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YOLOv8 to YOLO11: Review details architecture evolution and challenges

By PulseAugur Editorial · [1 sources] ·

This paper provides a detailed comparative review of the YOLOv8 through YOLO11 computer vision models. It aims to clarify the architectures and distinctions between these rapidly evolving object detection systems, many of which lack official documentation or scholarly publications. The analysis, based on academic papers, documentation, and source code scrutiny, highlights consistent architectural blocks across versions while noting improvements in feature extraction. AI

IMPACT Provides clarity on the architectural evolution of popular object detection models, aiding developers in understanding and utilizing them.

RANK_REASON This is a research paper analyzing existing models, not a release of a new model or a significant industry event.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

YOLOv8 to YOLO11: Review details architecture evolution and challenges

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This is a research paper analyzing existing models, not a release of a new model or a significant industry event.
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High
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151 days old
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Priyanto Hidayatullah, Nurjannah Syakrani, Muhammad Rizqi Sholahuddin, Trisna Gelar, Refdinal Tubagus ·

    YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review

    arXiv:2501.13400v3 Announce Type: replace Abstract: In the field of deep learning-based computer vision, YOLO is revolutionary. With respect to deep learning models, YOLO is also the one that is evolving the most rapidly. Unfortunately, not every YOLO model possesses scholarly pu…

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