Allow you to freeze the backbone encoder partially. For example, you can set encoder_freeze=0.8 which will make the first 80% layers untrainable, and leave the last 20% trainable.

This lets you tune the trainability of your network.

I freeze early layers first because: early layers learn basic features that are less likely to change between data sets. Late layers learn abstract features that may change more between data sets.

In my experiment, setting a 95% freeze was good for accuracy AND training speed for Unet + inceptionv3 on the Oxford-IIIT Pet dataset (code at https://github.com/AllWashedOut/pet_segmentation)

0% frozen

python .\model.py --patience=10 --encoder_freeze_percent=0.0
Test accuracy: 90.0%. Epoch time: 60s

95% frozen

python .\model.py --patience=10 --encoder_freeze_percent=0.95
Test accuracy: 91.13%. Epoch time: 52s

100% frozen

python .\model.py --patience=10 --encoder_freeze_percent=1.0
Test accuracy: 90.99%. Epoch time: 50s