REPOGEO REPORT · LITE

huawei-noah/Efficient-AI-Backbones

Default branch master · commit f90e129b · scanned 6/30/2026, 1:57:15 PM

GitHub: 4,417 stars · 736 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

highlicense#1

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root, or clearly state the applicable license(s) directly in the README if a custom license is intended.

highabout#2

Update the repository description

Why:

CURRENT

Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.

COPY-PASTE FIX

A comprehensive collection of efficient AI backbone architectures (GhostNet, TNT, AugViT, WaveMLP, ViG, ParameterNet) and pre-trained models from Huawei Noah's Ark Lab, optimized for efficient inference in computer vision.

mediumtopics#3

Add more specific topics to reinforce categorization

Why:

CURRENT

convolutional-neural-networks, efficient-inference, ghostnet, imagenet, model-compression, pretrained-models, pytorch, tensorflow, transformer, vision-transformer

COPY-PASTE FIX

convolutional-neural-networks, deep-learning-models, efficient-inference, ghostnet, imagenet, model-compression, model-zoo, pretrained-models, pytorch, tensorflow, transformer, vision-transformer

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

Of all named tools, what % are you?

Top rival

TensorFlow Lite

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

TensorFlow Lite · recommended 1×
PyTorch Mobile · recommended 1×
ONNX Runtime · recommended 1×
OpenVINO Toolkit · recommended 1×
Apache TVM · recommended 1×

CATEGORY QUERY
How to achieve efficient inference for deep learning models on edge devices?
you: not recommended
AI recommended (in order):
1. TensorFlow Lite
2. PyTorch Mobile
3. ONNX Runtime
4. OpenVINO Toolkit
5. Apache TVM
6. Core ML
7. NVIDIA TensorRT
AI recommended 7 alternatives but never named huawei-noah/Efficient-AI-Backbones. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find pre-trained efficient deep learning backbones for computer vision?
you: not recommended
AI recommended (in order):
1. torchvision.models (pytorch/vision)
2. TensorFlow Hub (tensorflow/hub)
3. Hugging Face Transformers (huggingface/transformers)
4. Keras Applications (keras-team/keras)
5. Timm (rwightman/pytorch-image-models)
6. MMClassification (open-mmlab/mmclassification)
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.

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MARKDOWN (README)

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Pro

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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