Pre-trained SOLOv2 for instance segmentation.

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

Generate C code for single and multicore devices on bare metal, RTOS, and middleware services

Gray land, white water basemap data for geographic visualization

Import, visualize, process, analyze and export hyperspectral data

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

Pretrained Xception model for image classification

Control Ryze Tello drones from MATLAB and acquire sensor and image data

Tools to compile a real-time application that runs on a Speedgoat target computer

Debug and test HDL code on Intel FPGAs and SoC FPGAs

Pretrained NasNet-Large network model for image classification

Pre-trained Segment Anything Model for instance segmentation

Debug and test HDL code on Microchip FPGAs using FPGA-in-the-loop

Generate testbenches for ASIC and advanced FPGA designs

Incorporate pre-trained TensorFlow Lite (TFLite) models into MATLAB and Simulink applications for simulation and deployment to hardware.

Pretrained GoogLeNet network trained on Places365 data set for image classification

Pre-trained Segment Anything Model for instance segmentation

Re-implementation of the spherical harmonics fitting tools offered in DIPY.