Skip to content

Using ISR with Docker


  1. Install Docker

  2. Clone our repository and cd into it:

git clone
cd image-super-resolution
  1. Build docker image for local usage docker build -t isr . -f Dockerfile.cpu

In order to train remotely on AWS EC2 with GPU

  1. Install Docker Machine

  2. Install AWS Command Line Interface

  3. Set up an EC2 instance for training with GPU support. You can follow our nvidia-docker-keras project to get started


Place your images (png, jpg) under data/input/<data name>, the results will be saved under /data/output/<data name>/<model>/<training setting>.

NOTE: make sure that your images only have 3 layers (the png format allows for 4).

Check the configuration file config.yml for more information on parameters and default folders.

The -d flag in the run command will tell the program to load the weights specified in config.yml. It is possible though to iteratively select any option from the command line.

Predict locally

Download the pre-trained weights as described here.

Update your config.yml according to the model you want to use. For example rrdn

# config.yml

  generator: rrdn # Use rrdn


weights_paths: # Point to the rrdn weights file
  generator: ./weights/rrdn-C4-D3-G32-G032-T10-x4_epoch299.hdf5

From the main folder run

docker run -v $(pwd)/data/:/home/isr/data -v $(pwd)/weights/:/home/isr/weights -v $(pwd)/config.yml:/home/isr/config.yml -it isr -p -d -c config.yml

Predict on AWS with nvidia-docker

From the remote machine run (using our DockerHub image)

sudo nvidia-docker run -v $(pwd)/isr/data/:/home/isr/data -v $(pwd)/isr/weights/:/home/isr/weights -v $(pwd)/isr/config.yml:/home/isr/config.yml -it idealo/image-super-resolution-gpu -p -d -c config.yml


Train either locally with (or without) Docker, or on the cloud with nvidia-docker and AWS.

Add you training set, including training and validation Low Res and High Res folders, under training_sets in config.yml.

Train on AWS with GPU support using nvidia-docker

To train with the default settings set in config.yml follow these steps: 1. From the main folder run bash scripts/ -m <name-of-ec2-instance> -b -i -u -d <data_name>. 2. ssh into the machine docker-machine ssh <name-of-ec2-instance> 3. Run training with sudo nvidia-docker run -v $(pwd)/isr/data/:/home/isr/data -v $(pwd)/isr/logs/:/home/isr/logs -v $(pwd)/isr/weights/:/home/isr/weights -v $(pwd)/isr/config.yml:/home/isr/config.yml -it isr -t -d -c config.yml

<data_name> is the name of the folder containing your dataset. It must be under ./data/<data_name>.


The log folder is mounted on the docker image. Open another EC2 terminal and run

tensorboard --logdir /home/ubuntu/isr/logs

and locally

docker-machine ssh <name-of-ec2-instance> -N -L 6006:localhost:6006


A few helpful details - DO NOT include a Tensorflow version in requirements.txt as it would interfere with the version installed in the Tensorflow docker image - DO NOT use Ubuntu Server 18.04 LTS AMI. Use the Ubuntu Server 16.04 LTS AMI instead

Train locally

Train locally with docker

From the main project folder run

docker run -v $(pwd)/data/:/home/isr/data -v $(pwd)/logs/:/home/isr/logs -v $(pwd)/weights/:/home/isr/weights -v $(pwd)/isr/config.yml:/home/isr/config.yml -it isr -t -d -c config.yml