ColorNet Image Colorization
Trained on
Places Data
Released in 2016, this net automatically colorizes a grayscale image, exploiting a combination of local and global image features. Local features are extracted in a fully convolutional fashion, while the extraction of global features was developed leveraging the labels of the Places dataset during training.
Number of layers: 62 |
Parameter count: 45,505,890 |
Trained size: 182 MB |
Examples
Resource retrieval
Get the pre-trained net:
Evaluation function
This net takes a grayscale image as input and outputs the A and B channels in the LAB color space. It needs an evaluation function to merge its output with the luminance of the input:
Basic usage
Colorize a grayscale image using the evaluation function:
Performance evaluation
Get a color image:
Compare the colorization performed by the net with the ground truth:
Net information
Inspect the number of parameters of all arrays in the net:
Obtain the total number of parameters:
Obtain the layer type counts:
Display the summary graphic:
Export to MXNet
Export the net into a format that can be opened in MXNet:
Export also creates a net.params file containing parameters:
Get the size of the parameter file:
The size is similar to the byte count of the resource object:
Represent the MXNet net as a graph:
Requirements
Wolfram Language
11.2
(September 2017)
or above
Resource History
Reference