The goal of this project is to upscale and improve the quality of low resolution images.
trained with Adversarial and VGG features losses, choose the option weights='gans' when creating a RRDN model.
The main parameters of the architecture structure are:
- T - number of Residual in Residual Dense Blocks (RRDB)
- D - number of Residual Dense Blocks (RDB) insider each RRDB
- C - number of convolutional layers stacked inside a RDB
- G - number of feature maps of each convolutional layers inside the RDBs
- G0 - number of feature maps for convolutions outside of RDBs and of each RBD output
cd client
cd npm run build
cd ../
pip install --upgrade pip
pip install -e ".[gpu]" --ignore-installed
pip3 install flask
pip3 install requests
pip3 install flask_cors
python3 ./src/server.py
sudo docker build -t {Docker Path}:{Tag} .
sudo docker run -p {Expose Port}:80 {Docker Path}:{Tag}