RRepoGEO

REPOGEO REPORT · LITE

Q-Future/Q-Align

Default branch main · commit 14dfdffb · scanned 6/5/2026, 1:47:05 PM

GitHub: 600 stars · 32 forks

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 Q-Future/Q-Align, 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
    Emphasize 'Foundation Model' and 'Efficient Fine-tuning' in README intro

    Why:

    COPY-PASTE FIX
    Add the following sentence directly after the H1 (or within the first paragraph): "Q-Align is an all-in-one foundation model designed for comprehensive visual scoring, capable of efficiently fine-tuning to various downstream image and video quality assessment datasets."
  • mediumlicense#2
    Clarify license details in README

    Why:

    COPY-PASTE FIX
    Add a section or line in the README, perhaps under a 'License' heading, clarifying the specific license(s) that apply to Q-Align, referencing the existing LICENSE file for full details.
  • lowreadme#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a 'Comparison' section to the README, briefly outlining how Q-Align differs from or improves upon common alternatives like LAION-Aesthetics V2 or NIMA for visual scoring 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 Q-Future/Q-Align
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LAION-Aesthetics V2
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LAION-Aesthetics V2 · recommended 2×
  2. CLIP · recommended 1×
  3. BLIP-2 · recommended 1×
  4. Open-Assistant's Aesthetic Predictor · recommended 1×
  5. NIMA · recommended 1×
  • CATEGORY QUERY
    What are the best foundation models for comprehensive visual quality and aesthetic scoring?
    you: not recommended
    AI recommended (in order):
    1. LAION-Aesthetics V2
    2. CLIP
    3. BLIP-2
    4. Open-Assistant's Aesthetic Predictor
    5. NIMA
    6. VQGAN+CLIP

    AI recommended 6 alternatives but never named Q-Future/Q-Align. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to efficiently fine-tune a model for image and video aesthetic quality assessment?
    you: not recommended
    AI recommended (in order):
    1. LAION-Aesthetics V2
    2. PyTorch Lightning (PyTorchLightning/pytorch-lightning)
    3. CLIP (openai/CLIP)
    4. Hugging Face Transformers (huggingface/transformers)
    5. BLIP-2 (salesforce/BLIP)
    6. PyTorch (pytorch/pytorch)
    7. MMAction2 (open-mmlab/mmaction2)
    8. TensorFlow Hub
    9. Keras Applications (keras-team/keras)
    10. TensorFlow/Keras (tensorflow/tensorflow)

    AI recommended 10 alternatives but never named Q-Future/Q-Align. 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 Q-Future/Q-Align?
    pass
    AI named Q-Future/Q-Align explicitly

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

  • If a team adopts Q-Future/Q-Align in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Q-Future/Q-Align 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 Q-Future/Q-Align solve, and who is the primary audience?
    pass
    AI named Q-Future/Q-Align 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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MARKDOWN (README)
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Q-Future/Q-Align — 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