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shinya7y / UniverseNet

USB: Universal-Scale Object Detection Benchmark (BMVC 2022)
Apache License 2.0
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mmdetection object-detection pytorch waymo-open-dataset

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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

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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English | [简体中文](README_zh-CN.md)

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](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) and [SimpleDet](https://github.com/TuSimple/simpledet). - **State of the art** The toolbox stems from the codebase developed by the *MMDet* team, who won [COCO Detection Challenge](http://cocodataset.org/#detection-leaderboard) 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