<div align="center">
<img width="600" alt="onedl-mmrotate" src="https://raw.githubusercontent.com/VBTI-development/onedl-mmrotate/main/resources/onedl-mmrotate-logo.png"/>
<div> </div>
<div align="center">
<a href="https://vbti.ai">
<b><font size="5">VBTI Website</font></b>
</a>
<a href="https://onedl.ai">
<b><font size="5">OneDL platform</font></b>
</a>
</div>
<div> </div>
[](https://onedl-mmrotate.readthedocs.io/en/latest/)
[](https://github.com/VBTI-development/onedl-mmrotate/blob/main/LICENSE)
[](https://pypi.org/project/onedl-mmrotate/)
[](https://pypi.org/project/onedl-mmrotate)
[](https://github.com/VBTI-development/onedl-mmrotate/actions/workflows/merge_stage_test.yml)
[](https://github.com/VBTI-development/onedl-mmrotate/issues)
[](https://github.com/VBTI-development/onedl-mmrotate/issues)
[📘Documentation](https://onedl-mmrotate.readthedocs.io/en/latest/) |
[🛠️Installation](https://onedl-mmrotate.readthedocs.io/en/latest/install.html) |
[👀Model Zoo](https://onedl-mmrotate.readthedocs.io/en/latest/model_zoo.html) |
[🆕Update News](https://onedl-mmrotate.readthedocs.io/en/latest/notes/changelog.html) |
[🚀Ongoing Projects](https://github.com/vbti-development/onedl-mmrotate/projects) |
[🤔Reporting Issues](https://github.com/vbti-development/onedl-mmrotate/issues/new/choose)
</div>
<div align="center">
English
</div>
<div align="center">
<a href="https://openmmlab.medium.com/" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://discord.com/channels/1037617289144569886/1046608014234370059" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://space.bilibili.com/1293512903" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a>
</div>
## Introduction
MMRotate is an open-source toolbox for rotated object detection based on PyTorch.
It is a part of the [OpenMMLab project](https://github.com/open-mmlab).
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
The VBTI development team is reviving MMLabs code, making it work with
newer pytorch versions and fixing bugs. We are only a small team, so your help
is appreciated.
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](https://arxiv.org/abs/2212.07784). Pre-trained models are [here](configs/rotated_rtmdet).
[](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real)
[](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real)
[](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real)
| 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>
**v1.0.0rc1** was released in 30/12/2022:
- Support [RTMDet](configs/rotated_rtmdet) rotated object detection models. The technical report of RTMDet is on [arxiv](https://arxiv.org/abs/2212.07784)
- Support [H2RBox](configs/h2rbox) models. The technical report of H2RBox is on [arxiv](https://arxiv.org/abs/2210.06742)
## Installation
Please refer to [Installation](https://onedl-mmrotate.readthedocs.io/en/latest/get_started.html) for more detailed instruction.
## Getting Started
Please see [Overview](https://onedl-mmrotate.readthedocs.io/en/latest/overview.html) for the general introduction of MMRotate.
For detailed user guides and advanced guides, please refer to our [documentation](https://onedl-mmrotate.readthedocs.io/en/latest/):
- User Guides
- [Train & Test](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/index.html#train-test)
- [Learn about Configs](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/config.html)
- [Inference with existing models](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/inference.html)
- [Dataset Prepare](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/dataset_prepare.html)
- [Test existing models on standard datasets](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/train_test.html)
- [Train predefined models on standard datasets](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/train_test.html)
- [Test Results Submission](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/test_results_submission.html)
- [Useful Tools](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/index.html#useful-tools)
- Advanced Guides
- [Basic Concepts](https://onedl-mmrotate.readthedocs.io/en/latest/advanced_guides/index.html#basic-concepts)
- [Component Customization](https://onedl-mmrotate.readthedocs.io/en/latest/advanced_guides/index.html#component-customization)
- [How to](https://onedl-mmrotate.readthedocs.io/en/latest/advanced_guides/index.html#how-to)
We also provide colab tutorial [](demo/MMRotate_Tutorial.ipynb).
To migrate from MMRotate 0.x, please refer to [migration](https://onedl-mmrotate.readthedocs.io/en/latest/migration.html).
