Anomalib vs PySAD

Anomalib is a free, Apache-2.0-licensed deep learning library for developing, benchmarking and deploying anomaly detection algorithms, especially for images and videos. Its modular Python API and command-line interface support training, inference and benchmarking, and the project includes ready-to-use algorithms and benchmark datasets. Model implementations are based on Lightning; inference options include Torch, Lightning, Gradio and OpenVINO. Most models can be exported to OpenVINO Intermediate Representation for accelerated inference on Intel hardware. Installation choices include CPU, NVIDIA CUDA on Linux or Windows, AMD ROCm on Linux and Intel XPU on Linux. Documented experiment-tracking integrations include Weights & Biases, Comet.ml and TensorBoard through PyTorch Lightning loggers. Anomalib Studio adds a low/no-code web application that accepts USB or IP cameras or image folders and can send output to industrial pipelines through ROS messages or MQTT. Studio is offered as a Docker container or standalone application, but is a pre-release under active development; features may change and functionality may be incomplete or unstable. Intel GPU training currently supports one GPU.

PySAD is a free, open-source Python framework for online anomaly detection in streaming data. Its models update when each new data instance arrives, supporting sequential detection for univariate and multivariate data. The project describes 16 online detectors, including xStream, LODA, RS-Hash, Half-Space Trees, and Robust Random Cut Forest. It also provides stream simulators, evaluators, preprocessors, statistic trackers, postprocessors, and probability calibrators. Integrations allow batch anomaly detectors from PyOD to run in a streaming setting. Experiments can use supervised, semi-supervised, or unsupervised settings. The documentation says streaming methods may retain one instance or a small recent window to meet memory and processing constraints. Installation is available through pip or the GitHub source; the current README lists Python 3.10 or newer on Linux, macOS, and Windows. PySAD is self-hosted and distributed under a BSD 3-Clause license. Its plan costs nothing, and 17 labelled benchmark datasets are downloaded on first use.