Are you ready to dive into the world of image-to-image translation using the Group-wise Deep Whitening-and-Coloring Transformation (GDWCT)? This blog post is your go-to guide for getting started with GDWCT, built using the powerful PyTorch framework. We’ll walk through the installation, dataset preparation, and running the model with ease!

Prerequisites

Before we jump into the installation, you’ll need to ensure your system is equipped with the following:

• Python 3.6
• PyTorch 0.4.0 or higher
• Linux operating system
• NVIDIA GPU with CUDA and CuDNN installed

Installation Steps

Follow these steps to get your GDWCT environment up and running:

1. Clone the GDWCT repository from GitHub:

git clone https://github.com/WonwoongCho/GDWCT.git

2. Navigate to the cloned directory:

cd GDWCT

Dataset Preparation

GDWCT requires several datasets for training and testing. Here’s how to prepare them:

1. Artworks Dataset

Download the Artworks dataset by visiting the CycleGAN GitHub repository. Make sure to download these specific datasets: monet2photo, cezanne2photo, ukiyoe2photo, and vangogh2photo.

2. CelebA Dataset

The data loader needs to arrange the CelebA dataset into subdirectories named trainA, trainB, testA, and testB. To accomplish this:

• Download the CelebA dataset.
• Run the following script to preprocess the data:

bash download.sh celeba

Separate the data according to the target attribute of translation, e.g., A: Male and B: Female.

3. BAM Dataset

The BAM dataset also requires preprocessing. You can download it by fulfilling a segmentation labeling task. Visit the BAM dataset link for detailed instructions.

Training and Testing

Once the datasets are ready, you can set various configurations in the config.yaml file. To train or test GDWCT, use the following command:

python run.py

Using Pretrained Models

To download pretrained models (such as Smile vs. Non-Smile or Bangs vs. Non-Bangs), run:

bash download.sh pretrained

When testing pretrained models, remember to modify several options in the config file as shown below:

N_GROUP: 4
SAVE_NAME: CelebA_Bangs_G4
MODEL_SAVE_PATH: pretrained_models
START: 320000
LOAD_MODEL: True
MODE: test

Results

Throughout the project, you might come across various results visualized in your output as images. Each result captures how GDWCT performs against other baseline models across datasets like CelebA and Artworks.

Troubleshooting Tips

If you encounter issues during installation or training, here are some troubleshooting ideas:

• Ensure that your versions of Python and PyTorch are compatible.
• Confirm that all required datasets are properly organized in their respective directories.
• If training takes too long, consider checking your GPU settings and CUDA installation.
• Don’t hesitate to reach out on community forums or explore fxis.ai for additional insights.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Happy coding!