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

Creative Commons License

Öksüz K., Çam B. C. , Akbaş E., Kalkan S.

2020 Conference on Neural Information Processing Systems, Montreal, Canada, 6 - 12 December 2020, vol.33, pp.15534-15545

  • Publication Type: Conference Paper / Full Text
  • Volume: 33
  • City: Montreal
  • Country: Canada
  • Page Numbers: pp.15534-15545
  • Middle East Technical University Affiliated: Yes


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 .