How can I further improve the accuracy of my CNN based on transfer-learning?

I employ existing widely used CNNs (e.g., `Xception`, `DenseNet169`, `InceptionV3`) via transfer-learning using Keras for a classification task of `10` classes. I've employed almost every available model but still experiencing overfitting as you can see via the attached accuracy-epoch graph, which was obtained during training. I re-calculate the weights of the layers that come with the existing models (*a.k.a.* base models). I've employed KerasTuner to optimize the hyperparameters such as the activation function, optimization algorithm, and learning rate. As a …

accuracy classification cnn cnns datascience graph keras overfitting training transfer