BIGWangYuDong / UniverseNet

Object detection. EfficientDet-D5 level COCO AP in 20 epochs. SOTA single-stage detector on Waymo Open Dataset.

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UniverseNets are state-of-the-art detectors for universal-scale object detection. Please refer to our paper for details.

universal-scale object detection COCO AP


  • recent:
    • Update codes for mmdet 2.21.0, mmcv-full 1.4.4
  • 21.12 (Dec. 2021):
    • Support finer scale-wise AP metrics
    • Add codes for TOOD, ConvMLP, PoolFormer
    • Update codes for PyTorch 1.9.0, mmdet 2.17.0, mmcv-full 1.3.13
  • 21.09 (Sept. 2021):
    • Support gradient accumulation to simulate large batch size with few GPUs (example)
    • Add codes for CBNetV2, PVT, PVTv2, DDOD
    • Update and fix codes for mmdet 2.14.0, mmcv-full 1.3.9
  • 21.04 (Apr. 2021):
  • 20.12 (Dec. 2020):
    • Add configs for Manga109-s dataset
    • Add ATSS-style TTA for SOTA accuracy (COCO test-dev AP 54.1)
    • Add UniverseNet 20.08s for realtime speed (> 30 fps)
  • 20.10 (Oct. 2020):
    • Add variants of UniverseNet 20.08
    • Update and fix codes for PyTorch 1.6.0, mmdet 2.4.0, mmcv-full 1.1.2
  • 20.08 (Aug. 2020): UniverseNet 20.08
    • Improve usage of batchnorm
    • Use DCN modestly by default for faster training and inference
  • 20.07 (July 2020): UniverseNet+GFL
    • Add GFL to improve accuracy and speed
    • Provide stronger pre-trained model (backbone: Res2Net-101)
  • 20.06 (June 2020): UniverseNet
    • Achieve SOTA single-stage detector on Waymo Open Dataset 2D detection
    • Win 1st place in NightOwls Detection Challenge 2020 all objects track

Features not in the original MMDetection

Methods and architectures:

Benchmarks and datasets:




Basic Usage

See MMDetection documents. Especially, see this document to evaluate and train existing models on COCO.


We show examples to evaluate and train UniverseNet-20.08 on COCO with 4 GPUs.

# evaluate pre-trained model
mkdir -p ${HOME}/data/checkpoints/
wget -P ${HOME}/data/checkpoints/
bash tools/ ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} --eval bbox

# train model
CONFIG_NAME=$(basename ${CONFIG_FILE} .py)
WORK_DIR="${HOME}/logs/coco/${CONFIG_NAME}_`date +%Y%m%d_%H%M%S`"
bash tools/ ${CONFIG_FILE} ${GPU_NUM} --work-dir ${WORK_DIR} --seed 0

Even if you have one GPU, we recommend using tools/ and tools/ to avoid a SyncBN issue.


  title={{USB}: Universal-Scale Object Detection Benchmark},
  author={Shinya, Yosuke},


Major parts of the code are released under the Apache 2.0 license. Plsease check NOTICE for exceptions.


Some codes are modified from the repositories of PoolFormer, ConvMLP, Swin Transformer, RelationNet++, SEPC, PVT, CBNetV2, GFLv2, DDOD, and NightOwls. When merging, please note that there are some minor differences from the above repositories and the original MMDetection repository.


English | ็ฎ€ไฝ“ไธญๆ–‡

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.


2.21.0 was released in 8/2/2022:

  • Support CPU training
  • Allow to set parameters about multi-processing to speed up training and testing

Please refer to for details and release history.

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

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
  • Components
    Backbones Necks Loss Common

    Some other methods are also supported in projects using MMDetection.


    Please refer to for installation.

    Getting Started

    Please see for the basic usage of MMDetection. We provide colab tutorial, and full guidance for quick run with existing dataset and with new dataset for beginners. There are also tutorials for finetuning models, adding new dataset, designing data pipeline, customizing models, customizing runtime settings and useful tools.

    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},

    Projects in OpenMMLab

    • MMCV: OpenMMLab foundational library for computer vision.
    • 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.
    • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
    • MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
    • MMTracking: OpenMMLab video perception toolbox and benchmark.
    • MMPose: OpenMMLab pose estimation toolbox and benchmark.
    • MMEditing: OpenMMLab image and video editing toolbox.
    • MMOCR: A comprehensive toolbox for text detection, recognition and understanding.
    • MMGeneration: OpenMMLab image and video generative models toolbox.
    • MMFlow: OpenMMLab optical flow toolbox and benchmark.
    • MMFewShot: OpenMMLab fewshot learning 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.


    Object detection. EfficientDet-D5 level COCO AP in 20 epochs. SOTA single-stage detector on Waymo Open Dataset.

    License:Apache License 2.0


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