Retrain image classifier using MobileNet v2 I am using my own dataset to retrain mobilenet_v2_100_224 model, I currently have 4 classes where each class have more than 100 images still I'm observing overfitting even though I've used --random_scale and --random_brightness parameters, how should I overcome this overfitting problem when there's no such regularization technique available in the retrain.py script? following is my TensorBoard visualization:

Retrain command:

python retrain.py --tfhub_module https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/2 --how_many_training_steps 500 --random_scale=5 --random_brightness=10 --output_graph=./retrained_graph.pb --output_labels=./retrained_labels.txt --image_dir ./data --summaries_dir /tmp/log
I do not see any overfitting there. Overfitting is when your validation loss becomes worse with time, while training loss improves. Just because your training error is less than the validation (which is expected) does not mean it overfits. 

But you can try to freeze the first layers (up to the fully connected one) to ensure the network does not lose generalization.