I published a guide on fine-tuning YOLO models for custom object detection, showing how to transform a generic 80-class detector into a specialized system (using soccer match analysis as an example).
A bit of context: I’ve been working on a YOLO library for Elixir that supports custom models via ONNX format. Since the library can load any custom YOLO model, I created this content to show how to train your own models using Ultralytics’ tooling. The approach is language-agnostic – the resulting model works with any framework supporting PyTorch or ONNX, though I demonstrate Elixir integration at the end.
This fine-tuning approach applies to various industries where domain-specific object detection is needed – sports analytics, manufacturing QC, etc.
Elixir YOLO library: [https://github.com/poeticoding/yolo\_elixir](https://github.com/poeticoding/yolo_elixir)
Video + Article about Elixir YOLO 0.2.0: [https://www.poeticoding.com/elixir-yolo-v0-2-0-yolox-support-custom-models-and-performance-boost/](https://www.poeticoding.com/elixir-yolo-v0-2-0-yolox-support-custom-models-and-performance-boost/)
Let me know if you would find interesting some videos about the details of the YOLO architecture
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