Deploy YOLOv2 to an NVIDIA Jetson скачать в хорошем качестве

Deploy YOLOv2 to an NVIDIA Jetson

In this video, Connell D’Souza joins Neha Goel to demonstrate how to deploy a deep neural network to an NVIDIA® embedded GPU using GPU Coder™ and the GPU Coder support package for NVIDIA GPUs. The example discussed in this video is deploying a multiclass YOLOv2 neural network to an NVIDIA Jetson® TX1. Connell first introduces the different MathWorks and third-party prerequisite libraries needed to generate and deploy CUDA code to an NVIDIA GPU and demonstrates how to verify the setup using the coder.checkGpuInstall app. Next, Connell will discuss preparing MATLAB code for GPU code generation. He demonstrate how to generate and deploy an executable to an NVIDIA Jetson TX1 to sample video frames from a camera connected to the Jetson and detect objects of interest in the video frames. Resources: Deep Learning with MATLAB: https://bit.ly/37APNSf GPU Coder: https://bit.ly/36zUTg6 Code Generation Training: https://bit.ly/2UdlugP Download the files used in this video: https://bit.ly/2U4pb8e Contact the MathWorks student competitions team: roboticsarena@mathworks.com Visit the MATLAB and Simulink Robotics Arena for additional videos: https://bit.ly/2U4vubN Request software for your student competition: https://bit.ly/2RxWrmU -------------------------------------------------------------------------------------------------------- Get a free product trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See what's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2019 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.