Identify objects in real-time video feed
Upload images to detect objects
Upload an image to detect objects
Detect objects in uploaded images
Detect objects in images
Find objects in your images
Detect objects in random images
Identify objects in an image
Identify objects in images
Identify objects in images
Upload images/videos to detect wildfires and smoke
Detect objects in images using a web app
State-of-the-art Object Detection YOLOV9 Demo
Streamlit Webrtc Example is an open-source application built using Streamlit and WebRTC (Web Real-Time Communication) technologies. It is designed for real-time object detection in video feeds, enabling users to identify objects within live or recorded video streams. This tool leverages AI models to analyze video frames and detect objects with high accuracy. Streamlit's simplicity and WebRTC's real-time capabilities make it an ideal combination for rapid development and deployment of video-based AI applications.
• Real-time Object Detection: Analyzes video frames in real-time to identify objects.
• WebRTC Integration: Utilizes WebRTC for low-latency video streaming directly in the browser.
• Customizable AI Models: Supports integration with various pre-trained AI models for object detection.
• User-Friendly Interface: Provides an intuitive interface for users to interact with the video feed and view detection results.
• Cross-Platform Compatibility: Works seamlessly across modern web browsers.
pip install streamlit webrtc to install the necessary dependencies.streamlit run your_script.py to launch the app.http://localhost:8501.What browsers are supported?
Streamlit Webrtc Example is compatible with modern browsers like Chrome, Firefox, and Edge.
Can I customize the AI model?
Yes, you can integrate custom AI models or use pre-trained models like YOLO or TensorFlow-based detectors.
How do I improve performance?
For better performance, use a high-resolution camera, optimize your AI model, and ensure a stable internet connection.