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Been working on an image classification model lately - the training accuracy looks great, but the validation accuracy seems to plateau around 88–90%.
I’ve already tried data augmentation and dropout tuning, but the improvement is marginal.
Curious to know - what’s your go-to approach when your model just refuses to generalise better? Do you usually tweak the architecture, try transfer learning, or focus on data cleaning?
Luo tili jo tänään nauttiaksesi yksinoikeusominaisuuksista ja osallistuaksesi mahtavaan yhteisöömme!
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