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Yolov5g is an advanced object detection model part of the YOLO (You Only Look Once) family, designed for real-time object detection in images and video streams. It is optimized for accuracy and speed, making it suitable for various applications, including surveillance, robotics, and autonomous systems.
• Object Detection and Recognition: Yolov5g identifies and classifies objects within images, providing bounding boxes and confidence scores. • Real-Time Detection: Optimized for fast inference, enabling real-time processing of video frames. • High Accuracy: Achieves state-of-the-art performance on benchmark datasets like COCO. • Multi-Object Detection: Detects multiple objects in a single image with high precision. • Customizable: Supports custom models for specific use cases and datasets.
python detect.py --source image.jpg1. What devices can Yolov5g run on?
Yolov5g can run on CPUs, GPUs, and TPUs, making it versatile for different hardware setups.
2. How do I use Yolov5g for video detection?
Run the detection script with a video file or camera input: python detect.py --source video.mp4 or python detect.py --source 0 for webcam.
3. Can I train Yolov5g on my own dataset?
Yes, Yolov5g supports custom training. Prepare your dataset in the YOLO format, update the configuration, and run the training script.