| # Probabilistic two-stage detection |
| Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network. |
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| <p align="center"> <img src='docs/centernet2_teaser.jpg' align="center" height="150px"> </p> |
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| > [**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))* |
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| Contact: [zhouxy@cs.utexas.edu](mailto:zhouxy@cs.utexas.edu). Any questions or discussions are welcomed! |
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| ## Summary |
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| - Two-stage CenterNet: First stage estimates object probabilities, second stage conditionally classifies objects. |
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| - Resulting detector is faster and more accurate than both traditional two-stage detectors (fewer proposals required), and one-stage detectors (lighter first stage head). |
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| - Our best model achieves 56.4 mAP on COCO test-dev. |
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| - 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). |
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| ## Main results |
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| 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). |
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| #### COCO |
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| | 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 | |
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| #### LVIS |
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| | Model | val mAP box | |
| | ------------------------- | ----------- | |
| | CenterNet2_R50_1x | 26.5 | |
| | CenterNet2_FedLoss_R50_1x | 28.3 | |
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| #### Objects365 |
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| | Model | val mAP | |
| |-------------------------------------------|----------| |
| | CenterNet2_R50_1x | 22.6 | |
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| ## Installation |
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| 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). |
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| We use the default detectron2 demo script. To run inference on an image folder using our pre-trained model, run |
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| ~~~ |
| python demo.py --config-file configs/CenterNet2_R50_1x.yaml --input path/to/image/ --opts MODEL.WEIGHTS models/CenterNet2_R50_1x.pth |
| ~~~ |
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| ## Benchmark evaluation and training |
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| 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). |
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| ## License |
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| 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). |
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| ## Citation |
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| If you find this project useful for your research, please use the following BibTeX entry. |
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| @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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