RRepoGEO

REPOGEO REPORT · LITE

gjy3035/Awesome-Crowd-Counting

Default branch master · commit b9463260 · scanned 6/26/2026, 1:58:12 PM

GitHub: 2,598 stars · 484 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)

3 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 gjy3035/Awesome-Crowd-Counting, 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 README to clarify repo's nature as an awesome list

    Why:

    COPY-PASTE FIX
    Add the following sentence immediately after the main heading in your README: 'This repository is a curated and comprehensive list of research papers, datasets, and code related to crowd counting and density estimation in computer vision.'
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root of the repository with a standard open-source license like MIT or Apache-2.0.
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    Set the repository homepage URL in the GitHub repository settings to `https://github.com/gjy3035/Awesome-Crowd-Counting`.

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 gjy3035/Awesome-Crowd-Counting
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CSRNet
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. CSRNet · recommended 1×
  2. MCNN · recommended 1×
  3. SFCN · recommended 1×
  4. Faster R-CNN · recommended 1×
  5. RetinaNet · recommended 1×
  • CATEGORY QUERY
    What are the best computer vision techniques for accurately counting people in dense crowds?
    you: not recommended
    AI recommended (in order):
    1. CSRNet
    2. MCNN
    3. SFCN
    4. Faster R-CNN
    5. RetinaNet
    6. DeepSORT
    7. ByteTrack

    AI recommended 7 alternatives but never named gjy3035/Awesome-Crowd-Counting. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find comprehensive datasets and benchmarks for crowd density estimation research?
    you: not recommended
    AI recommended (in order):
    1. ShanghaiTech Dataset
    2. UCF_CC_50 Dataset
    3. WorldExpo'10 Dataset
    4. Mall Dataset
    5. UCSD Dataset
    6. JHU-CROWD++ Dataset
    7. NWPU-Crowd Dataset

    AI recommended 7 alternatives but never named gjy3035/Awesome-Crowd-Counting. 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 gjy3035/Awesome-Crowd-Counting?
    pass
    AI did not name gjy3035/Awesome-Crowd-Counting — 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 gjy3035/Awesome-Crowd-Counting in production, what risks or prerequisites should they evaluate first?
    pass
    AI named gjy3035/Awesome-Crowd-Counting 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 gjy3035/Awesome-Crowd-Counting solve, and who is the primary audience?
    pass
    AI did not name gjy3035/Awesome-Crowd-Counting — 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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gjy3035/Awesome-Crowd-Counting — 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