RRepoGEO

REPOGEO REPORT · LITE

DavidZhangdw/Visual-Tracking-Development

Default branch master · commit 2b6725ed · scanned 6/11/2026, 6:22:30 AM

GitHub: 586 stars · 63 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 DavidZhangdw/Visual-Tracking-Development, 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
  • hightopics#1
    Add more specific topics to better categorize the repository

    Why:

    CURRENT
    benchmark, deep-learning, tracking
    COPY-PASTE FIX
    visual-tracking, object-tracking, deep-learning, computer-vision, tracking-framework, research, benchmark, evaluation
  • mediumlicense#2
    Add a standard open-source license file

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root, choosing a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that best suits your project's goals.

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 DavidZhangdw/Visual-Tracking-Development
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
YOLO (You Only Look Once)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. YOLO (You Only Look Once) · recommended 1×
  2. YOLOv5 · recommended 1×
  3. YOLOv7 · recommended 1×
  4. YOLOv8 · recommended 1×
  5. DeepSORT (Deep Learning SORT) · recommended 1×
  • CATEGORY QUERY
    How to implement real-time visual object tracking using deep learning models?
    you: not recommended
    AI recommended (in order):
    1. YOLO (You Only Look Once)
    2. YOLOv5
    3. YOLOv7
    4. YOLOv8
    5. DeepSORT (Deep Learning SORT)
    6. ByteTrack
    7. FairMOT (Fair Multi-Object Tracking)
    8. CenterTrack
    9. Track-R-CNN
    10. Deformable DETR (Detection Transformer)

    AI recommended 10 alternatives but never named DavidZhangdw/Visual-Tracking-Development. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best deep learning techniques for robust visual tracking evaluation?
    you: not recommended
    AI recommended (in order):
    1. OTB (Object Tracking Benchmark)
    2. VOT (Visual Object Tracking)

    AI recommended 2 alternatives but never named DavidZhangdw/Visual-Tracking-Development. 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 DavidZhangdw/Visual-Tracking-Development?
    pass
    AI named DavidZhangdw/Visual-Tracking-Development explicitly

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

  • If a team adopts DavidZhangdw/Visual-Tracking-Development in production, what risks or prerequisites should they evaluate first?
    pass
    AI named DavidZhangdw/Visual-Tracking-Development 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 DavidZhangdw/Visual-Tracking-Development solve, and who is the primary audience?
    pass
    AI did not name DavidZhangdw/Visual-Tracking-Development — 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?

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DavidZhangdw/Visual-Tracking-Development — 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