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Quick Reference: YOLO V8 vs V3

🚀 Quick Commands

YOLO V8 (Recommended - Default)

# Default (YOLO V8 Medium)
ros2 launch darknet_ros_3d darknet_ros_3d_complete.launch.py

# Choose model size
ros2 launch darknet_ros_3d darknet_ros_3d_complete.launch.py yolo_model:=yolov8n  # Fastest
ros2 launch darknet_ros_3d darknet_ros_3d_complete.launch.py yolo_model:=yolov8m  # Recommended
ros2 launch darknet_ros_3d darknet_ros_3d_complete.launch.py yolo_model:=yolov8l  # Accurate
ros2 launch darknet_ros_3d darknet_ros_3d_complete.launch.py yolo_model:=yolov8x  # Best accuracy

# Direct launch
ros2 launch darknet_ros_3d yolov8_3d.launch.py

YOLO V3 (Legacy)

# Option 1: Launch argument
ros2 launch darknet_ros_3d darknet_ros_3d_complete.launch.py use_yolov8:=false

# Option 2: Direct launch
ros2 launch darknet_ros_3d yolov3_3d.launch.py

📊 Quick Comparison

Aspect YOLO V8 YOLO V3
Accuracy ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐
Speed ⭐⭐⭐⭐⭐ ⭐⭐⭐
Model Size 6-136 MB 235 MB
Setup Auto-download Manual weights

🎯 When to Use What?

  • New projects: YOLO V8 ✅
  • Better performance: YOLO V8 ✅
  • Legacy compatibility: YOLO V3 ⚠️
  • Custom YOLO V3 weights: YOLO V3 ⚠️

📝 Configuration Files

  • YOLO V8: src/ultralytics_ros/config/yolov8m.yaml
  • YOLO V3: src/darknet_ros/darknet_ros/config/yolov3.yaml
  • 3D Detection: src/gb_visual_detection_3d/darknet_ros_3d/config/darknet_3d.yaml

🔍 Verify Which Version is Running

# Check nodes
ros2 node list

# YOLO V8 node: /yolo_detector_node
# YOLO V3 node: /darknet_ros

# Check topics (both use same topics)
ros2 topic echo /darknet_ros/bounding_boxes

📚 More Information