RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/ImageBind

Default branch main · commit 53680b02 · scanned 5/13/2026, 8:47:49 PM

GitHub: 9,026 stars · 846 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 facebookresearch/ImageBind, 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 improve categorization

    Why:

    COPY-PASTE FIX
    multimodal, embedding, cross-modal, zero-shot, deep-learning, pytorch, computer-vision, audio-processing, depth-sensing, thermal-imaging, imu-data, foundation-model, meta-ai, fair
  • highlicense#2
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License

This project is licensed under [describe the actual license(s) present in the LICENSE file, e.g., a custom research license, or a combination of licenses]. Please refer to the [LICENSE](LICENSE) file for full details.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    [The official project page URL, e.g., https://ai.meta.com/blog/imagebind-ai-model-binds-six-modalities/ or a dedicated project site]

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 facebookresearch/ImageBind
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI CLIP · recommended 1×
  2. Google's Perceiver IO · recommended 1×
  3. Facebook AI's Data2Vec · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. PyTorch Lightning · recommended 1×
  • CATEGORY QUERY
    How to create a unified embedding space for multiple data types like images, text, and audio?
    you: not recommended
    AI recommended (in order):
    1. OpenAI CLIP
    2. Google's Perceiver IO
    3. Facebook AI's Data2Vec
    4. Hugging Face Transformers
    5. PyTorch Lightning
    6. TensorFlow Keras

    AI recommended 6 alternatives but never named facebookresearch/ImageBind. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a deep learning model for cross-modal retrieval and zero-shot classification across various sensor inputs.
    you: not recommended
    AI recommended (in order):
    1. OpenAI CLIP (openai/CLIP)
    2. Google's Perceiver IO (deepmind/deepmind-research)
    3. Meta's Data2vec (facebookresearch/data2vec)
    4. Hugging Face Transformers (huggingface/transformers)
    5. PyTorch Metric Learning Library (KevinMusgrave/pytorch-metric-learning)

    AI recommended 5 alternatives but never named facebookresearch/ImageBind. 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 facebookresearch/ImageBind?
    pass
    AI named facebookresearch/ImageBind explicitly

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

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