RRepoGEO

REPOGEO REPORT · LITE

huawei-noah/Efficient-AI-Backbones

Default branch master · commit f90e129b · scanned 5/19/2026, 6:21:43 AM

GitHub: 4,414 stars · 737 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
28 /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
2 / 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 huawei-noah/Efficient-AI-Backbones, 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 statement to emphasize the collection's scope

    Why:

    CURRENT
    # Efficient AI Backbones 
including GhostNet, TNT (Transformer in Transformer), AugViT, WaveMLP and ViG developed by Huawei Noah's Ark Lab.
    COPY-PASTE FIX
    # Efficient AI Backbones 
This repository provides a comprehensive collection of state-of-the-art efficient AI backbone architectures, including GhostNet, TNT (Transformer in Transformer), AugViT, WaveMLP, and ViG, developed by Huawei Noah's Ark Lab for high-performance and resource-constrained computer vision applications.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root containing the full text of a standard open-source license, such as the Apache-2.0 License.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Set the repository's homepage URL to `https://www.huawei.com/en/research-innovation/noahs-ark-lab` or a dedicated project page for these backbones.

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 huawei-noah/Efficient-AI-Backbones
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MobileNetV3
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MobileNetV3 · recommended 2×
  2. EfficientNet · recommended 1×
  3. ResNet · recommended 1×
  4. RegNet · recommended 1×
  5. ConvNeXt · recommended 1×
  • CATEGORY QUERY
    Looking for efficient deep learning backbones for image classification with PyTorch.
    you: not recommended
    AI recommended (in order):
    1. EfficientNet
    2. MobileNetV3
    3. ResNet
    4. RegNet
    5. ConvNeXt
    6. Swin Transformer

    AI recommended 6 alternatives but never named huawei-noah/Efficient-AI-Backbones. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need lightweight neural network architectures for resource-constrained computer vision applications.
    you: not recommended
    AI recommended (in order):
    1. MobileNetV3
    2. EfficientNetV2
    3. ShuffleNetV2
    4. GhostNet
    5. NanoDet
    6. SqueezeNet

    AI recommended 6 alternatives but never named huawei-noah/Efficient-AI-Backbones. 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 huawei-noah/Efficient-AI-Backbones?
    pass
    AI named huawei-noah/Efficient-AI-Backbones explicitly

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

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

Embed your GEO score

Drop this badge into the README of huawei-noah/Efficient-AI-Backbones. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/huawei-noah/Efficient-AI-Backbones.svg)](https://repogeo.com/en/r/huawei-noah/Efficient-AI-Backbones)
HTML
<a href="https://repogeo.com/en/r/huawei-noah/Efficient-AI-Backbones"><img src="https://repogeo.com/badge/huawei-noah/Efficient-AI-Backbones.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

huawei-noah/Efficient-AI-Backbones — 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