RRepoGEO

REPOGEO REPORT · LITE

LAION-AI/CLIP_benchmark

Default branch main · commit 486a23ac · scanned 6/15/2026, 12:52:08 PM

GitHub: 812 stars · 103 forks

AI VISIBILITY SCORE
28 /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
2 / 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 LAION-AI/CLIP_benchmark, 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
    Strengthen the README's opening sentence to assert its role as *the* standardized benchmark

    Why:

    CURRENT
    The goal of this repo is to evaluate CLIP-like models on a standard set of datasets on different tasks such as zero-shot classification and zero-shot retrieval, and captioning.
    COPY-PASTE FIX
    CLIP Benchmark is the standardized, comprehensive, and reproducible suite for evaluating CLIP-like vision-language models across a wide range of datasets and tasks, including zero-shot classification, retrieval, and captioning.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/LAION-AI/CLIP_benchmark

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 LAION-AI/CLIP_benchmark
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. TorchMetrics · recommended 2×
  3. OpenCLIP · recommended 1×
  4. CLIP Benchmark · recommended 1×
  5. Hugging Face evaluate library · recommended 1×
  • CATEGORY QUERY
    How to benchmark zero-shot classification and retrieval performance of vision-language models?
    you: not recommended
    AI recommended (in order):
    1. OpenCLIP
    2. CLIP Benchmark
    3. Hugging Face evaluate library
    4. CLIPScore
    5. Hugging Face Transformers
    6. LAION's clip-benchmark
    7. TorchMetrics
    8. RetrievalMAP
    9. RetrievalRPrecision
    10. RetrievalRecall
    11. RetrievalPrecision
    12. scikit-learn
    13. numpy
    14. MMDetection
    15. MMTracking
    16. MMAction2

    AI recommended 16 alternatives but never named LAION-AI/CLIP_benchmark. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for comparing performance of different large-scale vision-language models across various datasets?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Datasets
    3. OpenMMLab
    4. MMCV
    5. MMEngine
    6. MMPretrain
    7. MMEval
    8. EleutherAI/lm-evaluation-harness (EleutherAI/lm-evaluation-harness)
    9. TorchMetrics
    10. PyTorch
    11. TensorFlow
    12. tqdm
    13. pandas
    14. matplotlib
    15. seaborn

    AI recommended 15 alternatives but never named LAION-AI/CLIP_benchmark. 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 LAION-AI/CLIP_benchmark?
    pass
    AI did not name LAION-AI/CLIP_benchmark — 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 LAION-AI/CLIP_benchmark in production, what risks or prerequisites should they evaluate first?
    pass
    AI named LAION-AI/CLIP_benchmark 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 LAION-AI/CLIP_benchmark solve, and who is the primary audience?
    pass
    AI named LAION-AI/CLIP_benchmark explicitly

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite