AMIP Project

Members Iantsa Provost, Lilian Rebiere-Pouyade, Bastien Soucasse, and Alexey Zhukov.

Paper Perceptual Losses for Real-Time Style Transfer and Super-Resolution.

Remotes GitHub, GitLab (CREMI, Université de Bordeaux)

Report

The report can be found in the report subfolder, containing the latest compiled PDF and the sources.

• references.bib is the references source file.
• report.pdf is tha latest compiled report.
• report.tex is the LaTeX main source file.

The images subfolder contains the images used in the report.tex source file.

To compile from the sources, you need a LaTeX compiler such as TeXLive.

Implementation

The actual implementation sources are in the src subfolder.

• datasets.py is the custom dataset implementation file.
• environment.py is the global parameters and variables file.
• models.py is the Image Transformer Network (and its blocks) and the Loss Network implementation file.
• sr_dataset.ipynb is the custom dataset experiments file.
• sr.ipynb is the model experiments file.
• test.py is the testing script implementation file.
• train.py is the training script implementation file.
• utils.py is an utilitary file.

PyTorch was used for the implementation. The data subfolder will contain the image data used for training, i.e., our custom dataset. The models subfolder will store the saved models after training (used for testing).