RRepoGEO

REPOGEO REPORT · LITE

amazon-science/patchcore-inspection

Default branch main · commit fcaa92f1 · scanned 5/24/2026, 5:38:08 AM

GitHub: 1,287 stars · 245 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 amazon-science/patchcore-inspection, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise description to the About section

    Why:

    COPY-PASTE FIX
    Official implementation of PatchCore for industrial anomaly detection and localization in images, achieving high recall and pixel-level accuracy.
  • mediumreadme#2
    Refine the README's opening to emphasize its application and method

    Why:

    CURRENT
    # Towards Total Recall in Industrial Anomaly Detection

This repository contains the implementation for `PatchCore` as proposed in Roth et al. (2021), <https://arxiv.org/abs/2106.08265>.
    COPY-PASTE FIX
    # Towards Total Recall in Industrial Anomaly Detection

This repository provides the official PyTorch implementation of `PatchCore`, a state-of-the-art method for unsupervised anomaly detection and localization in industrial images, as proposed in Roth et al. (2021), <https://arxiv.org/abs/2106.08265>.

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 amazon-science/patchcore-inspection
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Databricks Lakehouse Platform
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Databricks Lakehouse Platform · recommended 1×
  2. mlflow/mlflow · recommended 1×
  3. apache/spark · recommended 1×
  4. AWS SageMaker · recommended 1×
  5. Google Cloud Vertex AI · recommended 1×
  • CATEGORY QUERY
    How to implement high-recall anomaly detection for quality control in manufacturing?
    you: not recommended
    AI recommended (in order):
    1. Databricks Lakehouse Platform
    2. MLflow (mlflow/mlflow)
    3. Apache Spark (apache/spark)
    4. AWS SageMaker
    5. Google Cloud Vertex AI
    6. Azure Machine Learning
    7. TensorFlow (tensorflow/tensorflow)
    8. Keras (keras-team/keras)
    9. PyTorch (pytorch/pytorch)
    10. Scikit-learn (scikit-learn/scikit-learn)
    11. Pandas (pandas-dev/pandas)
    12. NumPy (numpy/numpy)

    AI recommended 12 alternatives but never named amazon-science/patchcore-inspection. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which frameworks offer state-of-the-art image anomaly detection with high localization accuracy?
    you: not recommended
    AI recommended (in order):
    1. MVTec AD Library
    2. Anomalib
    3. OpenVINO Anomaly Detection
    4. Deep Learning for Anomaly Detection (DLAD)
    5. TensorFlow Anomaly Detection

    AI recommended 5 alternatives but never named amazon-science/patchcore-inspection. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 amazon-science/patchcore-inspection?
    pass
    AI did not name amazon-science/patchcore-inspection — 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 amazon-science/patchcore-inspection in production, what risks or prerequisites should they evaluate first?
    pass
    AI named amazon-science/patchcore-inspection 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 amazon-science/patchcore-inspection solve, and who is the primary audience?
    pass
    AI did not name amazon-science/patchcore-inspection — 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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