Simple implementation of Yolov3 given in this https://pjreddie.com/media/files/papers/YOLOv3.pdf paper in TF 2 for understanding

The output of the three branches of the YOLOv3 network will be sent to the decode function to decode the channel information of the Feature Map. In the following picture: the black dotted box represents the a priori box (anchor), and the blue box represents the prediction box. The dimensions of the bounding box are predicted by applying a log-space transformation to the output and then multiplying with an anchor:

• b denote the length and width of the prediction frame respectively, and P denote the length and width of the a priori frame respectively.
• t represents the offset of the center of the object from the upper left corner of the grid, and C represents the coordinates of the upper left corner of the grid.

Non-Maximum Suppression, as the name implies, suppresses elements that are not maximal. NMS removes those bounding boxes that have a higher overlap rate and a lower score. The algorithm of NMS is straightforward, and the iterative process is as follows:

• Process 1: Determine whether the number of bounding boxes is greater than 0, if not, then end the iteration;
• Process 2: Select the bounding box A with the highest score according to the score order and remove it;
• Process 3: Calculate the IoU of this bounding box A and all remaining bounding boxes and remove those bounding boxes whose IoU value is higher than the threshold, repeat the above steps.

Run the Demo

Download the yolov3 weights from https://pjreddie.com/media/files/yolov3.weights and place in models/yolov3.weights

• ALL the core yolov3 implementation are found inside /yolov3 directory.
  • Or you can download weights by simply running

     python download_weights.py

  This will download yolov3.weights inside models diretory.
• Change the configuration inside yolov3/config.py

python detection_demo.py