Methods › Computer Vision › Convolutional Neural Networks › Fast-YOLOv3

Fast-YOLOv3

2 papers tagged archive 2025-07-28

Introduced by Joseph Redmon et al. in YOLOv3: An Incremental Improvement

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

The archive carries no description for this method.

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.

Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines
6 May 2020 · 0 repositories · arXiv:2005.03106
YOLOv3: An Incremental Improvement
8 Apr 2018 · 311 repositories · arXiv:1804.02767

Syntology ran 18 of 124 samples · 106 unverified · 19 pointer-only (licence)

Tasks archive 2025-07-28

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

Task	Papers
Classification	1
Dial Meter Reading	1
Image-based Automatic Meter Reading	1
Meter Reading	1
Object Detection	1
One-stage Anchor-free Oriented Object Detection	1
Pedestrian Detection	1
Real-Time Object Detection	1
Robust Object Detection	1

Usage over time archive 2025-07-28

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

Convolutional Neural Networks

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