![]() ![]() ![]() You will also learn how to use python commands in a jupyter notebook and in google colab to train and evaluate your model. More importantly, you will learn how to create a new project in DeepLabCut, how to label frames and how not to get lost in the project directory structure. ![]() In this example you will train a machine learning model to read an analogue clock, just for the fun of automating things. It is your first project, do whatever you want! I’m excited to hear about it and see how it turns out. The original papers track movement and pose in mice and other animals ( Mathis et al., 2018), but you can also track human facial expressions (see here) or even the location of a coin during magic tricks (see Zaghi-Lara et al., 2019). If you want to start right away you will find demo data and code here, but it will be more fun if you bring your own videos and a preliminary idea of what you want to track. You will need a computer with DeepLabCut installed (no GPU needed), as well as a working google account with some space left in your google drive. If you already have it installed, let’s go. Start your very own machine learning project for video-based tracking and markerless pose estimation today!įor a quick guide on how to install DeepLabCut refer to the previous post: Installing DeepLabCut - A three step guide. Do you have the impression everyone but you is using DeepLabCut, and you start feeling left out? Well, don’t.
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