Faster Inference of ONNX Models | Edge Innovation Series for Developers | Intel Software

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Published on ● Video Link: https://www.youtube.com/watch?v=s4TtEzULMGc


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Join Ragesh in his interview with Devang Aggarwal, a product manager at Intel with Intel’s OpenVINO™ AI framework engineering team doing work around deep learning and computer vision. Devang talks about OpenVINO™ Execution Provider for ONNX Runtime, and how developers creating ONNX models can accelerate inference with OpenVINO™ toolkit.

Read the Article, "Faster Inferencing on Intel® Hardware with Just One Extra Line of Code": https://intel.ly/3ASpDLl

Installation guide for OpenVINO™ Execution Provider: https://bit.ly/3VgjhOe

Installation guide for OpenVINO™ Execution Provider for ONNX Runtime: https://bit.ly/3ioBbzu

GitHub for Object detection with YOLOv4 in Python using OpenVINO™ Execution Provider: https://bit.ly/3OH5BJw

GitHub for Object detection with tinyYOLOv2 in Python using OpenVINO™ Execution Provider: https://bit.ly/3OIA89O

ONNX Runtime inferencing examples on GitHub: https://bit.ly/3Vx3FFz

Object detection with YOLOv3 in C# using OpenVINO Execution Provider inferencing example on GitHub: https://bit.ly/3gO0jzj

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Faster Inference of ONNX Models | Edge Innovation Series for Developers | Intel Software



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