Skip to content

Latest commit

 

History

History
46 lines (35 loc) · 1.92 KB

README.md

File metadata and controls

46 lines (35 loc) · 1.92 KB
Raw

CS7643-Project

This repository contains code for the CS7643 Deep Learning Project.

CycleGAN

There are two CycleGAN notebooks that implement Cycle GAN on a reduced version of the monet2photo dataset. These notebooks also test the implementation using the following metrics:

  • SSIM (Structural Similarity Index)
  • PSNR (Peak Signal-to-Noise Ratio)
  • Feature-Based
  • LPIPS (Learned Perceptual Image Patch Similarity)
  • FID (Fréchet Inception Distance) with 192 dimensions
  • ArtFID (Artwork Fréchet Inception Distance) with 192 dimensions

One notebook provides examples of single-image generation, while the other notebook evaluates the quality of generation across the content images of the dataset.

Neural Style Transfer (NST)

  • The Gatys et al. Neural Style Transfer (NST) model is implemented in style_transfer.py as a class that can be imported. It utilizes some modules coded in module.py.

  • The Jupyter notebook nst_experiments.ipynb contains tests and examples demonstrating how to use the style transfer model and how to evaluate its outputs using the class implemented in eval_metrics.py.

  • The evaluation process in NST follows the same metrics used in CycleGAN.

How to use the new style_transfer.py with the best hyperparameters:

style_transfer = StyleTransfer("path_to_content_img.jpg", "path_to_style_img.jpg")

output = style_transfer.run_style_transfer(
        style_transfer.content_img,
        style_transfer.style_img,
        style_transfer.content_img.clone(),
        num_steps=300,
        style_weight=1e6,
        content_weight=0.5,
        content_layers=['relu_1'],
        style_layers=['relu_1', 'relu_2', 'relu_3', 'relu_4', 'relu_5'],
        tv_weight = 0.0,
        optimizer_choice='lbfgs',
        loss_choice='generic'
    )

# Add code to save or plot the result image

Datasets:

  • Ensure that you have the evaluation dataset to run tests on the models, particularly for monet2photo testing.