# ThunderNet: Towards Real-time Generic Object Detection Source: [ThunderNet: Towards Real-time Generic Object Detection]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:08:55.299Z Authors: Zheng Qin, Zeming Li, Zhaoning Zhang, Yiping Bao, Gang Yu, Yuxing Peng, Jian Sun Published: 2019-03-28T01:48:09.000Z ## Abstract Real-time generic object detection on mobile platforms is a crucial but challenging computer vision task. However, previous CNN-based detectors suffer from enormous computational cost, which hinders them from real-time inference in computation-constrained scenarios. In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight two-stage detector named ThunderNet. In the backbone part, we analyze the drawbacks in previous lightweight backbones and present a lightweight backbone designed for object detection. In the detection part, we exploit an extremely efficient RPN and detection head design. To generate more discriminative feature representation, we design two efficient architecture blocks, Context Enhancement Module and Spatial Attention Module. At last, we investigate the balance between the input resolution, the backbone, and the detection head. Compared with lightweight one-stage detectors, ThunderNet achieves superior performance with only 40% of the computational cost on PASCAL VOC and COCO benchmarks. Without bells and whistles, our model runs at 24.1 fps on an ARM-based device. To the best of our knowledge, this is the first real-time detector reported on ARM platforms. Our code and models are available at \\url{https://github.com/qinzheng93/ThunderNet}. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on MS COCO (test-dev)](): Object Detection; MS COCO (test-dev); mAP@.5: 46.2. - [Object Detection on COCO (test-dev)](): Object Detection; COCO (test-dev); mAP: 28.1. - [Object Detection on VOC 2007 (test)](): Object Detection; VOC 2007 (test); AP@50: not recorded. - [Object Detection on MS COCO (test)](): Object Detection; MS-COCO (test); AP: not recorded. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()