Face-Super-resolution-Reconstruction-Based-on-Attention-Residual-Network

Face Super-resolution Reconstruction Based on Attention Residual Network I have tested the codes on

• Ubuntu 18.04
• CUDA 10.1
• Python 3.7, install required packages by pip3 install -r requirements.txt

Test with Pretrained Models

Pre-training model download address：pretrain_models Password：scsb

# helen
python test.py --gpus 1 --model Ours --name Ours \    --load_size 128 --dataset_name single --dataroot /home/wang107552002794/Ours/test_dirs/Helen_test/LR\    --pretrain_model_path /home/wang107552002794/Ours/pretrain_models/Ours.pth\    --save_as_dir results_helen/Ours_test/


# celeba
python test.py --gpus 1 --model Ours --name Ours \    --load_size 128 --dataset_name single --dataroot /home/wang107552002794/Ours/test_dirs/CelebA_test/LR \  --pretrain_model_path /home/wang107552002794/Ours/pretrain_models/Ours.pth\    --save_as_dir results_CelebA/Ours_test/

Train the Model

The commands used to train the released models are provided in script train.sh. Here are some train tips:

• You should download CelebA train Ours.
• To train Ours, we simply crop out faces from CelebA without pre-alignment, because for ultra low resolution face SR, it is difficult to pre-align the LR images.
• Please change the --name option for different experiments. Tensorboard records with the same name will be moved to check_points/log_archive, and the weight directory will only store weight history of latest experiment with the same name.

Acknowledgement

The codes are based on SPARNet . The project also benefits from DICNet.