Anomalib swMATH ID: 41238 Software Authors: Samet Akcay, Dick Ameln, Ashwin Vaidya, Barath Lakshmanan, Nilesh Ahuja, Utku Genc Description: 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. Homepage: https://openvinotoolkit.github.io/anomalib/ Source Code: https://github.com/openvinotoolkit/anomalib Dependencies: Python Keywords: Computer Vision; Pattern Recognition; arXiv_cs.CV; Machine Learning; arXiv_cs.LG; Python; Deep Learning; Anomaly Detection Related Software: CFLOW-AD; GANomaly; TensorFlow; PyTorch Lightning; Scikit; PaDiM; Albumentations; PyOD; Detectron; MMDetection; OpenVino; PyTorch; Python Cited in: 0 Documents Standard Articles 1 Publication describing the Software Year Anomalib: A Deep Learning Library for Anomaly Detection arXiv Samet Akcay, Dick Ameln, Ashwin Vaidya, Barath Lakshmanan, Nilesh Ahuja, Utku Genc 2022