mmrotate-dev 1.0.0rc1

Rotation Detection Toolbox and Benchmark

 

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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

Major Features

• 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.

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

v1.0.0rc1 was released in 30/12/2022:

• Support RTMDet rotated object detection models. The technical report of RTMDet is on arxiv
• Support H2RBox models. The technical report of H2RBox is on arxiv

Installation

Please refer to Installation for more detailed instruction.

Getting Started

Please see Overview for the general introduction of MMRotate.

For detailed user guides and advanced guides, please refer to our documentation:

• User Guides
  • Train & Test
    • Learn about Configs
    • Inference with existing models
    • Dataset Prepare
    • Test existing models on standard datasets
    • Train predefined models on standard datasets
    • Test Results Submission
  • Useful Tools
• Advanced Guides
  • Basic Concepts
  • Component Customization
  • How to

We also provide colab tutorial .

To migrate from MMRotate 0.x, please refer to migration.

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.

Supported algorithms:

• Rotated RetinaNet-OBB/HBB (ICCV'2017)
• Rotated FasterRCNN-OBB (TPAMI'2017)
• Rotated RepPoints-OBB (ICCV'2019)
• Rotated FCOS (ICCV'2019)
• RoI Transformer (CVPR'2019)
• Gliding Vertex (TPAMI'2020)
• Rotated ATSS-OBB (CVPR'2020)
• CSL (ECCV'2020)
• R³Det (AAAI'2021)
• S²A-Net (TGRS'2021)
• ReDet (CVPR'2021)
• Beyond Bounding-Box (CVPR'2021)
• Oriented R-CNN (ICCV'2021)
• GWD (ICML'2021)
• KLD (NeurIPS'2021)
• SASM (AAAI'2022)
• Oriented RepPoints (CVPR'2022)
• KFIoU (ICLR'2023)
• H2RBox (ICLR'2023)
• PSC (CVPR'2023)
• RTMDet (arXiv)
• H2RBox-v2 (arXiv)

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 appreciate the Student Innovation Center of SJTU for providing rich computing resources at the beginning of the project. 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},
  pages = {7331–7334},
  numpages = {4},
  year={2022}
}

License

This project is released under the Apache 2.0 license.

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