Image Classification and Segmentation Applications - MATLAB & Simulink (original) (raw)
Generate code for deep learning networks that perform image classification and segmentation
Generate code for image classification and segmentation applications and deploy on embedded targets.
Featured Examples
Code Generation for Deep Learning Networks
Get started with CUDA® code generation for image classification networks such asResNet.
Traffic Sign Detection and Recognition
Generate CUDA MEX for a traffic sign detection and recognition application that uses deep learning.
Logo Recognition Network
Generate code and classify an input image into 32 logo categories.
Deep Learning Prediction with NVIDIA TensorRT Library
Generate a CUDA MEX file that performs 32-bit, 16-bit floating point, and 8-bit integer prediction using TensorRT.
Generate Digit Images on NVIDIA GPU Using Variational Autoencoder
CUDA code generation for dlnetwork anddlarray objects.
Code Generation for Lidar Point Cloud Segmentation Network
Generate CUDA MEX for a network that can segment organized lidar point clouds belonging to three classes.
Code Generation for a Video Classification Network
Classify activities on Jetson™ Xavier using a network with convolutional and BiLSTM layers.
Deploy and Classify Webcam Images on NVIDIA Jetson Platform from Simulink
Deploy a Simulink® model on the NVIDIA® Jetson™ 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 MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE™ Platforms to capture the live video stream from a webcam and display the prediction results on a monitor connected to the Jetson platform.
Quantize Residual Network Trained for Image Classification and Generate CUDA Code
Quantize learnable parameters in the convolution layers of a residual network and generate CUDA code.
Deep Learning Prediction on ARM Mali GPU
Deploy image classification application to an ARM® Mali GPU.
Code Generation for Semantic Segmentation Network That Uses U-net
Generate CUDA code for the U-Net deep learning network for image segmentation.
Code Generation for Semantic Segmentation Network
Code generation for the SegNet image segmentation network.
Code Generation for Aerial Lidar Semantic Segmentation Using PointNet++ Deep Learning
Generate CUDA MEX code for a PointNet++ network for lidar semantic segmentation.