This repository is implementation of the "Fast and Accurate Single Image Super-Resolution via Information Distillation Network".
- PyTorch
- Tensorflow
- tqdm
- Numpy
- Pillow
Tensorflow is required for quickly fetching image in training phase.
Original | BICUBIC x2 | IDN x2 |
![]() |
![]() |
![]() |
When training begins, the model weights will be saved every epoch.
If you want to train quickly, you should use --use_fast_loader option.
python main.py --scale 2 \
--num_features 64 \
--d 16 \
--s 4 \
--images_dir "" \
--outputs_dir "" \
--patch_size 29 \
--batch_size 16 \
--num_epochs 20 \
--lr 1e-4 \
--loss "l1" \
--threads 8 \
--seed 123 \
--use_fast_loader
The fine-tuning artifacts are generated as "IDN_ft_epoch_{}.pth".
python main.py --scale 2 \
--num_features 64 \
--d 16 \
--s 4 \
--images_dir "" \
--outputs_dir "" \
--weights_path "" \
--patch_size 39 \
--batch_size 16 \
--num_epochs 20 \
--lr 1e-5 \
--loss "l2" \
--threads 8 \
--seed 123 \
--use_fast_loader
Output results consist of restored images by the BICUBIC and the IDN.
python example --scale 2 \
--num_features 64 \
--d 16 \
--s 4 \
--weights_path "" \
--image_path "" \
--outputs_dir ""