Images, labels, models and code are provided at  for the DeeplabCut model and GentleBoost classifier.  Read_me document describes each file.

Files included

DeepLabCut Model

Zipped file containing the images used to train DeepLabCut and the associated human annotated labels in excel spreadsheet

The yaml output from DLC after training completed

The test yaml output from DLC after training completed

The images with labels from human and model superimposed for train and test images.

GentleBoost Classifier Model

The output of DLC for each of the 40 training videos in H5 files.  Each frame of video is labelled with 159 points representing the x,y coordinates and likelihood values for 4 mice, 12 points per mouse, and 5 points on the arena

The classification of licking and no licking for each frame of video for one arena for each of the 40 videos. Arena indicated by file name.

Matlab structure of the GentleBoost classifier

Matlab code to create the necessary input to classifier.  Code is described using one file of  H5 output of the DLC model.  The output of the classifier is of licking or no licking for each video frame.  Data is typically binned to determine number of seconds of licking in a 5 minute bin.