diff --git a/QUICKSTART.md b/QUICKSTART.md
new file mode 100644
index 0000000..572e887
--- /dev/null
+++ b/QUICKSTART.md
@@ -0,0 +1,191 @@
+# Quick Start Guide
+
+Get the scraper running in minutes without downloading YOLO models!
+
+## Minimal Setup (No Object Detection)
+
+The scraper works perfectly fine **without** YOLO object detection. You can run it immediately and add object detection later if needed.
+
+### Step 1: Run the Scraper
+
+```bash
+# Using Maven
+mvn clean compile exec:java -Dexec.mainClass="com.auction.scraper.TroostwijkScraper"
+```
+
+Or in IntelliJ IDEA:
+1. Open `TroostwijkScraper.java`
+2. Right-click on the `main` method
+3. Select "Run 'TroostwijkScraper.main()'"
+
+### What You'll See
+
+```
+=== Troostwijk Auction Scraper ===
+
+Initializing scraper...
+⚠️  Object detection disabled: YOLO model files not found
+   Expected files:
+   - models/yolov4.cfg
+   - models/yolov4.weights
+   - models/coco.names
+   Scraper will continue without image analysis.
+
+[1/3] Discovering Dutch auctions...
+✓ Found 5 auctions: [12345, 12346, 12347, 12348, 12349]
+
+[2/3] Fetching lot details...
+  Processing sale 12345...
+
+[3/3] Starting monitoring service...
+✓ Monitoring active. Press Ctrl+C to stop.
+```
+
+### Step 2: Test Desktop Notifications
+
+The scraper will automatically send desktop notifications when:
+- A new bid is placed on a monitored lot
+- An auction is closing within 5 minutes
+
+**No setup required** - desktop notifications work out of the box!
+
+---
+
+## Optional: Add Email Notifications
+
+If you want email notifications in addition to desktop notifications:
+
+```bash
+# Set environment variable
+export NOTIFICATION_CONFIG="smtp:your.email@gmail.com:app_password:your.email@gmail.com"
+
+# Then run the scraper
+mvn exec:java -Dexec.mainClass="com.auction.scraper.TroostwijkScraper"
+```
+
+**Get Gmail App Password:**
+1. Enable 2FA in Google Account
+2. Go to: Google Account → Security → 2-Step Verification → App passwords
+3. Generate password for "Mail"
+4. Use that password (not your regular Gmail password)
+
+---
+
+## Optional: Add Object Detection Later
+
+If you want AI-powered image analysis to detect objects in auction photos:
+
+### 1. Create models directory
+```bash
+mkdir models
+cd models
+```
+
+### 2. Download YOLO files
+```bash
+# YOLOv4 config (small)
+curl -O https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg
+
+# YOLOv4 weights (245 MB - takes a few minutes)
+curl -LO https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights
+
+# COCO class names
+curl -O https://raw.githubusercontent.com/AlexeyAB/darknet/master/data/coco.names
+```
+
+### 3. Run again
+```bash
+mvn exec:java -Dexec.mainClass="com.auction.scraper.TroostwijkScraper"
+```
+
+Now you'll see:
+```
+✓ Object detection enabled with YOLO
+```
+
+The scraper will now analyze auction images and detect objects like:
+- Vehicles (cars, trucks, forklifts)
+- Equipment (machines, tools)
+- Furniture
+- Electronics
+- And 80+ other object types
+
+---
+
+## Features Without Object Detection
+
+Even without YOLO, the scraper provides:
+
+✅ **Full auction scraping** - Discovers all Dutch auctions
+✅ **Lot tracking** - Monitors bids and closing times
+✅ **Desktop notifications** - Real-time alerts
+✅ **SQLite database** - All data persisted locally
+✅ **Image downloading** - Saves all lot images
+✅ **Scheduled monitoring** - Automatic updates every hour
+
+Object detection simply adds:
+- AI-powered image analysis
+- Automatic object labeling
+- Searchable image database
+
+---
+
+## Database Location
+
+The scraper creates `troostwijk.db` in your current directory with:
+- All auction data
+- Lot details (title, description, bids, etc.)
