论文标题
ANOMALIB：用于异常检测的深度学习库
Anomalib: A Deep Learning Library for Anomaly Detection
论文作者
论文摘要
本文介绍了Anomalib，这是一个用于无监督异常检测和定位的新型库。考虑到可重复性和模块化，该开源库提供了文献和一套通过插件方法设计自定义异常检测算法的工具。 Anomalib包含最先进的异常检测算法，这些算法在基准上实现了最高性能，并且可以在现成的现场使用。此外，该库还提供了设计可以针对特定需求定制的自定义算法的组件。其他工具，包括实验跟踪器，可视化器和高参数优化器，使设计和实施异常检测模型变得易于使用。该库还支持OpenVino模型优化和实时部署的量化。总体而言，Anomalib是一个广泛的库，用于从数据到边缘的无监督异常检测模型的设计，实现和部署。
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.
