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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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[–] Toilet2000@alien.top 1 points 2 years ago

If that’s not something that you already do, use data augmentation. Especially scaling.

It might not help for features related to age (such as color changes and the likes), but it can definitely help remove the relative size bias, especially if your dataset was created in a single scenario with a fixed camera distance.

If you do know some features of "an older fish", you can also apply those transformations on masks of younger fish from your first trained but biaised model. Somewhat like a "semi-synthetic" data augmentation.

For example, if older fishes are browner, you can skew the hue of the masks by a certain amount to get brownish young fishes.