DeepLabCut is an open source Python package for markerless pose estimation of animals performing various tasks.
| Version | modulename |
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| 2.2.0.6 | anaconda3/2020.07 + conda/dlc |
Installing DeepLabCut in your directories
DeepLabCutis 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 have noticed that during the training phase, DeepLabCut tries to write data to system directories that is not permitted. For this reason, we recommend users to install DeepLabCut in their home directories. Please execute the steps as follows so that we can create an Open OnDemand Virtual Desktop application that will launch DeepLabCut for all users.
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| language | bash |
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| title | Load the anaconda3 module to put conda in your path |
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module load anaconda3 |
...
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| language | bash |
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| title | Create the conda environment |
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conda env create --prefix ${HOME}/condaenv/dlc -f /share/Apps/usr/etc/dlc.yml |
Open OnDemand Application
| Expand |
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| title | Select DeepLabCut from the Interactive Apps Dropdown |
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...
| Expand |
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| title | Use my own dlc environment |
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| Code Block |
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| language | bash |
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| title | Unload conda/dlc module |
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| module unload conda/dlc |
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| language | bash |
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| title | activate my own conda environment for DeepLabCut |
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| conda activate ${HOME}/condaenv/dlc |
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| language | bash |
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| title | Start DeepLabCut GUI (or JupyterLab) |
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| python -m deeplabcut
# OR
jupyter lab |
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For more information, visit http://www.mackenziemathislab.org/deeplabcut