Generalizing Your Model: An Example With EfficientNetV2 and Cats & Dogs

Consider this scenario. You are using the new fancy state-of-the-art CNN network architecture, EfficientNetV2, to train an image classifier. You’ve achieved impressive training accuracy (> 95%) but the model is not learning evaluation samples nearly as well as training samples.

As machine learning engineers, we understand that our models are only as good as they perform on unseen data. Which begs the question:

How can we increase the performance of our networks on unseen data?

When our …

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