Code Demo | Automated Self-Checkout Edge AI Reference Kit | Intel Software
8An in-depth exploration of the code that makes our Edge AI Reference Kit’s Automated Self-checkout application work. Our kits are ready-made with prebuilt components and pretrained models to accelerate your AI solution development.
Explore the inner workings behind each module that powers our application, and walk through how it runs step-by-step.
Follow us through installing the requirements for this application, converting and optimizing your models, and deploying the application itself. After exploring and understanding the code, you will be equipped to build on top of this code for your own custom solutions.
OpenVINO is an open source toolkit that accelerates AI inference on a wide variety of hardware, including CPUs and GPUs, and powers our Edge AI Reference Kits.
The kit in this video focuses on Automated Self-checkout, which harnesses computer vision and object detection with the addition of custom-defined zones to create self-checkout solutions for retail. Use OpenVINO, Ultralytics’s YOLOv8, and Roboflow’s Supervision to build an application to detect and track objects within zones. Gather real-time analytics data as to whether objects are added or removed.
Our Github with Jupyter Notebook: https://bit.ly/47OwAeu
Watch our solution overview companion video: https://youtu.be/VrJRr_thxcs
Learn more about our Edge AI Reference Kits: https://intel.ly/3YG3Q4t
Download OpenVINO: https://intel.ly/3uB3qRT
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Code Demo | Automated Self-Checkout Edge AI Reference Kit | Intel Software