Faster R-CNN下载

[AI100_小助手]

State-of-the-art object detection networks depend on region proposal algorithms
to hypothesize object locations. Advances like SPPnet [7] and Fast R-CNN [5]
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. RPNs are trained end-to-end to generate highquality
region proposals, which are used by Fast R-CNN for detection. With a
simple alternating optimization, RPN and Fast R-CNN can be trained to share
convolutional features. For the very deep VGG-16 model [19], 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 (73.2% mAP)
and 2012 (70.4% mAP) using 300 proposals per image. Code is available at
https://github.com/ShaoqingRen/faster_rcnn.
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