# MMDetection
**News**: We released the technical report on [ArXiv](https://arxiv.org/abs/1906.07155).
Documentation: https://mmdetection.readthedocs.io/
## 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](http://mmlab.ie.cuhk.edu.hk/).
![demo image](demo/coco_test_12510.jpg)
### 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](https://github.com/facebookresearch/Detectron), [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](https://github.com/open-mmlab/mmcv) for computer vision research, which is heavily depended on by this toolbox.
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Changelog
v1.0.0 was released in 30/1/2020, with more than 20 fixes and improvements.
Please refer to [CHANGELOG.md](docs/CHANGELOG.md) for details and release history.
## Benchmark and model zoo
Supported methods and backbones are shown in the below table.
Results and models are available in the [Model zoo](docs/MODEL_ZOO.md).
| | 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 | ✓ | ✓ | ☐ | ✗ | ✓ |
| Double-Head R-CNN | ✓ | ✓ | ☐ | ✗ | ✓ |
| Grid R-CNN (Plus) | ✓ | ✓ | ☐ | ✗ | ✓ |
| Hybrid Task Cascade| ✓ | ✓ | ☐ | ✗ | ✓ |
| Libra R-CNN | ✓ | ✓ | ☐ | ✗ | ✓ |
| Guided Anchoring | ✓ | ✓ | ☐ | ✗ | ✓ |
| FCOS | ✓ | ✓ | ☐ | ✗ | ✓ |
| RepPoints | ✓ | ✓ | ☐ | ✗ | ✓ |
| Foveabox | ✓ | ✓ | ☐ | ✗ | ✓ |
| FreeAnchor | ✓ | ✓ | ☐ | ✗ | ✓ |
| NAS-FPN | ✓ | ✓ | ☐ | ✗ | ✓ |
| ATSS | ✓ | ✓ | ☐ | ✗ | ✓ |
Other features
- [x] [CARAFE](configs/carafe/README.md)
- [x] [DCNv2](configs/dcn/README.md)
- [x] [Group Normalization](configs/gn/README.md)
- [x] [Weight Standardization](configs/gn+ws/README.md)
- [x] OHEM
- [x] Soft-NMS
- [x] [Generalized Attention](configs/empirical_attention/README.md)
- [x] [GCNet](configs/gcnet/README.md)
- [x] [Mixed Precision (FP16) Training](https://github.com/open-mmlab/mmdetection/blob/master/configs/fp16)
- [x] [InstaBoost](configs/instaboost/README.md)
## Installation
Please refer to [INSTALL.md](docs/INSTALL.md) for installation and dataset preparation.
## Get Started
Please see [GETTING_STARTED.md](docs/GETTING_STARTED.md) for the basic usage of MMDetection.
## Contributing
We appreciate all contributions to improve MMDetection. Please refer to [CONTRIBUTING.md](.github/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}
}
```
## Contact
This repo is currently maintained by Kai Chen ([@hellock](http://github.com/hellock)), Yuhang Cao ([@yhcao6](https://github.com/yhcao6)), Wenwei Zhang ([@ZwwWayne](https://github.com/ZwwWayne)), Jiangmiao Pang ([@OceanPang](https://github.com/OceanPang)) and Jiaqi Wang ([@myownskyW7](https://github.com/myownskyW7)).
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水下目标检测算法赛声学图像赛项第15名代码.zip (310个子文件)
make.bat 795B
.isort.cfg 323B
deform_conv_cuda.cpp 29KB
nms_cpu.cpp 7KB
carafe_cuda.cpp 5KB
deform_pool_cuda.cpp 4KB
roi_align_cuda.cpp 3KB
roi_pool_cuda.cpp 3KB
carafe_naive_cuda.cpp 3KB
masked_conv2d_cuda.cpp 3KB
sigmoid_focal_loss.cpp 2KB
compiling_info.cpp 1KB
nms_cuda.cpp 575B
deform_conv_cuda_kernel.cu 42KB
carafe_cuda_kernel.cu 20KB
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carafe_naive_cuda_kernel.cu 7KB
roi_pool_kernel.cu 7KB
sigmoid_focal_loss_cuda.cu 6KB
nms_kernel.cu 5KB
masked_conv2d_kernel.cu 5KB
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inference_demo.ipynb 1.02MB
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my_test.py 16KB
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resnext.py 7KB
maskiou_head.py 7KB
double_head_rcnn.py 7KB
free_anchor_retina_head.py 7KB
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