Open dereyly opened 7 years ago
The current codes set bounding box regression targets to be x10 of the real targets. The hyper parameter (=x10) comes from the original py-faster-rcnn. I guess it enhances the trained results but I haven't checked it.
Thank you.
Visualization with tr_boxes = bbox_transform_inv(boxes, 0.1*bbox_targets)
is correct.
I miss this point. But not understand why they not change loss_weight at 'bbox loss'.
The effects of modifying loss weight and of bounding box targets are different since the loss function is not linear.
Hello I have strange results when trying to visualize training process: fast_rcnn/train.py
I try to visualize targets of Fast-RCNN Red it is target (bbox_targets). I hope target boxes restore GT box from ROI boxes. Blue is answer (bbox_pred) of pvanet/comp/test.model
Second image is train from imagenet (finetune)