Update yolov8-p6.yaml with model parameters and GFLOPs

[glenn-jocher]

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Enhancements to YOLOv8-p6 model configurations with detailed summaries for each variant.

📊 Key Changes

• Added detailed comments to yolov8-p6.yaml:
  • YOLOv8n-p6: 220 layers, 4.9M parameters, 8.7 GFLOPs
  • YOLOv8s-p6: 220 layers, 17.9M parameters, 28.5 GFLOPs
  • YOLOv8m-p6: 285 layers, 44.9M parameters, 83.1 GFLOPs
  • YOLOv8l-p6: 350 layers, 62.4M parameters, 167.3 GFLOPs
  • YOLOv8x-p6: 350 layers, 97.4M parameters, 261.1 GFLOPs

🎯 Purpose & Impact

• Clarification: Provides detailed model summaries directly within the config file, making it easier for developers and researchers to understand the complexity and computational requirements of each model variant.
• Informed Choices: Helps users select the right model variant based on their hardware capabilities and performance needs.

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 70.40%. Comparing base (dacbd48) to head (ffe26c0).

Additional details and impacted files

@@           Coverage Diff           @@
##             main   #13862   +/-   ##
=======================================
  Coverage   70.40%   70.40%           
=======================================
  Files         124      124           
  Lines       15898    15898           
=======================================
  Hits        11193    11193           
  Misses       4705     4705           

Flag	Coverage Δ
Benchmarks	35.60% <ø> (ø)
GPU	37.10% <ø> (-0.02%)	⬇️
Tests	66.53% <ø> (ø)

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.