DeepLabCut

DeepLabCut is an open source Python package for markerless pose estimation of animals performing various tasks.

Installing DeepLabCut in your directories

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 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.

Load the anaconda3 module to put conda in your path

Create a directory in the <PI's userid>_proj folder

Create a symlink to the condaenv directory in your home directory

Create the conda environment

Open OnDemand Application

Select DeepLabCut from the Interactive Apps Dropdown

Open

Choose the resources you want to use. Click "Launch" to submit your request to the SLURM scheduler.

Open

When resources are available, a blue "Launch DeepLabCut" button will appear. Click the button to launch a Virtual Desktop for launching DeepLabCut.

Open

Virtual Desktop with shortcut for launching DeepLabCut GUI and JupyterLab

Open

DeepLabCut GUI

Open

JupyterLab session

Open

Use my own dlc environment

Unload conda/dlc module

module unload conda/dlc

activate my own conda environment for DeepLabCut

conda activate ${HOME}/condaenv/dlc

Start DeepLabCut GUI (or JupyterLab)

python -m deeplabcut # OR jupyter lab

Open

For more information, visit http://www.mackenziemathislab.org/deeplabcut