# CenterNet2

**Repository Path**: zhangming8/CenterNet2

## Basic Information

- **Project Name**: CenterNet2
- **Description**: https://github.com/xingyizhou/CenterNet2
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 1
- **Created**: 2021-06-23
- **Last Updated**: 2022-12-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Probabilistic two-stage detection
Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network.


<p align="center"> <img src='docs/centernet2_teaser.jpg' align="center" height="150px"> </p>

> [**Probabilistic two-stage detection**](http://arxiv.org/abs/2103.07461),            
> Xingyi Zhou, Vladlen Koltun, Philipp Kr&auml;henb&uuml;hl,        
> *arXiv technical report ([arXiv 2103.07461](http://arxiv.org/abs/2103.07461))*         

Contact: [zhouxy@cs.utexas.edu](mailto:zhouxy@cs.utexas.edu). Any questions or discussions are welcomed! 

## Summary

- Two-stage CenterNet: First stage estimates object probabilities, second stage conditionally classifies objects.

- Resulting detector is faster and more accurate than both traditional two-stage detectors (fewer proposals required), and one-stage detectors (lighter first stage head).

- Our best model achieves 56.4 mAP on COCO test-dev.

- This repo also includes a detectron2-based CenterNet implementation with better accuracy (42.5 mAP at 70FPS) and a new FPN version of CenterNet (40.2 mAP with Res50_1x).

## Main results

All models are trained with multi-scale training, and tested with a single scale. The FPS is tested on a Titan RTX GPU.
More models and details can be found in the [MODEL_ZOO](docs/MODEL_ZOO.md).

#### COCO

| Model                                     |  COCO val mAP |  FPS  |
|-------------------------------------------|---------------|-------|
| CenterNet-S4_DLA_8x                       |  42.5         |   71  |
| CenterNet2_R50_1x                         |  42.9         |   24  |
| CenterNet2_X101-DCN_2x                    |  49.9         |    8  |
| CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST |  56.1         |    5  |
| CenterNet2_DLA-BiFPN-P5_24x_ST            |  49.2         |   38  |


#### LVIS 

| Model                     | val mAP box |
| ------------------------- | ----------- |
| CenterNet2_R50_1x         | 26.5        |
| CenterNet2_FedLoss_R50_1x | 28.3        |


#### Objects365

| Model                                     |  val mAP |
|-------------------------------------------|----------|
| CenterNet2_R50_1x                         |  22.6    |

## Installation

Our project is developed on [detectron2](https://github.com/facebookresearch/detectron2). Please follow the official detectron2 [installation](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md).

We use the default detectron2 demo script. To run inference on an image folder using our pre-trained model, run

~~~
python demo.py --config-file configs/CenterNet2_R50_1x.yaml --input path/to/image/ --opts MODEL.WEIGHTS models/CenterNet2_R50_1x.pth
~~~

## Benchmark evaluation and training

Please check detectron2 [GETTING_STARTED.md](https://github.com/facebookresearch/detectron2/blob/master/GETTING_STARTED.md) for running evaluation and training. Our config files are under `configs` and the pre-trained models are in the [MODEL_ZOO](docs/MODEL_ZOO.md).


## License

Our code is under [Apache 2.0 license](LICENSE). `centernet/modeling/backbone/bifpn_fcos.py` are from [AdelaiDet](https://github.com/aim-uofa/AdelaiDet), which follows the original [non-commercial license](https://github.com/aim-uofa/AdelaiDet/blob/master/LICENSE).

## Citation

If you find this project useful for your research, please use the following BibTeX entry.

    @inproceedings{zhou2021probablistic,
      title={Probabilistic two-stage detection},
      author={Zhou, Xingyi and Koltun, Vladlen and Kr{\"a}henb{\"u}hl, Philipp},
      booktitle={arXiv preprint arXiv:2103.07461},
      year={2021}
    }