Anomalib: A Deep Learning Library for Anomaly Detection

• URL: http://arxiv.org/abs/2202.08341v1
• Date: Wed, 16 Feb 2022 21:15:59 GMT
• Title: Anomalib: A Deep Learning Library for Anomaly Detection
• Authors: Samet Akcay, Dick Ameln, Ashwin Vaidya, Barath Lakshmanan, Nilesh Ahuja, Utku Genc
• Abstract summary: Anomalib comprises state-of-the-art anomaly detection algorithms that achieve top performance on the benchmarks. Anomalib provides components to design custom algorithms that could be tailored towards specific needs. The library also supports OpenVINO model optimization and quantization for real-time deployment.
• Score: 0.39146761527401414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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