CenterNet Hourglass-104 MAP 42.1 updated with the latest ranking of this paper.

CenterNet is a one-stage object detector that detects each object as a triplet, rather than a pair, of keypoints. It utilizes 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.

I believe that CenterNet could get higher pts and implemented in a more elegant way, so I write this repo. 2019-12-30 Hello everyone! Currently I’ve started reading the paper of name “CenterNet: Objects as Points”. The general idea is to train a keypoint estimator using heat-map and then extend those detected keypoint to other task such as object detection, human-pose estimation, etc. But the thing that confused me is how to splat the ground truth keypoint onto a heat-map by using Gaussian kernel.

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2020-11-16

We build our framework upon a representative one-stage keypoint-based detector named The code to train and evaluate the proposed CenterNet is available here. For more technical details, please refer to our arXiv paper. We thank Princeton Vision & Learning Lab for providing the original implementation of CornerNet.

2019-12-30

I've been using variants of it for over a year in various applications and it is so much nicer than Yolo type networks, easier to understand, reason about and extend. Request PDF | Fruit Detection from Digital Images Using CenterNet | In this paper, CenterNet is chosen as the model to settle fruit detection problem from digital images.

Recovery Paper where method was first introduced: Method category (e.g. Activation Functions): If no match, add something for now then you can add a new category afterwards.
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The essential idea of the paper is to treat objects as points denoted by their centers rather than CenterNet: Keypoint Triplets for Object Detection. by Kaiwen Duan, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang and Qi Tian. The code to train and evaluate the proposed CenterNet is available here. For more technical details, please refer to our arXiv paper.. We thank Princeton Vision & Learning Lab for providing the original implementation of CornerNet.

The code to train and evaluate the proposed CenterNet is available here. For more technical details, please refer to our arXiv paper.. We thank Princeton Vision & Learning Lab for providing the original implementation of CornerNet.
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Paper where method was first introduced: Method category (e.g. Activation Functions): If no match, add something for now then you can add a new category afterwards. Markdown description (optional; $\LaTeX$ enabled): You can edit this later, so feel free to start with something succinct.

To enhance detection performance, we adop- Understanding Centernet 05 November 2019. Recently I came across a very nice paper Objects as Points by Zhou et al. I found the approach pretty interesting and novel. It doesn’t use anchor boxes and requires minimal post-processing. The essential idea of the paper is to treat objects as points denoted by their centers rather than CenterNet: Keypoint Triplets for Object Detection. by Kaiwen Duan, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang and Qi Tian. The code to train and evaluate the proposed CenterNet is available here.