Merge pull request #2 from renan-siqueira/develop

feat: update README

README.md

@@ -1 +1,55 @@
-# GAN Research
+# GAN Research with Dual Generators
+
+This project presents a novel approach to Generative Adversarial Networks (GANs) by employing two generators in competition with each other and against a common discriminator.
+
+## Overview
+
+Traditional GANs use a single generator and discriminator to learn and generate realistic images. This project explores the use of two  generators that both compete against a common discriminator. The aim is to study the performance and characteristics of dual generators in GAN training.
+
+## Features
+
+- Dual Generator Architecture.
+- Use of gradient penalty for improved training stability.
+- Dynamic directory creation for model checkpoints and generated samples.
+- Configurable parameters through a JSON file.
+- GPU support detection.
+
+## Getting Started
+
+### Prerequisites
+
+- Python 3.x
+- PyTorch
+- torchvision
+- tqdm
+
+### Installation
+
+1. Clone the repository.
+2. Install the required packages.
+
+[*See my other projects for more details about setup and configuration](https://github.com/renan-siqueira/my-own-WGAN-GP-implementation)
+
+### Usage
+
+1. Update the `src/settings/settings.py` with the correct paths.
+
+2. Configure the training parameters in `src/json/params.json`.
+
+3. Execute the training:
+
+```bash
+python run.py
+```
+
+## Structure
+
+- `run.py`: Entry point for training.
+- `src/app/training.py`: Contains training-related functions.
+- `src/utils/utils.py`: Utility functions.
+- `src/json/params.json`: Training parameters in JSON format.
+- `src/settings/settings.py`: Path settings.
+
+## License
+
+This project is licensed under the MIT License.