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# 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 Krähenbühl,
> *arXiv technical report ([arXiv 2103.07461](http://arxiv.org/abs/2103.07461))*
Contact: [[email protected]](mailto:[email protected]). 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}
}
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