Implementation of deep learning model in Keras for superresolution. Model is still in experimental phase (current results below).
Input image (left), predicted image (center), ground truth (right). Results after <2h of training on NVIDIA Quadro P5000 with 128x128 input size with 2x upscale factor.
- Python 3.x
- pip
Install pip packages using
$ pip install -r requirements.txt
Add .env file to project root with environmental variables
COMET_PROJECTNAME={comet_project_name}
COMET_WORKSPACE={comet_workspace}
COMET_API_KEY={comet_api_key}
[optional]
There is a Docker image included that was used for training in cloud. You can build it from local Dockerfile with
docker build -t ml-box .
or get it from Docker Hub
docker pull tomikeska/ml-box
Train model using command
$ python src/train.py
Code is released under the MIT License. Please see the LICENSE file for details.