This video was originally published at Intel’s YouTube channel. It is reprinted here with the permission of Intel.
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: https://bit.ly/3QHNxzy
Validated models supported by OpenVINO™ integration with TensorFlow: https://bit.ly/3A5dSlr
Dockerfiles and prebuilt images: https://bit.ly/3neu4co
Sample applications and tutorials on Intel® DevCloud: https://intel.ly/3OgVEBG
Object Detection Jupyter Notebook on GitHub: https://bit.ly/3OznOaG
How OpenVINO™ integration with TensorFlow really works under the hood?: https://intel.ly/3xO1YJK
Video playlist to learn about Intel® DevCloud container playground: https://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: https://bit.ly/3njwg2j
Please submit your questions, feature requests and bug reports via GitHub issues: https://bit.ly/3ni3teu
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