A convolutional neural network for colorizing black white pictures inspired by: https://arxiv.org/abs/1603.08511. This implementation is based on Keras and Tensorflow.
The net consists of 8 subsequent convolutional blocks, each consisting of 2 convolutional layers using leaky ReLU, one Dropout Layer and one Batch Normalization Layer. There are approx. 12,8 Millon trainable parameters.
There are 5 downsampling blocks, the first of them are bisecting the widht and height of the image by using a stride of 2 in the last convolutional layer. At the same time, the number of filters in increased each time. The last 2 blocks are upsampling blocks that double the size of the inputs while at the same time reducing the number of filters (see model.png for the exact architecture).
The trained version of the network can be downloaded here.
Because of limited hardware resources (no GPUs), the model has been trained on only 1024 images from the ImageNet Dataset. Traing was executed on a Google Compute Engine VM for approx. 3 days. (approx 150.000 epochs). It does not generalize very well on images it has never seen before.
To use the model, first place the weigths file (cp.h5) into the /model/checkpoints directory.
For applying the model to an image of your choice, run
python3 colorize.py -image myimage.jpgHere are some images from the ImageNet dataset for which the algorithm worked especially well:
While the Net performs quite well on some images, there are also some it fails on:
