In this chapter, we will be using version 3 of the YOLO object detection algorithm, which further improves upon the old version of YOLO in terms of both speed and accuracy. Let's see how YOLO is different from other object detection networks:
- YOLO looks at the whole image during the testing process, so the prediction of YOLO is informed by the global context of the image.
- In general, networks such as R-CNN require thousands of networks to predict a single image, but in the case of YOLO, only one network is required to look into the image and make predictions.
- Due to the use of a single neural network, YOLO is 1,000x faster than other object detection networks (https://pjreddie.com/darknet/yolo/).
- YOLO treats detection as a regression problem.
- YOLO is extremely fast and accurate.
YOLO works as follows:
- YOLO takes the input image and divides it into a grid of SxS. Every grid cell predicts one entity.
- YOLO applies image classification and localization...