Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Anomalib: A Deep Learning Library for Anomaly Detection   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.

22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin