RRepoGEO

REPOGEO REPORT · LITE

OpenGVLab/all-seeing

Default branch main · commit 5cb8cea0 · scanned 6/15/2026, 11:03:05 AM

GitHub: 509 stars · 18 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 OpenGVLab/all-seeing, 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
  • highreadme#1
    Reposition the README's opening to clearly state its category and purpose

    Why:

    CURRENT
    # The All-Seeing Project 

This is the official implementation of the following papers:
    COPY-PASTE FIX
    # The All-Seeing Project 

A unified, general-purpose multimodal foundation model for panoptic visual recognition, understanding, and general relation comprehension across the open world. This is the official implementation of the following papers:
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root, containing the text of a standard open-source license (e.g., MIT, Apache-2.0, or GPL-3.0).
  • mediumtopics#3
    Expand repository topics to include broader categories

    Why:

    CURRENT
    all-seeing, dataset, region-text
    COPY-PASTE FIX
    all-seeing, dataset, region-text, multimodal-ai, foundation-model, computer-vision, visual-recognition, visual-understanding, relation-comprehension, panoptic-segmentation

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 OpenGVLab/all-seeing
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Mask2Former
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Mask2Former · recommended 1×
  2. PanopticFPN · recommended 1×
  3. UPSNet · recommended 1×
  4. Swin Transformer · recommended 1×
  5. EfficientPS · recommended 1×
  • CATEGORY QUERY
    Looking for a model to achieve panoptic visual recognition and understanding of images.
    you: not recommended
    AI recommended (in order):
    1. Mask2Former
    2. PanopticFPN
    3. UPSNet
    4. Swin Transformer
    5. EfficientPS
    6. Detectron2

    AI recommended 6 alternatives but never named OpenGVLab/all-seeing. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help with general relation comprehension across different visual elements?
    you: not recommended
    AI recommended (in order):
    1. Tableau
    2. Microsoft Power BI
    3. Observable
    4. Gephi
    5. yEd Graph Editor
    6. Lucidchart
    7. Figma

    AI recommended 7 alternatives but never named OpenGVLab/all-seeing. 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 OpenGVLab/all-seeing?
    pass
    AI did not name OpenGVLab/all-seeing — 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 OpenGVLab/all-seeing in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenGVLab/all-seeing 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 OpenGVLab/all-seeing solve, and who is the primary audience?
    pass
    AI named OpenGVLab/all-seeing explicitly

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

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OpenGVLab/all-seeing — 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