Faster R-CNN
Faster R-CNN
**Faster R-CNN** is an object detection model that improves on [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) by utilising a region proposal network ([RPN](https://paperswithcode.com/method/rpn)) with the CNN model. The RPN shares full-image convolutional features with the detection network, enabling nearly cost-free region proposals. It 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, which are used by [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) for detection. RPN and Fast [R-CNN](https://paperswithcode.com/method/r-cnn) are merged into a single network by sharing their convolutional features: the RPN component tells the unified network where to look.

As a whole, Faster R-CNN consists of two modules. The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions.
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