Commit e00d6c3c authored by Rita-Josy Haddoub's avatar Rita-Josy Haddoub

Update README.md

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The Beton Dataset is a collection of self-taken photos of _Béton_ spontaneously found throughout the city of Beirut and its outskirts.
Scraping images from large online databases is the conventional way to collect images to work with GANS. Deep learning goes hand in hand with big data, as it deals with extracting meaningful information from a vast amount of information. Memo Aktins Deep Mediations illustrates GAN’s as a powerful medium for creative expression. Where his image dataset consist of ‘everything’. All scraped from Flickr with tags such as ‘life, love, faith, everything, etc.’.
Scraping images from large online databases is the conventional way to collect images to work with GANS. Deep learning goes hand in hand with big data, as it deals with extracting meaningful information from a vast amount of information. Memo Aktins [Deep Mediations](https://www.memo.tv/works/deep-meditations/) illustrates GAN’s as a powerful medium for creative expression. Where his image dataset consist of ‘everything’. All scraped from Flickr with tags such as ‘life, love, faith, everything, etc.’.
The power of GANs to learn the distribution of all of its input data, and to find forms, compositions, and patterns within its endless network of possible trajectories makes it a fascinating tool to understand big data better, and distinguishes it from other algorithms.
My raw image dataset currently consists of about only 40 original images. The model however has been trained on a total of 274 images, consisting of re-sized, cropped, rotated, and transformed adjustments from the original dataset. I then ran a batch process to rescale the images to 256x256. The method used for training is ‘inpainting’ from pix2pix-tensorflow.
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