TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Methods/Varifocal Loss

Varifocal Loss

GeneralIntroduced 20008 papers
Source Paper

Description

Varifocal Loss is a loss function for training a dense object detector to predict the IACS, inspired by focal loss. Unlike the focal loss that deals with positives and negatives equally, Varifocal Loss treats them asymmetrically.

VFL(p,q)=−q(qlog⁡(p)+(1−q)log⁡(1−p)) if q>0VFL\left(p, q\right) = −q\left(q\log\left(p\right) + \left(1 − q\right)\log\left(1 − p\right)\right) \text{ if } q > 0VFL(p,q)=−q(qlog(p)+(1−q)log(1−p)) if q>0

VFL(p,q)=−αpγlog⁡(1−p)VFL\left(p, q\right) = −\alpha{p^{\gamma}}\log\left(1-p\right)VFL(p,q)=−αpγlog(1−p)

where ppp is the predicted IACS and qqq is the target IoU score.

For a positive training example, qqq is set as the IoU between the generated bounding box and the ground-truth one (gt IoU), whereas for a negative training example, the training target qqq for all classes is 000.

Papers Using This Method

Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT2025-04-08Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentation2025-04-07Comparing Surface Landmine Object Detection Models on a New Drone Flyby Dataset2024-10-17How to Train an Accurate and Efficient Object Detection Model on Any Dataset2022-11-30Universal Lymph Node Detection in T2 MRI using Neural Networks2022-03-31Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection2022-02-14SWA Object Detection2020-12-23VarifocalNet: An IoU-aware Dense Object Detector2020-08-31