This is the Linux app named DeepLabCut whose latest release can be downloaded as Minorfixes.zip. It can be run online in the free hosting provider OnWorks for workstations.
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DeepLabCut
DESCRIPTION
DeepLabCut™ is an efficient method for 2D and 3D markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results (i.e. you can match human labeling accuracy) with minimal training data (typically 50-200 frames). We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. The package is open source, fast, robust, and can be used to compute 3D pose estimates or for multi-animals. Please see the original paper and the latest work below! This package is collaboratively developed by the Mathis Group & Mathis Lab at EPFL (releases prior to 2.1.9 were developed at Harvard University). The code is freely available and easy to install in a few clicks with Anaconda (and pypi). DeepLabCut is an open-source Python package for animal pose estimation.
Features
- We provide data and several Jupyter Notebooks
- Due to transfer learning it requires little training data for multiple, challenging behaviors
- DeepLabCut is embedding in a larger open-source eco-system
- This project is licensed under the GNU Lesser General Public License v3.0
- The code is freely available and easy to install in a few clicks
- We have online learning modules
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/deeplabcut.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
