RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/perception_models

Default branch main · commit 3e352cca · scanned 5/22/2026, 3:23:20 PM

GitHub: 2,286 stars · 156 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
22 /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
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 facebookresearch/perception_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
  • mediumreadme#1
    Refine README opening to emphasize codebase for SOTA models

    Why:

    CURRENT
    This repo is the home to the state-of-the-art for image and video _perception_: **Perception Encoder (PE)** for image, video, audio encoding, and **Perception Language Model (PLM)** for decoding.
    COPY-PASTE FIX
    This repository provides the **official codebase and state-of-the-art implementations** for image and video _perception_: featuring **Perception Encoder (PE)** for image, video, and audio encoding, and **Perception Language Model (PLM)** for decoding.
  • lowreadme#2
    Add a sentence to the README clarifying its research focus

    Why:

    COPY-PASTE FIX
    This repository serves as a foundational research codebase for advancing perception models, primarily targeting AI researchers and practitioners.

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/perception_models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4o
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4o · recommended 1×
  2. Gemini · recommended 1×
  3. Llama 3-V · recommended 1×
  4. LLaVA · recommended 1×
  5. CoCa · recommended 1×
  • CATEGORY QUERY
    What are the best multimodal large language models for image and video understanding?
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Gemini
    3. Llama 3-V
    4. LLaVA
    5. CoCa
    6. Flamingo

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

    Show full AI answer
  • CATEGORY QUERY
    Seeking a robust framework for encoding image, video, and audio data for AI tasks.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. torchvision
    3. torchaudio
    4. torchdata
    5. TensorFlow
    6. TensorFlow Datasets
    7. Keras
    8. MediaPipe
    9. Hugging Face Transformers
    10. Hugging Face Datasets
    11. Hugging Face Accelerate
    12. MMAction2
    13. MMSegmentation
    14. MMDetection
    15. FFmpeg

    AI recommended 15 alternatives but never named facebookresearch/perception_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
    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/perception_models?
    pass
    AI did not name facebookresearch/perception_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 facebookresearch/perception_models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named facebookresearch/perception_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 facebookresearch/perception_models solve, and who is the primary audience?
    pass
    AI did not name facebookresearch/perception_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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facebookresearch/perception_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