## 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](docs/en/model_zoo.md) page.
<details open>
<summary><b>Supported algorithms:</b></summary>
- [x] [Rotated RetinaNet-OBB/HBB](configs/rotated_retinanet/README.md) (ICCV'2017)
- [x] [Rotated FasterRCNN-OBB](configs/rotated_faster_rcnn/README.md) (TPAMI'2017)
- [x] [Rotated RepPoints-OBB](configs/rotated_reppoints/README.md) (ICCV'2019)
- [x] [Rotated FCOS](configs/rotated_fcos/README.md) (ICCV'2019)
- [x] [RoI Transformer](configs/roi_trans/README.md) (CVPR'2019)
- [x] [Gliding Vertex](configs/gliding_vertex/README.md) (TPAMI'2020)
- [x] [Rotated ATSS-OBB](configs/rotated_atss/README.md) (CVPR'2020)
- [x] [CSL](configs/csl/README.md) (ECCV'2020)
- [x] [R<sup>3</sup>Det](configs/r3det/README.md) (AAAI'2021)
- [x] [S<sup>2</sup>A-Net](configs/s2anet/README.md) (TGRS'2021)
- [x] [ReDet](configs/redet/README.md) (CVPR'2021)
- [x] [Beyond Bounding-Box](configs/cfa/README.md) (CVPR'2021)
- [x] [Oriented R-CNN](configs/oriented_rcnn/README.md) (ICCV'2021)
- [x] [GWD](configs/gwd/README.md) (ICML'2021)
- [x] [KLD](configs/kld/README.md) (NeurIPS'2021)
- [x] [SASM](configs/sasm_reppoints/README.md) (AAAI'2022)
- [x] [Oriented RepPoints](configs/oriented_reppoints/README.md) (CVPR'2022)
- [x] [KFIoU](configs/kfiou/README.md) (ICLR'2023)
- [x] [H2RBox](configs/h2rbox/README.md) (ICLR'2023)
- [x] [PSC](configs/psc/README.md) (CVPR'2023)
- [x] [RTMDet](configs/rotated_rtmdet/README.md) (arXiv)
- [x] [H2RBox-v2](configs/h2rbox_v2/README.md) (arXiv)
</details>
## Data Preparation
Please refer to [data_preparation.md](tools/data/README.md) to prepare the data.
## FAQ
Please refer to [FAQ](docs/en/notes/faq.md) for frequently asked questions.
## Contributing
We appreciate all contributions to improve MMRotate. Please refer to [CONTRIBUTING.md](.github/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](https://www.si.sjtu.edu.cn/) 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.
```bibtex
@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](LICENSE).
## Projects in VBTI-development
- [MMEngine](https://github.com/vbti-development/onedl-mmengine): OpenMMLab foundational library for training deep learning models.
- [MMCV](https://github.com/vbti-development/onedl-mmcv): OpenMMLab foundational library for computer vision.
- [MMPreTrain](https://github.com/vbti-development/onedl-mmpretrain): OpenMMLab pre-training toolbox and benchmark.
- [MMDetection](https://github.com/vbti-development/onedl-mmdetection): OpenMMLab detection toolbox and benchmark.
- [MMRotate](https://github.com/vbti-development/onedl-mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
- [MMSegmentation](https://github.com/vbti-development/onedl-mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
- [MMDeploy](https://github.com/vbti-development/onedl-mmdeploy): OpenMMLab model deployment framework.
- [MIM](https://github.com/vbti-development/onedl-mim): MIM installs OpenMMLab packages.
## Projects in OpenMMLab
- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.
- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.
Raw data
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"description": "<div align=\"center\">\n <img width=\"600\" alt=\"onedl-mmrotate\" src=\"https://raw.githubusercontent.com/VBTI-development/onedl-mmrotate/main/resources/onedl-mmrotate-logo.png\"/>\n <div> </div>\n <div align=\"center\">\n <a href=\"https://vbti.ai\">\n <b><font size=\"5\">VBTI Website</font></b>\n </a>\n \n <a href=\"https://onedl.ai\">\n <b><font size=\"5\">OneDL platform</font></b>\n </a>\n </div>\n<div> </div>\n\n[](https://onedl-mmrotate.readthedocs.io/en/latest/)\n[](https://github.com/VBTI-development/onedl-mmrotate/blob/main/LICENSE)\n\n[](https://pypi.org/project/onedl-mmrotate/)\n[](https://pypi.org/project/onedl-mmrotate)\n\n[](https://github.com/VBTI-development/onedl-mmrotate/actions/workflows/merge_stage_test.yml)\n[](https://github.com/VBTI-development/onedl-mmrotate/issues)\n[](https://github.com/VBTI-development/onedl-mmrotate/issues)\n\n[\ud83d\udcd8Documentation](https://onedl-mmrotate.readthedocs.io/en/latest/) |\n[\ud83d\udee0\ufe0fInstallation](https://onedl-mmrotate.readthedocs.io/en/latest/install.html) |\n[\ud83d\udc40Model Zoo](https://onedl-mmrotate.readthedocs.io/en/latest/model_zoo.html) |\n[\ud83c\udd95Update News](https://onedl-mmrotate.readthedocs.io/en/latest/notes/changelog.html) |\n[\ud83d\ude80Ongoing Projects](https://github.com/vbti-development/onedl-mmrotate/projects) |\n[\ud83e\udd14Reporting Issues](https://github.com/vbti-development/onedl-mmrotate/issues/new/choose)\n\n</div>\n\n<div align=\"center\">\n\nEnglish\n\n</div>\n\n<div align=\"center\">\n <a href=\"https://openmmlab.medium.com/\" style=\"text-decoration:none;\">\n <img