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Model Training Details
=> Performed with 150 Ąż 150 patch images to reduce the computational cost & achieve accurate classification results.
=> All networks underwent consistent training: 100 epochs, batch size 32, utilized ReLU activation function, employed the Adam optimizer with a fixed learning rate (0.0001), and employed categorical cross-entropy as the loss function.
=> Use early stopping to monitor the validation loss, and adjust the learning rate if the validation loss plateaus. Reduces the learning rate by a factor (factor) if there is no improvement in the validation loss for a certain number of epochs (patience).
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IEEE/ICACT20240405 Slide.10
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