RRepoGEO

REPOGEO REPORT · LITE

jasmcaus/opencv-course

Default branch master · commit c028d362 · scanned 5/10/2026, 2:43:28 PM

GitHub: 1,383 stars · 1,064 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 jasmcaus/opencv-course, 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
    Reposition README opening to emphasize 'tutorial' and 'learning resource'

    Why:

    CURRENT
    Notes and code used in my **Python and OpenCV course** on freeCodeCamp.org.
    COPY-PASTE FIX
    This repository contains the notes and code for a comprehensive **Python and OpenCV course and tutorial** on freeCodeCamp.org, designed as a practical learning resource for image and video processing.
  • mediumtopics#2
    Add specific 'tutorial' and 'learning' topics

    Why:

    CURRENT
    caer, concepts, face-detection, face-recognition, faces, freecodecamp, opencv, opencv-course, opencv-python, python, recognition, video
    COPY-PASTE FIX
    caer, concepts, face-detection, face-recognition, faces, freecodecamp, opencv, opencv-course, opencv-python, python, recognition, video, tutorial, learning, education, computer-vision-tutorial
  • lowreadme#3
    Clarify the role of 'Caer' within the course content

    Why:

    CURRENT
    Caer is a *lightweight, high-performance* Vision library for high-performance AI research. It simplifies your approach towards Computer Vision by abstracting away unnecessary boilerplate code giving you the **flexibility** to quickly prototype deep learning models and research ideas.
    COPY-PASTE FIX
    Caer is a *lightweight, high-performance* Vision library for high-performance AI research, **used as a key tool throughout this course.** It simplifies your approach towards Computer Vision by abstracting away unnecessary boilerplate code giving you the **flexibility** to quickly prototype deep learning models and research ideas.

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 jasmcaus/opencv-course
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
opencv/opencv
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. opencv/opencv · recommended 1×
  2. PyImageSearch · recommended 1×
  3. Real Python · recommended 1×
  4. Kaggle Learn · recommended 1×
  5. GeeksforGeeks · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive Python tutorial for image and video processing basics?
    you: not recommended
    AI recommended (in order):
    1. OpenCV (opencv/opencv)
    2. PyImageSearch
    3. Real Python
    4. Kaggle Learn
    5. GeeksforGeeks

    AI recommended 5 alternatives but never named jasmcaus/opencv-course. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What lightweight Python libraries are available for high-performance computer vision research?
    you: not recommended
    AI recommended (in order):
    1. OpenCV
    2. Pillow
    3. scikit-image
    4. imgaug
    5. Albumentations
    6. PyTorch

    AI recommended 6 alternatives but never named jasmcaus/opencv-course. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 jasmcaus/opencv-course?
    pass
    AI did not name jasmcaus/opencv-course — 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 jasmcaus/opencv-course in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jasmcaus/opencv-course 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 jasmcaus/opencv-course solve, and who is the primary audience?
    pass
    AI named jasmcaus/opencv-course explicitly

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

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