YOLO-Underhood

In this repo , I will display you the underhood of YOLO algorithm it could be useful for making your object detection more precise and flexible.

Let's breakdown the steps:

1. Your Deep-CNN yolo models take image as input and give you the emedding as output
2. We will now take this embedding and convert it to the best bounding box,scores and class name using some of our custom functions.

• Yolo filter boxes : It returns the filter boxes above the threshold from all H_image X W_image X anchor_box ,
• IoU : It is a function used in non-max suppression to remove boudning box above the IoU threshold.
• Non-Max suppression : It select the most confidence bounding boxes among all the boxes.
• Converting yolo model encoding to the bounding box, scores and class
• Putting all together : Using above function to detect object from the image using pretrained model.