# Unsupervised Domain Adaptation from Synthetic to Real Images for Anchorless Object Detection Source: [Unsupervised Domain Adaptation from Synthetic to Real Images for Anchorless Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:01:46.798Z Authors: Tobias Scheck, Ana Perez Grassi, Gangolf Hirtz Published: 2020-12-15T10:51:43.000Z ## Abstract Synthetic images are one of the most promising solutions to avoid high costs associated with generating annotated datasets to train supervised convolutional neural networks (CNN). However, to allow networks to generalize knowledge from synthetic to real images, domain adaptation methods are necessary. This paper implements unsupervised domain adaptation (UDA) methods on an anchorless object detector. Given their good performance, anchorless detectors are increasingly attracting attention in the field of object detection. While their results are comparable to the well-established anchor-based methods, anchorless detectors are considerably faster. In our work, we use CenterNet, one of the most recent anchorless architectures, for a domain adaptation problem involving synthetic images. Taking advantage of the architecture of anchorless detectors, we propose to adjust two UDA methods, viz., entropy minimization and maximum squares loss, originally developed for segmentation, to object detection. Our results show that the proposed UDA methods can increase the mAPfrom61 %to69 %with respect to direct transfer on the considered anchorless detector. The code is available: https://github.com/scheckmedia/centernet-uda. ## 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]()