This repository is the official NTIRE Challenge 2023 implementation of Team LVGroup_HFUT in Image Shadow Removal.
The restoration results of the tesing images and pretrained model can be downloaded from Google Drive.
cd your/script/path
python infer.py --data_source your/dataset/path --model_path ../pretrained/epoch_0090.pth --save_image --experiment your-experiment-name
cd your/script/path
python train.py --data_source your/dataset/path --experiment your-experiment
The format of the dataset should meet the following code in datasets.py:
img_paths_train = sorted(glob.glob(data_source + '/train' + '/input' + '/*.*'))
gt_paths_train = sorted(glob.glob(data_source + '/train' + '/gt' + '/*.*'))
or
self.img_paths = sorted(glob.glob(data_source + '/test' + '/input' + '/*.*'))
data_source is given by the command line.
when training and validating: the default path is '../results/your-experiment'
when testing: the default path is '../outputs/your-experiment/test'
when inferring: the default path is '../outputs/your-experiment/infer'