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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+.
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.
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 |
0.3.4 was released in 01/02/2023:
Please refer to changelog.md for details and release history.
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 .
Please see get_started.md for the basic usage of MMRotate. We provide colab tutorial, and other tutorials for:
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.
Please refer to data_preparation.md to prepare the data.
Please refer to FAQ for frequently asked questions.
We appreciate all contributions to improve MMRotate. Please refer to CONTRIBUTING.md for the contributing guideline.
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.
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}
}
This project is released under the Apache 2.0 license.
FAQs
Rotation Detection Toolbox and Benchmark
We found that mmrotate demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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