RRepoGEO

REPOGEO REPORT · LITE

tiiuae/Falcon-Perception

Default branch main · commit 627e10a8 · scanned 6/1/2026, 6:41:51 AM

GitHub: 691 stars · 67 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 tiiuae/Falcon-Perception, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Strengthen README's opening statement for core capabilities

    Why:

    CURRENT
    # Falcon Perception
A minimal, readable yet performant PyTorch inference engine implementation of **Falcon Perception** — a natively multimodal, dense, autoregressive Transformer model that performs **object detection**, **instance segmentation**, or **OCR** from natural language queries.
    COPY-PASTE FIX
    # Falcon Perception: Multimodal Object Detection, Segmentation, and OCR with Language Queries
A minimal, readable yet performant PyTorch inference engine implementation of **Falcon Perception** — a natively multimodal, dense, autoregressive Transformer model that performs **object detection**, **instance segmentation**, or **OCR** from natural language queries.
  • mediumhomepage#2
    Add a project homepage link

    Why:

    COPY-PASTE FIX
    https://vision.falcon.aidrc.tii.ae

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 tiiuae/Falcon-Perception
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Grounding DINO
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Grounding DINO · recommended 2×
  2. OWL-ViT · recommended 2×
  3. GLIP · recommended 2×
  4. CLIP · recommended 2×
  5. Segment Anything Model (SAM) · recommended 1×
  • CATEGORY QUERY
    How to perform object detection and instance segmentation using natural language prompts?
    you: not recommended
    AI recommended (in order):
    1. Grounding DINO
    2. Segment Anything Model (SAM)
    3. OWL-ViT
    4. GLIP
    5. CLIP
    6. GPT-4V
    7. Gemini

    AI recommended 7 alternatives but never named tiiuae/Falcon-Perception. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a PyTorch model to extract text and detect objects from images with language queries.
    you: not recommended
    AI recommended (in order):
    1. OWL-ViT
    2. Grounding DINO
    3. CLIP
    4. Faster R-CNN
    5. DETR
    6. GLIP
    7. ALBEF
    8. ViLT
    9. CoCa
    10. LayoutLMv3

    AI recommended 10 alternatives but never named tiiuae/Falcon-Perception. 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 tiiuae/Falcon-Perception?
    pass
    AI did not name tiiuae/Falcon-Perception — 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 tiiuae/Falcon-Perception in production, what risks or prerequisites should they evaluate first?
    pass
    AI named tiiuae/Falcon-Perception 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 tiiuae/Falcon-Perception solve, and who is the primary audience?
    pass
    AI did not name tiiuae/Falcon-Perception — 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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tiiuae/Falcon-Perception — 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