• Background Removal: Easily remove backgrounds from images in bulk.
• Automatic Augmentations: Generate variations of images in specified folders to enhance your dataset.
• Simple Setup: No coding required—just run the executable and select your input folder.
• Intuitive Folder Structure Requirement: Specify a parent_folder that includes organized subfolders of images to get the best output.

1. Download the latest release of MCLX ML EXE from the Releases page.
2. Run the executable mclx_ml_exe.exe.

1. Select the Parent Folder:
   • Organize your images in a parent_folder, with each subfolder containing images.
   • For example:

     parent_folder/
     ├── folder_a/
     │   ├── image1.jpg
     │   ├── image2.png
     ├── folder_b/
     │   ├── image1.jpg
     │   ├── image2.png
     

2. Run the Tool:
   • After launching mclx_ml_exe.exe, select the parent_folder.
   • The tool will process each folder’s images by removing backgrounds and generating augmentations.
3. Output:
   • The processed images and their augmentations will be saved in the selected output directory, ready for use!

• Parent Folder: This must contain subfolders with images. Each subfolder represents a unique class or set of images.
• Naming Convention: Ensure each subfolder has a distinct name for the best organization of augmented outputs.

After processing, your directory structure might look like this:

• OS: Windows 10 or higher
• RAM: 4 GB or more recommended for smooth processing
• Disk Space: Sufficient space to accommodate the augmented image files

Interested in enhancing MCLX ML EXE? Contributions are welcome! To get started:

1. Fork the repository on GitHub.
2. Make changes in your fork and test thoroughly.
3. Submit a pull request with a brief explanation of the changes.

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

For questions or suggestions, reach out via:

• Email: developer.mozadded@gmail.com
• GitHub Issues: Open an issue