# Matrix Nets: A New Deep Architecture for Object Detection

Source: [Matrix Nets: A New Deep Architecture for Object Detection](<https://www.sota2.com/research/paper/68fa663c1e209131cea0f110>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T04:08:22.070Z

Authors: Abdullah Rashwan, Agastya Kalra, Pascal Poupart

Published: 2019-08-13T13:54:31.000Z

## Abstract

We present Matrix Nets (xNets), a new deep architecture for object detection. xNets map objects with different sizes and aspect ratios into layers where the sizes and the aspect ratios of the objects within their layers are nearly uniform. Hence, xNets provide a scale and aspect ratio aware architecture. We leverage xNets to enhance key-points based object detection. Our architecture achieves mAP of 47.8 on MS COCO, which is higher than any other single-shot detector while using half the number of parameters and training 3x faster than the next best architecture.

## 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 COCO v2017 (test-dev)](<https://www.sota2.com/research/sota/object-detection-on-coco-v2017-test-dev.md>): Object Detection; COCO v2017 (test-dev); mAP: 47.8.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1908.04646>)

[PDF](<https://arxiv.org/pdf/1908.04646.pdf>)