- ✅ 2023-09-09: Release the codes and results of HMA.
- (To do) Release the pretrain models.
Benchmark results on SRx4.
| Model | Set5 | Set14 | BSD100 | Urban100 | Manga109 |
|---|---|---|---|---|---|
| SwinIR | 32.92 | 29.09 | 27.92 | 27.45 | 32.03 |
| HMA | 33.38 | 29.51 | 28.13 | 28.69 | 33.19 |
Comparison with the state-of-the-art methods.
Install Pytorch first. Then,
pip install -r requirements.txt
python setup.py develop
- Refer to
./options/trainfor the configuration file of the model to train. - Preparation of training data can refer to this page. ImageNet dataset can be downloaded at the official website.
- The training command is like
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --use_env --master_port=4321 hma/train.py -opt options/train/train_HMA_SRx4_from_Imagenet.yml --launcher pytorch
The training logs and weights will be saved in the ./experiments folder.
The inference results on benchmark datasets are available at Google Drive.
If you have any question, please email douzhichao2021@163.com to discuss with the authors.