OpenCVopencv/opencv
OpenCV has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS.
76 libraries, 42 updated this year
C++ is where the fast cores live. OpenCV, XGBoost, LightGBM, CatBoost and SentencePiece are written in C++ and exposed to Python, R and other languages through bindings. dlib and mlpack are full machine learning libraries you can use directly from C++, and Kaldi and Vosk do speech recognition.
76 libraries
OpenCV has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS.
A deep learning framework developed with cleanliness, readability, and speed in mind.
A real-time multi-person keypoint detection library for body, face, hands, and foot estimation
Open source engineering platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. (Source Code)
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
Microsoft's fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
The Computational Network Toolkit (CNTK) by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.
A relational column-oriented database designed for real-time analytics on time series and event data.
Kaldi is a toolkit for speech recognition written in C++ and licensed under the Apache License v2.0. Kaldi is intended for use by speech recognition researchers.
An offline speech recognition toolkit with C++ support, designed for low-resource devices and multiple languages.
DLib has C++ and Python interfaces for face detection and training general object detectors.
A C++ library for unsupervised text tokenization and detokenization, widely used in modern NLP models.
Uncover insights, surface problems, monitor and fine tune your generative LLM, CV and tabular models.
An open-source, low-code machine learning library in Python that automates machine learning workflows.
General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, contains fast inference implementation and supports CPU and GPU (even multi-GPU) computation.
Automatically apply SOTA optimization techniques to achieve the maximum inference speed-up on your hardware.
A library for automated feature engineering. It excels at transforming transactional and relational datasets into feature matrices for machine learning using reusable feature engineering "primitives".
A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving.
A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility.
An open-source cross-platform performance library for deep learning applications.
A fast parallel implementation of Connectionist Temporal Classification (CTC), on both CPU and GPU.
A platform for reproducible and scalable machine learning and deep learning.
A dynamic neural network library working well with networks that have dynamic structures that change for every training instance. Written in C++ with bindings in Python.
A modular scientific software framework. It provides all the functionalities needed to deal with big data processing, statistical analysis, visualization and storage.
A high performance, easy-to-use, and scalable machine learning package, which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning problems on large-scale sparse data, which is very common in Internet services such as online advertising and recommender systems.
C, C++, and Python tools for named entity recognition and relation extraction
A probabilistic programming language implementing full Bayesian statistical inference with Hamiltonian Monte Carlo sampling.
In-context learning framework that allows agents to learn from execution feedback.
A machine learning API and server written in C++11. It makes state of the art machine learning easy to work with and integrate into existing applications.
A C++ edge video AI engine for RTSP ingestion, CV/VLM inference, visual pipeline orchestration, alarms, and event delivery on Sophon and Rockchip NPUs.
An Easy-to-Use and High-Performance AI deployment framework.
A generic approach that allows to mimic most factorization models by feature engineering.
A data-intensive platform for AI with the industry's first open-source feature store. The Hopsworks Feature Store provides both a feature warehouse for training and batch based on Apache Hive and a feature serving database, based on MySQL Cluster, for online applications.
The fastest deep reinforcement learning library for continuous control, implemented header-only in pure, dependency-free C++ (Python bindings available as well).
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition.
MeTA : ModErn Text Analysis is a C++ Data Sciences Toolkit that facilitates mining big text data.
The Machine Learning Database is a database designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs.
CRFsuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data.
A high performance software library developed by Intel and optimized for Intel's architectures. Library provides algorithmic building blocks for all stages of data analytics and allows to process data in batch, online and distributed modes.
Open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data & other Natural Language Processing tasks.
A highly-modular C++ machine learning library for embedded electronics and robotics.