MMDetection

News: We released the technical report on ArXiv.

Introduction

The master branch works with PyTorch 1.1 or higher.

mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK.

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 now. The training speed is faster than or comparable to other codebases, including Detectron, 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.

License

This project is released under the Apache 2.0 license.

Updates

v1.0rc0 (27/07/2019)

• Implement lots of new methods and components (Mixed Precision Training, HTC, Libra R-CNN, Guided Anchoring, Empirical Attention, Mask Scoring R-CNN, Grid R-CNN (Plus), GHM, GCNet, FCOS, HRNet, Weight Standardization, etc.). Thank all collaborators!
• Support two additional datasets: WIDER FACE and Cityscapes.
• Refactoring for loss APIs and make it more flexible to adopt different losses and related hyper-parameters.
• Speed up multi-gpu testing.
• Integrate all compiling and installing in a single script.

v0.6.0 (14/04/2019)

• Up to 30% speedup compared to the model zoo.
• Support both PyTorch stable and nightly version.
• Replace NMS and SigmoidFocalLoss with Pytorch CUDA extensions.

v0.6rc0(06/02/2019)

• Migrate to PyTorch 1.0.

v0.5.7 (06/02/2019)

• Add support for Deformable ConvNet v2. (Many thanks to the authors and @chengdazhi)
• This is the last release based on PyTorch 0.4.1.

v0.5.6 (17/01/2019)

• Add support for Group Normalization.
• Unify RPNHead and single stage heads (RetinaHead, SSDHead) with AnchorHead.

v0.5.5 (22/12/2018)

• Add SSD for COCO and PASCAL VOC.
• Add ResNeXt backbones and detection models.
• Refactoring for Samplers/Assigners and add OHEM.
• Add VOC dataset and evaluation scripts.

v0.5.4 (27/11/2018)

• Add SingleStageDetector and RetinaNet.

v0.5.3 (26/11/2018)

• Add Cascade R-CNN and Cascade Mask R-CNN.
• Add support for Soft-NMS in config files.

v0.5.2 (21/10/2018)

• Add support for custom datasets.
• Add a script to convert PASCAL VOC annotations to the expected format.

v0.5.1 (20/10/2018)

• Add BBoxAssigner and BBoxSampler, the train_cfg field in config files are restructured.
• ConvFCRoIHead / SharedFCRoIHead are renamed to ConvFCBBoxHead / SharedFCBBoxHead for consistency.

Benchmark and model zoo

Supported methods and backbones are shown in the below table. Results and models are available in the Model zoo.

	ResNet	ResNeXt	SENet	VGG	HRNet
RPN	✓	✓	☐	✗	✓
Fast R-CNN	✓	✓	☐	✗	✓
Faster R-CNN	✓	✓	☐	✗	✓
Mask R-CNN	✓	✓	☐	✗	✓
Cascade R-CNN	✓	✓	☐	✗	✓
Cascade Mask R-CNN	✓	✓	☐	✗	✓
SSD	✗	✗	✗	✓	✗
RetinaNet	✓	✓	☐	✗	✓
GHM	✓	✓	☐	✗	✓
Mask Scoring R-CNN	✓	✓	☐	✗	✓
FCOS	✓	✓	☐	✗	✓
Grid R-CNN (Plus)	✓	✓	☐	✗	✓
Hybrid Task Cascade	✓	✓	☐	✗	✓
Libra R-CNN	✓	✓	☐	✗	✓
Guided Anchoring	✓	✓	☐	✗	✓

Other features

• DCNv2
• Group Normalization
• Weight Standardization
• OHEM
• Soft-NMS
• Generalized Attention
• GCNet
• Mixed Precision (FP16) Training

Installation

Please refer to INSTALL.md for installation and dataset preparation.

Get Started

Please see GETTING_STARTED.md for the basic usage of MMDetection.

Contributing

We appreciate all contributions to improve MMDetection. 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 colledges 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  = {Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li,
             Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng,
             Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu,
             Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, Dahua Lin},
  journal = {arXiv preprint arXiv:1906.07155},
  year    = {2019}
}

Contact

This repo is currently maintained by Kai Chen (@hellock), Jiangmiao Pang (@OceanPang), Jiaqi Wang (@myownskyW7) and Yuhang Cao (@yhcao6).