+- Downloaded image paths
+- Object labels (if detection enabled)
+
+View the database with any SQLite browser:
+```bash
+sqlite3 troostwijk.db
+.tables
+SELECT * FROM lots LIMIT 5;
+```
+
+---
+
+## Stopping the Scraper
+
+Press **Ctrl+C** to stop the monitoring service.
+
+---
+
+## Next Steps
+
+1. ✅ **Run the scraper** without YOLO to test it
+2. ✅ **Verify desktop notifications** work
+3. ⚙️ **Optional**: Add email notifications
+4. ⚙️ **Optional**: Download YOLO models for object detection
+5. 🔧 **Customize**: Edit monitoring frequency, closing alerts, etc.
+
+---
+
+## Troubleshooting
+
+### Desktop notifications not appearing?
+- **Windows**: Check if Java has notification permissions
+- **Linux**: Ensure desktop environment is running (not headless)
+- **macOS**: Check System Preferences → Notifications
+
+### OpenCV warnings?
+These are normal and can be ignored:
+```
+WARNING: A restricted method in java.lang.System has been called
+WARNING: Use --enable-native-access=ALL-UNNAMED to avoid warning
+```
+
+The scraper works fine despite these warnings.
+
+---
+
+## Full Documentation
+
+See [README.md](README.md) for complete documentation including:
+- Email setup details
+- YOLO installation guide
+- Configuration options
+- Database schema
+- API endpoints
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..e7f1ffa
--- /dev/null
+++ b/README.md
@@ -0,0 +1,233 @@
+# Troostwijk Auction Scraper
+
+A Java-based web scraper for Dutch auctions on Troostwijk Auctions with **100% free** desktop/email notifications, SQLite persistence, and AI-powered object detection.
+
+## Features
+
+- **Auction Discovery**: Automatically discovers active Dutch auctions
+- **Data Scraping**: Fetches detailed lot information via Troostwijk's JSON API
+- **SQLite Storage**: Persists auction data, lots, images, and detected objects
+- **Image Processing**: Downloads and analyzes lot images using OpenCV YOLO object detection
+- **Free Notifications**: Real-time notifications when:
+  - Bids change on monitored lots
+  - Auctions are closing soon (within 5 minutes)
+  - Via desktop notifications (Windows/macOS/Linux system tray) ✅
+  - Optionally via email (Gmail SMTP - free) ✅
+
+## Dependencies
+
+All dependencies are managed via Maven (see `pom.xml`):
+
+- **jsoup 1.17.2** - HTML parsing and HTTP client
+- **Jackson 2.17.0** - JSON processing
+- **SQLite JDBC 3.45.1.0** - Database operations
+- **JavaMail 1.6.2** - Email notifications (free)
+- **OpenCV 4.9.0** - Image processing and object detection
+
+## Setup
+
+### 1. Notification Options (Choose One)
+
+#### Option A: Desktop Notifications Only ⭐ (Recommended - Zero Setup)
+
+Desktop notifications work out of the box on:
+- **Windows**: System tray notifications
+- **macOS**: Notification Center
+- **Linux**: Desktop environment notifications (GNOME, KDE, etc.)
+
+**No configuration required!** Just run with default settings:
+```bash
+export NOTIFICATION_CONFIG="desktop"
+# Or simply don't set it - desktop is the default
+```
+
+#### Option B: Desktop + Email Notifications 📧 (Free Gmail)
+
+1. Enable 2-Factor Authentication in your Google Account
+2. Go to: **Google Account → Security → 2-Step Verification → App passwords**
+3. Generate an app password for "Mail"
+4. Set environment variable:
+   ```bash
+   export NOTIFICATION_CONFIG="smtp:your.email@gmail.com:your_app_password:recipient@example.com"
+   ```
+
+**Format**: `smtp:username:app_password:recipient_email`
+
+**Example**:
+```bash
+export NOTIFICATION_CONFIG="smtp:john.doe@gmail.com:abcd1234efgh5678:john.doe@gmail.com"
+```
+
+**Note**: This is completely free using Gmail's SMTP server. No paid services required!
