RRepoGEO

REPOGEO REPORT · LITE

plemeri/InSPyReNet

Default branch main · commit 692ad8a5 · scanned 6/7/2026, 5:28:10 PM

GitHub: 773 stars · 87 forks

AI VISIBILITY SCORE
28 /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
2 / 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 plemeri/InSPyReNet, 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
    Reposition README's opening to highlight problem-solving and audience

    Why:

    CURRENT
    The README currently starts with the paper title and links to PapersWithCode.
    COPY-PASTE FIX
    Add the following sentence immediately after the main title: "This repository provides the official PyTorch implementation of InSPyReNet, a state-of-the-art deep learning model specifically designed for highly accurate salient object detection and precise background removal in high-resolution images. It's ideal for researchers and practitioners needing robust solutions for fine-grained image segmentation tasks."
  • mediumtopics#2
    Correct typo in 'image-segmantation' topic

    Why:

    CURRENT
    image-segmantation
    COPY-PASTE FIX
    accv2022, background-removal, computer-vision, deep-learning, dichotomous-image-segmentation, high-resolution, image-matting, image-segmentation, pytorch, salient-object-detection
  • lowreadme#3
    Add a dedicated 'Key Features' or 'Why InSPyReNet?' section highlighting its unique technical advantages

    Why:

    COPY-PASTE FIX
    ## Key Features

- **Invertible Spatio-temporal Reasoning (ISR) Module:** Enables iterative, bi-directional refinement of saliency maps for superior accuracy.
- **High-Resolution Performance:** Specifically designed to handle and excel with high-resolution images, addressing a common challenge in salient object detection.
- **Precise Background Removal:** Offers advanced capabilities for accurate image matting and background removal tasks.

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 plemeri/InSPyReNet
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Detectron2
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Detectron2 · recommended 1×
  2. MMSegmentation · recommended 1×
  3. YOLO (You Only Look Once) with Segmentation · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. DINOv2 · recommended 1×
  • CATEGORY QUERY
    How to accurately identify and segment salient objects in high-resolution images?
    you: not recommended
    AI recommended (in order):
    1. Detectron2
    2. MMSegmentation
    3. YOLO (You Only Look Once) with Segmentation
    4. Hugging Face Transformers
    5. DINOv2
    6. SAM (Segment Anything Model)
    7. OWL-ViT
    8. TensorFlow Object Detection API

    AI recommended 8 alternatives but never named plemeri/InSPyReNet. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective deep learning methods for precise background removal or image matting?
    you: not recommended
    AI recommended (in order):
    1. MODNet
    2. U-2-Net
    3. Deep Image Matting (DIM)
    4. Background Matting V2
    5. Mask R-CNN
    6. BASNet

    AI recommended 6 alternatives but never named plemeri/InSPyReNet. 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 plemeri/InSPyReNet?
    pass
    AI named plemeri/InSPyReNet explicitly

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

  • If a team adopts plemeri/InSPyReNet in production, what risks or prerequisites should they evaluate first?
    pass
    AI named plemeri/InSPyReNet 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 plemeri/InSPyReNet solve, and who is the primary audience?
    pass
    AI did not name plemeri/InSPyReNet — likely talking about a different project

    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 plemeri/InSPyReNet. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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plemeri/InSPyReNet — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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