Best Way to create a bounding box for object detection
You can reference YOLO algorithm- this is the best algorithm for object detection. The first, input image will divide into SxS grid cell, Yolo will predict 5 bounding box for each cell and with each bounding box, Yolo also predict the center coordinates of box, width, height of box and confidence score of having any object in that box along with the probabilities that object will belong to M classes. After that, we use Non Max Suppression and IOU to calculate the accuracy between bounding box with ground truth and get only the most exactly bounding box for the object in the input image.
Currently, I am working to create a deep neural network for object detection, and i am also create my own dataset, and I use the bounding box to annotate my images, and my question is what are the rules to have the best bounding box for my images training. I mean if I wrap my object is it good to limit the background of my object or do I need t find a way to bound only my object.
Thanks,