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My brief understanding of Faster R-CNN

1. assign anchor box

2. finding anchor box objectness score (rather positive, negative, non) using ground truth

3. select a set of positive, negative boxes to train region proposal network

4-1. training classification layer using positive anchor box

4-2. training box regression layer using positive anchor box

 

Questions

Q1. When an anchor box has high IOU with several ground truth boxes is the highest box chosen as the target? Can an anchor box only have on target?

Q2. How is a positive anchor box and its target box matched in code? (Most explanation says an anchor box object contains four variables; center x, center y, width, height, which has no value for its target.)

Q3. Is ROI pooling referring to stage two?

Q4. Is the third stage training used to predict the objectness score for test set?

Q5. Is there a reason other than training speed, for not training anchor boxes that are not labeled as either positive or negative? (Aren’t all boxes’ objectness score estimated when training? Including positive, negative, non)

Q6. Shouldn’t 4-1 classification happen after moving of the box during 4-2 box regression? (Explanations say the two layers are independent and happen simultaneously) Shouldn’t the two layers have order?

Q7. Is the probability of an anchor box used for NMS referring to the classification score calculated in stage 4-1?

Q8. Unlike the RPN stage where all anchor boxes are used for training, the classification and regression stages only use a few positive anchor boxes. How does back-propagation happen when training a model like this, where some stages use only a part of the training set? (I heard the advantages of Faster-RCNN is the connection of all stages as a single deep-learning model. But if the latter models only use chosen boxes and if the classification/box regression stage works independently how can the full model work as one?)