Magical, OpenVINO™ Integration with TensorFlow | Edge Innovation for Developers | Intel Software

Опубликовано 27 июня 2022, 13:00

(EOL) - 4/11/2023

In this episode, Ragesh and Steph talk about OpenVINO™ Integration with Tensorflow which is used for speeding up inferencing of a TensorFlow based real-time application at the edge. Ragesh pulls and runs a docker image on his Intel® Core™ i7 CPU to showcase what it can do, uses a notebook that covers an object detection use case and demonstrates acceleration of YOLOv4 model, trained on coco dataset. In the end, Steph mentions 4 short training videos for you on Container Playground from Intel® DevCloud: Edge Workloads.

OpenVINO™ integration with TensorFlow GitHub Homepage: bit.ly/3QHNxzy

Validated models supported by OpenVINO™ integration with TensorFlow: bit.ly/3A5dSlr

Dockerfiles and prebuilt images: bit.ly/3neu4co

Sample applications and tutorials on Intel® DevCloud: intel.ly/3OgVEBG

Object Detection Jupyter Notebook on GitHub: bit.ly/3OznOaG

How OpenVINO™ integration with TensorFlow really works under the hood?: intel.ly/3xO1YJK

Video playlist to learn about Intel® DevCloud container playground: bit.ly/39NrK8Z

Performance improvement of SSD & Faster-RCNN object detection models are in progress and will be addressed in the upcoming release. Stay tuned to this page: bit.ly/3njwg2j

Please submit your questions, feature requests and bug reports via GitHub issues: bit.ly/3ni3teu

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Magical, OpenVINO™ Integration with TensorFlow | Edge Innovation for Developers | Intel Software