Questions tagged [faster-r-cnn]

For questions related to the faster R-CNN model, which was proposed in "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" (2015) by Shaoqing Ren et al. and published in NeurIPS. Faster R-CNN is an improved version of fast R-CNN, which, in turn, is an improved version of R-CNN.

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Why are the learned offsets of anchor boxes in the RCNN object detection models scale invariant?

In the original RCNN paper (https://arxiv.org/pdf/1311.2524.pdf) and continued in later RCNN papers such as faster RCNN (https://arxiv.org/pdf/1506.01497.pdf) the learned offsets of the anchor boxes ...
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Why the two identical "IoU comparing procedures" are needed in Faster R-CNN(RPN & RCNN)?

As far as I know, there are two same 'IoU comparing procedures' in RPN and RCNN, but why is the same operation held twice? The paragraph right below is what I've comprehended about the Faster R-CNN. ...
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In anchor based object detection, why don't the anchors share the same weights?

After reading about YOLO V3 and Faster R-CNN, I don't understand why the weights for the regression head aren't the same across all boxes of the same size. Given that the backbone of these systems is ...
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54 views

Confusion about faster RCNN neither object nor background label

I am trying to construct a faster RCNN from scratch using KERAS. I am generating the tensor which contains whether anchor at each location corresponds to object or background or neither for training ...
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How to design training loop in RPN?

I have a short question. I understand the concept of RPN but one small details keeps me from implementing it. How should I design the training loop given that I have to use only a subset of anchor ...
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117 views

How does the region proposal method work in Fast R-CNN?

I read so many articles and the Fast R-CNN paper, but I'm still confused about how the region proposal method works in Fast R-CNN. As you can see in the image below, they say they used a proposal ...
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1answer
433 views

In Faster R-CNN, how can I get the predicted bounding box given the neural network's output?

The RPN loss in Faster RCNN paper is $$ L({p_i}, {t_i}) = \frac{1}{N_{cls}} \sum_{i} L_{cls}(p_i,p_i^*) + \lambda \frac{1}{N_{reg}} \sum_i p_i^* L_{reg}(t_i, t_i^*) $$ For regression problems, we have ...