AUTOMATIC PICTURE COLORIZER HOW TO
AUTOMATIC PICTURE COLORIZER FULL

Under the Jobs tab on the FloydHub website, click on the Jupyter Notebook link and navigate to this file: floydhub/Alpha version/working_floyd_pink_light_full.ipynb This will make it approximately 50x faster If you have GPU credit, you can also add the GPU flag -gputo your command.We ran the job in Jupyter Notebook mode with -mode jupyter.We enabled Tensorboard with -tensorboard.You can explore and use this dataset (and many other public datasets) by viewing it on FloydHub dataemilwallner/datasets/colornet/2:data We mounted a public dataset on FloydHub (which I’ve already uploaded) at the datadirectory with the below line:.Okay, let’s run our job: floyd run -data emilwallner/datasets/colornet/2:data -mode jupyter -tensorboard Once that's done, go back to your terminal and run the same initcommand.

You will be prompted to create a new FloydHub project called colornet. The FloydHub web dashboard will open in your browser. cd Coloring-greyscale-images-in-Keras/floydhub floyd init colornet Once FloydHub is installed, use the following commands: git clone It’s the best (and easiest) way to train deep learning models on cloud GPUs. You can also make a local FloydHub installation with their 2-min installation, watch my 5-min video tutorial or check out my step-to-step guide.

AUTOMATIC PICTURE COLORIZER FULL

Run the code on FloydHubĬlick here to open a Workspace on FloydHub where you will find the same environment and dataset used for the Full version. We’ll start by stacking hundreds of filters and narrow them down into two layers, the a and b layers.īefore we get into detail into how it works, let’s run the code. The network can either create a new image from a filter or combine several filters into one image.įor a convolutional neural network, each filter is automatically adjusted to help with the intended outcome. They can highlight or remove something to extract information out of the picture. Each filter determines what we see in a picture. Think of them as the blue/red filters in 3D glasses. To turn one layer into two layers, we use convolutional filters. The values span from 0–255, from black to white. Each pixel has a value that corresponds to its brightness.

AUTOMATIC PICTURE COLORIZER HOW TO

In this section, I’ll outline how to render an image, the basics of digital colors, and the main logic for our neural network.īlack and white images can be represented in grids of pixels. You can also check out the three versions on FloydHub and GitHub, along with code for all the experiments I ran on FloydHub’s cloud GPUs. If you want to look ahead, here’s a Jupyter Notebook with the Alpha version of our bot. To make the coloring pop, we’ll train our neural network on portraits from Unsplash. We’ll use an Inception Resnet V2 that has been trained on 1.2 million images. We’ll be able to color images the bot has not seen before.įor our “final” version, we’ll combine our neural network with a classifier. The next step is to create a neural network that can generalize - our “beta” version. This well help us become familiar with the syntax.

There’s not a lot of magic in this code snippet. We’ll build a bare-bones 40-line neural network as an “alpha” colorization bot. The first section breaks down the core logic. I’ll show you how to build your own colorization neural net in three steps. Yet, if you’re new to deep learning terminology, you can read my previous two posts here and here, and watch Andrej Karpathy’s lecture for more background. A face alone needs up to 20 layers of pink, green and blue shades to get it just right. In short, a picture can take up to one month to colorize.

Automatic picture colorizer