RRepoGEO

REPOGEO REPORT · LITE

huytransformer/Awesome-Out-Of-Distribution-Detection

Default branch main · commit 62520224 · scanned 6/20/2026, 8:02:25 PM

GitHub: 1,009 stars · 80 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 huytransformer/Awesome-Out-Of-Distribution-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
  • highreadme#1
    Update the README's main heading to explicitly include 'Awesome List'

    Why:

    CURRENT
    # OOD Machine Learning: Detection, Robustness, and Generalization
    COPY-PASTE FIX
    # Awesome OOD Machine Learning: Detection, Robustness, and Generalization
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/huytransformer/Awesome-Out-Of-Distribution-Detection
  • lowreadme#3
    Ensure the README's introductory paragraph clearly states it's a 'curated list' or 'collection' of resources

    Why:

    CURRENT
    This repository aims to provide the most comprehensive, up-to-date, high-quality resource for **OOD detection, robustness, and generalization** in Machine Learning/Deep Learning. Your one-stop shop for everything OOD is here.
    COPY-PASTE FIX
    This **curated list** provides the most comprehensive, up-to-date, high-quality resources for **OOD detection, robustness, and generalization** in Machine Learning/Deep Learning. It's your one-stop shop for everything OOD.

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 huytransformer/Awesome-Out-Of-Distribution-Detection
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenMax
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenMax · recommended 1×
  2. Deep Open Classifier (DOC) · recommended 1×
  3. PROSER · recommended 1×
  4. uncertainty-toolbox · recommended 1×
  5. DeepProbLog · recommended 1×
  • CATEGORY QUERY
    How to detect when new data significantly differs from my model's training distribution?
    you: not recommended
    AI recommended (in order):
    1. OpenMax
    2. Deep Open Classifier (DOC)
    3. PROSER
    4. uncertainty-toolbox
    5. DeepProbLog
    6. PyOD
    7. ADTK
    8. Mahalanobis Distance
    9. ODIN
    10. Energy-based Models (EBMs)
    11. Variational Autoencoders (VAEs)
    12. Generative Adversarial Networks (GANs)
    13. Z-score
    14. Interquartile Range (IQR)

    AI recommended 14 alternatives but never named huytransformer/Awesome-Out-Of-Distribution-Detection. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find resources for improving machine learning model generalization and robustness?
    you: not recommended
    AI recommended (in order):
    1. Scikit-Learn
    2. Keras
    3. TensorFlow
    4. Papers With Code
    5. Kaggle Learn
    6. Adversarial Robustness Toolbox (ART) (IBM/adversarial-robustness-toolbox)

    AI recommended 6 alternatives but never named huytransformer/Awesome-Out-Of-Distribution-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 huytransformer/Awesome-Out-Of-Distribution-Detection?
    pass
    AI named huytransformer/Awesome-Out-Of-Distribution-Detection explicitly

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

  • If a team adopts huytransformer/Awesome-Out-Of-Distribution-Detection in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huytransformer/Awesome-Out-Of-Distribution-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 huytransformer/Awesome-Out-Of-Distribution-Detection solve, and who is the primary audience?
    pass
    AI did not name huytransformer/Awesome-Out-Of-Distribution-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?

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huytransformer/Awesome-Out-Of-Distribution-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