The YOLO family, based on the “You Only Look Once” concept emphasizes real-time object recognition while sacrificing accuracy.
contained a single neural network that directly predicted bounding boxes and class labels.
Despite the reduced prediction error, YOLO can operate at speeds of up to 155 frames per second. YOLOv2, also known as YOLO9000, addressed some of the shortcomings of the original model by predicting 9,000 object classes and introducing anchor boxes for more rigorous predictions.
more, with a wider feature detector network.
YOLOv3 improved even
The object recognition models in the YOLO (You Only phonelist Look Once) family have emerged as outstanding achievements in computer vision.
YOLO, introduced in 2015, prioritizes speed and real-time object identification by directly anticipating bounding boxes and class labels.
Although limited accuracy is provided, it analyzes images in real time, making it useful for time-critical applications.
The original YOLO model
YOLOv2 introduced anchor boxes to handle different object scales and trained on multiple datasets to predict over 9,000 object classes.
In 2018, YOLOv3 developed the family even further with a deeper feature detector network, increasing accuracy without sacrificing performance.
The YOLO family predicts bounding BLB Directory boxes, class probabilities, and instability scores by dividing the image into a grid. It effectively combines speed and precision, making it adaptable for use in surveillance, healthcare, and other fields.