# Minqi824/ADBench

Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.

Repository: https://github.com/Minqi824/ADBench
Canonical: https://ross.abutalabs.com/products/adbench
Language: Python
License: BSD-2-Clause
License Family: permissive
Topics: anomaly-detection, benchmark, data-mining, deep-learning, machine-learning, outlier-detection, semi-supervised-learning, supervised-learning, unsupervised-learning, data-sicence, ensemble-learning, neural-networks, python
Last push: 2026-01-08T09:24:20+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 61, release rhythm 35, longevity 100
- inputs: {"age_days": 1550, "days_push": 237, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1021, forks 152 (observed 2026-08-28T04:03:15.713060+00:00)

## What it is
ADBench is the official implementation of a comprehensive tabular anomaly detection benchmark published at NeurIPS 2022, comparing 30 algorithms across 57 datasets with nearly 100,000 experiments. It is available both as a research codebase and an installable Python package.

## Use cases
- benchmark anomaly detection algorithms on tabular data
- compare unsupervised vs semi-supervised vs supervised outlier detection methods
- find datasets for anomaly detection research
- evaluate algorithm robustness under data corruption
- reproduce NeurIPS 2022 anomaly detection benchmark results

## When to choose
- you need a rigorous, reproducible comparison of anomaly detection methods
- you want curated tabular datasets with anomaly labels
- you are researching the effect of supervision on outlier detection

## When to avoid
- you need anomaly detection for time-series, graphs, or images rather than tabular data
- you just want a production anomaly detection library rather than a benchmark
- you need streaming or real-time anomaly detection

## Facets
- artifact type: dataset
- maturity: active
- function: benchmarking, machine-learning, data-science
- domain: machine-learning, data-science, analytics
- platform: python, cross-platform
- tags: anomaly-detection, outlier-detection, tabular-data, benchmark, semi-supervised-learning, neurips-2022

## Member repositories
- Minqi824/ADBench (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:15.713060+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 2026-08-30T07:08:48.218288+00:00, confidence not recorded.
  - readme: https://github.com/Minqi824/ADBench (fetched 2026-08-28T04:03:15.713060+00:00, sha 7aab7a221245)
  - registry_pypi: https://pypi.org/pypi/adbench/json (fetched 2026-08-29T13:09:06.684894+00:00, sha 05b04831b361)
- Data as of 2026-08-30T08:39:29.467469+00:00.