+
+### 2. OpenCV Native Libraries
+
+Download and install OpenCV native libraries for your platform:
+
+**Windows:**
+```bash
+# Download from https://opencv.org/releases/
+# Extract and add to PATH or use:
+java -Djava.library.path="C:\opencv\build\java\x64" -jar scraper.jar
+```
+
+**Linux:**
+```bash
+sudo apt-get install libopencv-dev
+```
+
+**macOS:**
+```bash
+brew install opencv
+```
+
+### 3. YOLO Model Files
+
+Download YOLO model files for object detection:
+
+```bash
+mkdir models
+cd models
+
+# Download YOLOv4 config
+wget https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg
+
+# Download YOLOv4 weights (245 MB)
+wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights
+
+# Download COCO class names
+wget https://raw.githubusercontent.com/AlexeyAB/darknet/master/data/coco.names
+```
+
+## Building
+
+```bash
+mvn clean package
+```
+
+This creates:
+- `target/troostwijk-scraper-1.0-SNAPSHOT.jar` - Regular JAR
+- `target/troostwijk-scraper-1.0-SNAPSHOT-jar-with-dependencies.jar` - Executable JAR with all dependencies
+
+## Running
+
+### Quick Start (Desktop Notifications Only)
+
+```bash
+java -Djava.library.path="/path/to/opencv/lib" \
+     -jar target/troostwijk-scraper-1.0-SNAPSHOT-jar-with-dependencies.jar
+```
+
+### With Email Notifications
+
+```bash
+export NOTIFICATION_CONFIG="smtp:your@gmail.com:app_password:your@gmail.com"
+
+java -Djava.library.path="/path/to/opencv/lib" \
+     -jar target/troostwijk-scraper-1.0-SNAPSHOT-jar-with-dependencies.jar
+```
+
+### Using Maven
+
+```bash
+mvn exec:java -Dexec.mainClass="com.auction.scraper.TroostwijkScraper"
+```
+
+## Project Structure
+
+```
+src/main/java/com/auction/scraper/
+├── TroostwijkScraper.java          # Main scraper class
+│   ├── Lot                          # Domain model for auction lots
+│   ├── DatabaseService              # SQLite operations
+│   ├── NotificationService          # Desktop + Email notifications (FREE)
+│   └── ObjectDetectionService       # OpenCV YOLO object detection
+└── Main.java                        # Entry point
+```
+
+## Configuration
+
+Edit `TroostwijkScraper.main()` to customize:
+
+- **Database file**: `troostwijk.db` (SQLite database location)
+- **YOLO paths**: Model configuration and weights files
+- **Monitoring frequency**: Default is every 1 hour
+- **Closing alerts**: Default is 5 minutes before closing
+
+## Database Schema
+
+The scraper creates three tables:
+
+**sales**
+- `sale_id` (PRIMARY KEY)
+- `title`, `location`, `closing_time`
+
+**lots**
+- `lot_id` (PRIMARY KEY)
+- `sale_id`, `title`, `description`, `manufacturer`, `type`, `year`
+- `category`, `current_bid`, `currency`, `url`
+- `closing_time`, `closing_notified`
+
+**images**
+- `id` (PRIMARY KEY)
+- `lot_id`, `url`, `file_path`, `labels` (detected objects)
+
+## Notification Examples
+
+### Desktop Notification
+![System Tray Notification]
+```
+🔔 Kavel bieding update
+Nieuw bod op kavel 12345: €150.00 (was €125.00)
+```
+
+### Email Notification
+```
+From: your.email@gmail.com
+To: your.email@gmail.com
+Subject: [Troostwijk] Kavel bieding update
+
+Nieuw bod op kavel 12345: €150.00 (was €125.00)
+```
+
+**High Priority Alerts** (closing soon):
+```
+⚠️ Lot nearing closure
+Kavel 12345 sluit binnen 5 min.
