Can this be considered overfitting?

Question

I have 4 classes, each with 1350 images. The validation set has 20% of the total images (it is generated automatically). The training model uses MobilenetV2 network:

base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights='imagenet') 

The model is created:

model = tf.keras.Sequential([
  base_model,
  tf.keras.layers.Conv2D(32, 3, activation='relu', kernel_regularizer=regularizers.l2(0.001)),
  tf.keras.layers.Dropout(0.5),
  tf.keras.layers.MaxPool2D(),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(4, activation='softmax', kernel_regularizer=regularizers.l2(0.001))
]) 

The model is trained through 20 epochs and then fine tunning is done in 15 epochs. The result is as follows:

Image of the model trained before fine tunning

Image of the model trained after 15 epochs and fine tunning

Answer 1

A bit difficult to tell without the numeric values of validation loss but I would say the results before fine tuning are slightly over fitting and for after fine tuning less over fitting. A couple of additional things you could do. One is try using an adjustable learning rate using the callback tf.keras.callbacks.ReduceLROnPlateau. Set it up to monitor validation loss. Documentation is here. I set factor=.5 and patience=1. Second replace the Flatten layer with tf.keras.layers.GlobalMaxPool2D and see if it improves the validation loss.