RRepoGEO

REPOGEO REPORT · LITE

obss/sahi

Default branch main · commit 462443da · scanned 6/28/2026, 5:51:28 AM

GitHub: 5,377 stars · 756 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 obss/sahi, 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
    Add a clear disambiguation statement to the README.

    Why:

    COPY-PASTE FIX
    Add the following sentence prominently in the README, e.g., right after the main title/tagline: "Note: This SAHI is a computer vision library for object detection and instance segmentation, not a UI automation or browser testing tool."
  • mediumreadme#2
    Rephrase the README's opening tagline to emphasize the problem solved.

    Why:

    CURRENT
    A lightweight vision library for performing large scale object detection & instance segmentation
    COPY-PASTE FIX
    A lightweight vision library for **solving the challenge of small object detection in large images** by enabling sliced inference for object detection & instance segmentation.
  • lowcomparison#3
    Add a section clarifying SAHI's role alongside other frameworks.

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., "How SAHI Complements Existing Frameworks" or "SAHI and Other Detectors", explaining that SAHI is an inference layer designed to enhance the performance of existing object detection models (like YOLO, RetinaNet, Detectron2) on large images with small objects, rather than being a standalone model architecture.

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 obss/sahi
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
YOLO (You Only Look Look Once)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. YOLO (You Only Look Look Once) · recommended 1×
  2. OpenSlide · recommended 1×
  3. RetinaNet · recommended 1×
  4. EfficientDet · recommended 1×
  5. Mask R-CNN · recommended 1×
  • CATEGORY QUERY
    How to improve detection of small objects within very large images efficiently?
    you: not recommended
    AI recommended (in order):
    1. YOLO (You Only Look Look Once)
    2. OpenSlide
    3. RetinaNet
    4. EfficientDet
    5. Mask R-CNN
    6. SAHI (Slicing Aided Hyper Inference)
    7. Detectron2

    AI recommended 7 alternatives but never named obss/sahi. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python library for efficient object detection on large images using image slicing.
    you: not recommended
    AI recommended (in order):
    1. Detectron2 (facebookresearch/detectron2)
    2. YOLO (You Only Look Once)
    3. PyTorch (pytorch/pytorch)
    4. TensorFlow (tensorflow/tensorflow)
    5. Pillow (python-pillow/Pillow)
    6. OpenCV (opencv/opencv)
    7. MMDetection (open-mmlab/mmdetection)
    8. TensorFlow Object Detection API (tensorflow/models)

    AI recommended 8 alternatives but never named obss/sahi. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 obss/sahi?
    pass
    AI named obss/sahi explicitly

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

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

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

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obss/sahi — 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