Identify and label objects in images
Analyze images for object recognition
Identify objects in images
Detect objects in your images
Detect defects in images and videos
Detect objects in images and videos
Detect objects in images and highlight them
Identify objects in real-time video feed
Identify benthic supercategories in images
Detect and segment objects in images
Identify objects in images
Draw a box to detect objects
Find and label objects in images
Yolov5g is an advanced object detection model based on the YOLO (You Only Look Once) family of models. It is designed to identify and label objects within images with high accuracy and efficiency. Yolov5g is optimized for real-time detection, making it suitable for applications that require quick and reliable object recognition.
• High Accuracy: Yolov5g delivers precise object detection and labeling. • Real-Time Detection: Optimized for fast performance, enabling real-time applications. • Multi-Object Detection: Capable of detecting multiple objects in a single image. • Customizable: Can be trained on custom datasets for specific use cases. • Cross-Platform Support: Compatible with various devices, including mobile and edge devices. • Efficient: Lightweight architecture allows for deployment in resource-constrained environments.
Example command to run Yolov5g:
python detect.py --source your_image.jpg
What is the difference between Yolov5 and Yolov5g?
Yolov5g is a variant of Yolov5 optimized for specific use cases, often with improvements in speed or accuracy for particular tasks.
Can Yolov5g be used for video analysis?
Yes, Yolov5g can be applied to video streams by processing individual frames sequentially.
What are the minimum system requirements to run Yolov5g?
A modern CPU or GPU with sufficient RAM (at least 4GB) is recommended for smooth operation.