YOLOv9 Tutorial - Train Model on Custom Dataset

Learn to implement YOLOv9, a state-of-the-art object detection model, in this comprehensive tutorial video. Explore the model's architecture, set up the environment, and master the process of training YOLOv9 on custom datasets. Follow along as the instructor demonstrates pre-trained model inference, fine-tuning techniques, and model evaluation. Discover how to deploy your trained model using an inference package and gain insights into important considerations when working with YOLOv9. The tutorial concludes with a practical demo on building a self-service checkout system using YOLOv9. Access a wealth of resources, including GitHub repositories, research papers, and additional learning materials to further enhance your understanding of this powerful object detection algorithm.

- Intro
- Setting Up YOLOv9
- YOLOv9 Inference with Pre-Trained COCO Weights
- Training YOLOv9 on Custom Dataset
- YOLOv9 Model Evaluation
- YOLOv9 Inference with Fine-Tuned Model
- Model Deployment with Inference Package
- Important Considerations for Using YOLOv9
- Demo: Build Self-Service Checkout with YOLOv9
- Outro
- Community Session March 14 2024 at 09:00 AM PST / 12:00 PM EST / PM CET: https://roboflow.stream