Image Colorizer

Overview

This model is a Generative Adversarial Network (GAN) that was trained by the IBM CODAIT Team on COCO dataset images converted to grayscale and produces colored images. The input to the model is a grayscale image (jpeg or png), and the output is a colored 256 by 256 image (increased resolution will be added in future releases). The model is based on Christopher Hesse’s Tensorflow implementation of the pix2pix model.

Model Metadata

Domain Application Industry Framework Training Data Input Data Format
Vision Image Coloring General TensorFlow COCO Dataset Images

References

Licenses

Component License Link
Model GitHub Repository Apache 2.0 LICENSE
Model Code (3rd party) MIT TensorFlow pix2pix Repository
Model Weights Apache 2.0 LICENSE
Test Assets CC0 License Samples README

Options available for deploying this model

  • Deploy from Dockerhub:

    docker run -it -p 5000:5000 codait/max-image-colorizer
    
  • Deploy on Red Hat OpenShift:

    Follow the instructions for the OpenShift web console or the OpenShift Container Platform CLI in this tutorial and specify codait/max-image-colorizer as the image name.

  • Deploy on Kubernetes:

    kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Image-Colorizer/master/max-image-colorizer.yaml
    

    A more elaborate tutorial on how to deploy this MAX model to production on IBM Cloud can be found here.

  • Locally: Follow the instructions in the model README on GitHub

Example Usage

Once deployed you can upload an image using the UI:

Swagger Doc Screenshot

You can also test the model from the command line. For example:

curl -F "image=@samples/bw-city.jpg" -XPOST http://localhost:5000/model/predict > result.png && open result.png

Resources and Contributions

If you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions here.