RRepoGEO

REPOGEO REPORT · LITE

gokayfem/awesome-vlm-architectures

Default branch main · commit feaef8d6 · scanned 5/11/2026, 9:03:59 AM

GitHub: 1,247 stars · 55 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 gokayfem/awesome-vlm-architectures, 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
  • highabout#1
    Update repository description to emphasize 'curated guide'

    Why:

    CURRENT
    Famous Vision Language Models and Their Architectures
    COPY-PASTE FIX
    A curated guide to famous Vision Language Models (VLMs) and their architectures, including details on training and datasets.
  • mediumhomepage#2
    Add repository URL as homepage

    Why:

    COPY-PASTE FIX
    https://github.com/gokayfem/awesome-vlm-architectures
  • lowreadme#3
    Populate the 'Tools' section in README

    Why:

    CURRENT
    The 'Tools' section is present but empty in the README.
    COPY-PASTE FIX
    Add the following markdown under the `## Tools` heading in the README:
```markdown
- [ComfyUI VLM Nodes](https://github.com/gokayfem/ComfyUI_VLM_nodes): A custom node set for ComfyUI to integrate and experiment with various Vision Language Models.
```

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 gokayfem/awesome-vlm-architectures
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CLIP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. CLIP · recommended 2×
  2. UNITER · recommended 2×
  3. OSCAR · recommended 2×
  4. ResNet · recommended 1×
  5. VGG · recommended 1×
  • CATEGORY QUERY
    How do multimodal AI models combine image and text data for understanding?
    you: not recommended
    AI recommended (in order):
    1. ResNet
    2. VGG
    3. EfficientNet
    4. BERT
    5. RoBERTa
    6. GPT-3
    7. CLIP
    8. ViLBERT
    9. VisualBERT
    10. UNITER
    11. OSCAR

    AI recommended 11 alternatives but never named gokayfem/awesome-vlm-architectures. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find detailed explanations of various vision-language model designs and their capabilities?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. CLIP
    3. ViLT
    4. BLIP
    5. Flamingo
    6. LLaVA
    7. Hugging Face
    8. BLIP-2
    9. ViT-GPT2
    10. OpenCLIP
    11. Distill.pub
    12. ViT
    13. arXiv.org
    14. PaLI
    15. CoCa
    16. Gato
    17. Towards Data Science
    18. DALL-E 2
    19. Stable Diffusion
    20. Yannic Kilcher
    21. AI Coffee Break with Letitia
    22. PaLM-E
    23. Stanford CS224N
    24. CMU 11-777
    25. VL-BERT
    26. UNITER
    27. OSCAR

    AI recommended 27 alternatives but never named gokayfem/awesome-vlm-architectures. 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 gokayfem/awesome-vlm-architectures?
    pass
    AI did not name gokayfem/awesome-vlm-architectures — 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 gokayfem/awesome-vlm-architectures in production, what risks or prerequisites should they evaluate first?
    pass
    AI named gokayfem/awesome-vlm-architectures 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 gokayfem/awesome-vlm-architectures solve, and who is the primary audience?
    pass
    AI did not name gokayfem/awesome-vlm-architectures — 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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
gokayfem/awesome-vlm-architectures — RepoGEO report