faster rcnn

Faster R-CNN (Region-based Convolutional Neural Network) is a popular object detection model in computer vision. It was introduced by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun in 2015. The architecture of Faster R-CNN consists of two modules: a Region Proposal Network (RPN) and a Fast R-CNN detector. The RPN generates object proposals by sliding a small network over the convolutional feature map of the input image. The Fast R-CNN detector then uses these proposals to classify objects and refine their bounding boxes. The key advantage of Faster R-CNN over previous object detection models is its ability to learn the proposal generation process, which leads to faster and more accurate object detection.