RRepoGEO

REPOGEO REPORT · LITE

cszn/KAIR

Default branch master · commit fc1732f4 · scanned 5/19/2026, 12:06:54 AM

GitHub: 3,483 stars · 695 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 cszn/KAIR, 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 emphasize 'comprehensive PyTorch toolbox'

    Why:

    CURRENT
    ## Training and testing codes for USRNet, DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, BSRGAN, SwinIR, VRT, RVRT
    COPY-PASTE FIX
    KAIR (Kai's Image Restoration Toolbox) is a comprehensive PyTorch library providing training and testing codes for a wide range of state-of-the-art deep learning models in image restoration, including super-resolution, denoising, and deblurring.
  • mediumabout#2
    Refine repository description to include 'library' or 'framework' keywords

    Why:

    CURRENT
    Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR
    COPY-PASTE FIX
    KAIR: A comprehensive PyTorch library and toolbox for image restoration. Includes training and testing codes for state-of-the-art models like DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR, and more.
  • lowreadme#3
    Add a 'Key Features' or 'Why KAIR?' section to the README

    Why:

    COPY-PASTE FIX
    ## Key Features

- **Unified Framework:** A single, well-structured repository for multiple image restoration tasks.
- **Comprehensive Model Support:** Includes training and testing codes for a wide array of state-of-the-art models (e.g., USRNet, DnCNN, SwinIR, BSRGAN).
- **PyTorch-based:** Leverages the flexibility and power of PyTorch for deep learning research and development.
- **Easy to Use:** Designed for researchers and developers to quickly implement, train, and test image restoration algorithms.

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 cszn/KAIR
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
BasicSR
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. BasicSR · recommended 2×
  2. MMagic · recommended 1×
  3. TorchSR · recommended 1×
  4. PyTorch-Image-Restoration · recommended 1×
  5. SRFormer · recommended 1×
  • CATEGORY QUERY
    Need a comprehensive PyTorch library for various image super-resolution and denoising algorithms.
    you: not recommended
    AI recommended (in order):
    1. BasicSR
    2. MMagic
    3. TorchSR
    4. PyTorch-Image-Restoration
    5. SRFormer

    AI recommended 5 alternatives but never named cszn/KAIR. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find training and testing codes for modern image restoration techniques?
    you: not recommended
    AI recommended (in order):
    1. GitHub
    2. BasicSR
    3. MMEditing
    4. Awesome-Image-Restoration
    5. Papers With Code
    6. Kaggle
    7. PyTorch Hub
    8. TensorFlow Hub
    9. Hugging Face Transformers
    10. Google Colaboratory (Colab)

    AI recommended 10 alternatives but never named cszn/KAIR. 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 cszn/KAIR?
    pass
    AI named cszn/KAIR explicitly

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

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

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

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cszn/KAIR — RepoGEO report