Python(编程语言)
计算机科学
工具箱
图形用户界面
人工智能
管道(软件)
机器学习
深度学习
姿势
人机交互
分类器(UML)
计算机视觉
程序设计语言
作者
Tanmay Nath,Alexander Mathis,An Chi Chen,Amir Patel,Matthias Bethge
出处
期刊:Nature Protocols
[Springer Nature]
日期:2019-06-21
卷期号:14 (7): 2152-2176
被引量:807
标识
DOI:10.1038/s41596-019-0176-0
摘要
Noninvasive behavioral tracking of animals during experiments is critical to many scientific pursuits. Extracting the poses of animals without using markers is often essential to measuring behavioral effects in biomechanics, genetics, ethology, and neuroscience. However, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open-source toolbox called DeepLabCut that builds on a state-of-the-art human pose-estimation algorithm to allow a user to train a deep neural network with limited training data to precisely track user-defined features that match human labeling accuracy. Here, we provide an updated toolbox, developed as a Python package, that includes new features such as graphical user interfaces (GUIs), performance improvements, and active-learning-based network refinement. We provide a step-by-step procedure for using DeepLabCut that guides the user in creating a tailored, reusable analysis pipeline with a graphical processing unit (GPU) in 1-12 h (depending on frame size). Additionally, we provide Docker environments and Jupyter Notebooks that can be run on cloud resources such as Google Colaboratory.
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