# Reducing Label Noise in Anchor-Free Object Detection Source: [Reducing Label Noise in Anchor-Free Object Detection]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:18:45.252Z Authors: Nermin Samet, Samet Hicsonmez, Emre Akbas Published: 2020-08-03T20:02:46.000Z ## Abstract Current anchor-free object detectors label all the features that spatially fall inside a predefined central region of a ground-truth box as positive. This approach causes label noise during training, since some of these positively labeled features may be on the background or an occluder object, or they are simply not discriminative features. In this paper, we propose a new labeling strategy aimed to reduce the label noise in anchor-free detectors. We sum-pool predictions stemming from individual features into a single prediction. This allows the model to reduce the contributions of non-discriminatory features during training. We develop a new one-stage, anchor-free object detector, PPDet, to employ this labeling strategy during training and a similar prediction pooling method during inference. On the COCO dataset, PPDet achieves the best performance among anchor-free top-down detectors and performs on-par with the other state-of-the-art methods. It also outperforms all major one-stage and two-stage methods in small object detection (${AP}\_{S}$ $31.4$). Code is available at https://github.com/nerminsamet/ppdet ## 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)](): Object Detection; COCO (test-dev); mAP: 46.3. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()