Behavioral segmentation of open field in DeepLabCut, or B-SOID ("B-side"), is a pipeline that pairs unsupervised pattern recognition with supervised classification to achieve fast predictions of behaviors that are not predefined by users.
Hi, I'm considering using B-SOID with my pose estimation data from SLEAP (h5). My lab is interested in analyzing home cage behavior and we're particularly interested in when our mice are eating and sleeping/inactive. I was wondering how likely it would be that eating would be identified as a distinct behavioral category?
Hi, I'm considering using B-SOID with my pose estimation data from SLEAP (h5). My lab is interested in analyzing home cage behavior and we're particularly interested in when our mice are eating and sleeping/inactive. I was wondering how likely it would be that eating would be identified as a distinct behavioral category?