Oriented Object Detection: Oriented RepPoints + Swin Transformer/ReResNet

Overview

Oriented RepPoints for Aerial Object Detection

图片

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 DataAug out

Installation

Please refer to install.md for installation and dataset preparation.

Getting Started

This repo is based on mmdetection. Please see GetStart.md 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 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 - - - -

ImageNet-1K and ImageNet-22K Pretrained Models

name pretrain resolution [email protected] [email protected] #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. 微信截图_20210522135042

Visual results

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

  • Learning points

Learning Points

  • Oriented bounding box

Oriented Box

Citation

@article{Li2021oriented,
  title={Oriented RepPoints for Aerial Object Detection},
  author={Wentong Li and 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

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