RRepoGEO

REPOGEO REPORT · LITE

twhui/LiteFlowNet

Default branch master · commit e7d6c43b · scanned 5/31/2026, 11:18:17 AM

GitHub: 631 stars · 104 forks

AI VISIBILITY SCORE
80 /100
Healthy
Category recall
2 / 2
Avg rank #3.5 when recommended
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 twhui/LiteFlowNet, 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
  • highhomepage#1
    Add project homepage to About section

    Why:

    COPY-PASTE FIX
    http://mmlab.ie.cuhk.edu.hk/projects/LiteFlowNet/
  • highreadme#2
    Clarify custom license in README

    Why:

    COPY-PASTE FIX
    Please refer to the `LICENSE` file for the specific terms of use, as this repository utilizes a custom license.
  • mediumreadme#3
    Format key features as a bulleted list in README

    Why:

    CURRENT
    LiteFlowNet is a lightweight, fast, and accurate opitcal flow CNN. We develop several specialized modules including (1) pyramidal features, (2) cascaded flow inference (cost volume + sub-pixel refinement), (3) feature warping (f-warp) layer, and (4) flow regularization by feature-driven local convolution (f-lconv) layer.
    COPY-PASTE FIX
    LiteFlowNet is a lightweight, fast, and accurate optical flow CNN. Key features include:
*   Pyramidal features
*   Cascaded flow inference (cost volume + sub-pixel refinement)
*   Feature warping (f-warp) layer
*   Flow regularization by feature-driven local convolution (f-lconv) layer

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
2 / 2
100% of queries surface twhui/LiteFlowNet
Avg rank
#3.5
Lower is better. #1 = top recommendation.
Share of voice
17%
Of all named tools, what % are you?
Top rival
RAFT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. RAFT · recommended 2×
  2. PWC-Net · recommended 2×
  3. GMA · recommended 2×
  4. FlowNetS · recommended 1×
  5. UFlow · recommended 1×
  • CATEGORY QUERY
    What are lightweight deep learning models for efficient optical flow estimation in computer vision?
    you: #2
    AI recommended (in order):
    1. RAFT
    2. LiteFlowNet ← you
    3. PWC-Net
    4. GMA
    5. FlowNetS
    6. UFlow
    Show full AI answer
  • CATEGORY QUERY
    Seeking accurate and fast convolutional neural networks for robust motion estimation between video frames.
    you: #5
    AI recommended (in order):
    1. RAFT
    2. GMA
    3. PWC-Net
    4. FlowNet3D
    5. LiteFlowNet ← you
    6. MaskFlowNet
    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 twhui/LiteFlowNet?
    pass
    AI named twhui/LiteFlowNet explicitly

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

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

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

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MARKDOWN (README)
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twhui/LiteFlowNet — 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