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Welcome to the Intel Retail GitHub Organization!

The Retail AI Suite is part of Open Edge Platform.

Intel Retail is a collection of pipelines, tools, and resources designed to accelerate hardware decisions for Retail AI workloads at the edge, featuring use cases such as self-checkout and loss prevention.

Intel’s Open Edge Platform is a secure and optimized open platform for delivering scalable edge solutions. Retail constitutes one of its AI Software Suites, offering several sample applications that showcase the use of platform's software stack. The following are the main components of the suite:

  • Order Accuracy Pipeline System is an open-source reference implementation for building and deploying video analytics pipelines for retail order accuracy in Quick Service Restaurant (QSR) use cases. It leverages Intel® hardware and software, GStreamer, and OpenVINO™ to enable scalable, real-time object detection and classification at the edge.
  • Loss Prevention Pipeline System is an open-source reference implementation for building and deploying video analytics pipelines for retail loss prevention use cases. It leverages Intel® hardware and software, GStreamer, and OpenVINO™ to enable scalable, real-time object detection and classification at the edge.
  • Performance Tools is a dockerized performance tool suite for benchmarking a use case.
  • Automated Self-Checkout Reference Package is a sample design of a self-checkout system, optimized for Intel hardware and the Open Edge Platform ecosystem.

Unless otherwise noted, the Retail repositories are released under the Apache 2.0 license.

Access the documentation

User documentation of the main components is available at the official Open Edge Platform documentation pages.

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  1. automated-self-checkout automated-self-checkout Public

    The Intel® Automated Self-Checkout Reference Package provides critical components required to build and deploy a self-checkout use case using Intel® hardware, software, and other open-source compon…

    Jupyter Notebook 33 60

  2. rtsf-at-checkout-reference-design rtsf-at-checkout-reference-design Public archive

    Detect loss at self-checkout by seamlessly connecting different sensor devices, including weight scale sensors, cameras, and RFIDs.

    Go 12 18

  3. automated-vending automated-vending Public archive

    Deploy sensor fusion technology for an automated checkout that enables real-time insight about the products consumers are buying using the EdgeX Foundry* extensible framework.

    Go 11 12

  4. software-vsync-modulation-sample software-vsync-modulation-sample Public

    A SW Library for Digital Signage that provides references on how to modify MMIO PLL registers to slow/speed up VSYNC timings on a system to achieve synchronization. For documentation click on the l…

    C++ 10 6

  5. loss-prevention loss-prevention Public

    The Loss Prevention Pipeline System is an open-source reference implementation for building and deploying video analytics pipelines for retail loss prevention use cases. It leverages Intel® hardwar…

    Python 5 22

  6. retail-use-cases retail-use-cases Public

    Retail based use case profiles.

    Python 3 20

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