RetinaNet

RetinaNet is a deep learning model used for detecting objects within images, such as cars, animals, or faces. It works by analyzing the image at different scales to identify objects of various sizes. What sets RetinaNet apart is its use of a technique called "focal loss," which helps the model focus on harder-to-detect objects and reduces false positives. This makes it both accurate and efficient, particularly in complex scenes with many objects. Essentially, RetinaNet is a sophisticated tool that helps computers "see" and recognize multiple objects in visual data reliably.