RRepoGEO

REPOGEO REPORT · LITE

Turoad/CLRNet

Default branch main · commit 7269e9d1 · scanned 6/9/2026, 7:51:42 PM

GitHub: 579 stars · 118 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)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /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
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 Turoad/CLRNet, 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 specific topics for better AI categorization

    Why:

    COPY-PASTE FIX
    lane-detection, autonomous-driving, computer-vision, deep-learning, pytorch, cvpr2022, sota
  • highreadme#2
    Enhance README's initial description for clearer problem/solution fit

    Why:

    CURRENT
    Pytorch implementation of our paper "CLRNet: Cross Layer Refinement Network for Lane Detection" (CVPR2022 Acceptance).
    COPY-PASTE FIX
    CLRNet is a state-of-the-art PyTorch-based deep learning model for accurate and robust lane detection in autonomous driving, achieving top performance on CULane, Tusimple, and LLAMAS datasets. This repository provides the official implementation of our CVPR2022 paper, "CLRNet: Cross Layer Refinement Network for Lane Detection."
  • mediumhomepage#3
    Add a homepage link to the project's About section

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2203.16236

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 Turoad/CLRNet
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LaneATT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LaneATT · recommended 2×
  2. PolyLaneNet · recommended 2×
  3. LSTR · recommended 2×
  4. SCNN · recommended 2×
  5. PINet · recommended 1×
  • CATEGORY QUERY
    What are the best deep learning models for accurate lane detection in autonomous driving?
    you: not recommended
    AI recommended (in order):
    1. LaneATT
    2. PolyLaneNet
    3. LSTR
    4. PINet
    5. SCNN
    6. ENet
    7. BiSeNet
    8. SwiftNet

    AI recommended 8 alternatives but never named Turoad/CLRNet. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a PyTorch-based solution for real-time lane detection with state-of-the-art performance.
    you: not recommended
    AI recommended (in order):
    1. LaneATT
    2. PolyLaneNet
    3. LSTR
    4. SCNN
    5. ENet-LaneNet

    AI recommended 5 alternatives but never named Turoad/CLRNet. 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 Turoad/CLRNet?
    pass
    AI named Turoad/CLRNet explicitly

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

  • If a team adopts Turoad/CLRNet in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Turoad/CLRNet 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 Turoad/CLRNet solve, and who is the primary audience?
    pass
    AI named Turoad/CLRNet 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 Turoad/CLRNet. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/Turoad/CLRNet.svg)](https://repogeo.com/en/r/Turoad/CLRNet)
HTML
<a href="https://repogeo.com/en/r/Turoad/CLRNet"><img src="https://repogeo.com/badge/Turoad/CLRNet.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

Turoad/CLRNet — 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