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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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[–] KyxeMusic@alien.top 1 points 2 years ago (1 children)

No magic hyperparameter is going to do the trick here.

Your only real option is to try to find a dataset that approaches the real distribution, or to try to somehow augment your data to make it look more like it (although this is very challenging to do without introducing even more bias).

[–] 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.