train YOLOv9 on your dataset tutorial - run inference with a pre-trained COCO model - fine-tune model on custom dataset - evaluate the trained model - run inference with a fine-tuned model blogpost: ↓ read more

I have also prepared a notebook with an end-to-end example. - notebook: - paper: - code:

tutorial covers benchmarking the newly trained model and analyzing the results.

and also demonstrates how to use the model for inference.

I've also added the example to the notebooks repository; there, you will find tutorials not only about YOLOv9 but also about other SOTA models. notebooks repository:

hahah when the referee fell, it looked more like a player to the model makes sense

hahaha, if I see someone falling on the sidewalk and holding his leg, my first thought is, it is a football player

So many yolo versions) Could you highlight the advantages of yolov9?)

Apparently it is the fastest and the most accurate ;)

Thanks for sharing mate. Really accurate.

Is this then tracking player movement on the field with any sort of accuracy?

This demo not, but some time ago I build demo that did this