RRepoGEO

REPOGEO REPORT · LITE

levy-tech-spark/AViD

Default branch master · commit 6944d164 · scanned 6/5/2026, 6:33:27 AM

GitHub: 600 stars · 93 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 levy-tech-spark/AViD, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    vision-language-models, grounding-dino, fine-tuning, parameter-efficient-fine-tuning, lora, computer-vision, nlp, deep-learning
  • highreadme#2
    Strengthen the README's opening sentence to clarify its core purpose

    Why:

    CURRENT
    A streamlined toolkit for fine-tuning state-of-the-art vision-language detection models with parameter-efficient adaptation. Built on Grounding DINO with LoRA support and EMA stabilization.
    COPY-PASTE FIX
    AViD is a dedicated framework for fine-tuning state-of-the-art vision-language grounding models, specifically extending Grounding DINO with parameter-efficient adaptation (LoRA) and EMA stabilization for custom datasets.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    [Link to a relevant project page, documentation, or demo if available, otherwise consider creating one]

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 levy-tech-spark/AViD
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. PEFT · recommended 1×
  3. Accelerate · recommended 1×
  4. PyTorch Lightning · recommended 1×
  5. OpenCLIP · recommended 1×
  • CATEGORY QUERY
    How to fine-tune vision-language grounding models on custom datasets efficiently?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PEFT
    3. Accelerate
    4. PyTorch Lightning
    5. OpenCLIP
    6. MMDetection
    7. MMYOLO
    8. MMTracking
    9. DeepSpeed
    10. TensorFlow
    11. Keras
    12. Keras-CV
    13. Keras-NLP

    AI recommended 13 alternatives but never named levy-tech-spark/AViD. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks enable parameter-efficient adaptation for large vision-language models?
    you: not recommended
    AI recommended (in order):
    1. PEFT (huggingface/peft)
    2. OpenDelta (thunlp/OpenDelta)
    3. LoRA
    4. AdapterHub (Adapter-Hub/AdapterHub)
    5. UniPELT (microsoft/UniPELT)

    AI recommended 5 alternatives but never named levy-tech-spark/AViD. 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 levy-tech-spark/AViD?
    pass
    AI named levy-tech-spark/AViD explicitly

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

  • If a team adopts levy-tech-spark/AViD in production, what risks or prerequisites should they evaluate first?
    pass
    AI named levy-tech-spark/AViD 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 levy-tech-spark/AViD solve, and who is the primary audience?
    pass
    AI named levy-tech-spark/AViD 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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levy-tech-spark/AViD — 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