RRepoGEO

REPOGEO REPORT · LITE

CASIA-LMC-Lab/FastSAM

Default branch main · commit b4ed20c2 · scanned 5/19/2026, 10:22:06 PM

GitHub: 8,340 stars · 762 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

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    computer-vision, image-segmentation, segment-anything, sam, real-time, efficient, deep-learning, cnn, object-segmentation
  • highreadme#2
    Reposition README opening to align with 'real-time' and 'efficient' queries

    Why:

    CURRENT
    # Fast Segment Anything

The **Fast Segment Anything Model(FastSAM)** is a CNN Segment Anything Model...
    COPY-PASTE FIX
    # Fast Segment Anything: Real-time, Efficient Object Segmentation

The **Fast Segment Anything Model (FastSAM)** offers **real-time, efficient object segmentation**, achieving comparable performance with the original SAM method at **50× higher run-time speed** using only 2% of the SA-1B dataset.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/CASIA-LMC-Lab/FastSAM

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
PP-LiteSeg
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PP-LiteSeg · recommended 2×
  2. YOLACT · recommended 2×
  3. DeepLabv3+ · recommended 2×
  4. YOLO · recommended 1×
  5. YOLOv8 · recommended 1×
  • CATEGORY QUERY
    How can I achieve real-time object segmentation in images without sacrificing accuracy?
    you: not recommended
    AI recommended (in order):
    1. YOLO
    2. YOLOv8
    3. YOLO-NAS
    4. RT-DETR
    5. PP-LiteSeg
    6. YOLACT
    7. DeepLab
    8. DeepLabv3+
    9. MobileNet
    10. Xception
    11. Mask R-CNN
    12. ResNet-50-FPN
    13. MobileNetV2-FPN
    14. NVIDIA CUDA
    15. AMD ROCm
    16. NVIDIA TensorRT
    17. OpenVINO
    18. ONNX Runtime

    AI recommended 18 alternatives but never named CASIA-LMC-Lab/FastSAM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some efficient and lightweight models for quickly segmenting objects in images?
    you: not recommended
    AI recommended (in order):
    1. YOLOv8-seg
    2. YOLACT
    3. NanoDet-Plus-M-480
    4. MobileNetV3-Large/Small
    5. DeepLabv3+
    6. U-Net
    7. PP-LiteSeg
    8. EfficientNet

    AI recommended 8 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?

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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