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.
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.
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/notebooks: Jupyter notebooks detailing the exploration, preprocessing, training, and evaluation of the Bi-LSTM model. Access the notebooks using the following links:
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/models: Saved models after training for deployment or further analysis.
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Clone the Repository:
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Install Dependencies:
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Explore the Notebooks:
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Train the Model:
List the major dependencies for running the project. Include versions where necessary.
- Python 3.x
- Pytorch
- NumPy
- Pandas
- etc.
List potential enhancements or areas for improvement in the project.
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.
This project is licensed under the [LICENSE NAME] License - see the LICENSE.md file for details.
Thank you for exploring the project!