diff --git a/README.md b/README.md index 548f816b0d48cbf43bee983a90c5afd0bd8c974c..05fc88abcb61ad7bd286e3f6dca162ebc600d179 100644 --- a/README.md +++ b/README.md @@ -9,8 +9,14 @@ GAN is a generative adversarial network that we use to generate new handwritten 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); + + # cGAN tutorial. -cGAN is a conditional generative adversarial network (cGAN) and in this project I trained it to identify if building facades images have generated by a model or are real; +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; -To generate the facade images, a mask, already included in the database, passes through the generator, which delivers as output a believable image. +The images were generated by a generator (with a mask from the database) + +The results are showed below: + + diff --git a/Result_partie1.png b/Result_partie1.png new file mode 100644 index 0000000000000000000000000000000000000000..0e80c1c9afc0912e55d1f14289a480d2f758594b Binary files /dev/null and b/Result_partie1.png differ diff --git a/Result_partie2.png b/Result_partie2.png new file mode 100644 index 0000000000000000000000000000000000000000..797127470ffdc248b134952156a0babc5b47af44 Binary files /dev/null and b/Result_partie2.png differ