cell-segmentation-using-yolov-8

Cell Segmentation Using YOLO v8

This project focuses on cell segmentation using YOLO v8, a state-of-the-art deep learning model for object detection and segmentation. The model aims to segment cells in images accurately and efficiently.

Workflows

1. Constants: Contains constant values used across the project.
2. Entity: Defines the entity classes and data structures.
3. Components: Contains various components required for the project.
4. Pipelines: Describes the data pipelines and workflows.
5. App.py: Main application file that ties everything together.

How to Run?

Steps

1. Clone the repository:

    git clone https://github.com/yash-raj202134/cell-segmentation-using-yolov-8.git
   

2. Create a conda environment:

    conda create -n cell python=3.8 -y
    conda activate cell
   

3. Install the requirements:

    pip install -r requirements.txt
   

4. Run the application:

    python app.py
   

5. Access the application:

   Open up your local host and port in a web browser to access the application.

Azure CI/CD Deployment with GitHub Actions

Run from Terminal

1. Build the Docker image:

    docker build -t cellseg.azurecr.io/cell:latest .
   

2. Log in to Azure Container Registry:

    docker login cellseg.azurecr.io
   

3. Push the Docker image:

    docker push cellseg.azurecr.io/cell:latest
   

Deployment Steps

1. Build the Docker image of the source code.
2. Push the Docker image to Azure Container Registry.
3. Launch the web app server in Azure.
4. Pull the Docker image from the container registry to the web app server and run the application.

Save Pass

Save pass: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

This site is open source. Improve this page.