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Track objects in video
YOLOv12 Demo

YOLOv12 Demo

Identify objects in images and videos

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What is YOLOv12 Demo ?

YOLOv12 Demo is a state-of-the-art application built on the YOLOv12 (You Only Look Once version 12) model, designed for real-time object detection in images and videos. It leverages advanced deep learning techniques to identify and track objects with high accuracy and speed.

Features

• Real-Time Detection: Detects objects in real-time with minimal latency.
• High Accuracy: Utilizes the YOLOv12 model, which offers improved precision and recall compared to previous versions.
• Support for Images and Videos: Compatible with both still images and video streams, making it versatile for various use cases.
• User-Friendly Interface: Intuitive design allows users to easily upload media and view results.
• Customizable Settings: Options to adjust detection thresholds and other parameters for fine-tuned performance.
• Cross-Platform Compatibility: Can be used on multiple operating systems, including Windows, macOS, and Linux.

How to use YOLOv12 Demo ?

  1. Install the Application: Download and install the YOLOv12 Demo app from the official source.
  2. Upload Your Media: Select an image or video file you want to analyze.
  3. Run Detection: Click the "Detect" button to start the object detection process.
  4. Review Results: View the output, which includes bounding boxes and class labels for detected objects.
  5. Adjust Settings: Optionally, tweak settings like confidence thresholds to refine results.

Frequently Asked Questions

What devices does YOLOv12 Demo support?
YOLOv12 Demo is compatible with Windows, macOS, and Linux systems, as well as mobile devices with compatible browsers.

Can YOLOv12 Demo detect objects in live video streams?
Yes, YOLOv12 Demo supports real-time video streams. You can use it for live camera feeds or pre-recorded videos.

How accurate is YOLOv12 Demo compared to older YOLO versions?
YOLOv12 Demo offers improved accuracy and speed compared to older versions, thanks to advancements in the model architecture and training data.

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