src=\"https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://discord.com/channels/1037617289144569886/1046608014234370059\" style=\"text-decoration:none;\">\n <img src=\"https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://twitter.com/OpenMMLab\" style=\"text-decoration:none;\">\n <img src=\"https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://www.youtube.com/openmmlab\" style=\"text-decoration:none;\">\n <img src=\"https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://space.bilibili.com/1293512903\" style=\"text-decoration:none;\">\n <img src=\"https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://www.zhihu.com/people/openmmlab\" style=\"text-decoration:none;\">\n <img src=\"https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png\" width=\"3%\" alt=\"\" /></a>\n</div>\n\n## Introduction\n\nMMRotate is an open-source toolbox for rotated object detection based on PyTorch.\nIt is a part of the [OpenMMLab project](https://github.com/open-mmlab).\n\nThe master branch works with **PyTorch 1.6+**.\n\nhttps://user-images.githubusercontent.com/10410257/154433305-416d129b-60c8-44c7-9ebb-5ba106d3e9d5.MP4\n\n<details open>\n<summary><b>Major Features</b></summary>\n\n- **Support multiple angle representations**\n\n MMRotate provides three mainstream angle representations to meet different paper settings.\n\n- **Modular Design**\n\n We decompose the rotated object detection framework into different components,\n which makes it much easy and flexible to build a new model by combining different modules.\n\n- **Strong baseline and State of the art**\n\n The toolbox provides strong baselines and state-of-the-art methods in rotated object detection.\n\n</details>\n\n## What's New\n\n### Highlight\n\nThe VBTI development team is reviving MMLabs code, making it work with\nnewer pytorch versions and fixing bugs. We are only a small team, so your help\nis appreciated.\n\nWe 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](https://arxiv.org/abs/2212.07784). Pre-trained models are [here](configs/rotated_rtmdet).\n\n[](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real)\n[](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real)\n[](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real)\n\n| Task | Dataset | AP | FPS(TRT FP16 BS1 3090) |\n| ------------------------ | ------- | ------------------------------------ | ---------------------- |\n| Object Detection | COCO | 52.8 | 322 |\n| Instance Segmentation | COCO | 44.6 | 188 |\n| Rotated Object Detection | DOTA | 78.9(single-scale)/81.3(multi-scale) | 121 |\n\n<div align=center>\n<img src=\"https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png\"/>\n</div>\n\n**v1.0.0rc1** was released in 30/12/2022:\n\n- Support [RTMDet](configs/rotated_rtmdet) rotated object detection models. The technical report of RTMDet is on [arxiv](https://arxiv.org/abs/2212.07784)\n- Support [H2RBox](configs/h2rbox) models. The technical report of H2RBox is on [arxiv](https://arxiv.org/abs/2210.06742)\n\n## Installation\n\nPlease refer to [Installation](https://onedl-mmrotate.readthedocs.io/en/latest/get_started.html) for more detailed instruction.\n\n## Getting Started\n\nPlease see [Overview](https://onedl-mmrotate.readthedocs.io/en/latest/overview.html) for the general introduction of MMRotate.\n\nFor detailed user guides and advanced guides, please refer to our [documentation](https://onedl-mmrotate.readthedocs.io/en/latest/):\n\n- User Guides\n - [Train & Test](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/index.html#train-test)\n - [Learn about Configs](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/config.html)\n - [Inference with existing models](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/inference.html)\n - [Dataset Prepare](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/dataset_prepare.html)\n - [Test existing models on standard datasets](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/train_test.html)\n - [Train predefined models on standard datasets](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/train_test.html)\n - [Test Results Submission](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/test_results_submission.html)\n - [Useful Tools](https://onedl-mmrotate.readthedocs.io/en/latest/user_guides/index.html#useful-tools)\n- Advanced Guides\n - [Basic Concepts](https://onedl-mmrotate.readthedocs.io/en/latest/advanced_guides/index.html#basic-concepts)\n - [Component Customization](https://onedl-mmrotate.readthedocs.io/en/latest/advanced_guides/index.html#component-customization)\n - [How to](https://onedl-mmrotate.readthedocs.io/en/latest/advanced_guides/index.html#how-to)\n\nWe also provide colab tutorial [](demo/MMRotate_Tutorial.ipynb).\n\nTo migrate from MMRotate 0.x, please refer to [migration](https://onedl-mmrotate.readthedocs.io/en/latest/migration.html).