Anomalib

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About

Anomalib is ranked #17 of 19 in anomaly detection software on Inferse. It runs on Linux, Self-hosted, Web, Windows. There is a free plan.

Compared on anomaly detection software

Detection method: machine-learninggithub.com
Supported data: images, videosgithub.com
Deployment options: self-hostedgithub.com

Facts

Purpose: Anomalib is a deep learning library for benchmarking, developing, and deploying anomaly detection algorithms, with a focus on detecting or localizing anomalies in images and videos.github.com · 4 Oct 2026
Training and benchmarking: It provides a modular Python API and CLI for training, inference, and benchmarking.github.com · 4 Oct 2026
Algorithms and datasets: The project describes its collection as ready-to-use deep learning anomaly detection algorithms and benchmark datasets.github.com · 4 Oct 2026
Model framework: Its model implementations are based on Lightning.github.com · 4 Oct 2026
Edge inference: Most models can be exported to OpenVINO Intermediate Representation for accelerated inference on Intel hardware.github.com · 4 Oct 2026
Experiment tracking: The documented logging integrations include Weights & Biases, Comet.ml, and TensorBoard through PyTorch Lightning loggers.github.com · 4 Oct 2026
Deployment: Inference options include Torch, Lightning, Gradio, and OpenVINO.github.com · 4 Oct 2026
Studio: Anomalib Studio is a low/no-code web application that accepts USB or IP cameras or image folders and can output to industrial pipelines through ROS messages or MQTT.github.com · 4 Oct 2026
Studio availability: Studio is described as a pre-release under active development, with features that may change and functionality that may be incomplete or unstable; it is offered as a Docker container or standalone application.github.com · 4 Oct 2026
Hardware support: Installation options include CPU, CUDA on Linux or Windows with NVIDIA GPUs, ROCm on Linux with AMD GPUs, and Intel XPU on Linux.github.com · 4 Oct 2026
Intel GPU limit: The README says Intel GPU training currently supports only a single GPU and notes testing on Arc 750 and Arc 770.github.com · 4 Oct 2026
Security: The project documents continuous security scanning with CodeQL, Semgrep, Bandit, Zizmor, Trivy, and Dependabot, and directs vulnerability reports to Intel's vulnerability handling guidelines.github.com · 4 Oct 2026
License: The repository identifies its license as Apache-2.0.github.com · 4 Oct 2026