RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/ImageBind

Default branch main · commit 53680b02 · scanned 6/24/2026, 7:42:20 AM

GitHub: 9,047 stars · 842 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
53 /100
Needs work
Category recall
1 / 2
Avg rank #6.0 when recommended
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 the repository

    Why:

    COPY-PASTE FIX
    multimodal, embedding, deep-learning, pytorch, computer-vision, audio, text, depth, thermal, imu, ai-research, foundation-model
  • highreadme#2
    Reposition the README's opening paragraph to emphasize its core purpose as a foundation model for diverse sensory data

    Why:

    CURRENT
    PyTorch implementation and pretrained models for ImageBind. For details, see the paper: **ImageBind: One Embedding Space To Bind Them All**.

ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation.
    COPY-PASTE FIX
    ImageBind is a PyTorch implementation of a foundation model that learns a single, unified embedding space across six diverse sensory modalities: images, text, audio, depth, thermal, and IMU data. This enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation. For details, see the paper: **ImageBind: One Embedding Space To Bind Them All**.
  • mediumlicense#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    Add a section or line to the README: 'This project is licensed under the terms specified in the [LICENSE](LICENSE) file. Please refer to the file for full details on usage and distribution.'

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
1 / 2
50% of queries surface facebookresearch/ImageBind
Avg rank
#6.0
Lower is better. #1 = top recommendation.
Share of voice
5%
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. Google Perceiver IO · recommended 1×
  3. Meta AI ImageBind · recommended 1×
  4. Google Universal Speech Model (USM) · recommended 1×
  5. Microsoft BEiT · recommended 1×
  • CATEGORY QUERY
    What models provide a unified embedding space for diverse sensory data modalities?
    you: not recommended
    AI recommended (in order):
    1. OpenAI CLIP
    2. Google Perceiver IO
    3. Meta AI ImageBind
    4. Google Universal Speech Model (USM)
    5. Microsoft BEiT
    6. VATT

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

    Show full AI answer
  • CATEGORY QUERY
    How to perform cross-modal retrieval and generation using various data types?
    you: #6
    AI recommended (in order):
    1. OpenAI CLIP
    2. DALL-E 2
    3. DALL-E 3
    4. Imagen
    5. Parti
    6. ImageBind ← you
    7. Hugging Face Transformers Library
    8. BLIP
    9. CoCa
    10. Flamingo
    11. Stable Diffusion
    12. ControlNet
    13. LoRA
    14. CLIP
    15. PyTorch
    16. TensorFlow
    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

Drop this badge into the README of facebookresearch/ImageBind. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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facebookresearch/ImageBind — 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