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shinya7y / UniverseNet

USB: Universal-Scale Object Detection Benchmark (BMVC 2022)

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UniverseNet

This is the official repository of "USB: Universal-Scale Object Detection Benchmark" (BMVC 2022).

We established a new benchmark USB with fair protocols and designed state-of-the-art detectors UniverseNets for universal-scale object detection. This repository extends MMDetection with more features and allows for more comprehensive benchmarking and development.

Introduction

universal-scale object detection

Benchmarks, such as COCO, play a crucial role in object detection. However, existing benchmarks are insufficient in scale variation, and their protocols are inadequate for fair comparison. In this paper, we introduce the Universal-Scale object detection Benchmark (USB). USB has variations in object scales and image domains by incorporating COCO with the recently proposed Waymo Open Dataset and Manga109-s dataset. To enable fair comparison and inclusive research, we propose training and evaluation protocols. They have multiple divisions for training epochs and evaluation image resolutions, like weight classes in sports, and compatibility across training protocols, like the backward compatibility of the Universal Serial Bus. Specifically, we request participants to report results with not only higher protocols (longer training) but also lower protocols (shorter training). Using the proposed benchmark and protocols, we conducted extensive experiments using 15 methods and found weaknesses of existing COCO-biased methods.

Please refer to our paper for details. https://arxiv.org/abs/2103.14027

Changelog

  • 23.07 (July 2023):
    • Win Honorable Mention Solution Award in Small Object Detection Challenge for Spotting Birds
    • Add SOD4SB dataset
  • 22.10 (Oct 2022):
    • Our paper got accepted to BMVC 2022!
  • 22.06 (June 2022):
    • Add SwinV2, FocalNet, GBR COTS dataset
    • Update codes for mmdet 2.25.0, 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:

Usage

Installation

See get_started.md.

Basic Usage

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

Examples

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 ${HOME}/data/checkpoints/ https://github.com/shinya7y/UniverseNet/releases/download/20.08/universenet50_2008_fp16_4x4_mstrain_480_960_2x_coco_20200815_epoch_24-81356447.pth
CONFIG_FILE=configs/universenet/universenet50_2008_fp16_4x4_mstrain_480_960_2x_coco.py
CHECKPOINT_FILE=${HOME}/data/checkpoints/universenet50_2008_fp16_4x4_mstrain_480_960_2x_coco_20200815_epoch_24-81356447.pth
GPU_NUM=4
bash tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} --eval bbox

# train model
CONFIG_FILE=configs/universenet/universenet50_2008_fp16_4x4_mstrain_480_960_2x_coco.py
CONFIG_NAME=$(basename ${CONFIG_FILE} .py)
WORK_DIR="${HOME}/logs/coco/${CONFIG_NAME}_`date +%Y%m%d_%H%M%S`"
GPU_NUM=4
bash tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} --work-dir ${WORK_DIR} --seed 0

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

Citation

@inproceedings{USB_shinya_BMVC2022,
  title={{USB}: Universal-Scale Object Detection Benchmark},
  author={Shinya, Yosuke},
  booktitle={British Machine Vision Conference (BMVC)},
  year={2022}
}

License

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

Acknowledgements

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



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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

2.25.0 was released in 1/6/2022:

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

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

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
    		•
    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

    • 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.
    • 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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    USB: Universal-Scale Object Detection Benchmark (BMVC 2022)

    object-detectionpytorchmmdetectionwaymo-open-dataset

    License:Apache License 2.0

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