RRepoGEO

REPOGEO REPORT · LITE

cmhungsteve/Awesome-Transformer-Attention

Default branch main · commit 0653f4a1 · scanned 5/18/2026, 8:57:31 AM

GitHub: 5,034 stars · 496 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
22 /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
1 / 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 cmhungsteve/Awesome-Transformer-Attention, 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
    Strengthen the README's opening sentence to explicitly name the repo and its purpose

    Why:

    CURRENT
    This repo contains a comprehensive paper list of **Vision Transformer & Attention**, including papers, codes, and related websites.
    COPY-PASTE FIX
    The `cmhungsteve/Awesome-Transformer-Attention` repository is the ultimate, comprehensive, and actively updated list of research papers, codes, and related websites specifically focused on **Vision Transformers & Attention**.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root. For an awesome list of papers, consider a permissive license like [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) for the content, or [MIT](https://opensource.org/licenses/MIT) if you prefer a software-oriented license.
  • mediumabout#3
    Populate the 'Homepage' field in the repository's 'About' section

    Why:

    COPY-PASTE FIX
    Add a relevant URL to the 'Homepage' field in the repository's 'About' section, such as a project page, a related publication, or the author's academic profile.

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 cmhungsteve/Awesome-Transformer-Attention
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Papers With Code
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Papers With Code · recommended 2×
  2. arXiv · recommended 1×
  3. Google Scholar · recommended 1×
  4. Awesome-Vision-Transformer · recommended 1×
  5. Distill.pub · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of research papers on Vision Transformers and attention mechanisms?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. arXiv
    3. Google Scholar
    4. Awesome-Vision-Transformer
    5. Distill.pub
    6. Towards Data Science

    AI recommended 6 alternatives but never named cmhungsteve/Awesome-Transformer-Attention. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest research and code resources for visual transformer architectures in deep learning?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. PyTorch Image Models (timm) Library (rwightman/pytorch-image-models)
    3. Papers With Code
    4. Google AI Blog
    5. DeepMind
    6. Meta AI
    7. OpenMMLab
    8. MMDetection (open-mmlab/mmdetection)
    9. MMSegmentation (open-mmlab/mmsegmentation)
    10. MMClassification (open-mmlab/mmclassification)
    11. GitHub Trending

    AI recommended 11 alternatives but never named cmhungsteve/Awesome-Transformer-Attention. 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 cmhungsteve/Awesome-Transformer-Attention?
    pass
    AI did not name cmhungsteve/Awesome-Transformer-Attention — 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?

  • If a team adopts cmhungsteve/Awesome-Transformer-Attention in production, what risks or prerequisites should they evaluate first?
    pass
    AI named cmhungsteve/Awesome-Transformer-Attention 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 cmhungsteve/Awesome-Transformer-Attention solve, and who is the primary audience?
    pass
    AI did not name cmhungsteve/Awesome-Transformer-Attention — 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?

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cmhungsteve/Awesome-Transformer-Attention — 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