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Deep Learning Code Generation

Generate MATLAB® code or CUDA® and C++ code and deploy deep learning networks

Use Deep Network Designer to generate MATLAB code to recreate the network.

Use MATLAB Coder™ or GPU Coder™ together with Deep Learning Toolbox™ to generate C++ or CUDA code and deploy convolutional neural networks on embedded platforms that use Intel®, ARM®, or NVIDIA® Tegra® processors.

Topics

MATLAB Code Generation

Generate MATLAB Code from Deep Network Designer

Recreate a network created or edited in Deep Network Designer by generating MATLAB code.

GPU Code Generation

Code Generation for Deep Learning Networks

This example demonstrates code generation for an image classification application that uses deep learning.

Code Generation for Object Detection Using YOLO v2

This example shows how to generate CUDA® code for the Object Detection Using YOLO v2 Deep Learning (Computer Vision Toolbox) example from the Computer Vision Toolbox™.

Running an Embedded Application on the NVIDIA Jetson TX2 Developer Kit

This example shows how to generate CUDA® code from a SeriesNetwork object and target the NVIDIA® TX2 board with an external camera.

Deep Learning Prediction with NVIDIA TensorRT (GPU Coder)

This example demonstrates code generation for a deep learning application using NVIDIA TensorRT™ library.

Lane Detection Optimized with GPU Coder

This example shows how to generate CUDA® code from a deep learning network, represented by a SeriesNetwork object.

Integrating Deep Learning with GPU Coder into Simulink (GPU Coder)

This example shows how to integrate the CUDA® code generated for a deep learning network into Simulink®.

Traffic Sign Detection and Recognition

This example demonstrates how to generate CUDA® MEX code for a traffic sign detection and recognition application, that uses deep learning.

Logo Recognition Network

This example demonstrates code generation for a logo classification application that uses deep learning.

Pedestrian Detection

This example demonstrates code generation for pedestrian detection application that uses deep learning.

Code Generation for Denoising Deep Neural Network

This example shows how to generate CUDA® MEX from MATLAB® code and denoise grayscale images by using the denoising convolutional neural network (DnCNN [1]).

Code Generation for Semantic Segmentation Network

This example demonstrates code generation for an image segmentation application that uses deep learning.

Train and Deploy Fully Convolutional Networks for Semantic Segmentation

This example shows how to train and deploy a fully convolutional semantic segmentation network on an NVIDIA® GPU by using GPU Coder ™.

Code Generation for Semantic Segmentation Network using U-net

This example demonstrates code generation for an image segmentation application that uses deep learning.

Deep Learning with GPU Coder (GPU Coder)

Generate CUDA code for deep learning neural networks

CPU Code Generation

Code Generation for Deep Learning on ARM Targets

This example shows how to generate and deploy code for prediction on an ARM®-based device without using a hardware support package.

Code Generation for Deep Learning on Raspberry Pi

This example shows how to generate and deploy code for prediction on a Raspberry Pi™ by using codegen with the MATLAB Support Package for Raspberry Pi Hardware.

Deep Learning Prediction with ARM Compute Using cnncodegen

This example shows how to use cnncodegen to generate code for a Logo classification application that uses deep learning on ARM® processors.

Deep Learning Prediction with Intel MKL-DNN

This example shows how to use codegen to generate code for an image classification application that uses deep learning on Intel® processors.

Load Pretrained Networks for Code Generation (MATLAB Coder)

Create a SeriesNetwork or DAGNetwork object for code generation.

Deep Learning with MATLAB Coder (MATLAB Coder)

Generate C++ code for deep learning neural networks (requires Deep Learning Toolbox)

Featured Examples