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:

  • 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.

Note

KV Cache on iGPU / low-RAM systems: 16 GB RAM is sufficient for inference. For first-time model export, a higher-memory host (48–64 GB) is recommended. On iGPU platforms, the KV cache is allocated from system RAM. Set export CACHE_SIZE=2 before running setup_models.sh to reduce KV cache to 2 GB (default is 4 GB). See ovms-service/README.md — Tuning the KV Cache Size for a full per-platform guide.

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

What You Will 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

Step-by-Step Instructions

Dine-In Order Accuracy

1. Clone the Repository

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

2. Configure the Environment

   # Create .env from template
   make init-env
   # Edit .env if needed — defaults work for most setups
   
   # Initialize git submodules (for benchmark tools)
   make update-submodules
   

3. Setup OVMS Models (First Time Only)

   The setup script reads model configuration (device, precision, model name) from dine-in/.env (created in Step 2), so complete Step 2 before running this step.

   cd ../ovms-service
   ./setup_models.sh --app dine-in    # Downloads and exports model (~30-60 min first time)
   cd ../dine-in
   

   This downloads MiniCPM-V-4.5 and converts it to OpenVINO™ INT4 format. This is only needed once — the model files are shared with Take-Away.

4. Prepare Test Data Before running the application, you must prepare your test data:

   • Add Images: Place your food tray images in the images/ folder

     • Supported formats: .jpg, .jpeg or .png

     • Images should clearly show the food items on the tray

   • Update Orders: Edit configs/orders.json with your test orders

     • Each order should have an items_ordered entry, each with item and quantity

     • image_id should match your image filenames in the images/ folder

   • Update Inventory: Edit configs/inventory.json to match your menu items

     • Define all possible food items that can appear in orders

     • Include item names, categories, and any relevant metadata

5. Build and Start Services

   # Pull images from registry (default)
   make build && make up
   
   # OR build locally from source
   make build REGISTRY=false && make up
   

   This starts 4 containers:

   Container	Ports	Purpose
   dinein_app	7861, 8083	Gradio UI + FastAPI
   dinein_ovms_vlm	8002	VLM model server (OVMS)
   dinein_semantic_service	8081, 9091	Semantic matching
   metrics-collector	9000	System metrics

6. Access the Application

   • Gradio UI: http://localhost:7861

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

Take-Away Order Accuracy

1. Clone the Repository

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

2. Configure the Environment

   # Create .env from template
   make init-env
   # Edit .env if needed — defaults work for most setups
   
   # Initialize git submodules (for benchmark tools)
   make update-submodules
   

   Edit the generated .env file as needed. See Configuration in the dedicated Take-Away guide.

3. Setup OVMS Models (First Time Only)

   Set TARGET_DEVICE in your .env before running this step. The script reads that value to export the model in the correct format for the target device.

   cd ../ovms-service
   ./setup_models.sh --app take-away    # Downloads and exports model (~30-60 min first time)
   cd ../take-away
   

   This downloads and exports:

   • MiniCPM-V-4_5 INT4 (OpenVINO™ format)

   • YOLOv11 model (INT8 OpenVINO™)

   • EasyOCR detection and recognition models

   Note

   Re-run this step any time you change TARGET_DEVICE in .env.

4. Build and Start Services

   # Pull images from registry (default)
   make build
   make up
   
   # OR build locally from source
   make build REGISTRY=false
   make up
   

5. Access the Application

   • Gradio UI: http://localhost:7860

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

Verify the Installation

Dine-In

# API health check
make test-api

# Or directly
curl http://localhost:8083/health

# Check OVMS model
curl http://localhost:8002/v1/config | jq .

Open http://localhost:7861 for the Gradio UI, or http://localhost:8083/docs for the REST API docs.

Take-Away

• Health Check

  make test-api
  

  This checks both the order accuracy API (localhost:8000) and OVMS (localhost:8001).

• Service URLs

  Service	URL
  Order Accuracy API	http://localhost:8000
  OVMS VLM	http://localhost:8001
  Gradio UI	http://localhost:7860
  MinIO Console	http://localhost:9001
  Semantic Service	http://localhost:8080

• Service Status

  make status
  

First Order Validation

Via Gradio UI

Dine-In

1. Open http://localhost:7861

2. Select a scenario from the dropdown

3. Review the order manifest

4. Click “Validate Plate”

5. View accuracy score, matched/missing/extra items, and performance metrics

Take-Away

1. Open http://localhost:7860

2. Upload a test video or enter an RTSP URL

3. Click “Upload and Start Processing”

4. View results showing matched, missing, and extra items

Via REST API

Dine-In

The bundled MCD-1001.png image shows Filet-O-Fish and Cheesy Fries on the tray. Two test scenarios are provided:

Negative test case — order does not match tray (demonstrates mismatch detection):

curl -X POST "http://localhost:8083/api/validate" \
  -F "image=@images/MCD-1001.png" \
  -F 'order={"items":[{"name":"Cheeseburger","quantity":1},{"name":"French Fries","quantity":1}]}'
# Expected: order_complete=false, accuracy_score=0.0

Positive test case — order matches tray (demonstrates successful validation):

curl -X POST "http://localhost:8083/api/validate" \
  -F "image=@images/MCD-1001.png" \
  -F 'order={"items":[{"name":"Filet-O-Fish","quantity":1},{"name":"Cheesy Fries","quantity":1}]}'
# Expected: order_complete=true, accuracy_score=1.0

Take-Away

# Upload video and validate
curl -X POST http://localhost:8000/upload-video \
  -F "file=@storage/videos/test.mp4" \
  -F "video_id=test_001"

# Check results
curl http://localhost:8000/results/test_001

Via Make Target

Dine-In

# Services must be running first
make benchmark-single IMAGE_ID=MCD-1001

Take-Away

Note

Prerequisite: Ensure storage/videos/test.mp4 exists. Run make download-sample-video if needed.

make benchmark

Stop the Services

# Stop all services
make down

# Stop and remove volumes (clean restart)
make clean

Quick Start Reference

Dine-In 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 services

Take-Away 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

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.