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MIT License

Copyright (c) 2023 Machado E Silva Matheus

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# GAN & cGAN tutorial.

We recommand to use the notebook (.ipynb) but the Python script (.py) is also provided if more convenient for you.

Matheus MACHADO E SILVA

# GAN 

GAN is a generative adversarial network that we use to generate new handwritten digits, after feeding it with pictures from the MNIST database;

To reduce the time of execution we use Google Colab GPU;

The MNIST dataset uses black and white images so to adapt this tutorial to the MNIST database, I changed the number of channels for 1 (in the tutorial was 3 because of RGB data used);

![Results GAN](/Result_partie1.png)

# cGAN tutorial.

The idea of this project is to use cGAN (a conditional generative adversarial network) to identify and juge if building facades images are real or generated by a model;

The data used is from the CMP Facade Database;

The images were generated by a generator (with a mask from the database)

# How to submit your Work ?

The results are showed below:

This work must be done individually. The expected output is a repository named gan-cgan on https://gitlab.ec-lyon.fr. It must contain your notebook (or python files) and a README.md file that explains briefly the successive steps of the project. The last commit is due before 11:59 pm on Wednesday, March 29, 2023. Subsequent commits will not be considered.

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![Results cGAN](/Result_partie2.png)