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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

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

PWC PWC PWC

TaskDatasetAPFPS(TRT FP16 BS1 3090)
Object DetectionCOCO52.8322
Instance SegmentationCOCO44.6188
Rotated Object DetectionDOTA78.9(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:

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:

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> </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