RRepoGEO

REPOGEO REPORT · LITE

awaisrauf/Awesome-CV-Foundational-Models

Default branch main · commit 3f2f8f1c · scanned 6/14/2026, 4:53:01 AM

GitHub: 549 stars · 33 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 awaisrauf/Awesome-CV-Foundational-Models, 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
  • highabout#1
    Add a concise description to the About section

    Why:

    COPY-PASTE FIX
    A comprehensive survey and curated list of foundational models defining a new era in computer vision, accepted for publication by TPAMI.
  • mediumlicense#2
    Add a LICENSE file

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, specifying a standard open-source license (e.g., MIT or Apache-2.0) that applies to the content of this survey/list.

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 awaisrauf/Awesome-CV-Foundational-Models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ViT (Vision Transformer)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ViT (Vision Transformer) · recommended 1×
  2. Swin Transformer · recommended 1×
  3. MAE (Masked Autoencoders Are Scalable Vision Learners) · recommended 1×
  4. CLIP (Contrastive Language-Image Pre-training) · recommended 1×
  5. DALL-E 2 · recommended 1×
  • CATEGORY QUERY
    What are the latest advancements in large-scale vision models for general understanding?
    you: not recommended
    AI recommended (in order):
    1. ViT (Vision Transformer)
    2. Swin Transformer
    3. MAE (Masked Autoencoders Are Scalable Vision Learners)
    4. CLIP (Contrastive Language-Image Pre-training)
    5. DALL-E 2
    6. Stable Diffusion
    7. Midjourney
    8. Flamingo
    9. DINO (Self-supervised Vision Transformers with DINO)
    10. SimCLR
    11. MoCo (Momentum Contrast)
    12. CoCa (Contrastive Captioners are Image-Text Foundation Models)
    13. Data2vec

    AI recommended 13 alternatives but never named awaisrauf/Awesome-CV-Foundational-Models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I find resources on multi-modal AI models for advanced visual scene reasoning?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. Papers With Code
    4. Hugging Face Hub
    5. GitHub
    6. YouTube
    7. Medium
    8. Towards Data Science

    AI recommended 8 alternatives but never named awaisrauf/Awesome-CV-Foundational-Models. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 awaisrauf/Awesome-CV-Foundational-Models?
    pass
    AI did not name awaisrauf/Awesome-CV-Foundational-Models — 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 awaisrauf/Awesome-CV-Foundational-Models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named awaisrauf/Awesome-CV-Foundational-Models 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 awaisrauf/Awesome-CV-Foundational-Models solve, and who is the primary audience?
    pass
    AI did not name awaisrauf/Awesome-CV-Foundational-Models — 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?

Embed your GEO score

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awaisrauf/Awesome-CV-Foundational-Models — 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