Anomalib is a PyTorch-based library for visual anomaly detection, offering a modular framework, a comprehensive model zoo, and a benchmarking suite designed for industrial defect detection. It provides a wide range of algorithms—including generative, discriminative, teacher-student, and vision-language approaches—that support unsupervised, few-shot, and zero-shot settings.
The library enables deployment through model export to ONNX and OpenVINO for edge devices, and includes a no-code web application for training and inference. It also features a command-line interface for orchestrating multi-stage pipelines, grid-search benchmarking across models and datasets, and experiment tracking with multiple logging backends.
Data loading and preprocessing support image, video, and depth modalities, along with synthetic anomaly generation. The library also provides automated result visualization and evaluation metrics for anomaly detection performance.