You can use Simulink® Coder™ with the Deep Learning Toolbox™ to generate code from a trained convolutional neural network (CNN). Deep learning uses convolutional neural networks (CNNs) to learn useful representations of data directly from images. You can deploy the generated code to an embedded platform that uses an Intel® or ARM® processor. You can also generate generic C or C++ code from a trained CNN that does not depend on any third-party libraries.
- Workflow for Deep Learning C/C++ Code Generation for Simulink Models
Overview of C/C++ code generation workflow for deep learning neural networks.
- Generate Code for Deep Learning Networks Using MATLAB Function Block
Generate code for a model containing a MATLAB Function block that uses the GoogLeNet trained deep learning network.
- Generate Code for Blocks from Deep Neural Networks Library
Generate code for a model containing the GoogLeNet trained deep learning network.
- Code Generation for Deep Learning Simulink Model That Performs Lane and Vehicle Detection
This example shows how to generate C++ code from a Simulink® model that performs lane and vehicle detection using convolutional neural networks (CNN).
- Generate Generic C/C++ for Sequence-to-Sequence Deep Learning Simulink Models
Generate C/C++ code for a sequence-to-sequence deep learning Simulink model.
- Code Generation for Detect Defects on Printed Circuit Boards Using YOLOX Network
Generate code for a You Only Look Once X (YOLOX) object detector that can detect, localize, and classify defects in printed circuit boards (PCBs). (Since R2023b)