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OpenMMLab Detection Toolbox and Benchmark

Project description

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

The main branch works with PyTorch 1.8+.

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 tasks out of box

    The toolbox directly supports multiple detection tasks such as object detection, instance segmentation, panoptic segmentation, and semi-supervised object detection.

  • 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. The newly released RTMDet also obtains new state-of-the-art results on real-time instance segmentation and rotated object detection tasks and the best parameter-accuracy trade-off on object detection.

Apart from MMDetection, we also released MMEngine for model training and MMCV for computer vision research, which are heavily depended on by this toolbox.

What's New


v3.2.0 was released in 12/10/2023:

1. Detection Transformer SOTA Model Collection (1) Supported four updated and stronger SOTA Transformer models: DDQ, CO-DETR, AlignDETR, and H-DINO. (2) Based on CO-DETR, MMDet released a model with a COCO performance of 64.1 mAP. (3) Algorithms such as DINO support AMP/Checkpoint/FrozenBN, which can effectively reduce memory usage.

2. Comprehensive Performance Comparison between CNN and Transformer RF100 consists of a dataset collection of 100 real-world datasets, including 7 domains. It can be used to assess the performance differences of Transformer models like DINO and CNN-based algorithms under different scenarios and data volumes. Users can utilize this benchmark to quickly evaluate the robustness of their algorithms in various scenarios.

3. Support for GLIP and Grounding DINO fine-tuning, the only algorithm library that supports Grounding DINO fine-tuning The Grounding DINO algorithm in MMDet is the only library that supports fine-tuning. Its performance is one point higher than the official version, and of course, GLIP also outperforms the official version. We also provide a detailed process for training and evaluating Grounding DINO on custom datasets. Everyone is welcome to give it a try.

Model Backbone Style COCO mAP Official COCO mAP
Grounding DINO-T Swin-T Zero-shot 48.5 48.4
Grounding DINO-T Swin-T Finetune 58.1(+0.9) 57.2
Grounding DINO-B Swin-B Zero-shot 56.9 56.7
Grounding DINO-B Swin-B Finetune 59.7
Grounding DINO-R50 R50 Scratch 48.9(+0.8) 48.1

4. Support for the open-vocabulary detection algorithm Detic and multi-dataset joint training. 5. Training detection models using FSDP and DeepSpeed.

ID AMP GC of Backbone GC of Encoder FSDP Peak Mem (GB) Iter Time (s)
1 49 (A100) 0.9
2 39 (A100) 1.2
3 33 (A100) 1.1
4 25 (A100) 1.3
5 18 2.2
6 13 1.6
7 14 2.9
8 8.5 2.4

6. Support for the V3Det dataset, a large-scale detection dataset with over 13,000 categories.

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


Please refer to Installation for installation instructions.

Getting Started

Please see Overview for the general introduction of MMDetection.

For detailed user guides and advanced guides, please refer to our documentation:

We also provide object detection colab tutorial Open in Colab and instance segmentation colab tutorial Open in Colab.

To migrate from MMDetection 2.x, please refer to migration.

Overview of Benchmark and Model Zoo

Results and models are available in the model zoo.

Object Detection Instance Segmentation Panoptic Segmentation Other
  • Contrastive Learning
  • Distillation
  • Semi-Supervised Object Detection
  • Components
    Backbones Necks Loss Common

    Some other methods are also supported in projects using MMDetection.


    Please refer to FAQ for frequently asked questions.


    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 for the contributing guideline.


    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.


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

      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},


    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.
    • MMPreTrain: OpenMMLab pre-training toolbox and benchmark.
    • MMagic: OpenMMLab Advanced, Generative and Intelligent Creation toolbox.
    • 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.
    • MMYOLO: OpenMMLab YOLO series 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.
    • MIM: MIM installs OpenMMLab packages.
    • MMEval: A unified evaluation library for multiple machine learning libraries.
    • Playground: A central hub for gathering and showcasing amazing projects built upon OpenMMLab.

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