Colorizing B&W Photos with Neural Networks
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models Colornet v0 Jun 15, 2018
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.gitignore Serve (#1) Jul 25, 2018
README.md add serve example Jul 25, 2018
app.py Serve (#1) Jul 25, 2018
colornet.ipynb Fix freeze Sep 27, 2018
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serving.py Serve (#1) Jul 25, 2018
support.py Serve (#1) Jul 25, 2018

README.md

Colornet

Today, colorization is done by hand in Photoshop, a picture can take up to one month to colorize. It requires extensive research. A face alone needs up to 20 layers of pink, green and blue shades to get it just right. But something changed this year when Amir Avni used neural networks to troll the subreddit /r/Colorization - a community where people colorize historical black and white images manually using Photoshop. They were astonished with Amir’s deep learning bot - what could take up to a month of manual labour could now be done in just a few seconds.

Try it now

Run on FloydHub

Click this button to open a Workspace on FloydHub that will train this model.

Colorizing Black&White photos

Fascinated by Amir’s neural network, Emill reproduced it and documented the process in the famous blog post: Colorizing B&W Photos with Neural Networks. In this notebook we will reproduce Emil's work by using the Full Version of his experiments.

colorization The middle picture is done with our neural network and the picture to the right is the original color photo - Image from the Blog

We will:

  • Preprocess the image data for this CV task
  • Build and train the colornet model using Keras and Tensorflow
  • Evaluate our model on the test set
  • Run the model on your own black&white and colored pictures!

Serve an interactive web page for your own model

You can easily spin up a serve job on FloydHub to demo your model through an interactive web site. Just run the following command from workspace terminal or your local machine:

floyd run --mode serve

You should be able to see the following page when visiting the FloydHub serve url:

serve-example