cls loss about yolov5

[glenn-jocher]

Hello! Thanks for bringing up your question about the classification loss (cls-loss) used in YOLOv5.

Indeed, the classification loss in YOLOv5 is the Binary Cross-Entropy Loss (BCE). When dealing with multiple classes (nc > 1), BCE allows a model to predict each class label as independent probabilities; this means that each class is treated separately and there is no built-in mutual exclusiveness between classes in the loss itself.

However, the mutual exclusion you are referring to is typically managed by the architecture and post-processing steps, ensuring that only the most probable class label is selected for each detection. If strict mutual exclusivity is required during training, alterations to the loss function or additional mechanisms might be considered.

Hope that helps clarify your query! 🌟