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Introduction

MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.

The master branch works with PyTorch 1.5+.

Major features
  • Modular Design

    We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.

  • Support of multiple frameworks out of box

    The toolbox directly supports popular and contemporary detection frameworks, e.g. Faster RCNN, Mask RCNN, RetinaNet, etc.

  • High efficiency

    All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including Detectron2, maskrcnn-benchmark and SimpleDet.

  • State of the art

    The toolbox stems from the codebase developed by the MMDet team, who won COCO Detection Challenge in 2018, and we keep pushing it forward.

Apart from MMDetection, we also released a library mmcv for computer vision research, which is heavily depended on by this toolbox.

What's New

💎 Stable version

2.28.0 was released in 28/1/2023:

  • Support Objects365 Dataset, and Separated and Occluded COCO metric
  • Support acceleration of RetinaNet and SSD on Ascend
  • Deprecate the support of Python 3.6

Please refer to changelog.md for details and release history.

For compatibility changes between different versions of MMDetection, please refer to compatibility.md.

🌟 Preview of 3.x version

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

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

A brand new version of MMDetection v3.0.0rc5 was released in 26/12/2022:

Find more new features in 3.x branch. Issues and PRs are welcome!

Installation

Please refer to Installation for installation instructions.

Getting Started

Please see get_started.md for the basic usage of MMDetection. We provide colab tutorial and instance segmentation colab tutorial, and other tutorials for:

Overview of Benchmark and Model Zoo

Results and models are available in the model zoo.

Architectures
Object Detection Instance Segmentation Panoptic Segmentation Other
  • Contrastive Learning
  • Distillation
  • Receptive Field Search
  • Components
    Backbones Necks Loss Common

    Some other methods are also supported in projects using MMDetection.

    FAQ

    Please refer to FAQ for frequently asked questions.

    Contributing

    We appreciate all contributions to improve MMDetection. Ongoing projects can be found in out GitHub Projects. Welcome community users to participate in these projects. Please refer to CONTRIBUTING.md for the contributing guideline.

    Acknowledgement

    MMDetection 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 detectors.

    Citation

    If you use this toolbox or benchmark in your research, please cite this project.

    @article{mmdetection,
      title   = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
      author  = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
                 Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
                 Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
                 Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
                 Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
                 and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
      journal= {arXiv preprint arXiv:1906.07155},
      year={2019}
    }
    

    License

    This project is released under the Apache 2.0 license.

    Projects in OpenMMLab

    • MMEngine: OpenMMLab foundational library for training deep learning models.
    • MMCV: OpenMMLab foundational library for computer vision.
    • MMEval: A unified evaluation library for multiple machine learning libraries.
    • MIM: MIM installs OpenMMLab packages.
    • MMClassification: OpenMMLab image classification toolbox and benchmark.
    • MMDetection: OpenMMLab detection toolbox and benchmark.
    • MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
    • MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
    • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
    • MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
    • MMPose: OpenMMLab pose estimation toolbox and benchmark.
    • MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
    • MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
    • MMRazor: OpenMMLab model compression toolbox and benchmark.
    • MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
    • MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
    • MMTracking: OpenMMLab video perception toolbox and benchmark.
    • MMFlow: OpenMMLab optical flow toolbox and benchmark.
    • MMEditing: OpenMMLab image and video editing toolbox.
    • MMGeneration: OpenMMLab image and video generative models toolbox.
    • MMDeploy: OpenMMLab model deployment framework.
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