Faster R-CNN

Faster R-CNN (Region-based Convolutional Neural Network) is a popular object detection model that was proposed by Shaoqing Ren et al. in 2015. It is an upgraded version of the previous R-CNN and Fast R-CNN models. The main advantage of Faster R-CNN is its ability to perform object detection and localization in an efficient and accurate way.

Faster R-CNN uses a two-stage detection process, where the first stage proposes regions of interest (RoIs) using a Region Proposal Network (RPN), and the second stage classifies the RoIs into different object categories and predicts their bounding boxes. The RPN is a fully convolutional network that slides over the feature map generated by a convolutional neural network (CNN) to predict object proposals.

Faster R-CNN is known for its high accuracy on object detection tasks and is widely used in various applications such as autonomous driving, surveillance, and image analysis. It has also inspired many other advanced object detection models such as Mask R-CNN, Cascade R-CNN, and RetinaNet.