Code Demo | Explainable AI - Edge AI Reference Kit | Intel Software

Опубликовано 4 апреля 2024, 18:57

AI 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: bit.ly/4cQ1IxB

Introduction — Datumaro 2.0.0 documentation: bit.ly/3U4FvV8

Learn more about our Edge AI Reference Kits: intel.ly/3TKjxWe

OpenVINO™ toolkit: An open source AI toolkit that makes it easier to write once, deploy anywhere.: bit.ly/3xoHKty

OpenVINO 2023.0 — OpenVINO™ documentation: bit.ly/4aKSGjH
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Code Demo | Explainable AI - Edge AI Reference Kit | Intel Software