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 # gan-cgan
 
 This repository contains the work corresponding to the BE2 of the course MSO 3.4 - Deep structured Learning, by Julie Guerra.
-It contains the Python notebook with the code and the answers to the theoritical questions, and two images showing results.
+It contains the Python notebook with the code and the answers to the theoritical questions, and three images showing results in the img directory.
 
 The images showing the results should be in the Python notebook, but just in case they aren't, the main results are below.
 
 ## First part: DC-GAN
 
-The process for this assignment started with working on a DC-GAM with a database of handwritten numbers. Here is the result for 5 epochs of training:
+The assignment started by working on a DC-GAN with a database of handwritten numbers. Here is the result for 5 epochs of training:
 
-![results_part1](https://gitlab.ec-lyon.fr/guerraj/gan-cgan/-/raw/main/results_part1.png)
+![results_part1](https://gitlab.ec-lyon.fr/guerraj/gan-cgan/-/raw/main/img/results_part1.png)
 
 ## Second part: cGAN
 
-The second part consisted in working on a Conditional GAN (cGAN) for the construction of the image of a building from a template. I built the cGAN and trained it for 200 epochs, saving the model for 100 and 200 epochs. Here is the comparison of the result of the network for 100 and 200 epochs, on an image from the validation dataset:
+The second part consisted in working on a Conditional GAN (cGAN) for the construction of the image of a building from a template. I built the cGAN and trained it for 200 epochs, saving the model for 100 and 200 epochs. Here is the comparison of the result of the network for 100 and 200 epochs, on two images from the validation dataset:
 
-![results_part2](https://gitlab.ec-lyon.fr/guerraj/gan-cgan/-/raw/main/results_part2.png)
+![results_part2](https://gitlab.ec-lyon.fr/guerraj/gan-cgan/-/raw/main/img/results_part2.png)
+![results2_part2](https://gitlab.ec-lyon.fr/guerraj/gan-cgan/-/raw/main/img/results2_part2.png)