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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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[–] Saffie91@alien.top 1 points 2 years ago

Hyperparameter tuning is out. Its much harder to overfit anything nowadays unless you re trying to using something like yolov8.

Its always been about the dataset.