I’m using faster-rcnn for custom object detection.
If the network results in bad classification (the bounding box is classified correctly with a right category but has a low probability or the bounding box is not classified correctly) but not so bad detection (it finds most of the correct bounding boxes plus some incorrect ones) can I freeze all the network except for last classifier stage and train only it or it’s more complex than that?
I’m trying to detect manufacter logos/text labels.
They always have small enough size.
Does it mean that I should try to decrease my the rpn anchor_sizes for that case ( since at the moment I also have a problem finding small size bounding boxes) or this settings shouldn’t be changed?