mmrotate/configs/rotated_faster_rcnn

• Rotated Faster R-CNN
• • Abstract
  • Results and models
  • Citation

Rotated Faster R-CNN

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 [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.

Results and models

DOTA1.0

Backbone	mAP	Angle	lr schd	Mem (GB)	Inf Time (fps)	Aug	Batch Size	Configs	Download
ResNet50 (1024,1024,200)	73.40	le90	1x	8.46	16.5	-	2	rotated_faster_rcnn_r50_fpn_1x_dota_le90	model | log

Citation

@article{Ren_2017,
   title={Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks},
   journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
   year={2017},
   month={Jun},
}