is that the network is fully 3D, so that it can learn about 3D image features and how cameras and landmarks relate to one another in 3D space. We also pre-trained DANNCE using a large dataset of rat motion capture and synchronized video, so the standard network has extensive prior knowledge of rodent motions and poses. DANNCE's ability to track landmarks transfers well to mice and other mammals, and works across different camera views, camera types, and illumination conditions.
Files
dannce-1.1.0.zip
Files
(624.2 MB)
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