Order Accuracy

Overview

The Order Accuracy Pipeline System is an open-source reference implementation for building and deploying video analytics pipelines for retail order accuracy in Quick Servce 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.

📋 Prerequisites

• Ubuntu 24.04 or newer (Linux recommended), Desktop edition (or Server edition with GUI installed).
• Docker
• Make (sudo apt install make)
• Intel hardware (CPU, iGPU, dGPU, NPU)
• Intel drivers:
  • Intel GPU drivers
  • NPU
• Sufficient disk space for models, videos, and results

🚀 QuickStart

Clone the repo with the below command

git clone -b <release-or-tag> --single-branch https://github.com/intel-retail/order-accuracy

Replace with the version you want to clone (for example, v1.1.0).

git clone -b v1.1.0 --single-branch https://github.com/intel-retail/order-accuracy

NOTE:

By default the application runs by pulling the pre-built images. If you want to build the images locally and then run the application, set the flag:

REGISTRY=false

usage: make <command> REGISTRY=false (applicable for all commands like benchmark, benchmark-stream-density..)
Example: make run-demo REGISTRY=false

(If this is the first time, it will take some time to download videos, models, docker images and build images)

1. Step by step instructions:

1.1 Download the models using download_models/downloadModels.sh

make download-models

1.2 Update github submodules

make update-submodules

1.3 Download sample videos used by the performance tools

make download-sample-videos

1.4 Start Order Accuracy using the Docker Compose file.

make run-render-mode

• The above series of commands can be executed using only one command:

  make run-demo

2. To build the images locally step by step:

• Follow the following steps:

  make download-models REGISTRY=false
  make update-submodules
  make download-sample-videos
  make run-render-mode REGISTRY=false

• The above series of commands can be executed using only one command:

  make run-demo REGISTRY=false

3. Stop all containers

When pre-built images are pulled-

make down

When images are built locally-

make down REGISTRY=false

4. Run benchmarking on CPU/NPU/GPU.

make benchmark

• By default, the configuration is set to use the CPU. If you want to benchmark the application on GPU or NPU, please update the DEVICE_ENV variable.

  make benchmark DEVICE_ENV=res/all-gpu.env

5. See the benchmarking results.

make consolidate-metrics

cat benchmark/metrics.csv

🛠️ Other Useful Make Commands.

• make clean-images — Remove dangling Docker images
• make clean-models — Remove all the downloaded models from the system
• make clean-all — Remove all unused Docker resources

📁 Project Structure

• configs/ — Configuration files (txt file with sample video URLs for inference)
• docker/ — Dockerfiles for downloader and pipeline containers
• download-scripts/ — Scripts for downloading models and videos
• src/ — Main source code and pipeline runner scripts
• Makefile — Build automation and workflow commands

ⓘ Learn More

For detailed documentation and a comprehensive guide, please visit our project website.

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