SiddhiVTripathi/ColorCapsNet

Fully automatic (no guidance) image colorization with Capsule Networks (ColorCapsNet).

Image Colorization By Capsule Networks

Reproducing the results
Instructions

1. Before start training, datasets should be generated by command below:

    ./generate_patch_pairs.py
   

   If asked, datasets (ILSVRC 2012 and DIV2K) may be provided as npz files:

    ILSVRC 2012 		-> train_9_9_4_4.npz					
    DIV2K (train)		-> train_9_9_4.npz
    DIV2K (validation)	-> valid_9_9_4.npz
   

2. Training is performed by commands below:

    ./colorizer.py --batch_size 128 --complexity 6 --datapath ./train_9_9_4_4.npz --dataset ntire --epochs 10 --loss mse --optimizer adam --routings 1 --train --save --run 1
    ./colorizer.py --batch_size 128 --complexity 6 --datapath ./train_9_9_4.npz --dataset ntire --epochs 10 --loss mse --optimizer adam --routings 1 --train --save --run 2 --pretrained_model runs/1/weights-10.h5
    ./colorizer.py --batch_size 128 --complexity 6 --datapath ./valid_9_9_4.npz --dataset ntire --epochs 10 --loss mse --optimizer adam --routings 1 --train --save --run 3 --pretrained_model runs/2/weights-10.h5
   

3. Validation/testing is performed by commands below:

    ./colorizer.py --complexity 6 --epochs 10 --routings 1 --save --predict --testpath Validation_gray/ --run 3
    ./colorizer.py --complexity 6 --epochs 10 --routings 1 --save --predict --testpath Test_gray/ --run 3
   

Here, the model file runs/3/weights-10.h5 is used to predict all png files under Validation_gray or Test_gray directories.

Citation

@InProceedings{Ozbulak_2019_CVPR_Workshops,
author = {Ozbulak, Gokhan},
title = {Image Colorization by Capsule Networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}