REPOGEO REPORT · LITE

Jingkang50/OpenOOD

Default branch main · commit 3c35632e · scanned 5/22/2026, 7:07:52 PM

GitHub: 1,052 stars · 176 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

65 /100

Needs work

Category recall

1 / 2

Avg rank #2.0 when recommended

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 Jingkang50/OpenOOD, 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

Emphasize OpenOOD as a comprehensive tool for specific detection tasks in README intro

Why:

CURRENT

This repository reproduces representative methods within the `Generalized Out-of-Distribution Detection Framework`, aiming to make a fair comparison across methods that were initially developed for anomaly detection, novelty detection, open set recognition, and out-of-distribution detection.

COPY-PASTE FIX

OpenOOD is a comprehensive benchmarking platform and toolkit for Generalized Out-of-Distribution Detection. It provides a unified framework to reproduce and fairly compare methods across anomaly detection, novelty detection, open set recognition, and out-of-distribution detection.

mediumhomepage#2
Add the project homepage to the repository settings
Why:
COPY-PASTE FIX
```
https://zjysteven.github.io/OpenOOD/
```

lowreadme#3

Relocate the prominent citation warning in the README

Why:

CURRENT

:exclamation: When using OpenOOD in your research, it is vital to cite both the OpenOOD benchmark (versions 1 and 1.5) and the individual works that have contributed to your research. Accurate citation acknowledges the efforts and contributions of all researchers involved. For example, if your work involves the NINCO benchmark within OpenOOD, please include a citation for NINCO apart of OpenOOD.

COPY-PASTE FIX

Move this content to a dedicated 'Citation' section later in the README, or condense it significantly to a single line at the top.

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

1 / 2

50% of queries surface Jingkang50/OpenOOD

Avg rank

#2.0

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OOD-Bench

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

OOD-Bench · recommended 1×
PyTorch-OOD · recommended 1×
ODIN-Lib · recommended 1×
SHAP · recommended 1×
LIME · recommended 1×

CATEGORY QUERY
How to evaluate and compare different methods for detecting out-of-distribution samples?
you: #2
AI recommended (in order):
1. OOD-Bench
2. OpenOOD ← you
3. PyTorch-OOD
4. ODIN-Lib
5. SHAP
6. LIME
7. Uncertainty-Toolbox
8. Deep Uncertainty
9. Albumentations
10. imgaug
Show full AI answer
CATEGORY QUERY
What are the best tools for generalized anomaly detection and open-set recognition?
you: not recommended
AI recommended (in order):
1. PyTorch-Lightning
2. scikit-learn
3. ADTK
4. OpenMax
5. Deep Open Classifier (DOC)
6. G-OpenMax
7. ELKI
8. XGBoost
9. LightGBM
AI recommended 9 alternatives but never named Jingkang50/OpenOOD. 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 Jingkang50/OpenOOD?
pass
AI named Jingkang50/OpenOOD explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Jingkang50/OpenOOD in production, what risks or prerequisites should they evaluate first?
pass
AI named Jingkang50/OpenOOD 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 Jingkang50/OpenOOD solve, and who is the primary audience?
pass
AI named Jingkang50/OpenOOD explicitly
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)

[![RepoGEO](https://repogeo.com/badge/Jingkang50/OpenOOD.svg)](https://repogeo.com/en/r/Jingkang50/OpenOOD)

HTML

<a href="https://repogeo.com/en/r/Jingkang50/OpenOOD"><img src="https://repogeo.com/badge/Jingkang50/OpenOOD.svg" alt="RepoGEO" /></a>

Pro

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Jingkang50/OpenOOD — 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