\n\n## Model Zoo\n\nResults and models are available in the *README.md* of each method's config directory.\nA summary can be found in the [Model Zoo](docs/en/model_zoo.md) page.\n\n<details open>\n<summary><b>Supported algorithms:</b></summary>\n\n- [x] [Rotated RetinaNet-OBB/HBB](configs/rotated_retinanet/README.md) (ICCV'2017)\n- [x] [Rotated FasterRCNN-OBB](configs/rotated_faster_rcnn/README.md) (TPAMI'2017)\n- [x] [Rotated RepPoints-OBB](configs/rotated_reppoints/README.md) (ICCV'2019)\n- [x] [Rotated FCOS](configs/rotated_fcos/README.md) (ICCV'2019)\n- [x] [RoI Transformer](configs/roi_trans/README.md) (CVPR'2019)\n- [x] [Gliding Vertex](configs/gliding_vertex/README.md) (TPAMI'2020)\n- [x] [Rotated ATSS-OBB](configs/rotated_atss/README.md) (CVPR'2020)\n- [x] [CSL](configs/csl/README.md) (ECCV'2020)\n- [x] [R<sup>3</sup>Det](configs/r3det/README.md) (AAAI'2021)\n- [x] [S<sup>2</sup>A-Net](configs/s2anet/README.md) (TGRS'2021)\n- [x] [ReDet](configs/redet/README.md) (CVPR'2021)\n- [x] [Beyond Bounding-Box](configs/cfa/README.md) (CVPR'2021)\n- [x] [Oriented R-CNN](configs/oriented_rcnn/README.md) (ICCV'2021)\n- [x] [GWD](configs/gwd/README.md) (ICML'2021)\n- [x] [KLD](configs/kld/README.md) (NeurIPS'2021)\n- [x] [SASM](configs/sasm_reppoints/README.md) (AAAI'2022)\n- [x] [Oriented RepPoints](configs/oriented_reppoints/README.md) (CVPR'2022)\n- [x] [KFIoU](configs/kfiou/README.md) (ICLR'2023)\n- [x] [H2RBox](configs/h2rbox/README.md) (ICLR'2023)\n- [x] [PSC](configs/psc/README.md) (CVPR'2023)\n- [x] [RTMDet](configs/rotated_rtmdet/README.md) (arXiv)\n- [x] [H2RBox-v2](configs/h2rbox_v2/README.md) (arXiv)\n\n</details>\n\n## Data Preparation\n\nPlease refer to [data_preparation.md](tools/data/README.md) to prepare the data.\n\n## FAQ\n\nPlease refer to [FAQ](docs/en/notes/faq.md) for frequently asked questions.\n\n## Contributing\n\nWe appreciate all contributions to improve MMRotate. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.\n\n## Acknowledgement\n\nMMRotate 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](https://www.si.sjtu.edu.cn/) 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.\n\n## Citation\n\nIf you use this toolbox or benchmark in your research, please cite this project.\n\n```bibtex\n@inproceedings{zhou2022mmrotate,\n title = {MMRotate: A Rotated Object Detection Benchmark using PyTorch},\n author = {Zhou, Yue and Yang, Xue and Zhang, Gefan and Wang, Jiabao and Liu, Yanyi and\n Hou, Liping and Jiang, Xue and Liu, Xingzhao and Yan, Junchi and Lyu, Chengqi and\n Zhang, Wenwei and Chen, Kai},\n booktitle={Proceedings of the 30th ACM International Conference on Multimedia},\n pages = {7331\u20137334},\n numpages = {4},\n year={2022}\n}\n```\n\n## License\n\nThis project is released under the [Apache 2.0 license](LICENSE).\n\n## Projects in VBTI-development\n\n- [MMEngine](https://github.com/vbti-development/onedl-mmengine): OpenMMLab foundational library for training deep learning models.\n- [MMCV](https://github.com/vbti-development/onedl-mmcv): OpenMMLab foundational library for computer vision.\n- [MMPreTrain](https://github.com/vbti-development/onedl-mmpretrain): OpenMMLab pre-training toolbox and benchmark.\n- [MMDetection](https://github.com/vbti-development/onedl-mmdetection): OpenMMLab detection toolbox and benchmark.\n- [MMRotate](https://github.com/vbti-development/onedl-mmrotate): OpenMMLab rotated object detection toolbox and benchmark.\n- [MMSegmentation](https://github.com/vbti-development/onedl-mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.\n- [MMDeploy](https://github.com/vbti-development/onedl-mmdeploy): OpenMMLab model deployment framework.\n- [MIM](https://github.com/vbti-development/onedl-mim): MIM installs OpenMMLab packages.\n\n## Projects in OpenMMLab\n\n- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.\n- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.\n- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.\n- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.\n- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.\n- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.\n- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.\n- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.\n- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.\n- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.\n- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.\n- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.\n- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.\n- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.\n- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.\n- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.\n",
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