-
Hello, I've created a rich dataset where I'd like to cull low-scoring skeletons (below 0.7) during inference before tracking. As an example, if I have a video with two mice, and SLEAP provides two skeletons scoring 0.4 and 0.9, I'd like the 0.4 skeleton to be deleted before tracking. Perhaps I'm doing something wrong, but I can't figure out how to delete these skeletons aside from manually in the GUI ('Labels > Delete All Predictions with Low Score'). Here's my syntax: The result includes two tracks, which both have skeletons below 0.9. Is it possible that the above syntax culls skeletons until there are only two left, and then leaves them regardless of their score? Thanks! |
Beta Was this translation helpful? Give feedback.
Replies: 1 comment 1 reply
-
Hi @olinesn, There is an indirect way of filtering instances with a low score using the The Let us know if setting the Thanks, |
Beta Was this translation helpful? Give feedback.
Hi @olinesn,
There is an indirect way of filtering instances with a low score using the
--peak_threshold
argument (default: 0.2). This will determine whether to consider a proposed body part location valid based on the node score (not the score of the entire instance - which is the average of all the node scores). Setting a higherpeak_threshold
will yield less predicted nodes, but the nodes that are returned will have a higher score. We should just have a command line option to delete the instances based on score (i.e. what the user sees).The
pre_cull_iou_threshold
andclean_iou_threshold
arguments cull instances based on the intersection over union of the instances' bounding boxes (not…