SOD Embedded Source Package Release 1.1.9 (July 2023, Changelog )
Open Source License
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
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 sample set 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 Code SamplesPre-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 (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 (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 (One time fee) |
| Full | 80 | :full | 257MB | 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 (One time fee) |
| Sat | 72 | N/A | 170MB | 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 | Request Model. |
Pre-trained RealNets Models
Production ready, pre-trained models to be used in conjunction with the SOD RealNets interfaces.
| Model | Model Size | RAM Consumption | Description | Usage | Availability |
|---|---|---|---|---|---|
| Frontal Face Detector | 234 KB | < 10 MB | 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 (One time fee) |
| WebAssemby Face Model | 242 KB | < 5 MB | 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 (One time fee) |
Pre-trained RNN Models
Production ready, pre-trained text generation models to be used in conjunction with the SOD RNN interfaces.
| Model | Magic Word | Model Size | RAM Consumption | Description | Gist | Availability |
|---|---|---|---|---|---|---|
| Kant | :rnn | 34 MB | < 35 MB | RNN text generation model pre-trained on Immanuel Kant various essays. ID - kant-rnn.sod |
rnn_text.c | $19 (One time fee) |
| 4chan | :rnn | 34 MB | < 35 MB | RNN text generation model pre-trained on 4chan various Anon posts. ID - anon_4chan.sod |
rnn_text.c | $19 (One time fee) |
| Tolstoy | :rnn | 34 MB | < 35 MB | RNN text generation model pre-trained on Leo Tolstoy various essays. ID - tolstoy-rnn.sod |
rnn_text.c | $19 (One time fee) |
| Art History | :rnn | 34 MB | < 35 MB | RNN text generation model pre-trained on various art history essays. ID - arthistory_rnn.sod |
rnn_text.c | $19 (One time fee) |
| Shakespeare | :rnn | 34 MB | < 35 MB | RNN text generation model pre-trained on William Shakespeare various essays. ID - shk_rnn.sod |
rnn_text.c | $19 (One time fee) |
Miscellaneous Models
Models not tied with the SOD library.
| 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 (One time fee) |
Various Datasets
Useful public datasets.