# MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection

Source: [MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection](<https://www.sota2.com/research/paper/68fa66e41e209131cea12e23>)

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

Data updated: 2025-10-25T22:00:21.133Z

Authors: Abdullah Rashwan, Rishav Agarwal, Agastya Kalra, Pascal Poupart

Published: 2020-01-09T19:32:22.000Z

## Abstract

We present MatrixNets (xNets), a new deep architecture for object detection. xNets map objects with similar sizes and aspect ratios into many specialized layers, allowing xNets to provide a scale and aspect ratio aware architecture. We leverage xNets to enhance single-stage object detection frameworks. First, we apply xNets on anchor-based object detection, for which we predict object centers and regress the top-left and bottom-right corners. Second, we use MatrixNets for corner-based object detection by predicting top-left and bottom-right corners. Each corner predicts the center location of the object. We also enhance corner-based detection by replacing the embedding layer with center regression. Our final architecture achieves mAP of 47.8 on MS COCO, which is higher than its CornerNet counterpart by +5.6 mAP while also closing the gap between single-stage and two-stage detectors. The code is available at https://github.com/arashwan/matrixnet.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2001.03194>)

[PDF](<https://arxiv.org/pdf/2001.03194.pdf>)