#anomaly detection

Anomalib is a deep learning library for anomaly detection with a modular API and CLI. It supports visual anomaly detection and allows model export to OpenVINO for enhanced performance on Intel hardware. Equipped with benchmarking tools and experiment tracking integration, it aids in developing innovative detection models efficiently across datasets.

PyGOD is a Python library for graph outlier detection, featuring over 10 algorithms for anomaly detection in networks and security systems. Utilizing PyTorch and PyTorch Geometric bases, it provides a smooth API, comprehensive documentation, and versatile examples for node, edge, and graph-level tasks. PyGOD is designed for scalability and efficiently handles large graphs through mini-batch processing and sampling, enabling rapid outlier detection with concise code. The ongoing updates offer optimal performance and solid community backing from renowned research institutions.

TensorFlow Data Validation (TFDV) is designed for scalable exploration and validation of machine learning data, integrating seamlessly with TensorFlow and TFX. It allows computation of data summary statistics, supports anomaly detection to highlight inconsistencies, and auto-generates data schemas. The integration with Facets enhances data comparison, and comprehensive resources are available for effective deployment in diverse machine learning workflows.

Flow Forecast is an open-source deep learning framework for time series forecasting, implementing cutting-edge models such as transformers and attention mechanisms. It features easy cloud integration, provides model interpretability metrics, and supports model serving. The framework accommodates models including LSTM, GRU, and Transformer XL, suitable for classification, anomaly detection, and various forecasting tasks. Managed by Task-TS from CoronaWhy, it offers comprehensive tutorials and detailed documentation.