From cebb1069cfc4046a87908b26445272bb7800f89b Mon Sep 17 00:00:00 2001 From: Avinash Reddy Palleti Date: Tue, 9 Dec 2025 12:54:05 +0530 Subject: [PATCH 1/2] Update refs to latest retail AI suite --- retail-ai-suite/README.md | 6 +++--- retail-ai-suite/automated-self-checkout | 2 +- retail-ai-suite/loss-prevention | 2 +- retail-ai-suite/order-accuracy | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/retail-ai-suite/README.md b/retail-ai-suite/README.md index c62935e7c..0430d59db 100644 --- a/retail-ai-suite/README.md +++ b/retail-ai-suite/README.md @@ -8,8 +8,8 @@ Key use cases include: | Sample Application | Definitions | User Docs | |:-------------------|:------------|:----------------| -| [Automated Self-Checkout](https://github.com/intel-retail/automated-self-checkout/releases/tag/v3.6.3) | Product recognition (detection, classification, and tracking), full pipeline workflow (product, weight, text, and barcode), and age verification. | [Link](https://intel-retail.github.io/documentation/use-cases/automated-self-checkout/automated-self-checkout.html) | -| [Loss Prevention](https://github.com/intel-retail/loss-prevention/releases/tag/v4.3.2)| Fake scans, items in basket, multi-product identification, product switching, shopper behavior (obscuring/hiding an item), and event video summation. | [Link](https://intel-retail.github.io/documentation/use-cases/loss-prevention/loss-prevention.html) | -| [Order Accuracy](https://github.com/intel-retail/order-accuracy/releases/tag/v1.2.2)| An easily repeatable process for generating performance numbers across multi modalities on Intel’s heterogenous compute. The tool helps determine compute requirements for scaling retail edge AI workloads. | [Link](https://intel-retail.github.io/documentation/use-cases/order-accuracy/order-accuracy.html) | +| **Automated Self-Checkout** ) | Product recognition (detection, classification, and tracking), full pipeline workflow (product, weight, text, and barcode), and age verification. | [Link](https://intel-retail.github.io/documentation/use-cases/automated-self-checkout/automated-self-checkout.html) | +| **Loss Prevention** | Fake scans, items in basket, multi-product identification, product switching, shopper behavior (obscuring/hiding an item), and event video summation. | [Link](https://intel-retail.github.io/documentation/use-cases/loss-prevention/loss-prevention.html) | +| **Order Accuracy** | An easily repeatable process for generating performance numbers across multi modalities on Intel’s heterogenous compute. The tool helps determine compute requirements for scaling retail edge AI workloads. | [Link](https://intel-retail.github.io/documentation/use-cases/order-accuracy/order-accuracy.html) | The Retail AI Suite is built with modularity and extensibility in mind. It is not meant to be used as a series of reference applications, but rather as code to understand the hardware requirements for embedding AI workloads into retail applications. The pipelines provide clear guidance on how to use Intel’s hardware-optimized software stacks while primarily focusing on enabling partners to determine the hardware for scale deployments. There are many more retail use cases under consideration. Additional details and documentation are available [here](https://github.com/intel-retail). diff --git a/retail-ai-suite/automated-self-checkout b/retail-ai-suite/automated-self-checkout index 4d6956950..1d77763d0 160000 --- a/retail-ai-suite/automated-self-checkout +++ b/retail-ai-suite/automated-self-checkout @@ -1 +1 @@ -Subproject commit 4d695695009a152be46c3c95da40c9ce47d583a7 +Subproject commit 1d77763d086963bef754471938ebd30772615f3e diff --git a/retail-ai-suite/loss-prevention b/retail-ai-suite/loss-prevention index 9b4d81ccc..c9b0c732f 160000 --- a/retail-ai-suite/loss-prevention +++ b/retail-ai-suite/loss-prevention @@ -1 +1 @@ -Subproject commit 9b4d81ccc1725d96e77b65aa4ee0a48f0b73a698 +Subproject commit c9b0c732f3de4b0594b072e001d18f03c8250a38 diff --git a/retail-ai-suite/order-accuracy b/retail-ai-suite/order-accuracy index 5786e9def..c1716a743 160000 --- a/retail-ai-suite/order-accuracy +++ b/retail-ai-suite/order-accuracy @@ -1 +1 @@ -Subproject commit 5786e9def9eb460b269dae8f4163a2467b1d4710 +Subproject commit c1716a743ad2d5fecd250d1a481e8b8806865f9d From 44385b8993def28a2e26ebef86abdcbe011850a1 Mon Sep 17 00:00:00 2001 From: Avinash Reddy Palleti Date: Tue, 9 Dec 2025 12:57:20 +0530 Subject: [PATCH 2/2] Update README.md --- retail-ai-suite/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/retail-ai-suite/README.md b/retail-ai-suite/README.md index 0430d59db..b0dd637c5 100644 --- a/retail-ai-suite/README.md +++ b/retail-ai-suite/README.md @@ -8,7 +8,7 @@ Key use cases include: | Sample Application | Definitions | User Docs | |:-------------------|:------------|:----------------| -| **Automated Self-Checkout** ) | Product recognition (detection, classification, and tracking), full pipeline workflow (product, weight, text, and barcode), and age verification. | [Link](https://intel-retail.github.io/documentation/use-cases/automated-self-checkout/automated-self-checkout.html) | +| **Automated Self-Checkout** | Product recognition (detection, classification, and tracking), full pipeline workflow (product, weight, text, and barcode), and age verification. | [Link](https://intel-retail.github.io/documentation/use-cases/automated-self-checkout/automated-self-checkout.html) | | **Loss Prevention** | Fake scans, items in basket, multi-product identification, product switching, shopper behavior (obscuring/hiding an item), and event video summation. | [Link](https://intel-retail.github.io/documentation/use-cases/loss-prevention/loss-prevention.html) | | **Order Accuracy** | An easily repeatable process for generating performance numbers across multi modalities on Intel’s heterogenous compute. The tool helps determine compute requirements for scaling retail edge AI workloads. | [Link](https://intel-retail.github.io/documentation/use-cases/order-accuracy/order-accuracy.html) |