# A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection

Source: [A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection](<https://www.sota2.com/research/paper/68fa69121e209131cea2002f>)

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

Data updated: 2026-01-04T04:17:40.849Z

Authors: Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan

Published: 2020-09-28T19:24:51.000Z

## Abstract

We propose average Localisation-Recall-Precision (aLRP), a unified, bounded, balanced and ranking-based loss function for both classification and localisation tasks in object detection. aLRP extends the Localisation-Recall-Precision (LRP) performance metric (Oksuz et al., 2018) inspired from how Average Precision (AP) Loss extends precision to a ranking-based loss function for classification (Chen et al., 2020). aLRP has the following distinct advantages: (i) aLRP is the first ranking-based loss function for both classification and localisation tasks. (ii) Thanks to using ranking for both tasks, aLRP naturally enforces high-quality localisation for high-precision classification. (iii) aLRP provides provable balance between positives and negatives. (iv) Compared to on average $\\sim$6 hyperparameters in the loss functions of state-of-the-art detectors, aLRP Loss has only one hyperparameter, which we did not tune in practice. On the COCO dataset, aLRP Loss improves its ranking-based predecessor, AP Loss, up to around $5$ AP points, achieves $48.9$ AP without test time augmentation and outperforms all one-stage detectors. Code available at: https://github.com/kemaloksuz/aLRPLoss .

## 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 (test-dev)](<https://www.sota2.com/research/sota/object-detection-on-coco-test-dev.md>): Object Detection; COCO (test-dev); mAP: 50.2.

- [Object Detection on COCO 2017 (val)](<https://www.sota2.com/research/sota/object-detection-on-coco-2017-val.md>): Object Detection; COCO 2017 (val); AP: 40.7.

- [Object Detection on MS-COCO 2017 (val)](<https://www.sota2.com/research/sota/object-detection-on-ms-coco-2017-val.md>): Object Detection; MS-COCO 2017 (val); mAP: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2009.13592>)

[PDF](<https://arxiv.org/pdf/2009.13592.pdf>)

[Implementation](<https://github.com/kemaloksuz/aLRPLoss>)