RRepoGEO

REPOGEO REPORT · LITE

ZhengPeng7/BiRefNet

Default branch main · commit d83f3557 · scanned 7/1/2026, 2:07:32 AM

GitHub: 3,839 stars · 300 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 ZhengPeng7/BiRefNet, 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 concise problem/solution statement to the README's opening

    Why:

    COPY-PASTE FIX
    Add this sentence immediately after the main H1 title: "BiRefNet introduces a novel bilateral reference network to achieve state-of-the-art performance in high-resolution dichotomous image segmentation, particularly for challenging camouflaged objects."
  • mediumtopics#2
    Add broader deep learning and computer vision topics

    Why:

    CURRENT
    background-removal, birefnet, camouflaged-object-detection, dichotomous-image-segmentation, high-resolution-image-segmentation, image-segmentation, salient-object-detection
    COPY-PASTE FIX
    background-removal, birefnet, camouflaged-object-detection, computer-vision, deep-learning, dichotomous-image-segmentation, high-resolution-image-segmentation, image-segmentation, salient-object-detection
  • mediumabout#3
    Expand the repository description for clarity

    Why:

    CURRENT
    [CAAI AIR'24] Bilateral Reference for High-Resolution Dichotomous Image Segmentation
    COPY-PASTE FIX
    [CAAI AIR'24] BiRefNet: A novel Bilateral Reference network for state-of-the-art high-resolution dichotomous image segmentation, excelling in camouflaged object detection and robust background removal.

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 ZhengPeng7/BiRefNet
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepLabV3+
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepLabV3+ · recommended 2×
  2. Mask R-CNN · recommended 2×
  3. U-Net · recommended 1×
  4. HRNet · recommended 1×
  5. UNet++ · recommended 1×
  • CATEGORY QUERY
    What are effective methods for high-resolution dichotomous image segmentation tasks?
    you: not recommended
    AI recommended (in order):
    1. U-Net
    2. DeepLabV3+
    3. Mask R-CNN
    4. HRNet
    5. UNet++
    6. TransUNet

    AI recommended 6 alternatives but never named ZhengPeng7/BiRefNet. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to perform robust background removal for camouflaged objects in high-resolution images?
    you: not recommended
    AI recommended (in order):
    1. Segment Anything Model (SAM) (facebookresearch/segment-anything)
    2. RemBG (danielgatis/rembg)
    3. DeepLabV3+
    4. Mask R-CNN
    5. Adobe Photoshop
    6. BackgroundMattingV2 (PeterL1n/BackgroundMattingV2)

    AI recommended 6 alternatives but never named ZhengPeng7/BiRefNet. 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 ZhengPeng7/BiRefNet?
    pass
    AI named ZhengPeng7/BiRefNet explicitly

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

  • If a team adopts ZhengPeng7/BiRefNet in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ZhengPeng7/BiRefNet 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 ZhengPeng7/BiRefNet solve, and who is the primary audience?
    pass
    AI named ZhengPeng7/BiRefNet explicitly

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

Embed your GEO score

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ZhengPeng7/BiRefNet — 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