|
|
|
|
@ -58,16 +58,19 @@ Pre-trained models are provided in the GitHub releases. Training your own is a
|
|
|
|
|
rm -f ne4x*.pkl.bz2
|
|
|
|
|
|
|
|
|
|
# Pre-train the model using perceptual loss from paper [1] below.
|
|
|
|
|
python3.4 enhance.py --train "data/*.jpg" --scales=2 --epochs=50 \
|
|
|
|
|
python3.4 enhance.py --train "data/*.jpg" --model custom --scales=2 --epochs=50 \
|
|
|
|
|
--perceptual-layer=conv2_2 --smoothness-weight=1e7 --adversary-weight=0.0 \
|
|
|
|
|
--generator-blocks=4 --generator-filters=64
|
|
|
|
|
|
|
|
|
|
# Train the model using an adversarial setup based on [4] below.
|
|
|
|
|
python3.4 enhance.py --train "data/*.jpg" --scales=2 --epochs=250 \
|
|
|
|
|
python3.4 enhance.py --train "data/*.jpg" --model custom --scales=2 --epochs=250 \
|
|
|
|
|
--perceptual-layer=conv5_2 --smoothness-weight=2e4 --adversary-weight=2e5 \
|
|
|
|
|
--generator-start=5 --discriminator-start=0 --adversarial-start=5 \
|
|
|
|
|
--discriminator-size=64
|
|
|
|
|
|
|
|
|
|
# The newly trained model is output into this file...
|
|
|
|
|
ls ne4x-custom-*.pkl.bz2
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: docs/BankLobby_example.gif
|
|
|
|
|
|
|
|
|
|
|