RRepoGEO

REPOGEO REPORT · LITE

M-3LAB/awesome-industrial-anomaly-detection

Default branch main · commit 58d749af · scanned 5/15/2026, 9:48:09 AM

GitHub: 3,548 stars · 318 forks

AI VISIBILITY SCORE
22 /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
1 / 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 M-3LAB/awesome-industrial-anomaly-detection, 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
  • highreadme#1
    Clarify repo's nature as a curated list in README intro

    Why:

    CURRENT
    # Awesome Industrial Anomaly Detection
    COPY-PASTE FIX
    # Awesome Industrial Anomaly Detection

This repository is a curated and continuously updated collection of academic papers and public datasets focused on industrial image anomaly and defect detection.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the MIT License text.
  • mediumhomepage#3
    Update repository homepage URL

    Why:

    CURRENT
    https://link.springer.com/content/pdf/10.1007/s11633-023-1459-z.pdf
    COPY-PASTE FIX
    Change the repository homepage URL in the GitHub settings to: https://github.com/M-3LAB/awesome-industrial-anomaly-detection

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 M-3LAB/awesome-industrial-anomaly-detection
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
IEEE Xplore Digital Library
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. IEEE Xplore Digital Library · recommended 1×
  2. ACM Digital Library · recommended 1×
  3. arXiv · recommended 1×
  4. Google Scholar · recommended 1×
  5. SpringerLink · recommended 1×
  • CATEGORY QUERY
    Where can I find academic papers and datasets for industrial image defect detection?
    you: not recommended
    AI recommended (in order):
    1. IEEE Xplore Digital Library
    2. ACM Digital Library
    3. arXiv
    4. Google Scholar
    5. SpringerLink
    6. ScienceDirect (Elsevier)
    7. Kaggle
    8. MVTec AD (Anomaly Detection) Dataset
    9. NEU-DET (Northeastern University Defect Detection) Dataset
    10. GDXray (Global X-ray) Dataset
    11. Open Images Dataset V6/V7 (Google)
    12. Roboflow Universe
    13. UCI Machine Learning Repository

    AI recommended 13 alternatives but never named M-3LAB/awesome-industrial-anomaly-detection. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective deep learning techniques for industrial anomaly detection in manufacturing?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. Keras
    3. PyTorch
    4. Scikit-learn
    5. Apache MXNet
    6. Hugging Face Transformers
    7. PyOD (Python Outlier Detection)
    8. ADTK (Anomaly Detection Toolkit)

    AI recommended 8 alternatives but never named M-3LAB/awesome-industrial-anomaly-detection. 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 M-3LAB/awesome-industrial-anomaly-detection?
    pass
    AI did not name M-3LAB/awesome-industrial-anomaly-detection — 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?

  • If a team adopts M-3LAB/awesome-industrial-anomaly-detection in production, what risks or prerequisites should they evaluate first?
    pass
    AI named M-3LAB/awesome-industrial-anomaly-detection 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 M-3LAB/awesome-industrial-anomaly-detection solve, and who is the primary audience?
    pass
    AI did not name M-3LAB/awesome-industrial-anomaly-detection — 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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