Oriented RepPoints for Aerial Object Detection

The code for the implementation of “Oriented RepPoints + Swin Transformer/ReResNet”.

Introduction

Based on the Oriented Reppoints detector with Swin Transformer backbone, the 3rd Place is achieved on the Task 1 and the 2nd Place is achieved on the Task 2 of 2021 challenge of Learning to Understand Aerial Images (LUAI) held on ICCV’2021. The detailed information is introduced in this paper of "LUAI Challenge 2021 on Learning to Understand Aerial Images, ICCVW2021".

New Feature

• BackBone: add Swin-Transformer, ReResNet
• DataAug: add Mosaic4or9, Mixup, HSV, RandomPerspective, RandomScaleCrop

Installation

Please refer to for installation and dataset preparation.

Getting Started

This repo is based on . Please see for the basic usage.

Results and Models

The results on DOTA test-dev set are shown in the table below(password:aabb/swin/ABCD). More detailed results please see the paper.

Model	Backbone	MS Train/Test	DataAug	DOTAv1 mAP	DOTAv2 mAP	Download
OrientedReppoints	R-50	-	-	75.68	-	baidu(aabb)
OrientedReppoints	R-101	-	√	76.21	-	baidu(aabb)
OrientedReppoints	R-101	√	√	78.12	-	baidu(aabb)
OrientedReppoints	SwinT-tiny	-	√	-	59.93	baidu(aabb)

ImageNet-1K and ImageNet-22K Pretrained Models

name	pretrain	resolution	acc@1	acc@5	#params	FLOPs	FPS	22K model	1K model	Need to turn read version
Swin-T	ImageNet-1K	224x224	81.2	95.5	28M	4.5G	755	-	github/baidu(swin)/config	✔
Swin-S	ImageNet-1K	224x224	83.2	96.2	50M	8.7G	437	-	github/baidu(swin)/config	✔
Swin-B	ImageNet-1K	224x224	83.5	96.5	88M	15.4G	278	-	github/baidu(swin)/config	✔
Swin-B	ImageNet-1K	384x384	84.5	97.0	88M	47.1G	85	-	github/baidu(swin)/test-config	✔
Swin-B	ImageNet-22K	224x224	85.2	97.5	88M	15.4G	278	github/baidu(swin)	github/baidu(swin)/test-config	✔
Swin-B	ImageNet-22K	384x384	86.4	98.0	88M	47.1G	85	github/baidu(swin)	github/baidu(swin)/test-config	✔
Swin-L	ImageNet-22K	224x224	86.3	97.9	197M	34.5G	141	github/baidu(swin)	github/baidu(swin)/test-config	✔
Swin-L	ImageNet-22K	384x384	87.3	98.2	197M	103.9G	42	github/baidu(swin)	github/baidu(swin)/test-config	✔
ReResNet50	ImageNet-1K	224x224	71.20	90.28	-	-	-	-	google/baidu(ABCD)/log	-

The mAOE results on DOTAv1 val set are shown in the table below(password:aabb).

Model	Backbone	mAOE	Download
OrientedReppoints	R-50	5.93°	baidu(aabb)

Note：

• Wtihout the ground-truth of test subset, the mAOE of orientation evaluation is calculated on the val subset(original train subset for training).
• The orientation (angle) of an aerial object is define as below, the detail of mAOE, please see the paper. The code of mAOE is mAOE_evaluation.py.

Visual results

The visual results of learning points and the oriented bounding boxes. The visualization code is .

• Learning points

• Oriented bounding box

More details

DOTAv2遥感图像旋转目标检测竞赛经验分享（Swin Transformer + Anchor free/based方案）

Citation

@article{li2021oriented,
	title="Oriented RepPoints for Aerial Object Detection.",
	author="Wentong {Li}, Yijie {Chen}, Kaixuan {Hu}, Jianke {Zhu}.",
	journal="arXiv preprint arXiv:2105.11111",
	year="2021"
}

Acknowledgements

I have used utility functions from other wonderful open-source projects. Espeicially thank the authors of:

OrientedRepPoints

Swin-Transformer-Object-Detection

ReDet