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| # This CITATION.cff file was generated with cffinit. | |
| # Visit https://bit.ly/cffinit to generate yours today! | |
| 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: [email protected] | |
| affiliation: Intel | |
| - given-names: Dick | |
| family-names: Ameln | |
| email: [email protected] | |
| affiliation: Intel | |
| - given-names: Ashwin | |
| family-names: Vaidya | |
| email: [email protected] | |
| affiliation: Intel | |
| - given-names: Barath | |
| family-names: Lakshmanan | |
| email: [email protected] | |
| affiliation: Intel | |
| - given-names: Nilesh | |
| family-names: Ahuja | |
| email: [email protected] | |
| affiliation: Intel | |
| - given-names: Utku | |
| family-names: Genc | |
| email: [email protected] | |
| 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: [email protected] | |
| affiliation: Intel | |
| - given-names: Dick | |
| family-names: Ameln | |
| email: [email protected] | |
| affiliation: Intel | |
| - given-names: Ashwin | |
| family-names: Vaidya | |
| email: [email protected] | |
| affiliation: Intel | |
| - given-names: Barath | |
| family-names: Lakshmanan | |
| email: [email protected] | |
| affiliation: Intel | |
| - given-names: Nilesh | |
| family-names: Ahuja | |
| email: [email protected] | |
| affiliation: Intel | |
| - given-names: Utku | |
| family-names: Genc | |
| email: [email protected] | |
| 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 | |