Metadata-Version: 2.1
Name: mmrotate
Version: 1.0.0rc0
Summary: Rotation Detection Toolbox and Benchmark
Home-page: https://github.com/open-mmlab/mmrotate
Author: MMRotate Authors
Author-email: openmmlab@gmail.com
License: Apache License 2.0
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        [🛠️Installation](https://mmrotate.readthedocs.io/en/1.x/install.html) |
        [👀Model Zoo](https://mmrotate.readthedocs.io/en/1.x/model_zoo.html) |
        [🆕Update News](https://mmrotate.readthedocs.io/en/1.x/notes/changelog.html) |
        [🚀Ongoing Projects](https://github.com/open-mmlab/mmrotate/projects) |
        [🤔Reporting Issues](https://github.com/open-mmlab/mmrotate/issues/new/choose)
        
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        English | [简体中文](README_zh-CN.md)
        
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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](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
        
        **v1.0.0rc0** was released in 7/11/2022:
        
        - Unifies interfaces of all components based on [MMEngine](https://github.com/open-mmlab/mmengine) and [MMDetection 3.x](https://github.com/open-mmlab/mmdetection/tree/3.x).
        - Support data structures RotatedBoxes and QuadriBoxes to encapsulate different kinds of bounding boxes. This will unify the usages of different kinds of bounding boxes in MMDetection 3.x and MMRotate 1.x to simplify the implementation and reduce redundant codes.
        - Support [quadrilateral box detection](configs/gliding_vertex/).
        - Support RotatedCocoMetric, which can generate evaluation indicators in COCO format.
        - Support COCO style annotations.
        - Support two new SAR datasets: [RSDD](tools/data/rsdd) and [SRSDD](tools/data/srsdd).
        
        ## Installation
        
        Please refer to [Installation](https://mmrotate.readthedocs.io/en/1.x/get_started.html) for more detailed instruction.
        
        ## Getting Started
        
        Please see [Overview](https://mmrotate.readthedocs.io/en/1.x/overview.html) for the general introduction of MMRotate.
        
        For detailed user guides and advanced guides, please refer to our [documentation](https://mmrotate.readthedocs.io/en/1.x/):
        
        - User Guides
          - [Train & Test](https://mmrotate.readthedocs.io/en/1.x/user_guides/index.html#train-test)
            - [Learn about Configs](https://mmrotate.readthedocs.io/en/1.x/user_guides/config.html)
            - [Inference with existing models](https://mmrotate.readthedocs.io/en/1.x/user_guides/inference.html)
            - [Dataset Prepare](https://mmrotate.readthedocs.io/en/1.x/user_guides/dataset_prepare.html)
            - [Test existing models on standard datasets](https://mmrotate.readthedocs.io/en/1.x/user_guides/train_test.html)
            - [Train predefined models on standard datasets](https://mmrotate.readthedocs.io/en/1.x/user_guides/train_test.html)
            - [Test Results Submission](https://mmrotate.readthedocs.io/en/1.x/user_guides/test_results_submission.html)
          - [Useful Tools](https://mmrotate.readthedocs.io/en/1.x/user_guides/index.html#useful-tools)
        - Advanced Guides
          - [Basic Concepts](https://mmrotate.readthedocs.io/en/1.x/advanced_guides/index.html#basic-concepts)
          - [Component Customization](https://mmrotate.readthedocs.io/en/1.x/advanced_guides/index.html#component-customization)
          - [How to](https://mmrotate.readthedocs.io/en/1.x/advanced_guides/index.html#how-to)
        
        We also provide colab tutorial [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](demo/MMRotate_Tutorial.ipynb).
        
        To migrate from MMRotate 0.x, please refer to [migration](https://mmrotate.readthedocs.io/en/1.x/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) (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 OpenMMLab
        
        - [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
        - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
        - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
        - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
        - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
        - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
        - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
        - [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
        - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation 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.
        - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
        
Keywords: computer vision,object detection,rotation detection
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: tests
Provides-Extra: build
Provides-Extra: optional
Provides-Extra: mim
