Open Source C Neural Network Libraries

This is YOLO-v3 and v2 for Windows and Linux. YOLO (You only look once) is a state-of-the-art, real-time object detection system of Darknet, an open source neural network framework in C. YOLO is extremely fast and accurate. It uses a single neural network to divide a full image into regions, and then predicts bounding boxes and probabilities for each region. This project is a fork of the original Darknet project.

Darknet is an open source neural network framework written in C and CUDA, developed by Joseph Redmon. It is best known as the original implementation of the YOLO (You Only Look Once) real-time object detection system. Darknet is lightweight, fast, and easy to compile, making it suitable for research and production use. The repository provides pre-trained models, configuration files, and tools for training custom object detection models. With GPU acceleration via CUDA and OpenCV integration, it achieves high performance in image recognition tasks. Its simplicity, combined with powerful capabilities, has made Darknet one of the most influential projects in the computer vision community.

Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. Bindings to more than 15 programming languages are available. An easy to read introduction article and a reference manual accompanies the library with examples and recommendations on how to use the library. Several graphical user interfaces are also available for the library.

We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. In conclusion, RBF is a novel framework that performs strongly especially on data with large size.

Lightweight backpropagation neural network in C. Intended for programs that need a simple neural network and do not want needlessly complex neural network libraries. Includes example application that trains a network to recognize handwritten digits.

It's an object-oriented library written in C++ for creating arbitrary kind of neural networks. The user can use the classes provided to create neural network with arbitrary topology and mixed type of neurons. It's very easy add custom neurons.

NeMo is a high-performance spiking neural network simulator which simulates networks of Izhikevich neurons on CUDA-enabled GPUs. NeMo is a C++ class library, with additional interfaces for pure C, Python, and Matlab.

ANNGD is a artificial neural network gender detection application. The detection is made in real time images captured by webcam by opencv library. The package include applications to image pre-processing and artificial neural network back-propagation training.

The subject of this project is development of the Intrusion Detection System (IDS) with Artificial Neural Network (ANN). This model will allow the implementation of a system capable to analyze and to identify possible intrusions, based on the method of an

Imlementation of SOM (Self organizing networks) a neural network for IBM Cell Broadband Engine Architecture.

Foad is an open source software which receive an EKG Signal from scanner, WFDB database or heart sensors. Finding patient disease started by taking Fourier transform (FFT) from input signal and extract a single cycle. Based on some heuristic algorithm the most important feature like P , Q , R , S , T captured and feed to trained neural network. and so the final decision made by CNN library. As mentioned before this software also capable do some image processing on scanned paper to lower the final costs.

Grenade is a composable, dependently typed, practical, and fast recurrent neural network library for concise and precise specifications of complex networks in Haskell. Because the types are so rich, there's no specific term level code required to construct this network; although it is of course possible and easy to construct and deconstruct the networks and layers explicitly oneself. Networks in Grenade can be thought of as a heterogeneous list of layers, where their type includes not only the layers of the network but also the shapes of data that are passed between the layers. To perform back propagation, one can call the eponymous function which takes a network, appropriate input, and target data, and returns the back propagated gradients for the network. The shapes of the gradients are appropriate for each layer and may be trivial for layers like Relu which have no learnable parameters.

This Ruby program takes in a topology specification for an artificial neural network and emits optimized C code (using SSE intrinsics) that implements fast forward and backward propagation for that specific topology.

Neural Network class library: Its a C/C++ implementation that provides following three neural architecture - Feed-forward network, Radial Basis function network, multi-layer perceptron and Self-Organizing Maps.

Neural Network calculation & learning library

Thank you for your interest in Speedy Composer. Speedy Composer is an automated application for composing melodies for Speedy Net members. We recently made changes to the source code of Speedy Net, and converted it into the Python language and Django framework. Since Speedy Composer was originally written in PHP, it is not adapted to work with Speedy Net in its current form. So unfortunately we were forced to temporarily close the app Speedy Composer. But don't worry, we kept backups of all the tunes composed by Speedy Composer, and when the website is reopened we will upload them to the new site. Speedy Composer and Speedy Net are open source applications, free software. We are currently looking for volunteers to help us convert Speedy Composer to Python. If you are interested in volunteering, please contact me by email. Thank you and good luck, Uri Rodberg Founder and Director of Speedy Net and Speedy Composer, Speedy Paz Technologies Ltd. uri@speedy.net

SPLITNEURON is a completely new approach to large-scale, biologically plausible, neural-network simulation library based on data structures and methods directly coded into database and conceived to explicitly share load on multiple machines.

Syntactic is a simple C++ library for constructing general Neural networks. For now the library supports creation of multi layered networks for the Feedforward Backpropagation algorithm as well as Time Series Networks. More will be implemented soon.

XNNPACK is a highly optimized, low-level neural network inference library developed by Google for accelerating deep learning workloads across a variety of hardware architectures, including ARM, x86, WebAssembly, and RISC-V. Rather than serving as a standalone ML framework, XNNPACK provides high-performance computational primitives—such as convolutions, pooling, activation functions, and arithmetic operations—that are integrated into higher-level frameworks like TensorFlow Lite, PyTorch Mobile, ONNX Runtime, TensorFlow.js, and MediaPipe. The library is written in C/C++ and designed for maximum portability, efficiency, and performance, leveraging platform-specific instruction sets (e.g., NEON, AVX, SIMD) for optimized execution. It supports NHWC tensor layouts and allows flexible striding along the channel dimension to efficiently handle channel-split and concatenation operations without additional cost.

Yann is Yet Another Neural Network. Yann is a library to create fast neural networks. It is also a GUI to easily create, edit, train, execute and investigate networks. Multiple topologies, runtime properties and ensemble learning are supported.

Code of this library is partialy based on myCNN MATLAB class written by Nikolay Chemurin.

This tool allows to compute LOLIMOT models. LOLIMOT models are also called neuro-fuzzy models or fast neural network models. This tool can export the model into C, C++, Matlab.

A parallel-programming framework for concurrently running large numbers of small autonomous jobs, or microthreads, across multiple cores in a CPU or CPUs in a cluster. NeuraNEP emulates a distributed processing environment capable of handling millions of microthreads in parallel, for example running neural networks with millions of spiking cells. Microthreads are general processing elements that can also represent non-neural elements, such as cell populations, extracellular space, emulating sensory activity, etc. NeuraNEP handles microthread scheduling, synchronization, distribution and communication. This project is a fork of SpikeOS (sourceforge.net/projects/spikeos) and represents a major update to that code base, including a scripting interface and low-level rewrite of several components. SpikeOS was oriented towards computational modeling. NeuraNEP is oriented toward neural network research.

openlpr is a open source project to implement license plate recognition in Win32 and Linux platform. It will provide the basic image processing to locate plate and character split and a neural network design to recognize character.

this server side PostgreeSQL extension enables developer to create, store, train and apply artificial neural networks in the serverside context of PostgreSQL. It is based on fann, the Fast Artificial Neural Network Library by Steffen Nissen (Thanks!).