RRepoGEO

REPOGEO REPORT · LITE

mit-han-lab/efficientvit

Default branch master · commit de7d7733 · scanned 6/30/2026, 10:07:41 AM

GitHub: 3,326 stars · 252 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
35 /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
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 mit-han-lab/efficientvit, 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 value proposition paragraph to the README's introduction

    Why:

    CURRENT
    The README's content immediately following the H1 is a link and then '## News'.
    COPY-PASTE FIX
    Insert a new paragraph directly after the H1, before the 'News' section, that clearly states: "EfficientViT provides a suite of vision foundation models designed for high-resolution generation and perception tasks. Leveraging a novel Multi-Scale Linear Attention mechanism, it achieves a superior accuracy-latency trade-off, making it ideal for efficient deployment in demanding applications like image synthesis and dense prediction."
  • mediumhomepage#2
    Add a project homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    Add the official project page or a relevant research paper link (e.g., the main EfficientViT paper) as the homepage URL in the repository's 'About' section.
  • lowtopics#3
    Expand repository topics with specific technical differentiators and application areas

    Why:

    CURRENT
    deep-compression-autoencoder, efficient-diffusion-model, efficientvit, high-resolution, imagenet, segment-anything, segmentation, vision-transformer
    COPY-PASTE FIX
    deep-compression-autoencoder, efficient-diffusion-model, efficientvit, high-resolution, imagenet, segment-anything, segmentation, vision-transformer, multi-scale-attention, linear-attention, accuracy-latency-tradeoff, generative-ai, image-synthesis

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 mit-han-lab/efficientvit
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA StyleGAN
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA StyleGAN · recommended 1×
  2. PyTorch · recommended 1×
  3. PyTorch Lightning · recommended 1×
  4. TensorFlow · recommended 1×
  5. Keras · recommended 1×
  • CATEGORY QUERY
    How to build efficient vision models for high-resolution image generation and perception?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA StyleGAN
    2. PyTorch
    3. PyTorch Lightning
    4. TensorFlow
    5. Keras
    6. Hugging Face Transformers
    7. NVIDIA DALI
    8. OpenCV
    9. Weights & Biases

    AI recommended 9 alternatives but never named mit-han-lab/efficientvit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a deep compression autoencoder for efficient high-resolution image synthesis.
    you: not recommended
    AI recommended (in order):
    1. VQ-VAE
    2. VQ-GAN
    3. StyleGAN
    4. NVAE
    5. DALL-E 2
    6. Imagen
    7. LPIPS

    AI recommended 7 alternatives but never named mit-han-lab/efficientvit. 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 mit-han-lab/efficientvit?
    pass
    AI named mit-han-lab/efficientvit explicitly

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

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

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

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mit-han-lab/efficientvit — 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