+```
+
+## Why This Approach?
+
+✅ **100% Free** - No paid services (Twilio, Pushover, etc.)
+✅ **No External Dependencies** - Desktop notifications built into Java
+✅ **Works Offline** - Desktop notifications don't need internet
+✅ **Privacy First** - Your data stays on your machine
+✅ **Cross-Platform** - Windows, macOS, Linux supported
+✅ **Optional Email** - Add Gmail notifications if you want
+
+## Troubleshooting
+
+### Desktop Notifications Not Showing
+
+- **Windows**: Check if Java has notification permissions
+- **Linux**: Ensure you have a desktop environment running (not headless)
+- **macOS**: Check System Preferences → Notifications
+
+### Email Not Sending
+
+1. Verify 2FA is enabled in Google Account
+2. Confirm you're using an **App Password** (not your regular Gmail password)
+3. Check that "Less secure app access" is NOT needed (app passwords work with 2FA)
+4. Verify the SMTP format: `smtp:username:app_password:recipient`
+
+## Notes
+
+- Desktop notifications require a graphical environment (not headless servers)
+- For headless servers, use email-only notifications
+- Gmail SMTP is free and has generous limits (500 emails/day)
+- OpenCV native libraries must match your platform architecture
+- YOLO weights file is ~245 MB
+
+## License
+
+This is example code for educational purposes.
diff --git a/models/coco.names b/models/coco.names
new file mode 100644
index 0000000..ca76c80
--- /dev/null
+++ b/models/coco.names
@@ -0,0 +1,80 @@
+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+sofa
+pottedplant
+bed
+diningtable
+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush
diff --git a/models/yolov4.cfg b/models/yolov4.cfg
new file mode 100644
index 0000000..a7be12b
--- /dev/null
+++ b/models/yolov4.cfg
@@ -0,0 +1,1158 @@
+[net]
+batch=64
+subdivisions=8
+# Training
+#width=512
+#height=512
+width=608
+height=608
+channels=3
+momentum=0.949
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.0013
+burn_in=1000
+max_batches = 500500
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+#cutmix=1
+mosaic=1
+
+#:104x104 54:52x52 85:26x26 104:13x13 for 416
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-7
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-10
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-16
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=1
+stride=1
+pad=1
+activation=mish
+
+##########################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+### SPP ###
+[maxpool]
+stride=1
+size=5
+
+[route]
+layers=-2
+
+[maxpool]
+stride=1
+size=9
+
+[route]
+layers=-4
+
+[maxpool]
+stride=1
+size=13
+
+[route]
+layers=-1,-3,-5,-6
+### End SPP ###
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = 85
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = 54
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+##########################
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+scale_x_y = 1.2
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=256
+activation=leaky
+
+[route]
+layers = -1, -16
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+scale_x_y = 1.1
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=512
+activation=leaky
+
+[route]
+layers = -1, -37
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+scale_x_y = 1.05
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+
diff --git a/models/yolov4.weights b/models/yolov4.weights
new file mode 100644
index 0000000..921f440
Binary files /dev/null and b/models/yolov4.weights differ
diff --git a/src/main/java/com/auction/scraper/TroostwijkScraper.java b/src/main/java/com/auction/scraper/TroostwijkScraper.java
index b5ad33f..6d64fd8 100644
--- a/src/main/java/com/auction/scraper/TroostwijkScraper.java
+++ b/src/main/java/com/auction/scraper/TroostwijkScraper.java
@@ -354,6 +354,8 @@ public class TroostwijkScraper {
      * discovers Dutch auctions, scrapes lots, and begins monitoring.
