RRepoGEO

REPOGEO REPORT · LITE

bupt-ai-cz/LLVIP

Default branch main · commit c1a655cc · scanned 5/31/2026, 7:22:09 PM

GitHub: 831 stars · 74 forks

AI VISIBILITY SCORE
35 /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
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 bupt-ai-cz/LLVIP, 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 "LL" in LLVIP to prevent LLM/VLM misinterpretation

    Why:

    COPY-PASTE FIX
    Add the following sentence immediately after the main title or in the first paragraph of your README: "Note: In LLVIP, 'LL' stands for 'Low-Light', distinguishing it from Large Language Models (LLMs) or Visual Language Models (VLMs)."
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT, Apache-2.0, or GPL-3.0) in the repository root to clearly state the terms of use for the dataset and code.
  • mediumhomepage#3
    Add the project homepage URL to the repository's About section

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    Locate the URL for the "Project" link mentioned in your README and add it to the repository's homepage field in GitHub's repository settings.

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 bupt-ai-cz/LLVIP
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
FLIR Boson
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. FLIR Boson · recommended 1×
  2. Tau 2 · recommended 1×
  3. Hadron · recommended 1×
  4. Teledyne FLIR ADK (Automotive Development Kit) · recommended 1×
  5. Seek Thermal RevealPRO · recommended 1×
  • CATEGORY QUERY
    How to improve object detection performance in extremely low-light conditions using sensor fusion?
    you: not recommended
    AI recommended (in order):
    1. FLIR Boson
    2. Tau 2
    3. Hadron
    4. Teledyne FLIR ADK (Automotive Development Kit)
    5. Seek Thermal RevealPRO
    6. Velodyne Puck (VLP-16)
    7. Ouster OS0/OS1/OS2
    8. RoboSense RS-LiDAR-16/32
    9. Prophesee Metavision Sensors (e.g., EVK4, GenX320)
    10. Inivation DVS/DAVIS Sensors
    11. PyTorch
    12. TensorFlow
    13. OpenCV

    AI recommended 13 alternatives but never named bupt-ai-cz/LLVIP. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a paired visible-infrared dataset for training low-light pedestrian detection models?
    you: not recommended
    AI recommended (in order):
    1. FLIR ADAS Dataset
    2. KAIST Multi-Spectral Pedestrian Dataset
    3. OSU Thermal Pedestrian Dataset
    4. LLVIP (Low-Light Visible-Infrared Pedestrian) Dataset
    5. CVC-09 (Day/Night) Dataset
    6. MS-COCO

    AI recommended 6 alternatives but never named bupt-ai-cz/LLVIP. 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 bupt-ai-cz/LLVIP?
    pass
    AI named bupt-ai-cz/LLVIP explicitly

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

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

    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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bupt-ai-cz/LLVIP — 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