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| Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network. | ||
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| <p align="center"> <img src='projects/CenterNet2/centernet2_docs/centernet2_teaser.jpg' align="center" height="150px"> </p> | ||
| <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))* | ||
| Contact: [[email protected]](mailto:[email protected]). Any questions or discussions are welcomed! | ||
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| ## Abstract | ||
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| We develop a probabilistic interpretation of two-stage object detection. We show that this probabilistic interpretation motivates a number of common empirical training practices. It also suggests changes to two-stage detection pipelines. Specifically, the first stage should infer proper object-vs-background likelihoods, which should then inform the overall score of the detector. A standard region proposal network (RPN) cannot infer this likelihood sufficiently well, but many one-stage detectors can. We show how to build a probabilistic two-stage detector from any state-of-the-art one-stage detector. The resulting detectors are faster and more accurate than both their one- and two-stage precursors. Our detector achieves 56.4 mAP on COCO test-dev with single-scale testing, outperforming all published results. Using a lightweight backbone, our detector achieves 49.2 mAP on COCO at 33 fps on a Titan Xp. | ||
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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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| ## 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](projects/CenterNet2/centernet2_docs/MODEL_ZOO.md). | ||
| More models and details can be found in the [MODEL_ZOO](docs/MODEL_ZOO.md). | ||
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| #### COCO | ||
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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). All our code is under `projects/CenterNet2/`. In theory, you should be able to copy-paste `projects/CenterNet2/` to the latest detectron2 release or your own detectron2 repo to run our project. There might be API changes in future detectron2 releases that make it incompatible. | ||
| 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 projects/CenterNet2/demo/demo.py --config-file projects/CenterNet2/configs/CenterNet2_R50_1x.yaml --input path/to/image/ --opts MODEL.WEIGHTS models/CenterNet2_R50_1x.pth | ||
| 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 `projects/CenterNet2/configs` and the pre-trained models are in the [MODEL_ZOO](projects/CenterNet2/centernet2_docs/MODEL_ZOO.md). | ||
| 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 under `projects/CenterNet2/` is under [Apache 2.0 license](projects/CenterNet2/LICENSE). `projects/CenterNet2/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). The code from detectron2 follows the original [Apache 2.0 license](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). | ||
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| ## Citation | ||
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