YOLOv12 Demo

What is YOLOv12 Demo ?

YOLOv12 Demo is a state-of-the-art object detection application based on the YOLO (You Only Look Once) model. It is designed to detect objects in both images and videos with high accuracy and speed. YOLOv12 Demo is the latest iteration of the YOLO family, offering improved performance and efficiency compared to its predecessors.

Features

• Real-Time Object Detection: Detect objects in real-time with low latency for smooth video processing.
• High Accuracy: Benefiting from advanced neural network architecture, YOLOv12 Demo delivers state-of-the-art detection accuracy.
• Multi-Object Tracking: Capable of tracking multiple objects simultaneously in video streams.
• Cross-Platform Support: Runs on various platforms, including Windows, Linux, and macOS.
• Customization Options: Users can fine-tune the model for specific use cases by adjusting settings like detection thresholds.

How to use YOLOv12 Demo ?

1. Install Dependencies: Ensure you have the required libraries and frameworks installed (e.g., Python, OpenCV, and the YOLOv12 model weights).
2. Download the Model Weights: Access the pre-trained YOLOv12 model weights from the official repository.
3. Run the Demo Script: Execute the provided script to launch the object detection application.
4. Input Selection: Choose between image or video input. For video, you can use a file or webcam input.
5. View Output: The application will display detected objects with bounding boxes and class labels in real-time.
6. Adjust Settings: Modify settings like confidence thresholds or frame rates to optimize performance for your use case.
7. Review Results: Save or review the detection results for further analysis.

Frequently Asked Questions

1. What are the system requirements for running YOLOv12 Demo?
YOLOv12 Demo requires a compatible GPU for optimal performance. A minimum of 4GB VRAM is recommended, along with Python 3.8 or higher and OpenCV installed.

2. Can YOLOv12 Demo detect custom objects?
Yes, YOLOv12 Demo allows for custom object detection by retraining the model with your dataset. You can modify the model architecture and train it on specific classes.

3. Why am I experiencing lag during video processing?
Lag may occur due to high-resolution input or low GPU performance. Try reducing the video resolution or optimizing the model by enabling techniques like downsampling or quantization.