SOD Resources & Downloads

Download the latest release of the SOD embedded Computer Vision library & Get access to state-of-the-art pre-trained, Machine Learning models

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Version 2.197 (Release Notes ↗)

SOD Computer Vision Library Source Package Release 1.1.9 (Changelog )

Open Source License GPLv3 Logo

GPLv3 - Go Open Source

  • Free of charge for open source projects or applications that are not distributed to third parties.
  • Compatible with most open source licenses (GPLv3).
  • Built-in RealNets Training Interfaces.
  • Amalgamated, high code quality.
  • Community Support.
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Commercial License PixLab | Symisc Systems

Commercial Licensing

  • Multi-core CPU support for all platforms - Up to 3 ~ 10 times faster processing speed.
  • Built-in (C Code), high performance RealNets frontal face detector.
  • 75 days of integration & technical assistance.
  • Royalty-free commercial licenses without any GPL restrictions.
  • Application source code stays private.

New Model - Real-Time, WebAssemby, face detection model for Web apps. Find out more.

The REST API code samples are practical usage, real-world working code implemented in C intended to familiarize the reader with the SOD Embedded API and is also available to consult online here. For an introduction course to the API, see Getting Started with SOD and The C/C++ API Reference Guide. You’re welcome to copy/paste and run these examples to see the API in action.

Download img Download Code Samples

Pre-trained CNN Models

Production ready, pre-trained models to be used in conjunction with the SOD CNN interfaces.

Model Total Classes Magic Word Model Size Description Usage Availability
CNN Face Detector 1 :face 396 KB Real-time, robust, multi-scale & shape invariant (i.e. frontal, inclined, large, tiny, etc.) face detection CNN model.
ID - face_cnn.sod 		cnn_face.c 20 USD (One-Time Fee)
Tiny Voc 20 :voc 60 MB Smallest & fastest object detection CNN model pre-trained on the Pascal VOC dataset that is able to detect 20 classes of different objects (i.e. car, person, dog, chair, etc.). This model works at Real-time on a Core I7 and similar CPUs with proper compiler optimizations (AVX, SSE, etc.) or using the proprietary multi-core enabled SOD release.
ID - tiny20.sod 		cnn_voc.c 25 USD (One-Time Fee)
Tiny COCO 80 :coco 61 MB Small & fast object detection CNN model pre-trained on the MS COCO dataset that is able to detect 80 classes of different objects (i.e. bus, person, airplane, stop sign, etc.). This model works at Real-time on a Core I7 and similar CPUs with proper compiler optimizations (AVX, SSE, etc.) or using the proprietary multi-core enabled SOD release.
ID - tiny80.sod 		cnn_coco.c 20 USD (One-Time Fee)
Full 80 :full 257 MB Most accurate but largest & slowest (compared to :voc or :coco) object detection CNN model pre-trained on the MS COCO dataset that is able to detect 80 classes of different objects including car, motorbike, horse, bicycle, and so forth. This model does not work at Real-time even with the proprietary multi-core enabled SOD release.
ID - full.sod 		cnn_full.c 29 USD (One-Time Fee)
Sat 72 N/A 170 MB Distill useful information including building, shapes, forms, etc. from satellite imagery at Real-time using the proprietary multi-core enabled SOD release. This model require prior approbation before delivery.
ID - sat.sod		 N/A
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Pre-trained RealNets Models

Production ready, pre-trained models to be used in conjunction with the SOD RealNets interfaces.

Model Ram Consumption Model Size Description Usage Availability
Frontal Face Detector 234 KB < 10MB Real-time (5 ~ 15 ms on HD video stream), frontal face detector RealNet model pre-trained on the Genki-4K datatset.
This is the recommended model if you are capturing video stream from user's Webcam or smartphone frontal camera to implement Snapchat-like filters, face recognition and so forth.
RealNets are designed to analyze & extract useful information from video stream rather than static images thanks to their fast processing speed (less than 10 milliseconds on 1920*1080 HD stream) and low memory footprint making them suitable for use on mobile devices. You are encouraged to connect the RealNets APIs with the OpenCV Video capture interfaces or any proprietary Video capture API to see them in action.
ID - face.realnet.sod 		realnet_face.c 20 USD (One-Time Fee)
WebAssemby Face Model 242 KB < 5MB Frontal face detector, WebAssemby model pre-trained on the Genki-4K datatset for Web oriented applications.
The model is production ready, works at Real-Time on all modern browsers (mobile devices included). Usage instruction already included in the package.
ID - Webassembly.face.model 		usage.html 20 USD (One-Time Fee)

Miscellaneous Models

Various models independent of the SOD library are used by our other open-source libraries.

Model Model Size Description Web Demo Gist Availability
ASCII Art 2.6 MB 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.
The library repository is available on Github.This is the hex output model generated during the training phase. It contains both the codebook and the decision tree that let you render your images or video frames at Real-time.
ID - ascii_art.hex 		art.pixlab.io sample.c 25 USD (One-Time Fee)