RRepoGEO

REPOGEO REPORT · LITE

GoogleCloudPlatform/practical-ml-vision-book

Default branch master · commit 6456313d · scanned 6/12/2026, 2:18:23 PM

GitHub: 637 stars · 294 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 GoogleCloudPlatform/practical-ml-vision-book, 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

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

OVERALL DIRECTION
  • highabout#1
    Add a clear About description

    Why:

    COPY-PASTE FIX
    Companion code repository for the O'Reilly book 'Practical Machine Learning for Computer Vision', offering hands-on examples and resources for learning ML in computer vision.
  • mediumhomepage#2
    Add homepage link to the book

    Why:

    COPY-PASTE FIX
    https://www.amazon.com/Practical-Machine-Learning-Computer-Vision/dp/1098102363

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 GoogleCloudPlatform/practical-ml-vision-book
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow · recommended 2×
  2. PyTorch · recommended 2×
  3. scikit-learn · recommended 2×
  4. OpenCV · recommended 1×
  5. MMDetection / MMDetection3D · recommended 1×
  • CATEGORY QUERY
    How to implement machine learning models for practical computer vision tasks?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. PyTorch
    3. OpenCV
    4. scikit-learn
    5. MMDetection / MMDetection3D
    6. Hugging Face Transformers
    7. FastAI

    AI recommended 7 alternatives but never named GoogleCloudPlatform/practical-ml-vision-book. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for practical code examples to learn computer vision with machine learning.
    you: not recommended
    AI recommended (in order):
    1. OpenCV-Python
    2. PyTorch
    3. torchvision
    4. TensorFlow
    5. Keras
    6. Kaggle
    7. scikit-image
    8. scikit-learn
    9. Fast.ai
    10. fastai
    11. YOLO
    12. ultralytics/yolov5 (ultralytics/yolov5)
    13. ultralytics/yolov8 (ultralytics/yolov8)

    AI recommended 13 alternatives but never named GoogleCloudPlatform/practical-ml-vision-book. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 GoogleCloudPlatform/practical-ml-vision-book?
    pass
    AI did not name GoogleCloudPlatform/practical-ml-vision-book — 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 GoogleCloudPlatform/practical-ml-vision-book in production, what risks or prerequisites should they evaluate first?
    pass
    AI named GoogleCloudPlatform/practical-ml-vision-book 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 GoogleCloudPlatform/practical-ml-vision-book solve, and who is the primary audience?
    pass
    AI did not name GoogleCloudPlatform/practical-ml-vision-book — 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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GoogleCloudPlatform/practical-ml-vision-book — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite