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The visual above represents the complete training and evaluation pipeline. It uses EfficientNetV2B2 as a feature extractor, followed by custom CNN layers. The model is initially trained with a frozen base, then fine-tuned, and finally evaluated on multiple metrics like accuracy, loss curves, and confusion matrix.
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