ElixirStatus·40wFine-Tuning YOLO to Watch Soccer Matches
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.
