Skip to content

Latest commit

 

History

History
72 lines (51 loc) · 1.79 KB

README-Senn.md

File metadata and controls

72 lines (51 loc) · 1.79 KB
Raw

Senn6x

Senn6x is an experimental Residual Channel Attention Network designed for Image Super-Resolution.

Little guy

The code is built on RCAN and the model was trained on the DIV2K dataset.

Dependencies

  • Python 3
  • PyTorch >= 1.0.0
  • numpy
  • skimage
  • imageio
  • matplotlib
  • tqdm

Code

Clone this repository.

git clone https://github.com/Senn1/Senn6x
cd Senn6x

Training

Training dataset

    1. Download the DIV2K dataset here
    1. Set the directory to the HR (high resolution) and LR (Low Resolution) paths

Training the model

    1. CD to 'Trainingcode/model'
    1. Run the trainmain.py script
python trainmain.py

Quick Test

Run these following scripts:

Senn2x

python main.py --model san --data_test MyImage --save `save_name` --scale 2 ../model/Senn_BIX2.pt

Senn4x

python main.py --model senn6x --data_test MyImage --save `save_name` --scale 4 ../model/Senn_BIX4.pt

Senn6x

python main.py --model senn6x --data_test MyImage --save `save_name` --scale 6 ../model/Senn_BIX6.pt

Results

The results are in the /results folder. Test results for popular datasets can also be downloaded here. (placeholder link)

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT