Code Demo | Explainable AI - Edge AI Reference Kit | Intel Software
17AI and computer vision rely on making accurate predictions, and this can be fine-tuned for efficiency and performance with the help of data and model explainability. This kit uses a tech stack combining the OpenVINO™ Toolkit, Ultralytic’s YOLOv8, and the OpenVINO Datumaru library. It has practical usage with any computer vision application, with some industry examples being transportation, healthcare, and retail.
An in-depth demonstration of how the Explainable AI Kit uses data quality measurements and saliency maps to help developers understand the predictions of computer vision models.
Check out the Jupyter Notebook on GitHub here: https://github.com/openvinotoolkit/op...\n
Introduction — Datumaro 2.0.0 documentation: https://bit.ly/3U4FvV8
Learn more about our Edge AI Reference Kits: https://intel.ly/3TKjxWe
OpenVINO™ toolkit: An open source AI toolkit that makes it easier to write once, deploy anywhere.: https://bit.ly/3xoHKty
OpenVINO 2023.0 — OpenVINO™ documentation: https://bit.ly/4aKSGjH
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Code Demo | Explainable AI - Edge AI Reference Kit | Intel Software