REPOGEO REPORT · LITE

M-3LAB/awesome-industrial-anomaly-detection

Default branch main · commit d5b1c0cf · scanned 6/26/2026, 1:42:37 AM

GitHub: 3,642 stars · 326 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)

3 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 M-3LAB/awesome-industrial-anomaly-detection, 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 in the root directory of the repository with your chosen open-source license.

highreadme#2

Explicitly state the repository's nature as a curated list/survey in the README

Why:

CURRENT

We discuss public datasets and related studies in detail. Welcome to read our paper and make comments.

COPY-PASTE FIX

This repository serves as a comprehensive, curated list of papers and datasets specifically focused on industrial image anomaly/defect detection. We discuss public datasets and related studies in detail.

hightopics#3

Refine repository topics to remove misleading entries and add clarifying ones

Why:

CURRENT

anomaly-detection, anomaly-segmentation, computer-vision, dataset, deep-learning, defect-detection, industrial-image, medical

COPY-PASTE FIX

anomaly-detection, anomaly-segmentation, computer-vision, dataset, deep-learning, defect-detection, industrial-image, survey, awesome-list

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 M-3LAB/awesome-industrial-anomaly-detection

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

PyTorch

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

PyTorch · recommended 1×
TensorFlow · recommended 1×
Keras · recommended 1×
AnoGAN · recommended 1×
f-AnoGAN · recommended 1×

CATEGORY QUERY
What deep learning techniques are effective for identifying anomalies in industrial images?
you: not recommended
AI recommended (in order):
1. PyTorch
2. TensorFlow
3. Keras
4. AnoGAN
5. f-AnoGAN
6. ResNet
7. VGG
8. Scikit-learn
9. SimCLR
10. MoCo
11. PatchCore
12. SPADE
AI recommended 12 alternatives but never named M-3LAB/awesome-industrial-anomaly-detection. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find a curated list of datasets and research for industrial defect detection?
you: not recommended
AI recommended (in order):
1. Papers With Code
2. Kaggle
3. MVTec AD (Anomaly Detection) Dataset
4. Awesome Anomaly Detection GitHub Repository
5. IEEE Xplore
6. ACM Digital Library
7. arXiv
AI recommended 7 alternatives but never named M-3LAB/awesome-industrial-anomaly-detection. 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 M-3LAB/awesome-industrial-anomaly-detection?
pass
AI did not name M-3LAB/awesome-industrial-anomaly-detection — 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 M-3LAB/awesome-industrial-anomaly-detection in production, what risks or prerequisites should they evaluate first?
pass
AI named M-3LAB/awesome-industrial-anomaly-detection 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 M-3LAB/awesome-industrial-anomaly-detection solve, and who is the primary audience?
pass
AI did not name M-3LAB/awesome-industrial-anomaly-detection — 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

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MARKDOWN (README)

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HTML

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M-3LAB/awesome-industrial-anomaly-detection — 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