This project implements a web application for real-time detection and segmentation of microbial cells using the YOLOv8 deep learning model.
Microbial cell detection is essential in various fields, including environmental monitoring, public health, and biotechnology. This application provides a user-friendly interface for researchers and practitioners to quickly and accurately identify microbial cells in images.
- Utilizes YOLOv8 for real-time object detection and segmentation.
- Web-based interface for easy interaction and visualization.
- Supports uploading of images containing microbial cells.
- Displays segmented regions and precise locations of detected cells.
- Provides distribution analysis of identified microbial cells.
- Clone the repository:
git clone https://github.com/SYED-M-HUSSAIN/Microbial-cell-segmentation.git
- Install the required dependencies:
pip install -r requirements.txt
- Run the main file:
streamlit run app.py
.
โโโ README.md # Project documentation
โโโ app.py # Main application script
โโโ best.pt # Pre-trained YOLOv8 model
โโโ image_utils.py # Utility functions for image processing
โโโ segmentation.py # Script for segmentation functionality
โโโ sidebar.py # Script for sidebar components
โโโ Images # Directory to store uploaded images
โ โโโ uploaded_image.jpg # Example uploaded image
โโโ .gitignore # Git ignore file
โโโ requirements.txt # Dependencies
https://microbial-cell-detection-yolov8.streamlit.app/