SOD Embedded 1.1.7 Released

Symisc Systems is pleased to release the first major version of the SOD library! SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices.

SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products.

Notable SOD features

  • Built for real world and real-time applications.
  • State-of-the-art, CPU optimized deep-neural networks including the brand new, exclusive RealNets architecture.
  • Patent-free, advanced computer vision algorithms.
  • Support major image format.
  • Simple, clean and easy to use API.
  • Brings deep learning on limited computational resource, embedded systems and IoT devices.
  • Easy interpolatable with OpenCV or any other proprietary API.
  • Pre-trained models available for most architectures.
  • CPU capable, RealNets model training.
  • Production ready, cross-platform, high quality source code.
  • SOD is dependency free, written in C, compile and run unmodified on virtually any platform & architecture with a decent C compiler.
  • Amalgamated - All SOD source files are combined into a single C file (sod.c) for easy deployment.
  • Open-source, actively developed & maintained product.
  • Developer friendly support channels.

Programming Interfaces

The documentation works both as an API reference and a programming tutorial. It describes the internal structure of the library and guides one in creating applications with a few lines of code. Note that SOD is straightforward to learn, even for new programmer.

SOD in 5 minutes or less

A quick introduction to programming with the SOD Embedded C/C++ API with real-world code samples implemented in C.

C/C++ API Reference Guide

This document describes each API function in details. This is the reference document you should rely on.

SOD Github Repository

The official Github repository.

C/C++ Code Samples

Real world code samples on how to embed, load models and start experimenting with SOD.

Real-Time ASCII Art Rendering Library Released

The PixLab engineering team is pleased to announce the immediate availability of the Real-Time ASCII Art C/C++ Rendering Library.

ASCII Art is a single file C/C++ library that let you transform an input image or video frame into printable ASCII characters at real-time using a single decision tree. Real-time performance is achieved by using pixel intensity comparison inside internal nodes of the tree.

  1. For a general overview on how the algorithm works, please visit the demonstration page at
  2. The Github Repository at
  3. The ASCII Art API at


PixLab Officially Launched

Dear folks,

We are pleased to announce the immediate availability of our machine learning SaaS platform to the public.

PixLab is set of unified Restful APIs for all your media analysis & processing tasks. It is shipped with over 130 commands (API endpoints) including:

  1. Face detection, recognition, emotion, generation, lookup, landmarks, etc.
  2. Content Moderation & Extraction: nsfw, sfw, urlcapture, header, ocr, tagimg.
  3. Pixel Generation/Image processing.
  4. The ability to train your own object detector.

With this in hand, you can achieve amazing transformation to your input images & videos including:

  1. Mimic Snapchat filters
  2. Content filtering
  3. Blurring/Cropping human faces.
  4. MEME Creation.

and finally here is some useful links to start playing with:

  1. The PixLab API in 5 minutes or less:
  2. API Reference Guide:
  3. List of Images Analysis & Processing Commands:
  4. The PixLab Sample Set:

We are a small bootstrapped startup mostly composed of engineers distributed around the globe. It took us 10 months of tedious work to ship the first stable version of PixLab so we really hope that you enjoy it and we look forward to hear back from you guys!