README.md

Hi, 感谢大家对我工作的兴趣。这是我21年做的工作，开源代码时我们实验室的服务器出了意外，由于意外触发整个机房的消防管道喷水，机房内所有服务器泡水10小时左右，全部返厂维修，硬盘数据都清空了。所以这份论文的完整APR模块的代码没有保存下来，因为我没有保存训练好的模型到本地。 不过，我的这篇论文是第一篇开源的用DETR框架来做旋转目标检测的，大家可以用这份代码来作为Baseline，在上面修改自己的DETR系列的代码。

Introduction

AO2-DETR: Arbitrary-Oriented Object Detection Transformer (https://arxiv.org/abs/2205.12785).

In this paper, we propose an end-to-end transformer-based detector AO2-DETR for arbitrary-oriented object detection. The proposed AO2-DETR comprises dedicated components to address AOOD challenges, including an oriented proposal generation mechanism, an adaptive oriented proposal refinement module, and a rotation aware set matching loss in order to accurately detect oriented objects in images. The encoderdecoder architecture transforms the oriented proposals (served as object queries) into each corresponding object, which eliminates the need for hand-designed components and complex pre/post-processing. Our approach achieves state-of-theart performance compared to recently anchor-free and singlestage methods on the oriented datasets (DOTA, SKU110K-R and HRSC2016 datasets). We validate that the transformer can enable adaptive receptive fields for oriented objects, thus it can deal with oriented and irregular placed objects naturally. Furthermore, we hope that this encoder-decoder paradigm will promote future works in oriented object detection.

Deformable DETR

This is a standard code of Deformable Detr for training DOTA datasets. The complete paper code will be open-sourced later (if the paper is accepted).

Train

CUDA_VISIBLE_DEVICES=2,3 ./tools/dist_train.sh configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_dota.py 2

Test

CUDA_VISIBLE_DEVICES=5 ./tools/dist_test.sh configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_dota.py work_dirs/deformable_detr_twostage_refine_r50_16x2_50e_dota/epoch_50.pth 1 --format-only --eval-options submission_dir=work_dirs/deformable_detr_twostage_refine_r50_16x2_50e_dota/Task1_results 

Some Results

Installation

Please refer to install.md for installation guide.

Get Started

Please see get_started.md for the basic usage of MMRotate.

Data Preparation

Please refer to data_preparation.md to prepare the data.

Acknowledgement

MMRotate is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new methods.