Get Started#

This guide walks you through installation, configuration, and first run of the Dine-In Order Accuracy system.

Table of Contents#

  1. Prerequisites

  2. Installation

  3. Verifying Installation

  4. First Order Validation

Prerequisites#

  • Docker 24.0+ with Compose V2

  • Intel GPU with drivers installed

  • 16 GB RAM minimum (64 GB recommended for production)

  • 50 GB free disk space

Notes: 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.

docker --version
docker compose version

Installation#

Step 1: Clone the Repository#

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

Step 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

Step 3: Setup OVMS Model (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

Note: Only needed once. Model files are shared between Dine-In and Take-Away.

This downloads Qwen2.5-VL-7B-Instruct (~7 GB) and converts it to OpenVINO™ INT8 format. This is only needed once — the model files are shared with Take-Away.

Step 4: Prepare Test Data#

Before running the application, you must prepare your test data:

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

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

    • Images should clearly show the food items on the tray

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

    • Each order should have an order_id and list of items

    • order_id should match your image_id

  3. 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

Step 5: Build and Start#

# 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

8084

System metrics

Verifying Installation#

# 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.

First Order Validation#

Via Gradio UI#

  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

Via REST API#

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

Via Make#

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

Changing Inference Device#

To switch between GPU and CPU, update TARGET_DEVICE in .env and re-run model setup:

# In .env
TARGET_DEVICE=CPU

cd ../ovms-service && ./setup_models.sh --app dine-in && cd ../dine-in
make down && make up

Quick Reference#

make up                              # Start services (registry image)
make up REGISTRY=false               # Start with locally built image
make down                            # Stop services
make logs                            # View logs
make test-api                        # Health check
make benchmark-single IMAGE_ID=...   # Quick test
make benchmark                       # Full benchmark
make benchmark-stream-density        # Stream density test
make clean                           # Stop and remove volumes
make clean-images                    # Remove dangling Docker images
make clean-all                       # Remove all unused Docker resources
make help                            # All commands

Next Steps#