Perform code generation for an image classification application that uses deep learning. It uses the codegen command to generate a MEX function that runs prediction by using image classification networks such as MobileNet-v2, ResNet, and GoogLeNet.

Generate CUDA® code from a deep learning network, represented by a SeriesNetwork object. In this example, the series network is a convolutional neural network that can detect and output lane marker boundaries from an image.

Generate CUDA® MEX code for a traffic sign detection and recognition application that uses deep learning. Traffic sign detection and recognition is an important application for driver assistance systems, aiding and providing information to the driver about road signs.

Code generation for a logo classification application that uses deep learning. It uses the codegen command to generate a MEX function that performs prediction on a SeriesNetwork object called LogoNet.

Code generation for pedestrian detection application that uses deep learning. Pedestrian detection is a key issue in computer vision. Pedestrian detection has several applications in the fields of autonomous driving, surveillance, robotics, and so on.

Code generation for an image segmentation application that uses deep learning. It uses the codegen command to generate a MEX function that performs prediction on a DAG Network object for SegNet [1], a deep learning network for image segmentation.

Train and deploy a fully convolutional semantic segmentation network on an NVIDIA® GPU by using GPU Coder™.

Code generation for an image segmentation application that uses deep learning. It uses the codegen command to generate a MEX function that performs prediction on a DAG Network object for U-Net, a deep learning network for image segmentation.

Generate CUDA® MEX from MATLAB® code and denoise grayscale images by using the denoising convolutional neural network (DnCNN [1]). You can use the denoising network to estimate noise in a noisy image, and then remove it to obtain a denoised image.

Code generation for a deep learning application by using the NVIDIA TensorRT™ library. It uses the codegen command to generate a MEX file to perform prediction with a ResNet-50 image classification network by using TensorRT. A second example demonstrates usage of codegen command to generate a MEX file that performs 8-bit integer prediction by using TensorRT for a logo classification network.

Generate CUDA® MEX for a you only look once (YOLO) v2 object detector. A YOLO v2 object detection network is composed of two subnetworks. A feature extraction network followed by a detection network. This example generates code for the network trained in the Object Detection Using YOLO v2 Deep Learning example from Computer Vision Toolbox™. For more information, see Object Detection Using YOLO v2 Deep Learning (Computer Vision Toolbox). You can modify this example to generate CUDA® MEX for the network imported in the Import Pretrained ONNX YOLO v2 Object Detector example from Computer Vision Toolbox™. For more information, see Import Pretrained ONNX YOLO v2 Object Detector (Computer Vision Toolbox).

Generate CUDA® code for an SSD network (ssdObjectDetector object) and take advantage of the NVIDIA® cuDNN and TensorRT libraries. An SSD network is based on a feed-forward convolutional neural network that detect multiple objects within the image in a single shot. SSD network can be thought of as having two sub-networks. A feature extraction network, followed by a detection network.

Use the cnncodegen function to generate code for an image classification application that uses deep learning on ARM® Mali GPUs. The example uses the MobileNet-v2 DAG network to perform image classification. The generated code takes advantage of the ARM Compute library for computer vision and machine learning.

Demonstrates how to generate CUDA® code for a long short-term memory (LSTM) network. The example generates a MEX application that makes predictions at each step of an input timeseries. Two methods are demonstrated: a method using a standard LSTM network, and a method leveraging the stateful behavior of the same LSTM network. This example uses accelerometer sensor data from a smartphone carried on the body and makes predictions on the activity of the wearer. User movements are classified into one of five categories, namely dancing, running, sitting, standing, and walking. The example uses a pretrained LSTM network. For more information on training, see the Sequence Classification Using Deep Learning (Deep Learning Toolbox) example from Deep Learning Toolbox™.

Demonstrates how you can use powerful signal processing techniques and Convolutional Neural Networks together to classify ECG signals. We will also showcase how CUDA® code can be generated from the Simulink® model. This example uses the pretrained CNN network from the Classify Time Series Using Wavelet Analysis and Deep Learning example of the Wavelet Toolbox™ to classify ECG signals based on images from the CWT of the time series data. For information on training, see Classify Time Series Using Wavelet Analysis and Deep Learning (Wavelet Toolbox).

Generate CUDA® MEX code for a deep learning network for lidar semantic segmentation. This example uses a pretrained SqueezeSegV2 [1] network that can segment organized lidar point clouds belonging to three classes (background, car, and truck). For information on the training procedure for the network, see Lidar Point Cloud Segmentation Using SqueezeSegV2 Deep Learning Network. The generated MEX code takes a point cloud as input and performs prediction on the point cloud by using the DAGNetwork object for the SqueezeSegV2 network.

Generate CUDA® code for a deep learning network that classifies video and deploy the generated code onto the NVIDIA® Jetson Xavier board using the GPU Coder™ Support Package for NVIDIA GPUs. The deep learning network has both convolutional and bidirectional long short-term memory (BiLSTM) layers. The generated application reads the data from a specified video file as a sequence of video frames and outputs a label that classifies the activity in the video. This example generates code for the network trained in the Classify Videos Using Deep Learning example from the Deep Learning Toolbox (TM). For more information, see Classify Videos Using Deep Learning (Deep Learning Toolbox).

Generate CUDA® MEX for a you only look once (YOLO) v3 object detector with custom layers. The example uses YOLO v3 object detection to illustrate:

Develop a CUDA® application from a Simulink® model that performs lane and vehicle detection using convolutional neural networks (CNN). This example takes the frames of a traffic video as an input, outputs two lane boundaries that correspond to the left and right lanes of the ego vehicle, and detects vehicles in the frame. This example uses the pretrained lane detection network from the Lane Detection Optimized with GPU Coder example of the GPU Coder Toolbox™. For more information, see Lane Detection Optimized with GPU Coder. This example also uses the pretrained vehicle detection network from the Object Detection Using YOLO v2 Deep Learning example of the Computer Vision toolbox™. For more information, see Object Detection Using YOLO v2 Deep Learning (Computer Vision Toolbox).

Deploy a Simulink® model on the NVIDIA® Jetson TX2 board for classifying webcam images. This example classifies images from a webcam in real-time by using the pretrained deep convolutional neural network, ResNet-50. The Simulink model in the example uses the camera and display blocks from the GPU Coder™ Support Package for NVIDIA GPUs to capture the live video stream from a webcam and display the prediction results on a monitor connected to the Jetson platform.