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Data and codes for "SCANet: Self-paced semi-curricular attention network for non-homogeneous image dehazing"
dataset
posted on 2023-10-09, 08:12 authored by Yu GUO, Yuan GAO, Ryan Wen LIU, Yuxu LU, Jingxiang QU, Shengfeng HeShengfeng He, Wenqi RENThis 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).
Requirement
- Python 3.7
- Pytorch 1.9.1
Network Architecture
Train
- Place the training and test image pairs in the
data
folder. - Run
data/makedataset.py
to generate theNH-Haze20-21-23.h5
file. - Run
train.py
to start training.
Test
- Place the pre-training weight in the
checkpoint
folder. - Place test hazy images in the
input
folder. - Modify the weight name in the
test.py
.
parser.add_argument("--model_name", type=str, default='Gmodel_40', help='model name')
- Run
test.py
- The results is saved in
output
folder.
Pre-training Weight Download
History
Confidential or personally identifiable information
- I confirm that the uploaded data has no confidential or personally identifiable information.