mmrotate/configs/oriented_rcnn

• Oriented R-CNN
• • Abstract
  • Results and models
  • Citation

Oriented R-CNN

Oriented R-CNN for Object Detection

Abstract

Current state-of-the-art two-stage detectors generate oriented proposals through time-consuming schemes. This diminishes the detectors’ speed, thereby becoming the computational bottleneck in advanced oriented object detection systems. This work proposes an effective and simple oriented object detection framework, termed Oriented R-CNN, which is a general two-stage oriented detector with promising accuracy and efficiency. To be specific, in the first stage, we propose an oriented Region Proposal Network (oriented RPN) that directly generates high-quality oriented proposals in a nearly cost-free manner. The second stage is oriented R-CNN head for refining oriented Regions of Interest (oriented RoIs) and recognizing them.

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
ResNet50 (1024,1024,200)	75.63	le90	1x	7.37	21.2	-	2	oriented_rcnn_r50_fpn_fp16_1x_dota_le90	model | log
ResNet50 (1024,1024,200)	75.69	le90	1x	8.46	16.2	-	2	oriented_rcnn_r50_fpn_1x_dota_le90	model | log

Citation

@InProceedings{Xie_2021_ICCV,
  author = {Xie, Xingxing and Cheng, Gong and Wang, Jiabao and Yao, Xiwen and Han, Junwei},
  title = {Oriented R-CNN for Object Detection},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  month = {October},
  year = {2021},
  pages = {3520-3529} }