RPN
Region Proposal Network
A **Region Proposal Network**, or **RPN**, is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals. RPN and algorithms like [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) can be merged into a single network by sharing their convolutional features - using the recently popular terminology of neural networks with attention mechanisms, the RPN component tells the unified network where to look.

RPNs are designed to efficiently predict region proposals with a wide range of scales and aspect ratios. RPNs use anchor boxes that serve as references at multiple scales and aspect ratios. The scheme can be thought of as a pyramid of regression references, which avoids enumerating images or filters of multiple scales or aspect ratios.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://paperswithcode.com/paper/faster-r-cnn-towards-real-time-object
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks