Spaces:
Build error
Build error
File size: 3,117 Bytes
c8c12e9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
# 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
|