Fake Image Generator - Generative Adversarial Network for Custom Dataset: Generative Models

Overview

FakeImageGenerator (GAN-based Project for Synthesizing Grayscale images) is a machine learning project focused on generating synthetic images using Generative Adversarial Networks (GANs). Specifically, it is designed to work with the Custom dataset, a large database of handwritten digits commonly used for training various image processing systems.

Features

  • Utilizes PyTorch for implementing GAN models.
  • Provides scripts for easy training and generating synthetic images.
  • Command Line Interface for easy interaction.
  • Includes a custom data loader for the Custom dataset.
  • Customizable training parameters for experimenting with GAN.

Installation

Clone the repository:

git clone https://github.com/atikul-islam-sajib/FakeImageGenerate.git

cd FakeImageGenerate

Install dependencies

pip install -r requirements.txt

Usage

Examples of commands and their explanations.

python /path/to/FakeImageGenerate/src/cli.py --help

Options

  • --batch_size BATCH_SIZE: Set the batch size for the dataloader. (Default: specify if there's one)
  • --image_path: Define the dataset path.
  • --epochs EPOCHS: Set the number of training epochs.
  • --latent_space LATENT_SPACE: Define the size of the latent space for the model.
  • --lr LR: Specify the learning rate for training the model.
  • --num_samples SAMPLES: Determine the number of samples to generate after training.
  • --test: Run tests with synthetic data to validate model performance.
  • --device: Train the model with CPU, GPU, MPS.
  • --display: Display the critic loss and generator loss in each iterations[True/False]

Training and Generating Images(CLI)

Training the GAN Model

To train the GAN model with default parameters with mps:

!python /content/FakeImageGenerate/src/cli.py  --epochs 200 --latent_space 100 --image_size 64  --lr 0.00005 --device mps --batch_size 64 --image_path /image.zip/ --display True

To train the GAN model with default parameters with gpu:

!python /content/FakeImageGenerate/src/cli.py  --epochs 200 --latent_space 100 --image_size 64  --lr 0.00005 --device gpu --batch_size 64 --critic_steps 5 --image_path /image.zip --display True

To train the GAN model with default parameters with cpu:

!python /content/FakeImageGenerate/src/cli.py  --epochs 200 --latent_space 100 --image_size 64 --lr 0.00005 --device cpu --batch_size 64 --image_path /image.zip --display True

Generating Images

To generate images using the trained model:

!python /content/FakeImageGenerate/src/cli.py --num_samples 20 --latent_space 100 --test

Viewing Generated Images

Check the specified output directory for the generated images.

from IPython.display import Image
Image(filename='/content/FakeImageGenerate/outputs/fake_image.png')

Core Script Usage

The core script sets up the necessary components for training the GAN. Here's a quick overview of what each part does:

from src.dataloader import Loader
from src.generator import Generator
from src.discriminator import Discriminator
from src.trainer import Trainer
from src.test import Test

# Initialize the data loader with batch size
loader = Loader(
    image_path = "/content/drive/MyDrive/anime.zip",
    batch_size = 128,
    image_height = 64,
    image_width = 64,
    normalized = True)

    dataloader = loader.extract_features()

    loader.create_dataloader()

# Set up the trainer with learning rate, epochs, and latent space size
trainer = Trainer( device = "cuda", latent_space = 100, image_size = 64, lr = 0.0002, epochs = 20, display = True)

trainer.train()

# Test the generated dataset and display the synthetic images
test = Test( latent_space=100, num_samples =20, device="cuda")

test.test()

from IPython.display import Image

Image("/content/FakeImageGenerate/outputs/fake_image.png")

This script initializes the data loader, downloads the Custom dataset, and prepares the data loader. It then sets up and starts the training process for the GAN model.

Documentation

For detailed documentation on the implementation and usage, visit the FakeImageGenerator Documentation.

Notebook Training

For detailed documentation on the implementation and usage using notebook, visit the Notebook for CLI.

For detailed documentation on the implementation and usage using notebook, visit the Notebook for Import Modules.

Contributing

Contributions to improve the project are welcome. Please follow the standard procedures for contributing to open-source projects.

License

This project is licensed under MIT LICENSE. Please see the LICENSE file for more details.

Acknowledgements

Thanks to all contributors and users of the GPWGAN project. Special thanks to those who have provided feedback and suggestions for improvements.

Contact

For any inquiries or suggestions, feel free to reach out to [atikulislamsajib137@gmail.com].