      */
     public static void main(String[] args) throws Exception {
+        System.out.println("=== Troostwijk Auction Scraper ===\n");
+
         // Configuration parameters (replace with your own values)
         String databaseFile = "troostwijk.db";
 
@@ -366,27 +368,34 @@ public class TroostwijkScraper {
         // Example: "smtp:your.email@gmail.com:abcd1234efgh5678:recipient@example.com"
         // Get app password: Google Account > Security > 2-Step Verification > App passwords
 
-        String yoloCfg = "models/yolov4.cfg";      // path to YOLO config file
-        String yoloWeights = "models/yolov4.weights"; // path to YOLO weights file
-        String yoloClasses = "models/coco.names";   // list of class names
+        // YOLO model paths (optional - scraper works without object detection)
+        String yoloCfg = "models/yolov4.cfg";
+        String yoloWeights = "models/yolov4.weights";
+        String yoloClasses = "models/coco.names";
 
         // Load native OpenCV library
         System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
 
+        System.out.println("Initializing scraper...");
         TroostwijkScraper scraper = new TroostwijkScraper(databaseFile, notificationConfig, "",
                 yoloCfg, yoloWeights, yoloClasses);
 
         // Step 1: Discover auctions in NL
+        System.out.println("\n[1/3] Discovering Dutch auctions...");
         List<Integer> auctions = scraper.discoverDutchAuctions();
-        System.out.println("Found auctions: " + auctions);
+        System.out.println("✓ Found " + auctions.size() + " auctions: " + auctions);
 
         // Step 2: Fetch lots for each auction
+        System.out.println("\n[2/3] Fetching lot details...");
         for (int saleId : auctions) {
+            System.out.println("  Processing sale " + saleId + "...");
             scraper.fetchLotsForSale(saleId);
         }
 
         // Step 3: Start monitoring bids and closures
+        System.out.println("\n[3/3] Starting monitoring service...");
         scraper.scheduleMonitoring();
+        System.out.println("✓ Monitoring active. Press Ctrl+C to stop.\n");
     }
 
     // ----------------------------------------------------------------------
@@ -710,23 +719,53 @@ public class TroostwijkScraper {
     }
 
     /**
-     * Service for performing object detection on images using OpenCV’s DNN
+     * Service for performing object detection on images using OpenCV's DNN
      * module.  The DNN module can load pre‑trained models from several
      * frameworks (Darknet, TensorFlow, ONNX, etc.)【784097309529506†L209-L233】.  Here
      * we load a YOLO model (Darknet) by specifying the configuration and
      * weights files.  For each image we run a forward pass and return a
      * list of detected class labels.
+     *
+     * If model files are not found, the service operates in disabled mode
+     * and returns empty lists.
      */
     static class ObjectDetectionService {
         private final Net net;
         private final List<String> classNames;
+        private final boolean enabled;
+
         ObjectDetectionService(String cfgPath, String weightsPath, String classNamesPath) throws IOException {
-            // Load network
-            this.net = Dnn.readNetFromDarknet(cfgPath, weightsPath);
-            this.net.setPreferableBackend(DNN_BACKEND_OPENCV);
-            this.net.setPreferableTarget(DNN_TARGET_CPU);
-            // Load class names (one per line)
-            this.classNames = Files.readAllLines(Paths.get(classNamesPath));
+            // Check if model files exist
+            Path cfgFile = Paths.get(cfgPath);
+            Path weightsFile = Paths.get(weightsPath);
+            Path classNamesFile = Paths.get(classNamesPath);
+
+            if (!Files.exists(cfgFile) || !Files.exists(weightsFile) || !Files.exists(classNamesFile)) {
+                System.out.println("⚠️  Object detection disabled: YOLO model files not found");
+                System.out.println("   Expected files:");
+                System.out.println("   - " + cfgPath);
+                System.out.println("   - " + weightsPath);
+                System.out.println("   - " + classNamesPath);
+                System.out.println("   Scraper will continue without image analysis.");
+                this.enabled = false;
+                this.net = null;
+                this.classNames = new ArrayList<>();
+                return;
+            }
+
+            try {
+                // Load network
+                this.net = Dnn.readNetFromDarknet(cfgPath, weightsPath);
+                this.net.setPreferableBackend(DNN_BACKEND_OPENCV);
+                this.net.setPreferableTarget(DNN_TARGET_CPU);
+                // Load class names (one per line)
+                this.classNames = Files.readAllLines(classNamesFile);
+                this.enabled = true;
+                System.out.println("✓ Object detection enabled with YOLO");
+            } catch (Exception e) {
+                System.err.println("⚠️  Object detection disabled: " + e.getMessage());
+                throw new IOException("Failed to initialize object detection", e);
+            }
         }
         /**
          * Detects objects in the given image file and returns a list of
@@ -736,9 +775,13 @@ public class TroostwijkScraper {
          * post‑processing【784097309529506†L324-L344】.
