RRepoGEO

REPOGEO REPORT · LITE

CASIA-LMC-Lab/FastSAM

Default branch main · commit b4ed20c2 · scanned 7/1/2026, 8:26:43 AM

GitHub: 8,366 stars · 764 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 CASIA-LMC-Lab/FastSAM, 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
  • mediumabout#1
    Expand the repository's 'About' description

    Why:

    CURRENT
    Fast Segment Anything
    COPY-PASTE FIX
    Fast Segment Anything Model (FastSAM): a CNN-based, 50x faster alternative to SAM for real-time image segmentation, integrated with Ultralytics.
  • mediumreadme#2
    Update the README's main heading (H1) for clarity and keywords

    Why:

    CURRENT
    # Fast Segment Anything
    COPY-PASTE FIX
    # FastSAM: 50x Faster Real-time Image Segmentation Model

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 CASIA-LMC-Lab/FastSAM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
YOLOv8-seg
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. YOLOv8-seg · recommended 1×
  2. YOLACT · recommended 1×
  3. NanoDet-Plus-M-480 · recommended 1×
  4. MobileNetV3-Large/Small · recommended 1×
  5. PP-LiteSeg · recommended 1×
  • CATEGORY QUERY
    Need a high-speed image segmentation model for real-time applications, prioritizing inference performance.
    you: not recommended
    AI recommended (in order):
    1. YOLOv8-seg
    2. YOLACT
    3. NanoDet-Plus-M-480
    4. MobileNetV3-Large/Small
    5. PP-LiteSeg
    6. FastFCN

    AI recommended 6 alternatives but never named CASIA-LMC-Lab/FastSAM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient open-source solutions for segmenting objects quickly in images?
    you: not recommended
    AI recommended (in order):
    1. YOLO with Segmentation
    2. Detectron2
    3. MMDetection/MMSegmentation
    4. OpenCV
    5. MediaPipe

    AI recommended 5 alternatives but never named CASIA-LMC-Lab/FastSAM. 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 CASIA-LMC-Lab/FastSAM?
    pass
    AI named CASIA-LMC-Lab/FastSAM explicitly

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

  • If a team adopts CASIA-LMC-Lab/FastSAM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named CASIA-LMC-Lab/FastSAM 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 CASIA-LMC-Lab/FastSAM solve, and who is the primary audience?
    pass
    AI named CASIA-LMC-Lab/FastSAM 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 CASIA-LMC-Lab/FastSAM. 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/CASIA-LMC-Lab/FastSAM.svg)](https://repogeo.com/en/r/CASIA-LMC-Lab/FastSAM)
HTML
<a href="https://repogeo.com/en/r/CASIA-LMC-Lab/FastSAM"><img src="https://repogeo.com/badge/CASIA-LMC-Lab/FastSAM.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

CASIA-LMC-Lab/FastSAM — 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