Methods › Computer Vision › Object Detection Models › NAS-FCOS

NAS-FCOS

2 papers tagged archive 2025-07-28

Introduced by Ning Wang et al. in NAS-FCOS: Fast Neural Architecture Search for Object Detection

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

NAS-FCOS consists of two sub networks, an FPN f and a set of prediction heads h which have shared structures. One notable difference with other FPN-based one-stage detectors is that our heads have partially shared weights. Only the last several layers of the predictions heads (marked as yellow) are tied by their weights. The number of layers to share is decided automatically by the search algorithm. Note that both FPN and head are in our actual search space; and have more layers than shown in this figure.

PaperSource

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.

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.

TaskPapers
GPU2
Neural Architecture Search2
Object2
Object Detection2
object-detection2
Decoder1
FAD1
Instance Segmentation1
Region Proposal1
Reinforcement Learning1
Semantic Segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with NAS-FCOS: 2019 to 2020, peak 1 1 0 2019: 1 paper 2019 2020: 1 paper 2020
Papers per year the archive tags with this method, by the paper's archive date (2 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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