I am currently training a PyTorch Mask R-CNN based on ResNet50 backbone.
During my training session I noticed that the precision is very good, going up to 94%, but the recall only up to 79%. For my use case it is very important that I detect all objects, while the actual classification is less important.
There are tons of parameters in the MaskRCNN base class, and I wonder if any of them could be changed to move toward a higher recall in favor of some precision.
I tried playing around with box_fg_iou_thresh and box_bg_iou_thresh, but without any noticable change in recall or precision.
Has anyone a suggestion which parameters could be used to increase recall?