Mmrotate

<div align="center"> <img src="resources/mmrotate-logo.png" width="450"/> <div>&nbsp;</div> <div align="center"> <b><font size="5">OpenMMLab website</font></b> ^{<a href="https://openmmlab.com"> <i><font size="4">HOT</font></i> </a>} &nbsp;&nbsp;&nbsp;&nbsp; <b><font size="5">OpenMMLab platform</font></b> ^{<a href="https://platform.openmmlab.com"> <i><font size="4">TRY IT OUT</font></i> </a>} </div> <div>&nbsp;</div>

📘Documentation | 🛠️Installation | 👀Model Zoo | 🆕Update News | 🚀Ongoing Projects | 🤔Reporting Issues

</div> <!--中/英 文档切换--> <div align="center">

English | 简体中文

</div>

Introduction

MMRotate is an open-source toolbox for rotated object detection based on PyTorch. It is a part of the OpenMMLab project.

The master branch works with PyTorch 1.6+.

https://user-images.githubusercontent.com/10410257/154433305-416d129b-60c8-44c7-9ebb-5ba106d3e9d5.MP4

<details open> <summary><b>Major Features</b></summary>

• Support multiple angle representations

  MMRotate provides three mainstream angle representations to meet different paper settings.

• Modular Design

  We decompose the rotated object detection framework into different components, which makes it much easy and flexible to build a new model by combining different modules.

• Strong baseline and State of the art

  The toolbox provides strong baselines and state-of-the-art methods in rotated object detection.

</details>

What's New

Highlight

We are excited to announce our latest work on real-time object recognition tasks, RTMDet, a family of fully convolutional single-stage detectors. RTMDet not only achieves the best parameter-accuracy trade-off on object detection from tiny to extra-large model sizes but also obtains new state-of-the-art performance on instance segmentation and rotated object detection tasks. Details can be found in the technical report. Pre-trained models are here.

| Task | Dataset | AP | FPS(TRT FP16 BS1 3090) | | ------------------------ | ------- | ------------------------------------ | ---------------------- | | Object Detection | COCO | 52.8 | 322 | | Instance Segmentation | COCO | 44.6 | 188 | | Rotated Object Detection | DOTA | 78.9(single-scale)/81.3(multi-scale) | 121 |

<div align=center> <img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/> </div>

0.3.4 was released in 01/02/2023:

• Fix compatibility with numpy, scikit-learn, and e2cnn.
• Support empty patch in Rotate Transform
• use iof for RRandomCrop validation

Please refer to changelog.md for details and release history.

Installation

MMRotate depends on PyTorch, MMCV and MMDetection. Below are quick steps for installation. Please refer to Install Guide for more detailed instruction.

conda create -n open-mmlab python=3.7 pytorch==1.7.0 cudatoolkit=10.1 torchvision -c pytorch -y
conda activate open-mmlab
pip install openmim
mim install mmcv-full
mim install mmdet
git clone https://github.com/open-mmlab/mmrotate.git
cd mmrotate
pip install -r requirements/build.txt
pip install -v -e .

Get Started

Please see get_started.md for the basic usage of MMRotate. We provide colab tutorial, and other tutorials for:

• learn the basics
• learn the config
• customize dataset
• customize model
• useful tools

Model Zoo

Results and models are available in the README.md of each method's config directory. A summary can be found in the Model Zoo page.

<details open> <summary><b>Supported algorithms:</b></summary>

• [x] Rotated RetinaNet-OBB/HBB (ICCV'2017)
• [x] Rotated FasterRCNN-OBB (TPAMI'2017)
• [x] Rotated RepPoints-OBB (ICCV'2019)
• [x] Rotated FCOS (ICCV'2019)
• [x] RoI Transformer (CVPR'2019)
• [x] Gliding Vertex (TPAMI'2020)
• [x] Rotated ATSS-OBB (CVPR'2020)
• [x] CSL (ECCV'2020)
• [x] R³Det (AAAI'2021)
• [x] S²A-Net (TGRS'2021)
• [x] ReDet (CVPR'2021)
• [x] Beyond Bounding-Box (CVPR'2021)
• [x] Oriented R-CNN (ICCV'2021)
• [x] GWD (ICML'2021)
• [x] KLD (NeurIPS'2021)
• [x] SASM (AAAI'2022)
• [x] Oriented RepPoints (CVPR'2022)
• [x] KFIoU (arXiv)
• [x] G-Rep (stay tuned)

</details>

Data Preparation

Please refer to data_preparation.md to prepare the data.

FAQ

Please refer to FAQ for frequently asked questions.

Contributing

We appreciate all contributions to improve MMRotate. Please refer to CONTRIBUTING.md for the contributing guideline.

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.

Citation

If you use this toolbox or benchmark in your research, please cite this project.

@inproceedings{zhou2022mmrotate,
  title   = {MMRotate: A Rotated Object Detection Benchmark using PyTorch},
  author  = {Zhou, Yue and Yang, Xue and Zhang, Gefan and Wang, Jiabao and Liu, Yanyi and
             Hou, Liping and Jiang, Xue and Liu, Xingzhao and Yan, Junchi and Lyu, Chengqi and
             Zhang, Wenwei and Chen, Kai},
  booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
  year={2022}
}

License

This project is released under the Apache 2.0 license.

Projects in OpenMMLab

• MMCV: OpenMMLab foundational library for computer vision.
• MIM: MIM installs OpenMMLab packages.
• MMClassification: OpenMMLab image classification toolbox and benchmark.
• MMDetection: OpenMMLab detection toolbox and benchmark.
• MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
• MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
• MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
• MMOCR: Op