Welcome to the UNet Image Segmentation project! This repository provides code for training and using a UNet model for image segmentation tasks, following the original paper "U-Net: Convolutional Networks for Biomedical Image Segmentation".

• Introduction
• Installation
• Usage
  • Training
  • Inference
• Results
• Contributing
• License

This project implements the UNet architecture, inspired by the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation", which pioneered the use of a fully convolutional network for efficient biomedical image segmentation. The UNet model is highly versatile and can be applied to various image segmentation tasks.

Note: While initially developed for biomedical applications, this UNet model is suitable for broader image segmentation challenges as well.

To get started, clone the repository and install the necessary dependencies:

git clone https://github.com/rk-vashista/unet-image-segmentation.git
cd unet-image-segmentation
pip install -r requirements.txt

To train the model, use the following command:

python main.py

Hyperparameters such as learning rate, batch size, and epochs can be adjusted in the main.py file.

Run predictions on images in the test/ directory:

python auto.py

Alternatively, you can use single_image_inference in metest.py for a single image:

from metest import single_image_inference
single_image_inference("path/to/your/image.jpg")

Segmented outputs will be saved in the Result/ directory.

Example segmented images are saved in the Result/ directory.

Segmented Image 1	Segmented Image 2

Contributions are welcome! Please open an issue for suggestions or bug fixes, or submit a pull request with improvements.

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