ImgAug_ClassSeg_CodeHub

Welcome to ImgAug_ClassSeg_CodeHub, your go-to resource for state-of-the-art image augmentation codes tailored for classification and segmentation tasks. This repository houses a comprehensive collection of meticulously crafted scripts and utilities designed to enhance your image datasets and empower your machine learning and deep learning models.

Key Features

1. Versatile Augmentation Techniques: Explore a diverse range of image augmentation techniques, carefully curated to bolster the performance of your classification and segmentation models. Techniques include:

   • Image Rotation: Rotate images at various angles to improve model generalization.
   • Image Brightness Adjustment: Adjust the brightness of images to simulate different lighting conditions.
   • Image Flip: Flip images horizontally or vertically to increase dataset diversity.
   • Color Jittering: Introduce controlled variations in image color for improved robustness.

2. Codebase for Classification and Segmentation: Organized and annotated codebase specifically tailored for both image classification and segmentation tasks, ensuring flexibility and ease of integration into your projects.

3. Extensive Documentation: Clear and concise documentation to guide you through the implementation of augmentation techniques, enabling seamless integration into your workflows.

4. Compatibility: Built with compatibility in mind, the codebase supports popular deep learning frameworks such as TensorFlow and PyTorch, providing you with the flexibility to choose the framework that best suits your needs.

How to Use

1. Clone the Repository:

   git clone https://github.com/Neural-Ninja/ImgAug_ClassSeg_CodeHub.git
   

2. Image Augmentation Inside the scripts folder use Image Augmentation.ipynb file.

3. Segmentation Augmentation Inside the scripts folder use Segmentation File Augmentation.ipynb file.

4. Semantic Augmentation Inside the scripts use Augmentation Semnatic Segmentation.ipynb file.

Contributions:

Contributions and feedback are highly encouraged! Whether you want to suggest improvements, report issues, or add your own augmentation techniques, we welcome your collaboration in making ImgAug_ClassSeg_CodeHub a comprehensive resource for the community.

Happy Coding!