Bildklassifizierungs- und Segmentierungs-Anwendungen
R2025bGenerieren von Code für Bildklassifizierungs- und Segmentierungs-Anwendungen und Bereitstellung auf eingebetteten Zielen.
Enthaltene Beispiele
Generate Code for a Deep Learning Network for x86-64 Platforms Using Advanced Vector Instructions
Generate code for deep learning networks, targeting an Intel® processor.
Generate Code and Deploy SqueezeNet Network to Raspberry Pi
Generate and run the generic C code on Raspberry Pi® hardware using processor-in-the loop (PIL) workflow.
Deep Learning Prediction with ARM Compute Using codegen
Generate code for deep learning networks, targeting an ARM® processor.
Generate Code and Deploy MobileNet-v2 Network to Raspberry Pi
Use the MobileNet-V2 network for object prediction on external hardware.
Code Generation for Semantic Segmentation Application on ARM Neon Targets That Uses U-Net
Generate a static library that performs image segmentation by using the deep learning network U-Net on ARM targets.
Generate Code for TensorFlow Lite (TFLite) Model and Deploy on Raspberry Pi
Generate code that uses a TensorFlow™ Lite model for inference.
Deploy Classification Application Using Mobilenet-V3 TensorFlow Lite Model on Host and Raspberry Pi
Generate code for a classification segmentation application that uses TensorFlow Lite model.
Deploy Semantic Segmentation Application Using TensorFlow Lite Model on Host and Raspberry Pi
Generate code for an image segmentation application that uses TensorFlow Lite model.
Deploy Pose Estimation Application Using TensorFlow Lite Model (TFLite) Model on Host and Raspberry Pi
Simulate and generate code for a human pose estimation TFLite model.
