Faster R-CNN is a unified end-to-end object detection network that significantly improved object detection by introducing the concept of a Region Proposal Network (RPN). The key idea behind it was to use the RPN to generate region proposals, which are essentially potential bounding boxes containing objects of interest, and the model then uses these initial proposals for further classification and refinement of the bounding boxes.The RPN itself is a fully convolutional network that performs two tasks simultaneously: predicting object bounds and objectness scores for each position. When trained end-to-end, the model becomes capable of producing high-quality region proposals, which are subsequently utilized by Fast R-CNN for object detection.Trained on datasets like PASCAL VOC and MS COCO, Faster R-CNN has shown its effectiveness in various object detection tasks.
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