OBBDetection

news: We are now updating OBBDetection to new vision based on MMdetection v2.10, which has more advanced models and more efficient features.

introduction

OBBDetection is a open source oriented object detection toolbox based on the MMdetection.

Major features

• MMdetection inheritance

  OBBDetection is modified from MMdetection v2.2, where all additive codes are put at newly created folders named obb. The structure of MMdetection isn't change, so our OBBDetection inherits all features from MMdetection.

• Support of multiple frameworks out of box

  Except for horizontal detection frameworks, the toolbox supports popular oriented detection frameworks, e.g. Faster RCNN OBB, RoI Transformer, Gliding Vertex.

• Flexible representation of boxes

  This toolbox supports three type of bounding boxes, horizontal bounding boxes (HBB), oriented bounding boxes (OBB), and 4 point boxes (POLY). Each type of boxes can transforms to others directly.

• Efficiency of training and testing big images

  We optimize the training and testing process of big image datasets. It can directly generate full image results without any postprocessing in AerialDetection. Besides, our OBBDtection also has a better proformance than AerialDetection.

License

This project is released under the Apache 2.0 license.

Benchmark and model zoo

Results and models are available in the model zoo.

Supported backbones:

• ResNet
• ResNeXt
• VGG
• HRNet
• RegNet
• Res2Net

Supported oriented detection methods:

• Faster R-CNN OBB
• RetinaNet OBB
• Gliding Vertex
• RoI Transformer

Supported horizontal detection methods:

• RPN
• Fast R-CNN
• Faster R-CNN
• Mask R-CNN
• Cascade R-CNN
• Cascade Mask R-CNN
• SSD
• RetinaNet
• GHM
• Mask Scoring R-CNN
• Double-Head R-CNN
• Hybrid Task Cascade
• Libra R-CNN
• Guided Anchoring
• FCOS
• RepPoints
• Foveabox
• FreeAnchor
• NAS-FPN
• ATSS
• FSAF
• PAFPN
• Dynamic R-CNN
• PointRend
• CARAFE
• DCNv2
• Group Normalization
• Weight Standardization
• OHEM
• Soft-NMS
• Generalized Attention
• GCNet
• Mixed Precision (FP16) Training
• InstaBoost
• GRoIE
• DetectoRS
• Generalized Focal Loss

Installation

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

Get Started

Oriented models training and testing

If you want to train or test a oriented model, please refer to oriented_model_starting.md.

How to use MMDetection

If you are not familiar with MMdetection, please see getting_started.md for the basic usage of MMDetection. There are also tutorials for finetuning models, adding new dataset, designing data pipeline, and adding new modules.

Acknowledgement

This toolbox is based on MMdetection. If you use this toolbox or benchmark in your research, please cite the following information.

@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}
}