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Live Inference with Streamlit Application using Ultralytics YOLOv8

Introduction

Streamlit makes it simple to build and deploy interactive web applications. Combining this with Ultralytics YOLOv8 allows for real-time object detection and analysis directly in your browser. YOLOv8 high accuracy and speed ensure seamless performance for live video streams, making it ideal for applications in security, retail, and beyond.

Aquaculture Animals husbandry
Fish Detection using Ultralytics YOLOv8 Animals Detection using Ultralytics YOLOv8
Fish Detection using Ultralytics YOLOv8 Animals Detection using Ultralytics YOLOv8

Advantages of Live Inference

  • Seamless Real-Time Object Detection: Streamlit combined with YOLOv8 enables real-time object detection directly from your webcam feed. This allows for immediate analysis and insights, making it ideal for applications requiring instant feedback.
  • User-Friendly Deployment: Streamlit's interactive interface makes it easy to deploy and use the application without extensive technical knowledge. Users can start live inference with a simple click, enhancing accessibility and usability.
  • Efficient Resource Utilization: YOLOv8 optimized algorithm ensure high-speed processing with minimal computational resources. This efficiency allows for smooth and reliable webcam inference even on standard hardware, making advanced computer vision accessible to a wider audience.

Streamlit Application Code

Ultralytics Installation

Before you start building the application, ensure you have the Ultralytics Python Package installed. You can install it using the command pip install ultralytics

Streamlit Application

from ultralytics import solutions

solutions.inference()

### Make sure to run the file using command `streamlit run <file-name.py>`
yolo streamlit-predict

This will launch the Streamlit application in your default web browser. You will see the main title, subtitle, and the sidebar with configuration options. Select your desired YOLOv8 model, set the confidence and NMS thresholds, and click the "Start" button to begin the real-time object detection.

You can optionally supply a specific model in Python:

Streamlit Application with a custom model

from ultralytics import solutions

# Pass a model as an argument
solutions.inference(model="path/to/model.pt")

### Make sure to run the file using command `streamlit run <file-name.py>`

Conclusion

By following this guide, you have successfully created a real-time object detection application using Streamlit and Ultralytics YOLOv8. This application allows you to experience the power of YOLOv8 in detecting objects through your webcam, with a user-friendly interface and the ability to stop the video stream at any time.

For further enhancements, you can explore adding more features such as recording the video stream, saving the annotated frames, or integrating with other computer vision libraries.

Share Your Thoughts with the Community

Engage with the community to learn more, troubleshoot issues, and share your projects:

Where to Find Help and Support

Official Documentation

  • Ultralytics YOLOv8 Documentation: Refer to the official YOLOv8 documentation for comprehensive guides and insights on various computer vision tasks and projects.

FAQ

How can I set up a real-time object detection application using Streamlit and Ultralytics YOLOv8?

Setting up a real-time object detection application with Streamlit and Ultralytics YOLOv8 is straightforward. First, ensure you have the Ultralytics Python package installed using:

pip install ultralytics

Then, you can create a basic Streamlit application to run live inference:

Streamlit Application

from ultralytics import solutions

solutions.inference()

### Make sure to run the file using command `streamlit run <file-name.py>`
yolo streamlit-predict

For more details on the practical setup, refer to the Streamlit Application Code section of the documentation.

What are the main advantages of using Ultralytics YOLOv8 with Streamlit for real-time object detection?

Using Ultralytics YOLOv8 with Streamlit for real-time object detection offers several advantages:

  • Seamless Real-Time Detection: Achieve high-accuracy, real-time object detection directly from webcam feeds.
  • User-Friendly Interface: Streamlit's intuitive interface allows easy use and deployment without extensive technical knowledge.
  • Resource Efficiency: YOLOv8's optimized algorithms ensure high-speed processing with minimal computational resources.

Discover more about these advantages here.

How do I deploy a Streamlit object detection application in my web browser?

After coding your Streamlit application integrating Ultralytics YOLOv8, you can deploy it by running:

streamlit run <file-name.py>

This command will launch the application in your default web browser, enabling you to select YOLOv8 models, set confidence, and NMS thresholds, and start real-time object detection with a simple click. For a detailed guide, refer to the Streamlit Application Code section.

What are some use cases for real-time object detection using Streamlit and Ultralytics YOLOv8?

Real-time object detection using Streamlit and Ultralytics YOLOv8 can be applied in various sectors:

  • Security: Real-time monitoring for unauthorized access.
  • Retail: Customer counting, shelf management, and more.
  • Wildlife and Agriculture: Monitoring animals and crop conditions.

For more in-depth use cases and examples, explore Ultralytics Solutions.

How does Ultralytics YOLOv8 compare to other object detection models like YOLOv5 and RCNNs?

Ultralytics YOLOv8 provides several enhancements over prior models like YOLOv5 and RCNNs:

  • Higher Speed and Accuracy: Improved performance for real-time applications.
  • Ease of Use: Simplified interfaces and deployment.
  • Resource Efficiency: Optimized for better speed with minimal computational requirements.

For a comprehensive comparison, check Ultralytics YOLOv8 Documentation and related blog posts discussing model performance.



Created 2024-07-05, Updated 2024-07-21
Authors: rulosanti@gmail.com (1), glenn-jocher (1)

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