Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network.

Probabilistic two-stage detection,
Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl,
arXiv technical report (arXiv 2103.07461)

Contact: zhouxy@cs.utexas.edu. Any questions or discussions are welcomed!

• 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).

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.

Our project is developed on detectron2. Please follow the official detectron2 installation.

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

Please check detectron2 GETTING_STARTED.md for running evaluation and training. Our config files are under configs and the pre-trained models are in the MODEL_ZOO.

Our code is under Apache 2.0 license. centernet/modeling/backbone/bifpn_fcos.py are from AdelaiDet, which follows the original non-commercial license.

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}
}