# S4OD: Semi-Supervised learning for Single-Stage Object Detection Source: [S4OD: Semi-Supervised learning for Single-Stage Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:07:48.255Z Authors: Yueming Zhang, Xingxu Yao, Chao Liu, Feng Chen, Xiaolin Song, Tengfei Xing, Runbo Hu, Hua Chai, Pengfei Xu, Guoshan Zhang Published: 2022-04-09T15:19:37.000Z ## Abstract Single-stage detectors suffer from extreme foreground-background class imbalance, while two-stage detectors do not. Therefore, in semi-supervised object detection, two-stage detectors can deliver remarkable performance by only selecting high-quality pseudo labels based on classification scores. However, directly applying this strategy to single-stage detectors would aggravate the class imbalance with fewer positive samples. Thus, single-stage detectors have to consider both quality and quantity of pseudo labels simultaneously. In this paper, we design a dynamic self-adaptive threshold (DSAT) strategy in classification branch, which can automatically select pseudo labels to achieve an optimal trade-off between quality and quantity. Besides, to assess the regression quality of pseudo labels in single-stage detectors, we propose a module to compute the regression uncertainty of boxes based on Non-Maximum Suppression. By leveraging only 10% labeled data from COCO, our method achieves 35.0% AP on anchor-free detector (FCOS) and 32.9% on anchor-based detector (RetinaNet). ## 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]() [PDF]()