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evaluate.sh
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evaluate.sh
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set -ex
# example training scripts for AAAI-21
# Split then Refine: Stacked Attention-guided ResUNets for Blind Single Image Visible Watermark Removal
CUDA_VISIBLE_DEVICES=0 python /data/home/yb87432/s2am/main.py --epochs 100\
--schedule 100\
--lr 1e-3\
-c eval/10kgray/1e3_bs4_256_hybrid_ssim_vgg\
--arch vvv4n\
--sltype vggx\
--style-loss 0.025\
--ssim-loss 0.15\
--masked True\
--loss-type hybrid\
--limited-dataset 1\
--machine vx\
--input-size 256\
--train-batch 4\
--test-batch 1\
--base-dir $HOME/watermark/10kgray/\
--data _images
# example training scripts for TIP-20
# Improving the Harmony of the Composite Image by Spatial-Separated Attention Module
# * in the original version, the res = False
# suitable for the iHarmony4 dataset.
python /data/home/yb87432/mypaper/s2am/main.py --epochs 200\
--schedule 150\
--lr 1e-3\
-c checkpoint/normal_rasc_HAdobe5k_res \
--arch rascv2\
--style-loss 0\
--ssim-loss 0\
--limited-dataset 0\
--res True\
--machine s2am\
--input-size 256\
--train-batch 16\
--test-batch 1\
--base-dir $HOME/Datasets/\
--data HAdobe5k