CV-segmentation

This repository contains code for an application that performs automatic inference using a fine-tuned segmentation model to segment images uploaded by the user.

Features

• Image Upload: Users can upload images for segmentation.
• Automatic Inference: The app utilizes a fine-tuned segmentation model to process and segment the uploaded images.
• User-Friendly Interface: A simple and intuitive interface for seamless user experience.

Installation

1. Clone the Repository:

   git clone https://github.com/DamiFass/CV-segmentation.git
   cd CV-segmentation

2. Install Dependencies:

   pip install -r requirements.txt

Usage

1. Run the Application:

   Streamlit run stApp.py

2. Access the App:

   Open your web browser to interact with the application.

3. Upload and Segment Images:

   • Click on the "Upload Image" button to select an image from your device.
   • The app will automatically perform segmentation on the uploaded image and display the result.

4. Access Streamlit app directly:

   Alternatively, one can try the Streamlit app already deployed here.

Project Structure

• src/: Contains the source code for the segmentation model and related utilities.
• stApp.py: The main script to run the Streamlit application.
• requirements.txt: Lists the Python dependencies required for the project.

Dependencies

The application relies on several Python libraries, including but not limited to:

• streamlit: For building the web application interface.
• torch: For loading and running the segmentation model.
• PIL: For image processing.

For a complete list of dependencies, refer to the requirements.txt file.

Contributing

Contributions are welcome! If you have suggestions or improvements, please open an issue or submit a pull request.

License

This project is licensed under the MIT License.