Anomalib is a deep learning library for benchmarking, developing, and deploying anomaly detection algorithms with a focus on visual anomaly detection in images and videos.
The library addresses the need for accessible, production-ready anomaly detection by providing state-of-the-art algorithm implementations alongside tools for experiment management, hyperparameter optimization, and model deployment. It uses PyTorch and Lightning as its foundation, reducing boilerplate code and allowing developers to focus on algorithm specifics. The tool supports both training and inference workflows, with particular emphasis on edge deployment through OpenVINO export for accelerated inference on Intel hardware.
Anomalib suits teams building visual anomaly detection systems who want to avoid reimplementing published algorithms from scratch. It works well for benchmarking against public datasets or adapting models to private data. The library provides a modular API and command-line interface for common tasks like training, inference, and hyperparameter optimization, making it accessible whether you need a standard algorithm or plan to develop custom models. The collection of ready-to-use implementations and benchmark datasets reduces setup friction compared to starting from research papers.
The project maintains active engagement with its community through GitHub Issues and Discussions. Development includes attention to security concerns, as evidenced by fixes addressing archive extraction vulnerabilities. The team addresses specific bugs in core functionality such as random tiling behavior and metric computation. Dataset management receives ongoing attention, with efforts to improve accessibility by migrating datasets to standard platforms rather than requiring manual requests.
