RRepoGEO

REPOGEO REPORT · LITE

OlafenwaMoses/ImageAI

Default branch master · commit 2156d1a3 · scanned 5/12/2026, 8:21:56 AM

GitHub: 8,869 stars · 2,197 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 OlafenwaMoses/ImageAI, 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
    Insert a clarifying paragraph about ImageAI's core features after the opening sentence

    Why:

    CURRENT
    An open-source python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code.
    COPY-PASTE FIX
    ImageAI simplifies the integration of advanced computer vision features like object detection, image recognition, and video analysis into your Python projects. It's designed for robust, offline-capable performance, making it ideal for applications requiring self-contained AI vision.
  • mediumhomepage#2
    Update homepage URL to be specific to ImageAI

    Why:

    CURRENT
    https://www.genxr.co/#products
    COPY-PASTE FIX
    A URL directly linking to ImageAI's dedicated page, documentation, or its specific section within the main product site.
  • lowabout#3
    Refine the repository description for clearer problem statement

    Why:

    CURRENT
    A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities
    COPY-PASTE FIX
    A Python library for easy, self-contained Computer Vision integration, offering robust offline image recognition and object detection for developers.

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 OlafenwaMoses/ImageAI
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. scikit-image/scikit-image · recommended 1×
  3. python-pillow/Pillow · recommended 1×
  4. davisking/dlib · recommended 1×
  5. tensorflow/tensorflow · recommended 1×
  • CATEGORY QUERY
    How can I easily integrate computer vision features into my Python projects?
    you: not recommended
    AI recommended (in order):
    1. OpenCV (opencv/opencv)
    2. scikit-image (scikit-image/scikit-image)
    3. Pillow (python-pillow/Pillow)
    4. Dlib (davisking/dlib)
    5. TensorFlow (tensorflow/tensorflow)
    6. Keras (keras-team/keras)
    7. PyTorch (pytorch/pytorch)

    AI recommended 7 alternatives but never named OlafenwaMoses/ImageAI. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some robust Python libraries for offline image recognition and object detection?
    you: not recommended
    AI recommended (in order):
    1. YOLO (You Only Look Once)
    2. Darknet
    3. OpenCV DNN
    4. Detectron2
    5. PyTorch
    6. TensorFlow Object Detection API
    7. TensorFlow
    8. OpenCV
    9. Caffe
    10. MMDetection
    11. OpenMMLab
    12. Keras-RetinaNet
    13. Keras-YOLO
    14. Keras
    15. JAX

    AI recommended 15 alternatives but never named OlafenwaMoses/ImageAI. 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 OlafenwaMoses/ImageAI?
    pass
    AI named OlafenwaMoses/ImageAI explicitly

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

  • If a team adopts OlafenwaMoses/ImageAI in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OlafenwaMoses/ImageAI 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 OlafenwaMoses/ImageAI solve, and who is the primary audience?
    pass
    AI did not name OlafenwaMoses/ImageAI — 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?

Embed your GEO score

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OlafenwaMoses/ImageAI — 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