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Codes for our paper "CenterNet: Keypoint Triplets for Object Detection" .
| Date | Stars |
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| 2026-07-24 | 1886 |
| 2026-07-25 | 1886 |
| 2026-07-28 | 1886 |
| 2026-07-30 | 1886 |
| 2026-08-06 | 1886 |
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# Updates - (April, 2022) We released a pyramid framework for CenterNet: [PyCenterNet](https://github.com/Duankaiwen/PyCenterNet). # [CenterNet: Keypoint Triplets for Object Detection](https://arxiv.org/abs/1904.08189) by [Kaiwen Duan](https://scholar.google.com/citations?hl=zh-CN&user=TFHRaZUAAAAJ&scilu=&scisig=AMD79ooAAAAAXLv9_7ddy26i4c6z5n9agk05m97faUdN&gmla=AJsN-F78W-h98Pb2H78j6lTKbjdn0fklhe2X_8CCPqRU2fC4KJEIbllhD2c5F0irMR3zDiehKt_SH26N2MHI1HlUMw6qRba9HMbiP3vnQfJqD82FrMAPdlU&sciund=10706678259143520926&gmla=AJsN-F5cOpNUdnI6YrZ9joRa6JE2nP6wFKU1GKVkNIfCmmgjk431Lg2BYCS6wn5WWZxdnzBjLfaUwdUJtvPXo53vfoOQoTGP5fHh2X0cCssVtXm8BI4PaM3_oQvKYtCx7o1wivIt1l49sDK6AZPvHLMxxPbC4GbZ1Q&sciund=10445692451499027349), [Song Bai](http://songbai.site/), [Lingxi Xie](http://lingxixie.com/Home.html), [Honggang Qi](http://people.ucas.ac.cn/~hgqi), [Qingming Huang](https://scholar.google.com/citations?user=J1vMnRgAAAAJ&hl=zh-CN) and [Qi Tian](https://scholar.google.com/citations?user=61b6eYkAAAAJ&hl=zh-CN) **The code to train and evaluate the proposed CenterNet is available here. For more technical details, please refer to our [arXiv paper](https://arxiv.org/abs/1904.08189).** **We thank [Princeton Vision & Learning Lab](https://github.com/princeton-vl) for providing the original implementation of [CornerNet](https://github.com/princeton-vl/CornerNet).** **CenterNet is an one-stage detector which gets trained from scratch. On the MS-COCO dataset, CenterNet achieves an AP of 47.0%, which surpasses all known one-stage detectors, and even gets very close to the top-performance two-stage detectors.** ## Abstract In object detection, keypoint-based approaches often suffer a large number of incorrect object bounding boxes, arguably due to the lack of an additional look into the cropped regions. This paper presents an efficient solution which explores the visual patterns within each cropped region with minimal costs. We build our framework upon a representative one-stage keypoint-based detector named CornerNet. Our approach, named CenterNet, detects each object as a triplet, rather than a pair, of keypoints, which improves both precision and recall. Accordingly, we design two customized modules named cascade corner pooling and center pooling, which play the roles of enriching information collected by both top-left and bottom-right corners and providing more recognizable information at the central regions, respectively. On the MS-COCO dataset, CenterNet achieves an AP of 47.0%, which outperforms all existing one-stage detectors by a large margin. Meanwhile, with a faster inference speed, CenterNet demonstrates quite comparable performance to the top-ranked two-stage detectors. ## Introduction CenterNet is a framework for object detection with deep convolutional neural networks. You can use the code to train and evaluate a network for object detection on the MS-COCO dataset. * It achieves state-of-the-art performance (an AP of 47.0%) on one of the most challenging dataset: MS-COCO. * Our code is written in Python, based on [CornerNet](https://github.com/princeton-vl/CornerNet). *More detailed descriptions of our approach and code will be made available soon.* **If you encounter any problems in using our code, please contact Kaiwen Duan: [email protected].** ## Architecture  ## Comparison with other methods    In terms of speed, we test the inference speed of both CornerNet and CenterNet on a NVIDIA Tesla P100 GPU. We obtain that the average inference time of CornerNet511-104 (means that the resolution of input images is 511X511 and the backbone is Hourglass-104) is 300ms per image and that of CenterNet511-104 is 340ms. Mean
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matched fp:948eab8c6e96cbcc, topic:object-detection, desc:object detection, readme:object detection