RRepoGEO

REPOGEO REPORT · LITE

bubbliiiing/yolov8-pytorch

Default branch master · commit c245eb01 · scanned 6/26/2026, 6:07:01 AM

GitHub: 1,004 stars · 109 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface bubbliiiing/yolov8-pytorch, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add specific topics for better categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    yolov8, pytorch, object-detection, deep-learning, computer-vision, machine-learning, custom-dataset-training
  • highabout#2
    Update repository description for clarity and English-first discoverability

    Why:

    CURRENT
    这是一个yolov8-pytorch的仓库，可以用于训练自己的数据集。
    COPY-PASTE FIX
    A clean, from-scratch PyTorch re-implementation of YOLOv8 for object detection, supporting custom dataset training. (YOLOv8目标检测模型的PyTorch实现，支持自定义数据集训练)
  • mediumreadme#3
    Add an explicit English introductory sentence to the README

    Why:

    CURRENT
    ## YOLOV8：You Only Look Once目标检测模型在pytorch当中的实现
    COPY-PASTE FIX
    This repository provides a clean, from-scratch PyTorch re-implementation of the YOLOv8 object detection model, designed for training on custom datasets.

## YOLOV8：You Only Look Once目标检测模型在pytorch当中的实现

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface bubbliiiing/yolov8-pytorch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MMDetection
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MMDetection · recommended 2×
  2. Detectron2 · recommended 2×
  3. YOLOv5 (Ultralytics) · recommended 1×
  4. PyTorch-Lightning · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How can I train a custom object detection model using a PyTorch implementation?
    you: not recommended
    AI recommended (in order):
    1. MMDetection
    2. Detectron2
    3. YOLOv5 (Ultralytics)
    4. PyTorch-Lightning
    5. Hugging Face Transformers
    6. torchvision.models

    AI recommended 6 alternatives but never named bubbliiiing/yolov8-pytorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good PyTorch libraries for real-time object detection with various optimization techniques?
    you: not recommended
    AI recommended (in order):
    1. YOLOv8 (Ultralytics YOLO)
    2. MMDetection
    3. PyTorch-YOLOv3 / PyTorch-YOLOv4
    4. Detectron2
    5. Timm (PyTorch Image Models)

    AI recommended 5 alternatives but never named bubbliiiing/yolov8-pytorch. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of bubbliiiing/yolov8-pytorch?
    pass
    AI named bubbliiiing/yolov8-pytorch explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts bubbliiiing/yolov8-pytorch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bubbliiiing/yolov8-pytorch explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo bubbliiiing/yolov8-pytorch solve, and who is the primary audience?
    pass
    AI did not name bubbliiiing/yolov8-pytorch — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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bubbliiiing/yolov8-pytorch — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite