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The previous commit was meant to remove .github/workflows and removed every tracked path instead — 892 files, the whole repo. This puts back the 874 that were never in scope; the eighteen workflow files stay gone, for the reasons that commit gives. Content is byte-identical to 9d2aec9 outside .github/workflows.
93 lines
3.0 KiB
YAML
93 lines
3.0 KiB
YAML
# This CITATION.cff file was generated with cffinit.
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# Visit https://bit.ly/cffinit to generate yours today!
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cff-version: 1.2.0
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title: "Anomalib: A Deep Learning Library for Anomaly Detection"
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message: "If you use this library and love it, cite the software and the paper \U0001F917"
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authors:
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- given-names: Samet
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family-names: Akcay
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email: [email protected]
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affiliation: Intel
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- given-names: Dick
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family-names: Ameln
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email: [email protected]
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affiliation: Intel
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- given-names: Ashwin
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family-names: Vaidya
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email: [email protected]
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affiliation: Intel
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- given-names: Barath
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family-names: Lakshmanan
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email: [email protected]
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affiliation: Intel
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- given-names: Nilesh
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family-names: Ahuja
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email: [email protected]
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affiliation: Intel
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- given-names: Utku
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family-names: Genc
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email: [email protected]
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affiliation: Intel
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version: 0.2.6
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doi: https://doi.org/10.48550/arXiv.2202.08341
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date-released: 2022-02-18
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references:
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- type: article
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authors:
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- given-names: Samet
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family-names: Akcay
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email: [email protected]
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affiliation: Intel
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- given-names: Dick
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family-names: Ameln
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email: [email protected]
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affiliation: Intel
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- given-names: Ashwin
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family-names: Vaidya
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email: [email protected]
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affiliation: Intel
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- given-names: Barath
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family-names: Lakshmanan
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email: [email protected]
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affiliation: Intel
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- given-names: Nilesh
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family-names: Ahuja
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email: [email protected]
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affiliation: Intel
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- given-names: Utku
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family-names: Genc
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email: [email protected]
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affiliation: Intel
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title: "Anomalib: A Deep Learning Library for Anomaly Detection"
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year: 2022
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journal: ArXiv
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doi: https://doi.org/10.48550/arXiv.2202.08341
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url: https://arxiv.org/abs/2202.08341
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abstract: >-
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This paper introduces anomalib, a novel library for
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unsupervised anomaly detection and localization.
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With reproducibility and modularity in mind, this
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open-source library provides algorithms from the
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literature and a set of tools to design custom
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anomaly detection algorithms via a plug-and-play
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approach. Anomalib comprises state-of-the-art
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anomaly detection algorithms that achieve top
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performance on the benchmarks and that can be used
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off-the-shelf. In addition, the library provides
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components to design custom algorithms that could
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be tailored towards specific needs. Additional
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tools, including experiment trackers, visualizers,
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and hyper-parameter optimizers, make it simple to
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design and implement anomaly detection models. The
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library also supports OpenVINO model optimization
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and quantization for real-time deployment. Overall,
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anomalib is an extensive library for the design,
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implementation, and deployment of unsupervised
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anomaly detection models from data to the edge.
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keywords:
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- Unsupervised Anomaly detection
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- Unsupervised Anomaly localization
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license: Apache-2.0
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