It's time to build a coloring deep neural network or colornet. As discussed in the previous section, if we utilize an alternate colorspace, such as LAB (or YUV), we can transform the colorization task into a mathematical transformation. The transformation is as follows:
![](https://static.packt-cdn.com/products/9781788831307/graphics/assets/3a9b6edb-d7da-4cc8-9007-9ebd67ed7f35.png)
Mathematical formulations and creativity are fine, but where are the images to learn these transformations? Deep learning networks are data hungry, but luckily, we have a huge collection of diverse images from various open source datasets. For the purposes of this chapter, we will rely on a few sample images from ImageNet itself. Since ImageNet is a huge dataset, we have randomly selected a few color images for our problem statement. In later sections, we will discuss why we selected this subset and a few of its nuances.
We relied upon the image-extraction utility...