[P] Yolov8 hyperparameters for best generalization

I’m training an object detection model on yolov8 but my training data is a little biased because it doesn’t represent the real life distribution.
(I want to count objects of one class but different shape in a video and need them to be detected with near equal probability. )
How can I make sure to generalise the model enough so that the bias doesn’t have too much of an effect?
I know it will come with more false positives, but …

count data detection distribution life machinelearning near objects probability them training training data video yolov8