Image Segmentation using Deep Learning

This project focuses on segmenting images into meaningful parts using deep learning techniques. The goal is to partition an image into segments that simplify or change the representation of an image into something more meaningful and easier to analyze.

Features

• Segment images into different regions or objects.
• Use deep learning models such as U-Net or Mask R-CNN for accurate segmentation.
• Visualize segmentation results with overlaid masks or boundaries.
• Handle a variety of image types, including medical imaging, satellite images, and more.

Installation

To set up this image segmentation project on your local machine, follow the steps below:

1. Clone the Repository

   git clone https://github.com/username/image-segmentation.git
   cd image-segmentation

2. Set Up Virtual Environment (Optional but Recommended)

   python3 -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Required Packages

   pip install -r requirements.txt

Usage

To start the image segmentation system, use the following command:

python image_segmentation.py --input  --output