A Summary of How YOLO Makes Predictions
Explore the prediction process of the YOLO object detection model. Understand how it divides an image into grids, uses anchor boxes for diverse object sizes, predicts confidence scores and class probabilities, decodes bounding boxes, and applies non-maximum suppression to finalize accurate object detections.
We'll cover the following...
We'll cover the following...
Here’s a summary of how YOLO makes predictions:
A high-level summary of object detection in YOLO
Dividing the input image: The image is divided into a grid of size S × S. Each grid cell is responsible for predicting the objects whose centers fall within it.
Using anchor boxes: Each grid cell uses predefined anchor boxes (or bounding box priors) with different shapes and ...