Microbial Cell Detection with YOLOv8

This project implements a web application for real-time detection and segmentation of microbial cells using the YOLOv8 deep learning model.

Overview

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.

Features

• 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.

Usage

1. Clone the repository:

git clone https://github.com/SYED-M-HUSSAIN/Microbial-cell-segmentation.git

2. Install the required dependencies:

  pip install -r requirements.txt

3. Run the main file:

  streamlit run app.py

Directory Structure

.
├── README.md                # Project documentation
├── app.py                   # Main application script
├── best.pt                  # Pre-trained YOLOv8 model
├── image_utils.py           # Utility functions for image processing
├── segmentation.py          # Script for segmentation functionality
├── sidebar.py               # Script for sidebar components
├── Images                    # Directory to store uploaded images
│   └── uploaded_image.jpg   # Example uploaded image
├── .gitignore               # Git ignore file
└── requirements.txt         # Dependencies

Website Link

https://bio200.streamlit.app/

References:

• Ultralytics YOLOv8
• Streamlit
• Deploying an Image Segmentation