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Yolov5g is a state-of-the-art object detection model designed to recognize and classify objects within images. It belongs to the YOLO (You Only Look Once) family of models, known for their high-speed and accuracy in real-time object detection tasks. Yolov5g is optimized for high performance and efficiency, making it suitable for a wide range of applications, from surveillance to autonomous systems.
pip install -r requirements.txt
from yolov5g import detect
results = detect(image_path="input.jpg")
What makes Yolov5g different from other YOLO models?
Yolov5g is optimized for better performance and efficiency compared to previous versions, with improved detection accuracy and faster inference speeds.
Can Yolov5g handle multiple object detections in one image?
Yes, Yolov5g is designed to detect multiple objects in a single image, providing bounding boxes and classifications for each object.
How do I improve the accuracy of Yolov5g for my specific use case?
You can fine-tune the model using your dataset by retraining it with samples relevant to your application. This helps the model learn features specific to your use case.