RRepoGEO

REPOGEO REPORT · LITE

obss/sahi

Default branch main · commit c68e731c · scanned 5/17/2026, 4:26:26 AM

GitHub: 5,288 stars · 746 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
  • highabout#1
    Update the 'About' description to be problem-solution focused

    Why:

    CURRENT
    Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
    COPY-PASTE FIX
    A lightweight, framework-agnostic library for accurate small object detection and instance segmentation in large images using slicing/tiling inference.
  • mediumreadme#2
    Refine the README's main heading (H4) to emphasize the core problem it solves

    Why:

    CURRENT
    <h4> A lightweight vision library for performing large scale object detection & instance segmentation </h4>
    COPY-PASTE FIX
    <h4> A lightweight vision library for **accurately detecting small objects in large images** using slicing-aided inference for object detection & instance segmentation. </h4>
  • lowcomparison#3
    Add a dedicated section to the README differentiating SAHI from general object detection frameworks

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., '## Why SAHI? Differentiating from General Object Detectors' or '## SAHI's Unique Edge'. This section should clearly articulate how SAHI specifically addresses the challenges of small object detection in large images, differentiating itself from general-purpose object detection frameworks like YOLO, Detectron2, or MMDetection by highlighting its slicing-aided inference approach and its benefits (e.g., improved accuracy for small objects, handling large image resolutions).

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
Detectron2
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Detectron2 · recommended 2×
  2. MMDetection · recommended 2×
  3. YOLO (You Only Look Once) · recommended 1×
  4. OpenCV · recommended 1×
  5. Google Cloud Vision AI · recommended 1×
  • CATEGORY QUERY
    Seeking a method to accurately identify small objects within extremely large images.
    you: not recommended
    AI recommended (in order):
    1. YOLO (You Only Look Once)
    2. Detectron2
    3. MMDetection
    4. OpenCV
    5. Google Cloud Vision AI

    AI recommended 5 alternatives but never named obss/sahi. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a flexible computer vision solution for detecting objects in large-scale images.
    you: not recommended
    AI recommended (in order):
    1. YOLO (You Only Look Once) series
    2. Detectron2
    3. MMDetection
    4. TensorFlow Object Detection API
    5. OpenCV with DNN module

    AI recommended 5 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