  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.
