The best approach
In this section, we will be trying the approach named YOLO. YOLO stands for You Only Look Once. This technique gives us good accuracy, is fast, and its memory management is easy. This section will be divided into two parts:
Understanding YOLO
Implementing the best approach using YOLO
In the first section, we will understand the basics about YOLO. During the implementation, we will be use YOLO with the pre-trained YOLO model. So, let's begin!
Understanding YOLO
YOLO is a state-of-the-art, real-time object detection system. On GPU Titan X, it processes images at 40-90 FPS and has a mAP on the PASCAL VOC dataset of 78.6% and a mAP of 48.1% on the coco test-dev dataset. So, now, we will look at how YOLO works and processes the images in order to identify the objects. We are using YOLOv2 (YOLO version 2) as it is a faster version.
The working of YOLO
YOLO reframes the object detection problem. It considers the object recognition task as single regression problem, right from the image...
