Adding a YOLOv9 Implementation Notebook to Help New Users

[deepinvalue]

I've been playing around with YOLOv9 since it's out and noticed there aren't many resources yet for beginners. So, I put together a Jupyter Notebook that covers the basics of getting started with detection, tracking, and counting using YOLOv9 and the Supervision library.

It's pretty straightforward and includes:

• How to set up YOLOv9 for object detection.
• Steps for real-time object tracking.
• Counting objects, and even doing so in selected areas.
• Extending the Supervision library to work with YOLOv9.
• Some basic video processing techniques.

It might be useful for others who are new to YOLOv9 or looking for ways to get started with their projects. I'm suggesting we could add a link to this notebook in the README or wherever you think it fits best for community resources. It could be a nice way to help folks get up to speed.

You can check out the notebook here: deepinvalue/yolov9-supervision-tracking-counting

[migsdigs]

Hi there,

I am particularly interested in the object tracking. I have not seen any example of performance, although I think I will play around with your Notebook. Can you comment on multi-object tracking and if it support object ID persistance please?

Thank you

[WongKinYiu]

added to readme.

readme also contains yolov9+bytetrack, which is a mot framework.

[deepinvalue]

@migsdigs Sure, I have used ByteTrack (as included in Supervision) for multi-object tracking. I believe it supports ID persistence. I have now included a simple demonstration GIF in readme. Check it out!