Methods › Computer Vision › Object Detection Models › DAFNe
DAFNe
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
DAFNe is a dense one-stage anchor-free deep model for oriented object detection. It is a deep neural network that performs predictions on a dense grid over the input image, being architecturally simpler in design, as well as easier to optimize than its two-stage counterparts. Furthermore, it reduces the prediction complexity by refraining from employing bounding box anchors. This enables a tighter fit to oriented objects, leading to a better separation of bounding boxes especially in case of dense object distributions. Moreover, it introduces an orientation-aware generalization of the center-ness function to arbitrary quadrilaterals that takes into account the object's orientation and that, accordingly, accurately down-weights low-quality predictions
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Deep Anatomical Federated Network (Dafne): An open client-server framework for the continuous, collaborative improvement of deep learning-based medical image segmentation 13 Feb 2023 · 1 repository · arXiv:2302.06352
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DAFNe: A One-Stage Anchor-Free Approach for Oriented Object Detection 13 Sep 2021 · 1 repository · arXiv:2109.06148
Tasks archive 2025-07-28
11 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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