Today we’re diving into something really cool: fine-tuning YOLO models for soccer detection. We’re going to detect balls, referees, players, and goalkeepers in soccer matches using Ultralytics’ fine-tuning tools. The popular YOLO models you’ll find out there, like Ultralytics YOLOv11, and YOLOX in different sizes, are mostly pre-trained on something called the COCO dataset. What’s […]

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Fine-tuning pre-trained YOLO models for specialized object detection tasks requires significantly less data and training time than building from scratch. Using a soccer dataset with 7,010 training images, the author demonstrates how to adapt a COCO-trained YOLOv11 model to detect balls, players, referees, and goalkeepers with 88% mAP50 accuracy. The process involves using Ultralytics tools for training, monitoring key metrics like loss values and mAP50, and converting the final PyTorch model to ONNX format for deployment in Elixir applications. The fine-tuned model shows superior contextual understanding compared to generic models, focusing on field action while filtering out background spectators.

Fine-Tuning YOLO to Watch Soccer Matches

So you want to automate relaxing and entertainment? Would you like to do my work for me instead?

welp its usefull for referees and organisers,but for us it’s completely useless.
But it’s cool keep it up!

Basically it’s just calling ultralytics library. Nothing new. There are a lot of ways to make the project more interesting