PyOD is a comprehensive Python library for anomaly/outlier detection offering 60+ detectors across tabular, time series, graph, text, image, and audio modalities. It provides a classic scikit-learn-compatible API (fit/predict/decision_function) via pyod.models, a high-level orchestration engine (ADEngine) for automatic profiling, detector selection, consensus scoring, and reporting, and an agentic workflow layer for LLM-driven anomaly detection through CLI, MCP server, and packaged AI skills. The library is primarily a software library (application) with strong data research influence: it comes with benchmark-backed routing rules, academic references, and Jupyter notebooks for research reprocibility. Its architecture follows a three-layer design (classic detectors, ADEngine orchestration, agentic skills) that allows progressive engagement from simple model fitting to full automated pipelines.
Linfa is a comprehensive machine learning library for Rust, providing implementations of classical ML algorithms including supervised learning (regression, classification, SVM, naive Bayes, decision trees, ensemble methods) and unsupervised learning (clustering, dimensionality reduction, ICA). The library features a modular architecture with core traits defining standard ML interfaces (Fit, Predict, Transformer), a flexible Dataset abstraction for data handling, and algorithm-specific crates following consistent patterns. It emphasizes performance through optional BLAS backends and parallel processing, while maintaining usability via builder-pattern hyperparameter configuration and comprehensive example code.
GrowSP is an unsupervised semantic segmentation framework for 3D point clouds that learns from growing superpoints. The system processes raw point cloud data through preparation pipelines, generates initial superpoints using voxelization and region growing, then trains a Feature Pyramid Network (Res16FPN18) to learn discriminative features. During training, superpoints are progressively merged (growing superpoints strategy) to create hierarchical representations, with pseudo-labels generated via clustering of superpoint features. The framework includes specialized dataset classes for S3DIS, ScanNet, and SemanticKITTI, evaluation scripts using Hungarian matching for unsupervised performance measurement, and utility libraries for 3D augmentations and PLY file handling. The architecture follows a modular design with clear separation between data preparation, dataset handling, model definitions, and training/evaluation logic.
This repository is a comprehensive, curated list of anomaly detection (outlier detection) resources, maintained by the author of the PyOD library. It aggregates books, academic papers, tutorials, open-source toolkits, datasets, and relevant conferences/journals, covering tabular, time-series, graph, and text data modalities. The repository also includes two utility scripts: a downloader for paper PDFs and a robust URL checker for verifying links in the documentation. While the core value is the curated resource list itself, the utility scripts demonstrate clean engineering practices and are reusable for similar link-checking and batch download tasks. This is not a software application or data pipeline; it is a reference catalog for researchers and practitioners.
soynlp is a pure-Python library for unsupervised Korean natural language processing, providing statistical methods for tokenization, noun extraction, POS tagging, lemmatization, and text normalization. The architecture is modular, with subpackages for hangle (Hangul decomposition/composition), lemmatizer, normalizer, noun extraction (v1, v2, news), POS tagging (dictionary-based and LR-template-based), tokenization (LTokenizer, MaxScoreTokenizer, RegexTokenizer), vectorization (TF-IDF-like), word extraction (cohesion, branching entropy), and utility data structures (LRGraph, EojeolCounter). Key entry points include WordExtractor, LRNounExtractor_v2, LTokenizer, MaxScoreTokenizer, BaseVectorizer, and DoublespaceLineCorpus. Data flows typically involve reading a corpus of double-space-separated sentences, building LR substring frequency graphs, extracting word/noun scores, and then using those scores for tokenization or tagging. External dependencies are limited to numpy and scikit-learn (older versions). The repository also includes research-oriented tutorials (Jupyter notebooks) and pre-built linguistic resources (frequency dictionaries, trained noun predictors), making it a hybrid of a production library and a research codebase. The unsupervised approach is well-suited for homogeneous document collections where domain-specific adaptation is needed.
This repository implements Dinomaly, a reconstruction-based unsupervised anomaly detection method for industrial inspection datasets. It uses vision transformers (primarily DINOv2) as feature extractors with a noisy bottleneck and linear attention mechanism. The code supports both class-separated and unified training across MVTec-AD, VisA, and Real-IAD datasets. Key components include dataset loaders, custom optimizers, training utilities, and evaluation metrics. While the research code achieves state-of-the-art results, it contains hardcoded paths, version-specific dependencies, and dataset-specific logic that limit immediate reproducibility.