mmdetection/configs/rpn

RPN

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Abstract

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An 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, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN 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. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks.

Results and Models

Backbone	Style	Lr schd	Mem (GB)	Inf time (fps)	AR1000	Config	Download
R-50-FPN	caffe	1x	3.5	22.6	58.7	config	model | log
R-50-FPN	pytorch	1x	3.8	22.3	58.2	config	model | log
R-50-FPN	pytorch	2x	-	-	58.6	config	model | log
R-101-FPN	caffe	1x	5.4	17.3	60.0	config	model | log
R-101-FPN	pytorch	1x	5.8	16.5	59.7	config	model | log
R-101-FPN	pytorch	2x	-	-	60.2	config	model | log
X-101-32x4d-FPN	pytorch	1x	7.0	13.0	60.6	config	model | log
X-101-32x4d-FPN	pytorch	2x	-	-	61.1	config	model | log
X-101-64x4d-FPN	pytorch	1x	10.1	9.1	61.0	config	model | log
X-101-64x4d-FPN	pytorch	2x	-	-	61.5	config	model | log

Citation

@inproceedings{ren2015faster,
  title={Faster r-cnn: Towards real-time object detection with region proposal networks},
  author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
  booktitle={Advances in neural information processing systems},
  year={2015}
}