Get Started#

Prerequisites#

  • Ubuntu 24.04 or newer (Linux recommended), Desktop edition (or Server edition with GUI installed)

  • Docker 24.0+

  • Docker Compose V2+

  • Make (sudo apt install make)

  • Intel hardware (CPU, iGPU, dGPU)

  • Intel drivers:

  • Sufficient disk space for models, videos, and results (50GB minimum)

Note: First-time setup downloads AI models (~7GB) and Docker images - this may take 30-60 minutes depending on your internet connection.

Choose Your Application#

Criteria

Dine-In Order Accuracy

Take-Away Order Accuracy

Purpose

Validate food plates at serving stations before delivery to tables

Real-time order validation for drive-through and counter service

Use When

You need image-based validation for restaurant table service

You need continuous video stream validation at multiple stations

Input

Static images of food trays/plates

RTSP video streams

Features

Gradio web interface, REST API for POS integration

Multi-station parallel processing, VLM request batching

Step-by-Step Instructions#

  1. Clone the Repository

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

    Replace <release-or-tag> with the version you want to clone (for example, v2026.0).

    git clone -b v2026.0 --single-branch https://github.com/intel-retail/order-accuracy
cd order-accuracy/dine-in

  2. Setup OVMS Models (First Time Only)

    cd ../ovms-service
./setup_models.sh
cd ../dine-in

    This downloads and converts the Qwen2.5-VL-7B model (~7GB). This only needs to be done once.

  3. Prepare Test Data

    • Add your food tray/plate images to the images/ folder

    • Update configs/orders.json with test orders

    • Update configs/inventory.json with your menu items

  4. Build and Start Services

    # Using pre-built images (recommended for first run)
make build
make up

# OR build locally from source
make build REGISTRY=false
make up

  5. Access the Application

    • Gradio UI: http://localhost:7861

    • REST API Docs: http://localhost:8083/docs

  1. Clone the Repository

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

    Replace <release-or-tag> with the version you want to clone (for example, v2026.0).

    git clone -b v2026.0 --single-branch https://github.com/intel-retail/order-accuracy
cd order-accuracy/take-away

  2. Setup OVMS Models (First Time Only)

    cd ../ovms-service
./setup_models.sh
cd ../take-away

    This downloads the VLM and EasyOCR models. This only needs to be done once.

  3. Initialize Environment

    make init-env

  4. Build and Start Services

    # Single worker mode (development/testing)
make build
make up

# OR Parallel mode (production)
make build
make up-parallel WORKERS=4

  5. Access the Application

    • Gradio UI: http://localhost:7860

    • MinIO Console: http://localhost:9001 (minioadmin/minioadmin)

What You’ll See When Working#

Dine-In Results

Take-Away Results

Visual

Gradio UI displays detected items with confidence scores

Real-time frame processing with item detection

Validation

Order match/mismatch status with detailed comparison

Continuous order validation against POS data

Logging

Detection results and semantic matching scores

Frames and results stored in MinIO buckets

Expected Performance#

  • Startup Time: 2-5 minutes (first run includes model loading)

  • Processing: Sub-15-second validation latency (Dine-In), real-time stream processing (Take-Away)

  • Results: JSON files appear in results/ directory

Verify Results#

After starting the application, you can verify it is working:

# Check running containers
docker ps

# View application logs
make logs

# Test the API
curl http://localhost:8083/health

# Check running containers
docker ps

# View application logs
make logs

# Check MinIO storage
# Visit http://localhost:9001

Stop the Services#

# Stop all services
make down

# Stop and remove volumes (clean restart)
make down-volumes

Quick Start Reference#

Configuration

Command

Description

Start Services

make up

Start all dine-in services

Build Locally

make build REGISTRY=false

Build images from source

View Logs

make logs

View service logs

Stop Services

make down

Stop all containers

Configuration

Command

Description

Single Mode

make up

Start in single worker mode (development)

Parallel Mode

make up-parallel WORKERS=4

Start with 4 parallel workers (production)

Build Locally

make build REGISTRY=false

Build images from source

View Logs

make logs

View service logs

Note: Single Mode is best for development and testing. Parallel Mode is recommended for production with multiple camera stations.

Advanced Settings#

See the Advanced Settings guide for detailed configuration options, including environment variables, service modes, and troubleshooting tips.