Pretrained ShuffleNet model for image classification

Pretrained NasNet-Large network model for image classification

Pretrained Xception model for image classification

Pretrained DarkNet-53 network model for image classification

Pretrained NasNet-Mobile network model for image classification

Pretrained Resnet-101 network model for image classification

Pretrained Inception-ResNet-v2 network model for image classification

Connect to Velodyne LiDAR sensors and stream point clouds directly into MATLAB for processing and visualization

Send and receive CAN messages with NI-XNET CAN devices.

Gray terrain basemap data for geographic visualization

This support package enables you to deploy a deep learning processor on Intel® FPGA and SoC based hardware from MATLAB

Acquire video and images from Teledyne DALSA Sapera cameras.

Import OpenCV functions into Simulink.

Use GPU Coder to generate optimized CUDA code for deep learning networks

Use NI-845x I2C/SPI devices to connect to your chips, circuit boards, sensors.

Automatically program the VEX EDR V5 Robot Brain using Simulink

Generate code for the FPGA portion of RFSoC devices

Control NI-FGEN devices and create arbitrary waveforms.

Add analytics to your Kafka streaming pipeline with MATLAB Production Server, and interact with Kafka streams using the MATLAB desktop.