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At this point, we were thinking to ourselves: this stuff works.
Let us go ahead and try it on a bunch of well-known public figures.
Keep in mind that despite popular beliefs deep learning is not a magical crystal ball that tells you where a person is located in the gender spectrum.
The model is as good as its input, which in this case is the visuals of a person’s face.
Curious readers might notice that many time the validation loss is lower than training loss. First, Keras calculates the training loss by averaging the losses of each batch and validation loss at the end of all batches.
In this article, we are going to use the terms as those who listed themselves as male-to-female transgender.
The most readily available sources for frontal-face images are online dating sites.
Unfortunately for us, these websites are usually tailored for specific genders and it was almost impossible to find a website with a sizeable number of girl and ladyboy profiles.
But as you can see, we still end up with a girl dataset that is a little more spread out in terms of age.
Since VGG-Face takes input of a 224-by-224-pixel frontal face image, we need some preprocessing to crop out all those faces and fit them to the dimensions required.
Thus, we needed to scraped 8,038 public profiles of girls from Date in Asia and 8,153 public profiles of ladyboys from My Ladyboy Date.