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Automatic Image Colorization

PRs Welcome License: GPL v3 Template from jarvis


This is a TensorFlow implementation of the Residual Encoder Network based on Automatic Colorization and the pre-trained VGG16 model from

For latest TensorFlow with estimator support, check tf-1.12 branch. (still under development, the training code is working now)


  • config variables like batch size, training_iters and so on
  • all functions related to image manipulation
  • all functions related to input
  • the residual encoder model
  • the common part for training and testing, which is mainly the workflow for this model
  • train the residual encoder model using TensorFlow built-in AdamOptimizer
  • test your own images and save the output images

TensorFlow graph


How to use

  • Download pre-trained VGG16 model vgg16.npy to vgg folder

  • Option 1: Use pre-trained residual encoder model

    • Download model here
    • Unzip all files to summary_path (you can change this path in
  • Option 2: Train your own model!

    1. Change the batch_size and training_iters if you want.
    2. Change training_dir to your directory that has all your training jpg images
    3. Run python
  • Test

    1. Change testing_dir to your directory that has all your testing jpg images
    2. Run python






GNU GPL 3.0 for personal or research use. COMMERCIAL USE PROHIBITED.

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