This project uses Keras and Python to convert a grayscale image to color without any additional information.
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comparison Add files via upload Oct 17, 2017
fruit_colorizer Add files via upload Oct 17, 2017
landscape_colorizer
LICENSE Rename LICENSE.txt to LICENSE Oct 17, 2017
README Update README Oct 17, 2017

README

Image Colorization

== Notes ==
Note1 : Some architectures need very large memory
(We used AWS EC2 P2 instance that has 61 GB memory to do the experiments)

Note2 : Our results can be view from google drive:
https://drive.google.com/open?id=0BzxDU3VAcOkQck9rYThkd3JmQVE

Execution time : Run at least 30 minutes (1 hour for some larger architecture) for each code

Results : View the result image in corresponding folder namde predict_output_(architecture)

== Dependencies ==
OS: Ubuntu 16.04
Software: python 
Deep learning framework: Theano, Keras
Dependencies: openCV

== Dataset Download Links: ==
Since we rearranged the dataset, we uploaded the dataset to the following links

1.fruitdata : https://drive.google.com/open?id=0BzxDU3VAcOkQUVRhTlJ6SXlIYnc

2.combined : https://drive.google.com/open?id=0BzxDU3VAcOkQTnpmZjA2aGszWGM

After download the dataset, fruitdata should be put into the fruit_colorizer folder,
and combined should be put into landscape_colorizer folder



== Run Code ==
1.For training and doing predicting for Fruit dataset:
$ cd fruit_colorizer

$ colorization_auto_encoder.py             # Use deep auto-encoder architecture 
$ colorization_auto_encoder_shallow.py     # Use shallow auto-encoder architecture 


2.For training and doing predicting for Open Country(Landscape) dataset:
$ cd landscape_colorizer

$ python colorization_use_pretrained_conv_layers.py          # Use our pre-trained conv-layer and Dense layer.
                                                             # This is our baseline model

$ python colorization_concate_pretrained_conv_layers.py      # Use concat conv layer architecture
$ python colorization_auto_encoder.py                        # Use auto-encoder architecture
$ python colorization_use_vgg.py                             # Use vgg as feature extractor
$ python colorization_use_concate_vgg.py                     # Use concat vgg conv layers architecture

3. For comparison between color images and gray scale images testing on a VGG-16 fine-tuned network. The result shows loss and accuracy.
$ cd comparison
$ python vgg16_cifar_RGB_gray_comparison.py