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[P] Yolov8 hyperparameters for best generalization (alien.top)

submitted 2 years ago by Banntu@alien.top to c/machinelearning@academy.garden

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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 that’s not a problem.

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[–] DisWastingMyTime@alien.top 1 points 2 years ago

What are the differences between the training objects and the real targets?

Generally speaking you're not going to bridge the gap with the hyperparameters, you may do so with augmentations or synthetic data, share more of the issue.