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symisc/sod

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An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)

FreeFree tier
Inputs: image
Type
Open Source
Company
symisc
Visit Website GitHub (38 stars)

About symisc/sod

SOD is an embedded, cross-platform computer vision and machine learning library written in C, optimized for CPU performance and designed for real-time applications on limited-resource systems and IoT devices. It provides a comprehensive set of APIs for deep learning, including Convolutional Neural Networks (CNN) for multi-class object detection (20 and 80 classes), Recurrent Neural Networks (RNN) for text generation (e.g., Shakespeare, Python code), decision trees for real-time object detection, and the proprietary RealNets architecture. The library is dependency-free, amalgamated into a single C file (sod.c) for easy deployment, and includes pre-trained models. Key capabilities include real-time face and object detection, license plate extraction, intrusion detection, Snapchat-like filters, human action classification, eye pupil tracking, and image segmentation. SOD is open-source and actively maintained.

Key Features

Multi-class object detection with CNNs (20 & 80 classes)
RNN for text generation (Shakespeare, code, etc.)
Decision trees for real-time object detection
Proprietary RealNets architecture for CPU-optimized training and inference
Dependency-free, amalgamated single C file (sod.c)
Cross-platform – compiles on virtually any platform with a C compiler
Pre-trained models available for most architectures
Patent-free, advanced computer vision algorithms
Support for major image formats
Simple, clean C/C++ API (easy to learn and integrate with OpenCV)

Pros & Cons

Pros
  • Lightweight and optimized for embedded/IoT devices with limited resources
  • No external dependencies – easy to build and deploy
  • Amalgamated single-file distribution simplifies integration
  • Cross-platform compatibility (Linux, Windows, macOS, embedded RTOS, etc.)
  • Real-time performance on CPU (no GPU required)
  • Pre-trained models included for common tasks
  • Clean C/C++ API with extensive documentation and examples
  • Actively maintained open-source project with community support
Cons
  • No native GPU acceleration (CPU-only, may be slower than GPU-optimized libraries for large models)
  • Limited to C/C++ API (no Python or other high-level language bindings)
  • Model zoo is focused on specific RealNets architectures; less extensive than TensorFlow or PyTorch
  • Documentation and community resources are less extensive than mainstream frameworks

Best For

Real-time face and object detection & recognitionLicense plate extraction and recognitionIntrusion detection in surveillance systemsAugmented reality filters (e.g., Snapchat-style)Human action classificationEye pupil trackingBody shape extraction and facial landmarkingImage and frame segmentation

FAQ

What is SOD?
SOD is an embedded, cross-platform computer vision and machine learning library optimized for CPU and IoT devices. It provides APIs for deep learning, object detection, text generation, and image processing, all in a single dependency-free C file.
What platforms does SOD support?
SOD is designed to compile and run unmodified on virtually any platform with a decent C compiler, including Linux, Windows, macOS, and various embedded systems.
Does SOD require any dependencies?
No, SOD is completely dependency-free. All source code is amalgamated into a single C file (sod.c) for easy deployment.
Is SOD free to use?
Yes, SOD is open-source. The repository includes a LICENSE file (MIT) and is freely available for both personal and commercial use.
Can SOD perform real-time object detection?
Yes, SOD is built for real-time applications. It includes decision trees and CNNs (with RealNets) capable of real-time multi-class object detection on CPU.
Does SOD support GPU training?
No, SOD is CPU-optimized and does not include GPU acceleration. Training is performed on CPU using the RealNets architecture.
What are the main neural network architectures in SOD?
SOD includes CNNs for object detection (20 and 80 classes), RNNs for text generation, decision trees for real-time detection, and its own RealNets architecture designed specifically for embedded systems.