Model Deployment with Docker

This repository contains the necessary files and instructions to deploy a machine learning model using Docker. The setup ensures a consistent and reproducible environment for model deployment.

Project Structure

yolo-docker-app/
├── app.py
├── requirements.txt
├── Dockerfile
├── docker-compose.yml
├── your_model.pt           # Your YOLO model
├── templates/
│   └── index.html
├── uploads/               # Temporary uploads
└── results/              # Output images

• Dockerfile: Defines the Docker image configuration.
• requirements.txt: Lists Python dependencies for the project.
• app.py: Entry point for the application (e.g., API or inference script).
• best.pt: My prediction model trained by YOLO.

Setup Instructions

1. Clone the Repository

   git clone https://github.com/khacluat03/deploy-the-model-with-Docker.git
   cd deploy-the-model-with-Docker

2. Build the Docker Image

   docker build -t model-deployment .

3. Run the Docker Container

   docker run -p 8000:8000 model-deployment

   • The application will be accessible at http://localhost:8000.
   • Adjust the port mapping (8000:8000) as needed.

The interface:

The sample I use:

The results: