RRepoGEO

REPOGEO REPORT · LITE

xinyu1205/recognize-anything

Default branch main · commit 7cb804a8 · scanned 5/8/2026, 8:37:44 PM

GitHub: 3,636 stars · 325 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 xinyu1205/recognize-anything, 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
    Clarify the project's core purpose in the README's opening to differentiate from object detection.

    Why:

    CURRENT
    This project aims to develop a series of open-source and strong fundamental image recognition models.
    COPY-PASTE FIX
    This project develops a series of open-source and strong fundamental models for **image recognition and tagging**, designed to accurately identify and categorize diverse objects and concepts within images, going beyond simple object detection to provide comprehensive understanding.
  • hightopics#2
    Add broader, more descriptive topics to improve categorization.

    Why:

    CURRENT
    recognize-anything, tag2text-iclr2024
    COPY-PASTE FIX
    image-recognition, image-tagging, vision-language-model, computer-vision, deep-learning, foundation-model, open-vocabulary
  • mediumreadme#3
    Emphasize the vision-language model (VLM) capabilities in the README's 'Highlight' section.

    Why:

    COPY-PASTE FIX
    Add a new sub-section under '## :bulb: Highlight':

### **Integrated Vision-Language Capabilities**
Leveraging models like Tag2Text, this project offers advanced vision-language functionalities, enabling simultaneous image tagging and comprehensive caption generation.

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 xinyu1205/recognize-anything
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
YOLO Series
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. YOLO Series · recommended 1×
  2. YOLOv8 · recommended 1×
  3. YOLOv7 · recommended 1×
  4. YOLOv6 · recommended 1×
  5. YOLOv5 · recommended 1×
  • CATEGORY QUERY
    What open-source models can accurately tag diverse objects in images?
    you: not recommended
    AI recommended (in order):
    1. YOLO Series
    2. YOLOv8
    3. YOLOv7
    4. YOLOv6
    5. YOLOv5
    6. DETR
    7. DINO
    8. Hugging Face DETR
    9. Mask R-CNN
    10. EfficientDet
    11. CenterNet

    AI recommended 11 alternatives but never named xinyu1205/recognize-anything. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a robust vision-language model for both image tagging and detailed caption generation.
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Google Gemini
    3. Llama 3
    4. CoCa
    5. BLIP-2
    6. CLIP

    AI recommended 6 alternatives but never named xinyu1205/recognize-anything. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 xinyu1205/recognize-anything?
    pass
    AI named xinyu1205/recognize-anything explicitly

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

  • If a team adopts xinyu1205/recognize-anything in production, what risks or prerequisites should they evaluate first?
    pass
    AI named xinyu1205/recognize-anything 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 xinyu1205/recognize-anything solve, and who is the primary audience?
    pass
    AI named xinyu1205/recognize-anything explicitly

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

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xinyu1205/recognize-anything — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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