RRepoGEO

REPOGEO REPORT · LITE

roboflow/awesome-openai-vision-api-experiments

Default branch main · commit 7adff72c · scanned 6/21/2026, 11:03:06 PM

GitHub: 1,686 stars · 134 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)

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

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 roboflow/awesome-openai-vision-api-experiments, 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 README opening to emphasize practical examples and integrations

    Why:

    CURRENT
    The must-have resource for anyone who wants to experiment with and build on the OpenAI Vision API. This repository serves as a hub for innovative experiments, showcasing a variety of applications ranging from simple image classifications to advanced zero-shot learning models.
    COPY-PASTE FIX
    This repository is the must-have collection of practical examples and ready-to-use integrations for anyone who wants to experiment with and build on the OpenAI Vision API. It serves as a hub for innovative experiments, showcasing a variety of applications from simple image classifications to advanced zero-shot learning models, often combining GPT-4V with foundational models like GroundingDINO or Segment Anything (SAM).
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT or Apache-2.0) to the repository root.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add a homepage URL to the repository settings, linking to a relevant project page or documentation.

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 roboflow/awesome-openai-vision-api-experiments
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI CLIP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI CLIP · recommended 1×
  2. Google ALIGN · recommended 1×
  3. Meta DINOv2 · recommended 1×
  4. LAION-5B · recommended 1×
  5. Hugging Face `transformers` library · recommended 1×
  • CATEGORY QUERY
    How can I experiment with advanced computer vision models for zero-shot image classification?
    you: not recommended
    AI recommended (in order):
    1. OpenAI CLIP
    2. Google ALIGN
    3. Meta DINOv2
    4. LAION-5B
    5. Hugging Face `transformers` library
    6. PyTorch Image Models (timm)

    AI recommended 6 alternatives but never named roboflow/awesome-openai-vision-api-experiments. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources help integrate large vision models with foundational models for object detection?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. 🤗 Datasets Library
    3. Hugging Face Hub
    4. PyTorch
    5. PyTorch Lightning
    6. MMDetection
    7. Detectron2
    8. YOLOv5/YOLOv8 (Ultralytics)
    9. OpenCV
    10. TensorFlow Object Detection API

    AI recommended 10 alternatives but never named roboflow/awesome-openai-vision-api-experiments. 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 roboflow/awesome-openai-vision-api-experiments?
    pass
    AI did not name roboflow/awesome-openai-vision-api-experiments — 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 roboflow/awesome-openai-vision-api-experiments in production, what risks or prerequisites should they evaluate first?
    pass
    AI named roboflow/awesome-openai-vision-api-experiments 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 roboflow/awesome-openai-vision-api-experiments solve, and who is the primary audience?
    pass
    AI did not name roboflow/awesome-openai-vision-api-experiments — 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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MARKDOWN (README)
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roboflow/awesome-openai-vision-api-experiments — 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