Methods › Computer Vision › Object Detection Models › PAFNet

Paddle Anchor Free Network

PAFNet

1 paper tagged archive 2025-07-28

Introduced by Ying Xin et al. in PAFNet: An Efficient Anchor-Free Object Detector Guidance

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

PAFNet is an anchor-free detector for object detection that removes pre-defined anchors and regresses the locations directly, which can achieve higher efficiency. The overall network is composed of a backbone, an up-sampling module, an AGS module, a localization branch and a regression branch. Specifically, ResNet50-vd is chosen as the backbone for server side, and MobileNetV3 for mobile side. Besides, for mobile side, we replace traditional convolution layers with lite convolution operators.

PaperSource

Papers archive 2025-07-28

1 shown of 1, 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.

Tasks archive 2025-07-28

5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
CPU1
GPU1
Object1
Object Detection1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with PAFNet: 2021 to 2021, peak 1 1 0 2021: 1 paper 2021
Papers per year the archive tags with this method, by the paper's archive date (1 dated). Bars are counts, not a trend claim.

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

Object Detection Models

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