-
Notifications
You must be signed in to change notification settings - Fork 712
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
* Added citation * added citation to docs * added paper to citation.cff * fix version * remove family name from givenname * renamed the name of the library * revert the title
- Loading branch information
1 parent
dbca627
commit 38725ad
Showing
3 changed files
with
139 additions
and
17 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,92 @@ | ||
| # 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: 0.2.4 | ||
| 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 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters