Getting 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:
  • Intel GPU 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

🍽️ Dine-In Order Accuracy

Purpose: Validate food plates at serving stations before delivery to tables
Use When: You need image-based validation for restaurant table service
Input: Static images of food trays/plates
Features: Gradio web interface, REST API for POS integration

🥡 Take-Away Order Accuracy

Purpose: Real-time order validation for drive-through and counter service
Use When: You need continuous video stream validation at multiple stations
Input: RTSP video streams
Features: Multi-station parallel processing, VLM request batching

Quick Start Reference

🍽️ Dine-In Quick Commands

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

🥡 Take-Away Quick Commands

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

Tip

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

Step-by-Step Instructions

Option 1: Dine-In Order Accuracy

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

---

Option 2: Take-Away Order Accuracy

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

• Visual: Gradio UI displays detected items with confidence scores
• Validation: Order match/mismatch status with detailed comparison
• Logs: Detection results and semantic matching scores

🥡 Take-Away Results

• Visual: Real-time frame processing with item detection
• Validation: Continuous order validation against POS data
• Storage: 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's working:

Dine-In:

# Check running containers
docker ps

# View application logs
make logs

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

Take-Away:

# 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

Proceed to Advanced Settings