RRepoGEO

REPOGEO REPORT · LITE

mbzuai-oryx/groundingLMM

Default branch main · commit 5afa462a · scanned 6/16/2026, 7:28:16 AM

GitHub: 959 stars · 56 forks

AI VISIBILITY SCORE
40 /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
3 / 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 mbzuai-oryx/groundingLMM, 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 core description to the top of the README

    Why:

    COPY-PASTE FIX
    Add the following sentence directly after the main H1 title in your README: "GLaMM is the first-of-its-kind model capable of generating natural language responses that are seamlessly integrated with object segmentation masks."
  • mediumtopics#2
    Add more specific topics to highlight unique output

    Why:

    CURRENT
    foundation-models, llm-agent, lmm, vision-and-language, vision-language-model
    COPY-PASTE FIX
    foundation-models, llm-agent, lmm, vision-and-language, vision-language-model, multimodal-generation, image-segmentation, grounded-language, text-to-mask
  • lowreadme#3
    Clarify GLaMM's unique output in the overview section

    Why:

    COPY-PASTE FIX
    In the 'GLaMM Overview' section, add a sentence like: "Unlike models that only provide bounding box grounding or general vision-language understanding, GLaMM uniquely generates natural language responses that are seamlessly integrated with precise object segmentation masks, enabling a new level of detailed visual communication."

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 mbzuai-oryx/groundingLMM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
facebookresearch/segment-anything
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. facebookresearch/segment-anything · recommended 1×
  2. facebookresearch/detectron2 · recommended 1×
  3. YOLO · recommended 1×
  4. opencv/opencv · recommended 1×
  5. openai/CLIP · recommended 1×
  • CATEGORY QUERY
    How to generate descriptive text responses that include precise object segmentation masks?
    you: not recommended
    AI recommended (in order):
    1. Segment Anything Model (SAM) (facebookresearch/segment-anything)
    2. Detectron2 (facebookresearch/detectron2)
    3. YOLO
    4. OpenCV (opencv/opencv)
    5. CLIP (openai/CLIP)
    6. ViT
    7. Swin Transformer
    8. GPT-4
    9. LLaMA (facebookresearch/llama)
    10. Alpaca (tatsu-lab/stanford_alpaca)
    11. Vicuna (lmsys/vicuna)
    12. BLIP-2 (salesforce/BLIP)
    13. FlanT5
    14. OPT

    AI recommended 14 alternatives but never named mbzuai-oryx/groundingLMM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a vision-language model capable of grounding textual output to specific image regions.
    you: not recommended
    AI recommended (in order):
    1. OWL-ViT
    2. GLIP
    3. Grounding DINO
    4. MDETR
    5. VL-T5
    6. OFA
    7. ViLT

    AI recommended 7 alternatives but never named mbzuai-oryx/groundingLMM. 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 mbzuai-oryx/groundingLMM?
    pass
    AI named mbzuai-oryx/groundingLMM explicitly

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

  • If a team adopts mbzuai-oryx/groundingLMM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mbzuai-oryx/groundingLMM 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 mbzuai-oryx/groundingLMM solve, and who is the primary audience?
    pass
    AI named mbzuai-oryx/groundingLMM explicitly

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

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mbzuai-oryx/groundingLMM — 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