RRepoGEO

REPOGEO REPORT · LITE

deepglint/unicom

Default branch main · commit d71992ed · scanned 6/4/2026, 12:42:33 PM

GitHub: 702 stars · 34 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 deepglint/unicom, 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
    Explicitly clarify the project's identity and domain in the README's opening

    Why:

    CURRENT
    The README's first content line after the title is "This repository focuses on building foundational visual models...".
    COPY-PASTE FIX
    Add a very explicit, disambiguating sentence immediately after the main title, e.g., "deepglint/unicom is the official repository for UNICOM & MLCD, state-of-the-art foundational visual models designed for large multimodal language models (LLMs) using large-scale datasets like LAION400M and COYO700M."
  • mediumtopics#2
    Correct typo in existing topics list

    Why:

    CURRENT
    large-sacle-pretrained-model
    COPY-PASTE FIX
    large-scale-pretrained-model
  • mediumcomparison#3
    Add explicit differentiators against top competitors in README

    Why:

    COPY-PASTE FIX
    Add a dedicated section or expand an existing one (e.g., "Key Differentiators" or "Why UNICOM/MLCD?") that clearly articulates what makes UNICOM/MLCD unique or superior compared to established models like CLIP, DINOv2, or MAE, beyond just raw numbers. For example, "Unlike [Competitor X], UNICOM/MLCD excels in [specific aspect] due to [unique approach]."

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 deepglint/unicom
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI CLIP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI CLIP · recommended 2×
  2. Hugging Face Transformers · recommended 1×
  3. PyTorch-Lightning · recommended 1×
  4. Meta MAE · recommended 1×
  5. Microsoft BEiT · recommended 1×
  • CATEGORY QUERY
    What are effective methods for training visual foundation models for multimodal language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI CLIP
    2. Hugging Face Transformers
    3. PyTorch-Lightning
    4. Meta MAE
    5. Microsoft BEiT
    6. Timm
    7. Salesforce BLIP
    8. Google CoCa
    9. DeepSpeed
    10. PyTorch
    11. Hugging Face Accelerate
    12. OpenVINO Toolkit
    13. Detectron2
    14. MMDetection
    15. MMSegmentation
    16. TensorFlow Object Detection API

    AI recommended 16 alternatives but never named deepglint/unicom. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a large-scale visual representation model using contrastive learning for diverse tasks.
    you: not recommended
    AI recommended (in order):
    1. OpenAI CLIP
    2. Meta DINOv2
    3. Google SimCLR
    4. Facebook MoCo
    5. Google PaLI
    6. Meta Data2vec

    AI recommended 6 alternatives but never named deepglint/unicom. 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 deepglint/unicom?
    pass
    AI named deepglint/unicom explicitly

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

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

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

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deepglint/unicom — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
deepglint/unicom — RepoGEO report