Identify objects in images and videos
Control object motion in videos using 2D trajectories
Upload and detect objects in videos
Object_detection_from_Video
Process videos to detect and track objects
A UI for drone detection for YOLO-powered detection system.
Car detection testing
Video captioning/open-vocabulary/zero-shot
Track objects in uploaded videos
Analyze video to recognize actions or objects
Automated Insect Detection
Detect objects in real-time from your webcam
Track and count vehicles in real-time
YOLOv12 Demo is a cutting-edge object detection tool designed to identify and track objects within images and videos. It leverages the YOLO (You Only Look Once) v12 algorithm, known for its high accuracy and real-time processing capabilities. This demo provides a user-friendly interface to experience state-of-the-art object detection, making it ideal for developers, researchers, and enthusiasts exploring computer vision applications.
• Object Detection in Images and Videos: Detect and classify objects in both static images and video streams.
• Real-Time Tracking: Track moving objects across frames with high precision.
• High Accuracy: Benefits from the latest advancements in YOLOv12, ensuring robust detection even in complex scenes.
• No External Dependencies Required: Runs efficiently on standard hardware with minimal setup.
• Support for Multiple Input Formats: Accepts various image and video formats for versatile use cases.
• User-Friendly Interface: Intuitive design makes it easy to use, even for beginners.
pip install -r requirements.txt to install dependencies.python yolo_demo.py).What frameworks does YOLOv12 Demo support?
YOLOv12 Demo primarily supports PyTorch, ensuring compatibility with the latest AI advancements.
Can I use YOLOv12 Demo for commercial projects?
Yes, YOLOv12 Demo is free to use for both personal and commercial projects, though specific licensing terms may apply depending on your use case.
How accurate is YOLOv12 compared to previous versions?
YOLOv12 offers significant improvements in accuracy and speed compared to earlier versions, making it more suitable for real-time applications.