r4m4kr15hn4/road_segmentation_unet

Road Segmentation Using UNET with PyTorch

Overview

This project focuses on implementing road segmentation using the U-Net architecture with PyTorch. Leveraging a labeled dataset, the U-Net model will be trained to accurately distinguish road and non-road areas in images. The architecture's contracting and expansive paths enable effective pixel-level segmentation.Future enhancements may involve fine-tuning for specific environments and integration with dynamic video feeds.

Project Description

TThis project focuses on implementing a road segmentation model using PyTorch and the U-Net architecture. The model will be trained on a labeled dataset to distinguish road and non-road areas in images. Deployment aims to provide real-time road segmentation on new images, with potential enhancements including fine-tuning for specific environments and integration with dynamic video feeds.

• /notebooks: Jupyter notebooks detailing the exploration, preprocessing, training, and evaluation of the Bi-LSTM model. Access the notebooks using the following links:

  • Exploration Notebook

• /models: Saved models after training for deployment or further analysis.

How to Use

1. Clone the Repository:

2. Install Dependencies:

3. Explore the Notebooks:

4. Train the Model:

Dependencies

List the major dependencies for running the project. Include versions where necessary.

• Python 3.x
• Pytorch
• NumPy
• Pandas
• etc.

Future Improvements

List potential enhancements or areas for improvement in the project.

Contributing

If you would like to contribute to this project, please follow the standard GitHub flow: Fork the repository, create a branch, make changes, and submit a pull request.

License

This project is licensed under the [LICENSE NAME] License - see the LICENSE.md file for details.

Thank you for exploring the project!