Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
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Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right checkpoint per request.
💫 Industrial-strength Natural Language Processing (NLP) in Python
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation.
Refine high-quality datasets and visual AI models
Techniques for deep learning with satellite & aerial imagery
Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications
Accelerated deep learning R&D
The machine learning toolkit for time series analysis in Python
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
NFStream: a Flexible Network Data Analysis Framework.
A tool for semi-automatic cell type classification
CellViT: Vision Transformers for Precise Cell Segmentation and Classification
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.