CITATION.cff

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cff-version: 1.2.0
title: "Anomalib: A Deep Learning Library for Anomaly Detection"
message: "If you use this library and love it, cite the software and the paper \U0001F917"
authors:
  - given-names: Samet
    family-names: Akcay
    email: samet.akcay@intel.com
    affiliation: Intel
  - given-names: Dick
    family-names: Ameln
    email: dick.ameln@intel.com
    affiliation: Intel
  - given-names: Ashwin
    family-names: Vaidya
    email: ashwin.vaidya@intel.com
    affiliation: Intel
  - given-names: Barath
    family-names: Lakshmanan
    email: barath.lakshmanan@intel.com
    affiliation: Intel
  - given-names: Nilesh
    family-names: Ahuja
    email: nilesh.ahuja@intel.com
    affiliation: Intel
  - given-names: Utku
    family-names: Genc
    email: utku.genc@intel.com
    affiliation: Intel
version: 0.2.6
doi: https://doi.org/10.48550/arXiv.2202.08341
date-released: 2022-02-18
references:
  - type: article
    authors:
      - given-names: Samet
        family-names: Akcay
        email: samet.akcay@intel.com
        affiliation: Intel
      - given-names: Dick
        family-names: Ameln
        email: dick.ameln@intel.com
        affiliation: Intel
      - given-names: Ashwin
        family-names: Vaidya
        email: ashwin.vaidya@intel.com
        affiliation: Intel
      - given-names: Barath
        family-names: Lakshmanan
        email: barath.lakshmanan@intel.com
        affiliation: Intel
      - given-names: Nilesh
        family-names: Ahuja
        email: nilesh.ahuja@intel.com
        affiliation: Intel
      - given-names: Utku
        family-names: Genc
        email: utku.genc@intel.com
        affiliation: Intel
    title: "Anomalib: A Deep Learning Library for Anomaly Detection"
    year: 2022
    journal: ArXiv
    doi: https://doi.org/10.48550/arXiv.2202.08341
    url: https://arxiv.org/abs/2202.08341

abstract: >-
  This paper introduces anomalib, a novel library for
  unsupervised anomaly detection and localization.
  With reproducibility and modularity in mind, this
  open-source library provides algorithms from the
  literature and a set of tools to design custom
  anomaly detection algorithms via a plug-and-play
  approach. Anomalib comprises state-of-the-art
  anomaly detection algorithms that achieve top
  performance on the benchmarks and that can be used
  off-the-shelf. In addition, the library provides
  components to design custom algorithms that could
  be tailored towards specific needs. Additional
  tools, including experiment trackers, visualizers,
  and hyper-parameter optimizers, make it simple to
  design and implement anomaly detection models. The
  library also supports OpenVINO model optimization
  and quantization for real-time deployment. Overall,
  anomalib is an extensive library for the design,
  implementation, and deployment of unsupervised
  anomaly detection models from data to the edge.
keywords:
  - Unsupervised Anomaly detection
  - Unsupervised Anomaly localization
license: Apache-2.0