README.md

# MMDetection

**News**: We released the technical report on [ArXiv](https://arxiv.org/abs/1906.07155).

## Introduction

The master branch works with **PyTorch 1.1** or higher. If you would like to use PyTorch 0.4.1,
please checkout to the [pytorch-0.4.1](https://github.com/open-mmlab/mmdetection/tree/pytorch-0.4.1) branch.

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**

  One can easily construct a customized object detection framework by combining different components.

- **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 nearly 2x faster than Detectron and comparable to maskrcnn-benchmark.

- **State of the art**

  This was the codebase of the *MMDet* team, who won the [COCO Detection 2018 challenge](http://cocodataset.org/#detection-leaderboard), 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).

## Updates

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](https://github.com/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](MODEL_ZOO.md).

|                    | ResNet   | ResNeXt  | SENet    | VGG      |
|--------------------|:--------:|:--------:|:--------:|:--------:|
| RPN                | ✓        | ✓        | ☐        | ✗        |
| Fast R-CNN         | ✓        | ✓        | ☐        | ✗        |
| Faster R-CNN       | ✓        | ✓        | ☐        | ✗        |
| Mask R-CNN         | ✓        | ✓        | ☐        | ✗        |
| Cascade R-CNN      | ✓        | ✓        | ☐        | ✗        |
| Cascade Mask R-CNN | ✓        | ✓        | ☐        | ✗        |
| SSD                | ✗        | ✗        | ✗        | ✓        |
| RetinaNet          | ✓        | ✓        | ☐        | ✗        |
| Hybrid Task Cascade| ✓        | ✓        | ☐        | ✗        |
| FCOS               | ✓        | ✓        | ☐        | ✗        |
| Libra R-CNN        | ✓        | ✓        | ☐        | ✗        |

Other features
- [x] DCNv2
- [x] Group Normalization
- [x] Weight Standardization
- [x] OHEM
- [x] Soft-NMS
- [ ] Mixed Precision (FP16) Training (coming soon)


## Installation

Please refer to [INSTALL.md](INSTALL.md) for installation and dataset preparation.


## Get Started

Please see [GETTING_STARTED.md](GETTING_STARTED.md) for the basic usage of MMDetection.

## Contributing

We appreciate all contributions to improve MMDetection. Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline.


## 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}
}
```