Welcome to the GAN_Scratch repository! This project contains a GAN (Generative Adversarial Network) model built from scratch, designed to provide a hands-on learning experience in GAN fundamentals. The repository is structured to enable contributors to build, train, and experiment with custom GAN implementations and enhance them with various functionalities.
Generative Adversarial Networks (GANs) are powerful neural networks used for generating synthetic data that resembles real-world data. This project is focused on image generation and includes a series of issues for contributors to solve, guiding them through essential GAN concepts, optimizations, and practical implementations. By following the issues, you’ll learn how to optimize, extend, and evaluate GAN models step-by-step.
- Python 3.8+: Ensure you have an up-to-date version of Python.
- PyTorch 1.7+: This project is built on PyTorch, a popular deep learning framework.
- CUDA (Optional): GPU support is recommended for faster training times with CUDA.
- Clone the Repository:
git clone https://github.com/your-username/GAN_Scratch.git cd GAN_Scratch
Install Dependencies: PyTorch installation depends on your system configuration. Refer to the official PyTorch installation guide to set it up.
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For the other dependencies, you can install them with:
pip install numpy torchvision
Run main.py: The main.py file in GAN and DCGAN contains the core GAN implementation from scratch.
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main.py: The primary GAN and DCGAN code. This file implements a GAN model from scratch and includes essential functions for training and image generation. The code is structured with clear functions for the generator and discriminator, along with a training loop.
The Issues tab on this repository contains a series of tasks designed to guide contributors through improvements and feature extensions. Issues are arranged in a logical order, with some tasks dependent on the completion of previous ones. It’s recommended to tackle them sequentially.
We welcome contributions from beginners and experts alike. This project is aimed at providing a learning experience, so don’t hesitate to try out the issues and contribute your solutions. Feel free to create new issues or pull requests if you have ideas for improving this repository.