open-edge-platform/anomalib

Health v2 · maintenance only

Adoption not part of the score

6088 stars · 974 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Anomalib is a Python deep learning library for anomaly detection, offering state-of-the-art unsupervised algorithms for detecting and localizing anomalies in images. It includes experiment management, hyper-parameter optimization, and export/edge inference support via OpenVINO.

Use cases

• detect defects in industrial images
• train an unsupervised anomaly detection model
• segment anomalous regions in images
• benchmark anomaly detection algorithms
• deploy anomaly detection to edge devices
• optimize hyperparameters for anomaly models
• visual inspection of manufactured parts

When to choose

• you need ready-to-use state-of-the-art anomaly detection or localization models
• you want PyTorch-based training with experiment tracking and HPO built in
• you need to export models for edge inference with OpenVINO

When to avoid

• you need supervised classification rather than unsupervised anomaly detection
• you need anomaly detection on time series or tabular data rather than images
• you want a lightweight inference-only tool without training features

Facets

library · maturity active

machine-learning deep-learning computer-vision image-processing benchmarking llm-training computer-vision machine-learning deep-learning image-processing python cross-platform anomaly-detection anomaly-segmentation unsupervised-learning openvino pytorch edge-inference industrial-inspection gpu

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markdown · JSON · MCP: product_card(name="open-edge-platform/anomalib")