RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/multimodal

Default branch main · commit 3c2a85a3 · scanned 5/16/2026, 9:37:41 PM

GitHub: 1,716 stars · 170 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
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/multimodal, 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
  • mediumreadme#1
    Refine README introduction to emphasize framework for building and scaling

    Why:

    CURRENT
    TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale, including both content understanding and generative models.
    COPY-PASTE FIX
    TorchMultimodal is a PyTorch framework providing modular and composable building blocks for researchers and practitioners to build, train, and scale state-of-the-art multimodal and multi-task models, including both content understanding and generative AI applications.
  • lowhomepage#2
    Add a homepage URL to the repository About section

    Why:

    COPY-PASTE FIX
    Add a relevant project website or documentation URL (e.g., `https://pytorch.org/multimodal` or similar) to the repository's 'About' section.

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/multimodal
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. Lightning-AI/pytorch-lightning · recommended 1×
  3. mlfoundations/open_clip · recommended 1×
  4. huggingface/diffusers · recommended 1×
  5. pytorch/vision · recommended 1×
  • CATEGORY QUERY
    What PyTorch libraries are available for building and scaling multimodal AI models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    3. OpenCLIP (mlfoundations/open_clip)
    4. Hugging Face Diffusers (huggingface/diffusers)
    5. TorchVision (pytorch/vision)
    6. fairseq (facebookresearch/fairseq)
    7. MMDetection (open-mmlab/mmdetection)
    8. MMSegmentation (open-mmlab/mmsegmentation)
    9. MMAction2 (open-mmlab/mmaction2)

    AI recommended 9 alternatives but never named facebookresearch/multimodal. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I efficiently implement state-of-the-art multimodal deep learning models using PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. OpenCLIP
    4. MMDetection / MMDetection3D / MMSegmentation (OpenMMLab)
    5. TorchVision
    6. Fairseq

    AI recommended 6 alternatives but never named facebookresearch/multimodal. 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/multimodal?
    pass
    AI named facebookresearch/multimodal 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/multimodal in production, what risks or prerequisites should they evaluate first?
    pass
    AI named facebookresearch/multimodal 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/multimodal solve, and who is the primary audience?
    pass
    AI named facebookresearch/multimodal 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/multimodal. 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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MARKDOWN (README)
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facebookresearch/multimodal — 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