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Track objects in video
YOLOv12 Demo

YOLOv12 Demo

Detect objects in images or videos

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What is YOLOv12 Demo ?

The YOLOv12 Demo is a real-time object detection system built on the YOLO (You Only Look Once) framework. It is designed to detect objects in images or videos with high accuracy and speed. This demo serves as a showcase for the capabilities of the YOLOv12 model, making it accessible for users to test and explore its features.

Features

• Real-Time Object Detection: Detect objects in images and videos with low latency.
• High Accuracy: Leveraging advanced algorithms for precise object recognition.
• Multiple Object Detection: Identify and track multiple objects within a single frame.
• Versatile Input Support: Process inputs from images, videos, or webcam feeds.
• Customizable: Adjust detection thresholds and settings to suit your needs.
• Cross-Platform Compatibility: Runs on various operating systems and devices.

How to use YOLOv12 Demo ?

  1. Install the Demo: Download and install the YOLOv12 Demo application or clone the repository if using the source code.
  2. Set Up Dependencies: Ensure you have the required libraries and frameworks installed (e.g., Python, OpenCV, etc.).
  3. Load the Model: Initialize the YOLOv12 model within the application.
  4. Select Input: Choose an input source (image, video file, or webcam).
  5. Run Detection: Execute the detection process to analyze the input.
  6. View Results: The demo will display the output with bounding boxes and class labels for detected objects.

Frequently Asked Questions

What devices or platforms does YOLOv12 Demo support?
The YOLOv12 Demo is designed to run on Windows, macOS, and Linux systems. It can also be optimized for mobile devices with appropriate configurations.

Can I use YOLOv12 Demo for real-time video analysis?
Yes, the demo supports real-time video analysis using webcam feeds or pre-recorded video files.

How accurate is YOLOv12 compared to previous versions?
YOLOv12 offers improved accuracy and speed compared to earlier versions, thanks to advancements in its neural network architecture and training methods.

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