Unlock the power of YOLOv9 with Ultralytics! 🚀 In Episode 51, we dive deep into training a custom YOLOv9 model on an industrial package dataset. This comprehensive tutorial covers everything from exploring the Ultralytics documentation to hands-on examples using both command-line and Python scripts. Learn how to set up your dataset, train the model, and validate your results effectively.
Key Moments ✨:
0:00 - Introduction to YOLOv9 and Ultralytics
0:27 - Detailed Walkthrough of Ultralytics Documentation
0:36 - In-depth YOLOv9 Model Overview
0:53 - Practical Usage Example with Ultralytics Package
1:21 - Supported Tasks and Modes in YOLOv9
1:43 - Industrial Package Segmentation Dataset Overview
2:38 - Step-by-Step Dataset Usage Code Sample
2:53 - Sample Data and Annotations Overview
3:15 - Training YOLOv9 Using CLI
3:59 - Training YOLOv9 with Python Script
6:04 - Training and Validation Metrics for YOLOv9
8:20 - Summary and Key Takeaways
In this video, explore the latest innovations in YOLOv9, including PGI and GELAN, and discover how to implement and run segmentation tasks on industrial packages moving along a conveyor belt. With over 2,000 annotated training images, this dataset is perfect for real-world applications like package counting and quality control.
Call-to-Action:
If you enjoyed this video, make sure to like, subscribe, and hit the notification bell for more tutorials on cutting-edge AI technologies. For more detailed guides, visit the links below:
📚 Ultralytics Documentation: [ Ссылка ]
📂 Package Segmentation Dataset: [ Ссылка ]
🚀 Ultralytics HUB: [ Ссылка ]
🏠 Ultralytics Home: [ Ссылка ]
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