How many bounding boxes will be returned in Yolo?
YOLO, therefore, has 13×13 = 169 different bounding box predictors, and each of these is assigned to look only at a specific location in the image.
How many proposals bounding boxes are considered in the vanilla Yolo algorithm?
How Does YOLO Work? YOLO divides up the image into a grid of 13 by 13 cells: Each of these cells is responsible for predicting 5 bounding boxes. A bounding box describes the rectangle that encloses an object.
How are bounding boxes used in Yolo machine?
YOLO divides up the image into a grid of 13 by 13 cells: Each of these cells is responsible for predicting 5 bounding boxes. A bounding box describes the rectangle that encloses an object. YOLO also outputs a confidence score that tells us how certain it is that the predicted bounding box actually encloses some object.
How does Yolo predict a bounding box regression?
For every positive position, the network predicts a regression on the bounding box precise position and dimension. In the second version of Yolo, these predictions are relative to the grid position and anchor size (instead of the full image) as in the Faster-RCNN models for better performance:
Do you use anchor boxes in Yolo model?
YOLOv1, which is the paper you linked, does not use anchor boxes so its not exactly the same. They key to understanding how the bounding boxes are formed is to first understand how the output is encoded. To which, I’ll recommend this link: https://hackernoon.com/understanding-yolo-f5a74bbc7967
Why do you only look at one bounding box?
It signifies that those two bounding boxes are covering the same object but the other one has a low probabilty for the same, thus it is eliminated. Once done, algorithm finds the bunding box with next highest class probabilities and does the same process, it is done until we are left with all the different bounding boxes.