DeepLabCut is a toolbox for markerless tracking of body parts of animals in lab settings performing various tasks, like trail tracking, reaching in mice and various Drosophila behaviors during egg-laying (see Mathis et al. for details). There is, however, nothing specific that makes the toolbox only applicable to these tasks and/or species. Please check out www.mousemotorlab.org/deeplabcut for video demonstrations of automated tracking. The implementation of DeepLabCut given here was created by NeuroCAAS developers to benchmark performance of our platform. To try out DeepLabCut, look at the template job provided. Data for both training and testing is provided from the DeepLabCut repo.
The NeuroCAAS implementation of DeepLabCut works with the version 2.0 version of DeepLabCut. The analysis here offers both training and testing modes. For training, we assume that frames are labeled elsewhere by the user. For testing, we assume users want to label videos. IMPORTANT: we additionally assume that the user will only be analyzing videos with one shuffle and one training fraction at a time. Different parameters are required for the training and testing routines, as described below.
TRAINING:
Args:
-Input: (zip file) A zipped folder corresponding to a DLC model folder (with format {taskname}_{scorername}_{date}), containing the training frames to be used for training. This zipped file should include the folder itself. It should not contain any trained models. You can already have generated a training dataset, or simply have labeled data (see Demo Link for demo data).
-Config: (yaml) A YAML file indicating the mode (train or test), type of videos to analyze, and other neurocaas specific parameters. See template config for details.
PREDICTION:
Args:
-Input: (zip file) A zipped folder corresponding to a DLC model folder (with format {taskname}_{scorername}_{date}), containing the training frames to be used for training. This zipped file should include the folder itself. This model folder should include a trained model.
-Config: (yaml) A YAML file indicating the mode (train or test), type of videos to analyze, and other neurocaas specific parameters. See template config for details.
Outputs:
-(zipfile) If training, full models fit from the data, as zip files results{modelname}.zip. If prediction only, returns only a zipfile of the videos and corresponding traces.
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