RRepoGEO

REPOGEO REPORT · LITE

mit-han-lab/efficientvit

Default branch master · commit de7d7733 · scanned 5/19/2026, 2:47:45 AM

GitHub: 3,308 stars · 246 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
    Reposition the README's opening to clarify its role as a research library/framework

    Why:

    CURRENT
    # Efficient Vision Foundation Models for High-Resolution Generation and Perception

[](https://paperswithcode.com/sota/image-generation-on-imagenet-512x512?p=deep-compression-autoencoder-for-efficient)

## News
    COPY-PASTE FIX
    # Efficient Vision Foundation Models for High-Resolution Generation and Perception

This repository provides the official codebase for EfficientViT, a collection of efficient vision foundation models and architectures designed for researchers and developers working on high-resolution image generation and perception tasks.

[](https://paperswithcode.com/sota/image-generation-on-imagenet-512x512?p=deep-compression-autoencoder-for-efficient)

## News
  • mediumabout#2
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    Add the official project page URL (e.g., a dedicated research page or project website) to the repository's homepage field in the 'About' section.
  • mediumtopics#3
    Add more specific topics to emphasize its role as a deep learning framework/library

    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, deep-learning-framework, model-architecture, computer-vision-library, ai-research

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
Stable Diffusion
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Diffusion · recommended 1×
  2. Automatic1111 web UI · recommended 1×
  3. ComfyUI · recommended 1×
  4. Fooocus · recommended 1×
  5. Midjourney · recommended 1×
  • CATEGORY QUERY
    How to generate high-resolution images efficiently using state-of-the-art diffusion models?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. Automatic1111 web UI
    3. ComfyUI
    4. Fooocus
    5. Midjourney
    6. SDXL
    7. Clipdrop
    8. Kandinsky 2.2
    9. DeepFloyd IF
    10. RunwayML Gen-1
    11. RunwayML Gen-2

    AI recommended 11 alternatives but never named mit-han-lab/efficientvit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are performant and efficient vision models for high-resolution image segmentation?
    you: not recommended
    AI recommended (in order):
    1. YOLOv8-seg
    2. Mask R-CNN
    3. DeepLabv3+
    4. HRNet
    5. Segment Anything Model (SAM)
    6. FastSAM
    7. UNet++

    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