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Anomalib is a deep learning library that aims to collect state-of-the-art anomaly detection algorithms for benchmarking on both public and private datasets. Anomalib provides several ready-to-use implementations of anomaly detection algorithms described in the recent literature, as well as a set of tools that facilitate the development and implementation of custom models. The library has a strong focus on visual anomaly detection, where the goal of the algorithm is to detect and/or localize anomalies within images or videos in a dataset. Anomalib is constantly updated with new algorithms and training/inference extensions, so keep checking!

Anomalib provides two ways to install the library. The first is through PyPI, and the second is through a local installation. PyPI installation is recommended if you want to use the library without making any changes to the source code. If you want to make changes to the library, then a local installation is recommended.

This will install Anomalib CLI using the dependencies in the pyproject.toml file. Anomalib CLI is a command line interface for training, inference, benchmarking, and hyperparameter optimization. If you want to use the library as a Python package, you can install the library with the following command:

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