Data and codes for "SCANet: Self-paced semi-curricular attention network for non-homogeneous image dehazing"
datasetposted on 2023-10-09, 08:12 authored by Yu GUO, Yuan GAO, Ryan Wen LIU, Yuxu LU, Jingxiang QU, Shengfeng HeShengfeng He, Wenqi REN
This record contains the data and codes for the paper "SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing" published in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
- Python 3.7
- Pytorch 1.9.1
- Place the training and test image pairs in the
data/makedataset.pyto generate the
train.pyto start training.
- Place the pre-training weight in the
- Place test hazy images in the
- Modify the weight name in the
parser.add_argument("--model_name", type=str, default='Gmodel_40', help='model name')
- The results is saved in
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