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

arXiv: https://arxiv.org/abs/2009.13592 · HTML: https://mlsanity.com/paper/2009.13592/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 25 of 25)

- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [TOOD: Task-aligned One-stage Object Detection](https://mlsanity.com/paper/2108.07755/) (2021)
- [Rank-DETR for High Quality Object Detection](https://mlsanity.com/paper/2310.08854/) (2023)
- [Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence](https://mlsanity.com/paper/2104.08736/) (2021)
- [Rank & Sort Loss for Object Detection and Instance Segmentation](https://mlsanity.com/paper/2107.11669/) (2021)
- [AutoLoss-Zero: Searching Loss Functions from Scratch for Generic Tasks](https://mlsanity.com/paper/2103.14026/) (2021)
- [Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration](https://mlsanity.com/paper/2307.00934/) (2023)
- [RankED: Addressing Imbalance and Uncertainty in Edge Detection Using Ranking-based Losses](https://mlsanity.com/paper/2403.01795/) (2024)
- [MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection](https://mlsanity.com/paper/2309.14976/) (2023)
- [Momentum Accelerates the Convergence of Stochastic AUPRC Maximization](https://mlsanity.com/paper/2107.01173/) (2021)
- [Prediction-Guided Distillation for Dense Object Detection](https://mlsanity.com/paper/2203.05469/) (2022)
- [Long-tail Detection with Effective Class-Margins](https://mlsanity.com/paper/2301.09724/) (2023)
- [Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability](https://mlsanity.com/paper/2209.13262/) (2022)
- [Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities](https://mlsanity.com/paper/2111.02400/) (2021)
- [Dome-DETR: DETR with Density-Oriented Feature-Query Manipulation for Efficient Tiny Object Detection](https://mlsanity.com/paper/2505.05741/) (2025)
- [Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection](https://mlsanity.com/paper/2207.12042/) (2022)
- [Correlation Loss: Enforcing Correlation between Classification and Localization](https://mlsanity.com/paper/2301.01019/) (2023)
- [Class Uncertainty: A Measure to Mitigate Class Imbalance](https://mlsanity.com/paper/2311.14090/) (2023)
- [Bucketed Ranking-based Losses for Efficient Training of Object Detectors](https://mlsanity.com/paper/2407.14204/) (2024)
- [QAMRO: Quality-aware Adaptive Margin Ranking Optimization for Human-aligned Assessment of Audio Generation Systems](https://mlsanity.com/paper/2508.08957/) (2025)
- [Searching Parameterized AP Loss for Object Detection](https://mlsanity.com/paper/2112.05138/) (2021)
- [Reformulating van Rijsbergen's Fβ metric for weighted binary cross-entropy](https://mlsanity.com/paper/2210.16458/) (2022)
- [Real-Time Detection and Tracking of Foreign Object Intrusions in Power Systems via Feature-Based Edge Intelligence](https://mlsanity.com/paper/2509.13396/) (2025)

## Abstract (arXiv)

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 ~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 .