          *
          * @param imagePath absolute path to the image
-         * @return list of detected class names
+         * @return list of detected class names (empty if detection disabled)
          */
         List<String> detectObjects(String imagePath) {
+            if (!enabled) {
+                return new ArrayList<>();
+            }
+
             List<String> labels = new ArrayList<>();
             Mat image = Imgcodecs.imread(imagePath);
             if (image.empty()) return labels;
diff --git a/target/classes/com/auction/scraper/Main.class b/target/classes/com/auction/scraper/Main.class
new file mode 100644
index 0000000..d400324
Binary files /dev/null and b/target/classes/com/auction/scraper/Main.class differ
diff --git a/target/classes/com/auction/scraper/TroostwijkScraper$DatabaseService.class b/target/classes/com/auction/scraper/TroostwijkScraper$DatabaseService.class
new file mode 100644
index 0000000..7069d86
Binary files /dev/null and b/target/classes/com/auction/scraper/TroostwijkScraper$DatabaseService.class differ
diff --git a/target/classes/com/auction/scraper/TroostwijkScraper$Lot.class b/target/classes/com/auction/scraper/TroostwijkScraper$Lot.class
new file mode 100644
index 0000000..9af6273
Binary files /dev/null and b/target/classes/com/auction/scraper/TroostwijkScraper$Lot.class differ
diff --git a/target/classes/com/auction/scraper/TroostwijkScraper$NotificationService$1.class b/target/classes/com/auction/scraper/TroostwijkScraper$NotificationService$1.class
new file mode 100644
index 0000000..3ebb139
Binary files /dev/null and b/target/classes/com/auction/scraper/TroostwijkScraper$NotificationService$1.class differ
diff --git a/target/classes/com/auction/scraper/TroostwijkScraper$NotificationService.class b/target/classes/com/auction/scraper/TroostwijkScraper$NotificationService.class
new file mode 100644
index 0000000..526cd75
Binary files /dev/null and b/target/classes/com/auction/scraper/TroostwijkScraper$NotificationService.class differ
diff --git a/target/classes/com/auction/scraper/TroostwijkScraper$ObjectDetectionService.class b/target/classes/com/auction/scraper/TroostwijkScraper$ObjectDetectionService.class
new file mode 100644
index 0000000..f82c6a4
Binary files /dev/null and b/target/classes/com/auction/scraper/TroostwijkScraper$ObjectDetectionService.class differ
diff --git a/target/classes/com/auction/scraper/TroostwijkScraper.class b/target/classes/com/auction/scraper/TroostwijkScraper.class
new file mode 100644
index 0000000..c04fa8f
Binary files /dev/null and b/target/classes/com/auction/scraper/TroostwijkScraper.class differ
diff --git a/troostwijk.db b/troostwijk.db
new file mode 100644
index 0000000..729611b
Binary files /dev/null and b/troostwijk.db differ