RRepoGEO

REPOGEO REPORT · LITE

CASIA-LMC-Lab/AnomalyGPT

Default branch main · commit f21c51b9 · scanned 5/18/2026, 11:13:29 AM

GitHub: 1,106 stars · 144 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 CASIA-LMC-Lab/AnomalyGPT, 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
    Clarify project type and scope in README introduction

    Why:

    CURRENT
    The README immediately follows the H1 with links and a catalogue, lacking an explicit introductory paragraph.
    COPY-PASTE FIX
    Add the following paragraph immediately after the H1 and before the links:

```
This repository presents AnomalyGPT, an open-source research project and framework from our AAAI 2024 Oral paper. It provides a robust solution for detecting industrial anomalies by leveraging large vision-language models, offering a practical implementation for researchers and practitioners in multimodal AI and manufacturing defect identification.
```
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    anomaly-detection, vision-language-models, industrial-ai, multimodal-ai, deep-learning, llm, computer-vision, manufacturing-defects, research-project, pytorch, aaai-2024
  • mediumlicense#3
    Clarify the existing license in the README

    Why:

    CURRENT
    The README excerpt does not explicitly mention the license.
    COPY-PASTE FIX
    Add a section to the README, perhaps under a 'License' heading, stating:

```
## License

This project is released under the terms specified in the `LICENSE` file. Please refer to the `LICENSE` file for full details regarding usage and distribution.
```

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 CASIA-LMC-Lab/AnomalyGPT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Vertex AI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Vertex AI · recommended 2×
  2. OpenAI's CLIP · recommended 1×
  3. GPT-4V · recommended 1×
  4. Vision AI · recommended 1×
  5. Natural Language AI · recommended 1×
  • CATEGORY QUERY
    What are the best tools for detecting industrial anomalies with vision-language AI?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's CLIP
    2. GPT-4V
    3. Google Cloud Vertex AI
    4. Vision AI
    5. Natural Language AI
    6. Hugging Face Transformers
    7. ViLT
    8. BLIP
    9. LLaVA
    10. Amazon Rekognition Custom Labels
    11. Amazon Comprehend
    12. Microsoft Azure AI Vision
    13. Azure OpenAI Service
    14. Domino Data Lab

    AI recommended 14 alternatives but never named CASIA-LMC-Lab/AnomalyGPT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a robust solution to identify manufacturing defects using multimodal AI.
    you: not recommended
    AI recommended (in order):
    1. Landing AI (LandingLens)
    2. Google Cloud Vertex AI
    3. AWS SageMaker
    4. Microsoft Azure Machine Learning
    5. OpenVINO (Intel)
    6. PyTorch
    7. TensorFlow
    8. NVIDIA Clara Guardian
    9. NVIDIA Metropolis

    AI recommended 9 alternatives but never named CASIA-LMC-Lab/AnomalyGPT. 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 CASIA-LMC-Lab/AnomalyGPT?
    pass
    AI named CASIA-LMC-Lab/AnomalyGPT explicitly

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

  • If a team adopts CASIA-LMC-Lab/AnomalyGPT in production, what risks or prerequisites should they evaluate first?
    pass
    AI named CASIA-LMC-Lab/AnomalyGPT 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 CASIA-LMC-Lab/AnomalyGPT solve, and who is the primary audience?
    pass
    AI named CASIA-LMC-Lab/AnomalyGPT explicitly

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

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CASIA-LMC-Lab/AnomalyGPT — 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