Image Classification and Segmentation Applications

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 as ResNet.

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Traffic Sign Detection and Recognition

Generate CUDA MEX for a traffic sign detection and recognition application that uses deep learning.

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Logo Recognition Network

Generate code and classify an input image into 32 logo categories.

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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.

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Generate Digit Images on NVIDIA GPU Using Variational Autoencoder

CUDA code generation for dlnetwork and dlarray objects.

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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.

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Code Generation for a Video Classification Network

Classify activities on Jetson™ Xavier using a network with convolutional and BiLSTM layers.

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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.