Welcome to the Dataset Class Equalizer repository, developed by the ISL Intelligent Systems Lab. This tool is designed to help balance class distributions in datasets, which is particularly useful for enhancing the performance of machine learning models affected by class imbalance.

• Class Distribution Analysis: Provides tools to analyze the distribution of classes within your dataset.
• Equalization Process: Automates adjustments to datasets to achieve balanced class distributions.
• Customization: Offers configurable parameters to tailor the process to specific needs.

To set up the Dataset Class Equalizer on your system, start by cloning this repository:

git clone https://github.com/ISL-INTELLIGENT-SYSTEMS-LAB/Dataset_Class_Equalizer.git

Ensure you have Python 3.11 or later installed. Then install the required libraries using:

pip install -r requirements.txt

Dependencies include Pillow and tqdm which are listed in the requirements.txt file.

Follow these steps to utilize the Dataset Class Equalizer:

1. Prepare Your Dataset: Format your data in a manner compatible with the tool.
2. Configure: Modify the settings when prompted when running entry.py
3. Run the Tool: Execute the script to start the equalization process:

python entry.py

We appreciate contributions from the community, whether they are feature enhancements, bug fixes, or documentation improvements. If you're interested in contributing, please:

1. Fork the repository.
2. Create a new branch for your feature (git checkout -b feature-branch).
3. Commit your changes (git commit -am 'Add some feature').
4. Push to the branch (git push origin feature-branch).
5. Submit a pull request.

This project is licensed under the MIT License - see the LICENSE.md file for details.

Should you have any questions, or require assistance, feel free to reach out to me at [email protected].

Thank you for exploring the Dataset Class Equalizer. We hope it serves your needs effectively!