Anomalib
7.4#2 in anomaly detection softwareFree planFree planWebAPILinuxSelf-hosted
VisitAnomalib vs PySAD
What both publish
| Fact | Anomalib | PySAD |
|---|---|---|
| Installation | The recommended standard installation is through PyPI using pip or uv, with source installation available for development. | The project can be installed with pip or from its GitHub source, and its current README lists Python 3.10 or newer on Linux, macOS, and Windows. |
| License | The repository identifies its license as Apache-2.0. | The project is distributed under a BSD 3-Clause license. |
| 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. | PySAD is an open-source Python framework for anomaly detection on streaming multivariate data. |
| 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. | The repository links to a security policy, but the policy page could not be opened during this research. |
Anomalib plans
- Open-source Anomalib libraryFree
- Open-source libraryFree
PySAD plans
- PySADFree
Pick Anomalib if…
Anomalib suits developers building or benchmarking image and video anomaly detection, including those deploying inference on supported hardware. Studio may appeal to users seeking a low/no-code web application, provided they can work with pre-release software.
Pick PySAD if…
PySAD suits Python users building or evaluating anomaly detection for streaming univariate or multivariate data. It also fits those experimenting with supervised, semi-supervised, and unsupervised approaches.