automated_self_checkout

Automated Self-Checkout with OpenVINO™ Toolkit

🏠 About the Kits · 📔 Jupyter Notebook · 📺 Overview Video · 👨‍💻 Code Demo Video · 📚 Step‑by‑step Tutorial

Automated Self-Checkout is designed to help automate checkout for retail businesses by analyzing video streams and detecting and tracking interactions with retail products. It uses OpenVINO™, a toolkit that enables developers to deploy deep learning models on various hardware platforms.

This kit uses the following technology stack:

• OpenVINO Toolkit (docs)
• Ultralytic YOLOv8
• Roboflow Supervision

Check out our AI Reference Kits repository for other kits.

What's New

New updates will be added here.

Table of Contents

• Getting Started
  • Installing Prerequisites
  • Setting Up Your Environment
  • Running the Application
• Benchmarking the Model with OpenVINO's Benchmark_App
• Additional Resources

Getting Started

Now, let's dive into the steps starting with installing Python. We recommend using Ubuntu to set up and run this project.

Star the Repository

Star the repository (optional, but recommended :))

Installing Prerequisites

This project requires Python 3.9 or higher and a few libraries. If you don't have Python installed on your machine, go to https://www.python.org/downloads/ and download the latest version for your operating system. Follow the prompts to install Python, making sure to check the option to add Python to your PATH environment variable.

Install libraries and tools:

sudo apt install git git-lfs gcc python3-venv python3-dev

NOTE: If you are using Windows, you will probably need to install Microsoft Visual C++ Redistributable also.

Setting Up Your Environment

Cloning the Repository

To clone the repository, run the following command:

git clone https://github.com/openvinotoolkit/openvino_build_deploy.git

The above will clone the repository into a directory named "openvino_build_deploy" in the current directory. Then, navigate into the directory using the following command:

cd openvino_build_deploy/ai_ref_kits/automated_self_checkout

Then pull the video sample:

git lfs -X= -I=data/ pull

Creating a Virtual Environment

To create a virtual environment, open your terminal or command prompt and navigate to the directory where you want to create the environment. Then, run the following command:

python3 -m venv venv

This will create a new virtual environment named "venv" in the current directory.

Activating the Environment

Activate the virtual environment using the following command:

source venv/bin/activate   # For Unix-based operating systems such as Linux or macOS

NOTE: If you are using Windows, use venv\Scripts\activate command instead.

This will activate the virtual environment and change your shell's prompt to indicate that you are now working within that environment.

Installing the Packages

To install the required packages, run the following commands:

python -m pip install --upgrade pip 
pip install -r requirements.txt

Running the Application

You can run self-checkout-recipe.ipynb to learn more about the inference process.

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Benchmarking the Model with OpenVINO's benchmark_app

Benchmarking provides insight into your model's real-world performance. Performance may vary based on use and configuration.

Benchmark Results

Benchmarking was performed on an Intel® Xeon® Platinum 8480+ (1 socket, 56 cores) running Ubuntu 22.04.2 LTS. The tests utilized the YOLOv8m model with OpenVINO 2023.0. For complete configuration, please check the Appendix section.

Running the Benchmark

Use the following command to run the benchmark:

!benchmark_app -m $int8_model_det_path -d $device -hint latency -t 30

Replace int8_model_det_path with the path to your INT8 model and $device with the specific device you're using (CPU, GPU, etc.). This command performs inference on the model for 30 seconds. Run benchmark_app --help for additional command-line options.

Congratulations! You have successfully set up and run the Detection and Tracking for Automated Self-Checkout application with OpenVINO.

Appendix

Platform Configurations for Performance Benchmarks for YOLOv8m Model

Type Device		CPU			GPU
System Board	Intel Corporation D50DNP1SBB	AAEON UPN-ADLN01 V1.0 220950173	Intel® Client Systems NUC12SNKi72	Intel Corporation M50CYP2SBSTD	Intel® Client Systems NUC12SNKi72	Intel® Client Systems NUC12SNKi72
CPU	Intel(R) Xeon(R) Platinum 8480+	Intel® Core™ i3-N305 @ 3.80 GHz	12th Gen Intel® Core™ i7-12700H @ 2.30 GHz	Intel(R) Xeon(R) Gold 6348 CPU @ 2.60GHz	12th Gen Intel® Core™ i7-12700H @ 2.30 GHz	12th Gen Intel® Core™ i7-12700H @ 2.30 GHz
Sockets / Physical cores	1 / 56 (112 Threads)	1 / 8 (8 Threads)	1 /14 (20 Threads)	2 / 28 (56 Threads)	1 /14 (20 Threads)	1 /14 (20 Threads)
HyperThreading / Turbo Setting	Enabled / On	Disabled	Enabled / On	Enabled / On	Enabled / On	Enabled / On
Memory	512 GB DDR4 @ 4800 MHz	16GB DDR5 @4800 MHz	64 GB DDR4 @ 3200 MHz	256 GB DDR4 @ 3200 MHz	64 GB DDR4 @ 3200 MHz	64 GB DDR4 @ 3200 MHz
OS	Ubuntu 22.04.2 LTS	Ubuntu 22.04.2 LTS	Windows 11 Enterprise v22H2	Ubuntu 22.04.2 LTS	Windows 11 Enterprise v22H2	Windows 11 Enterprise v22H2
Kernel	5.15.0-72-generic	5.15.0-1028-intel-iotg	22621.1702	5.15.0-57-generic	22621.1702	22621.1702
Software	OpenVINO 2023.0	OpenVINO 2023.0	OpenVINO 2023.0	OpenVINO 2023.0	OpenVINO 2023.0	OpenVINO 2023.0
BIOS	Intel Corp. SE5C7411.86B.9525 .D13.2302071333	American Megatrends International, LLC. UNADAM10	Intel Corp. SNADL357.0053 .2022.1102.1218	Intel Corp. SE5C620.86B.01 .01.0007.2210270543	Intel Corp. SNADL357.0053 .2022.1102.1218	Intel Corp. SNADL357.0053 .2022.1102.1218
BIOS Release Date	02/07/2023	12/15/2022	11/02/2022	10/27/2022	11/02/2022	11/02/2022
GPU	N/A	N/A	1x Intel® Arc A770™ 16GB, 512 EU	1x Intel® Iris® Xe Graphics	1x Intel® Data Center GPU Flex 170	1x Intel® Arc A770™ 16GB, 512 EU
Workload: Codec, resolution, frame rate Model, size (HxW), BS	Yolov8m Model – input size [640, 640], batch 1 FP16 | int8	Yolov8m Model – input size [640, 640], batch 1 FP16 | int8	Yolov8m Model – input size [640, 640], batch 1 FP16 | int8	Yolov8m Model – input size [640, 640], batch 1 FP16 | int8	Yolov8m Model – input size [640, 640], batch 1 FP16 | int8	Yolov8m Model – input size [640, 640], batch 1 FP16 | int8
TDP	350W	15W	45W	235W	45W	45W
Benchmark Date	May 31, 2023	May 29, 2023	June 15, 2023	May 29, 2023	June 15, 2023	May 29, 2023
Benchmarked by	Intel Corporation	Intel Corporation	Intel Corporation	Intel Corporation	Intel Corporation	Intel Corporation

Additional Resources

• Learn more about OpenVINO
• Explore OpenVINO’s documentation

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