RRepoGEO

REPOGEO REPORT · LITE

iSEE-Laboratory/LLMDet

Default branch main · commit 53366243 · scanned 6/8/2026, 11:48:13 PM

GitHub: 594 stars · 31 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 iSEE-Laboratory/LLMDet, 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
    Clarify README introduction to prevent misinterpretation of project domain

    Why:

    CURRENT
    This is the official PyTorch implementation of LLMDet.
    COPY-PASTE FIX
    This repository provides the official PyTorch implementation of LLMDet, a cutting-edge approach to **open-vocabulary object detection** that harnesses the power of **Large Language Models (LLMs)** for enhanced supervision and performance in **computer vision** tasks. Unlike tools for detecting LLM-generated content, LLMDet applies LLMs to advance visual object recognition.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/YOUR_PAPER_ID_HERE

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 iSEE-Laboratory/LLMDet
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Grounding DINO · recommended 1×
  2. OWL-ViT · recommended 1×
  3. GLIP · recommended 1×
  4. DINO · recommended 1×
  5. MDETR · recommended 1×
  • CATEGORY QUERY
    How to build open-vocabulary object detectors leveraging large language models effectively?
    you: not recommended
    AI recommended (in order):
    1. Grounding DINO
    2. OWL-ViT
    3. GLIP
    4. DINO
    5. MDETR
    6. ViT-Det

    AI recommended 6 alternatives but never named iSEE-Laboratory/LLMDet. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a PyTorch framework to improve object detection using large language model supervision.
    you: not recommended
    AI recommended (in order):
    1. MMDetection (open-mmlab/mmdetection)
    2. Detectron2 (facebookresearch/detectron2)
    3. Hugging Face Transformers (huggingface/transformers)
    4. OpenCLIP (mlfoundations/open_clip)
    5. PyTorch-Lightning (Lightning-AI/pytorch-lightning)

    AI recommended 5 alternatives but never named iSEE-Laboratory/LLMDet. 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 iSEE-Laboratory/LLMDet?
    pass
    AI named iSEE-Laboratory/LLMDet explicitly

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

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

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

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iSEE-Laboratory/LLMDet — 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