YASH-13-lab

🌆 segmentation-cityscape - Easy Semantic Segmentation on Cityscapes

🚀 Getting Started

Welcome to segmentation-cityscape! This application allows you to perform semantic segmentation using PyTorch DeepLabV3/V3+ on Cityscapes images. You can train models, evaluate their performance, and export results ready for submission.

📥 Download

To get started, visit the Releases page to download the software.

⚙️ System Requirements

Before you begin, ensure your system meets the following requirements:

• Operating System: Windows, macOS, or Linux
• Memory: At least 8 GB of RAM
• Graphics Card: NVIDIA GPU with CUDA support (for accelerated training)
• Python: Version 3.6 or higher
• Dependencies: PyTorch, albumentations, and other required Python libraries (detailed below)

📦 Installation Steps

1. Visit the Releases Page: Go to the Releases page.

2. Download the Application: Click on the latest release version. You will see various download options. Choose the version that matches your operating system.

3. Install Dependencies:
   • Open a command prompt or terminal.
   • Create a virtual environment (optional but recommended):

     python -m venv segmentation-env
     

   • Activate the virtual environment:
     • Windows:

       segmentation-env\Scripts\activate
       

     • macOS/Linux:

       source segmentation-env/bin/activate
       

   • Install necessary packages:

     pip install torch torchvision albumentations
     

4. Run the Application:
   • Navigate to the folder where you have downloaded the application.
   • Execute the following command to launch the software:

     python run_segmentation.py
     

🔧 Features

• Model Training: Train semantic segmentation models on the Cityscapes dataset with simple commands.
• Evaluation Metrics: Compute Mean Intersection over Union (mIoU) scores to assess model performance.
• Inference Overlays: Generate and view segmentation overlays directly on images for quick analysis.
• Export Labels: Easily export label IDs in the required format for submissions.

🛠️ Using the Application

1. Training a Model

To train a model, use the following command in your terminal:

python train_model.py --config config.yaml

Adjust the config.yaml file according to your dataset paths and parameters.

2. Evaluating a Model

After training, evaluate the model with:

python evaluate_model.py --model_path path/to/your/model.pth

This command will give you mIoU scores and other performance metrics.

3. Generating Overlays

To create segmentation overlays, run:

python generate_overlays.py --image_path path/to/image.jpg

This will display the segmented version of the input image.

4. Exporting Label IDs

To export label IDs, use:

python export_labels.py --output_path path/to/output.json

This command creates a file suitable for submission.

📚 Documentation

For in-depth instructions on each feature, consult the project’s Wiki. You’ll find examples, tips, and detailed explanations.

🛠️ Troubleshooting

If you run into issues:

• Ensure all dependencies are installed.
• Check the config file for errors.
• Refer to the documentation for potential solutions.

🤝 Contributing

We welcome contributions! If you have suggestions, feel free to open an issue or submit a pull request.

📖 License

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

🌍 Community

Join our community on GitHub Discussions or participate in our forums to share experiences and ask for help.

For any specific issues or questions, feel free to reach out in the Issues section on GitHub. Your feedback can help make segmentation-cityscape better for everyone.