# Jingkang50/OpenOOD

Benchmarking Generalized Out-of-Distribution Detection

Repository: https://github.com/Jingkang50/OpenOOD
Canonical: https://ross.abutalabs.com/products/openood
Language: Python
License: MIT
License Family: permissive
Topics: out-of-distribution-detection, anomaly-detection, open-set-recognition, novelty-detection, outlier-detection, robustness
Last push: 2025-12-01T10:40:18+00:00

## Health v2 (maintenance only)
Score: 48/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 55, release rhythm 8, longevity 100
- inputs: {"age_days": 1738, "days_push": 275, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1070, forks 179 (observed 2026-08-28T04:03:27.861838+00:00)

## What it is
OpenOOD is a benchmark framework that reproduces and fairly compares methods for generalized out-of-distribution detection, spanning anomaly detection, novelty detection, open set recognition, and OOD detection. It provides standardized datasets, 35+ implemented methods, and a public leaderboard for evaluating detection performance.

## Use cases
- benchmark out-of-distribution detection methods
- compare anomaly detection algorithms fairly
- evaluate open set recognition models
- reproduce OOD detection baselines
- find the best novelty detection method for my model
- test classifier robustness to out-of-distribution inputs

## When to choose
- you need a standardized, reproducible comparison of OOD detection methods
- you are doing research on anomaly, novelty, or open set detection and want baselines
- you want access to a leaderboard of 35+ detection methods on common benchmarks

## When to avoid
- you need a production-ready OOD detection service rather than a research benchmark
- you need multimodal or text-based OOD detection, which is still future work (v2.0)
- you just want a simple plug-and-play anomaly detector without benchmarking overhead

## Facets
- artifact type: dataset
- maturity: active
- function: benchmarking, machine-learning, computer-vision, testing
- domain: machine-learning, computer-vision, data-science
- platform: python
- tags: out-of-distribution-detection, anomaly-detection, open-set-recognition, novelty-detection, outlier-detection, research-benchmark, model-robustness, algorithms, gpu, linux

## Member repositories
- Jingkang50/OpenOOD (main) score 48

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:27.861838+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:53:59.878367+00:00, confidence not recorded.
  - readme: https://github.com/Jingkang50/OpenOOD (fetched 2026-08-28T04:03:27.861838+00:00, sha fc38c72bb1e0)
- Data as of 2026-08-30T08:39:29.467469+00:00.