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- W2941352015 abstract "Quantitative behavioral measurements are important for answering questions across scientific disciplines—from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal’s body parts directly from images or videos. However, currently available animal pose estimation methods have limitations in speed and robustness. Here, we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings—including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences." @default.
- W2941352015 created "2019-05-03" @default.
- W2941352015 creator A5005722714 @default.
- W2941352015 creator A5008389022 @default.
- W2941352015 creator A5009776734 @default.
- W2941352015 creator A5055944620 @default.
- W2941352015 creator A5077072770 @default.
- W2941352015 creator A5079322290 @default.
- W2941352015 creator A5084419988 @default.
- W2941352015 date "2019-10-01" @default.
- W2941352015 modified "2023-10-17" @default.
- W2941352015 title "DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning" @default.
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