# Weighted boxes fusion: Ensembling boxes from different object detection models

arXiv: https://arxiv.org/abs/1910.13302 · HTML: https://mlsanity.com/paper/1910.13302/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 20 of 20)

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- [Optimizing YOLO Architectures for Optimal Road Damage Detection and Classification: A Comparative Study from YOLOv7 to YOLOv10](https://mlsanity.com/paper/2410.08409/) (2024)
- [How To Effectively Train An Ensemble Of Faster R-CNN Object Detectors To Quantify Uncertainty](https://mlsanity.com/paper/2310.04829/) (2023)
- [Ensemble Learning for Microbubble Localization in Super-Resolution Ultrasound](https://mlsanity.com/paper/2411.07376/) (2024)
- [YOLO-PVC: 2D-to-3D Consolidation of Slice-wise Detections for Volumetric Liver Tumor Localization in MRI](https://mlsanity.com/paper/2608.04642/) (2026)

## Abstract (arXiv)

In this work, we present a novel method for combining predictions of object detection models: weighted boxes fusion. Our algorithm utilizes confidence scores of all proposed bounding boxes to constructs the averaged boxes. We tested method on several datasets and evaluated it in the context of the Open Images and COCO Object Detection tracks, achieving top results in these challenges. The source code is publicly available at https://github.com/ZFTurbo/Weighted-Boxes-Fusion