Efficient Object Detection with YOLOv7

This program enables object detection using the YOLOv7 model, supporting various input sources such as images, videos, and live feeds. It processes data to identify and filter objects based on confidence and intersection-over-union thresholds. Users can save results, view detections in real-time, and apply blurring effects to detected objects. The model can leverage pre-trained weights and allows for enhanced inference, with customizable parameters for diverse applications. Overall, the system aims to deliver efficient and accurate detection of objects across different scenarios.