![]() When coloring images, a horizontal line might have a different meaning than a vertical line. When detecting a face in an image, we want to detect it even when the image is rotated. Usually in image detection, it’s common to rotate and mirror the images to gain more data. To avoid this, I took an image every 0.5 seconds. A long dialogue, for example, might cause a model to overfit. If you watch the movie (and you should) you can see that there are scenes where the picture doesn’t really change. However, the problem is that most of the good pictures are basically the same image and not all of the results are really related to Frozen. Searching for Frozen images in Google returned some good results. Starting from a color picture and applying filters to make it look like it was taken from a coloring book. ![]() So, my solution was to go the opposite way. While I would really like to color thousands of coloring books and scan them, I don’t have the time to do that. In my case, the training dataset is made of images before and after they were colored. My theory is that Iron Man will be blue and Moana will be colored with a white color scheme and bright hair color. In my experiment, I want to have a model trained only on images from the movie Frozen and use this to model to color other cartoons, including Iron Man and Moana. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. Transfer learning is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, if the training set contains only pictures taken at night, it will never color the sky as blue. The main goal of this post is to train a few models to color the same images as Gili, but each network will “witness” different color combinations and will probably have different results coming from a different point of view. After verifying that Gili isn’t color blind, my thoughts were that she was probably choosing colors I wouldn’t because I have seen more color combinations and patterns than she has (elderly wisdom) and I was using this knowledge while coloring with her. While playing with her, I noticed that she kept choosing different colors from the ones that I would choose. The problem is that my nephew, Yali, is playing chess and he is freakishly good at it.įortunately, Yali’s younger sister Gili is into coloring books. So, I asked him what game he’s playing at the moment and hoped to write something that can also play it (and maybe even better them him). I’ve made it a routine to try and create AI that competes with my nephew in games he’s playing (just like in my previous posts). After a few months with no side projects on my plate, I was eager to create something new.
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