RRepoGEO

REPOGEO REPORT · LITE

microsoft/X-Decoder

Default branch v2.0 · commit 165f8a63 · scanned 5/25/2026, 10:41:24 AM

GitHub: 1,344 stars · 162 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 microsoft/X-Decoder, 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
    computer-vision, deep-learning, segmentation, panoptic-segmentation, instance-segmentation, semantic-segmentation, vision-language-models, generalized-decoding, cvpr-2023
  • highreadme#2
    Add a concise problem/solution statement to the README's opening

    Why:

    CURRENT
    The current README starts with `# X-Decoder: Generalized Decoding for Pixel, Image, and Language` followed by links and authors.
    COPY-PASTE FIX
    # X-Decoder: Generalized Decoding for Pixel, Image, and Language
X-Decoder is a universal vision decoder designed to unify diverse image segmentation tasks—including semantic, instance, and panoptic segmentation—with open-vocabulary capabilities, using a single, pre-trained model.
  • mediumreadme#3
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with State-of-the-Art

X-Decoder offers a unified approach to generalized decoding across pixel, image, and language tasks, distinguishing itself from models like SAM (focused on interactive segmentation) or Grounding DINO (focused on open-set object detection) by providing a single framework for diverse segmentation and vision-language tasks.

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 microsoft/X-Decoder
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. PyTorch-Lightning · recommended 1×
  3. OpenMMLab · recommended 1×
  4. Keras · recommended 1×
  5. JAX/Flax · recommended 1×
  • CATEGORY QUERY
    What are the best frameworks for unified vision and language processing models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch-Lightning
    3. OpenMMLab
    4. Keras
    5. JAX/Flax

    AI recommended 5 alternatives but never named microsoft/X-Decoder. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a library for generalized semantic segmentation and object detection with text prompts.
    you: not recommended
    AI recommended (in order):
    1. Grounding DINO
    2. Segment Anything Model (SAM)
    3. OWL-ViT
    4. CLIPSeg
    5. OneFormer
    6. Mask2Former

    AI recommended 6 alternatives but never named microsoft/X-Decoder. 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 microsoft/X-Decoder?
    pass
    AI did not name microsoft/X-Decoder — 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 microsoft/X-Decoder in production, what risks or prerequisites should they evaluate first?
    pass
    AI named microsoft/X-Decoder 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 microsoft/X-Decoder solve, and who is the primary audience?
    pass
    AI named microsoft/X-Decoder 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 microsoft/X-Decoder. 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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HTML
<a href="https://repogeo.com/en/r/microsoft/X-Decoder"><img src="https://repogeo.com/badge/microsoft/X-Decoder.svg" alt="RepoGEO" /></a>
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microsoft/X-Decoder — 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