Introduction

The full name of yolov2’s paper is yolov000: better, faster, stronger, which won CVPR 2017 best paper honorable meaning. In this paper, the author first proposes an improved yolov2 based on yolov1, and then proposes a joint training method of detection and classification. Using this joint training method, the yolov000 model is trained on coco detection data set and Imagenet classification data set, which can detect more than 9000 kinds of objects. Therefore, this article actually contains two models: yolov2 and yolo9000, but the latter is based on the former, and the main structure of the two models is consistent. Compared with yoov1, yoov2 has made many improvements, which also makes the map of yoov2 significantly improved. Moreover, the speed of yoov2 is still very fast, maintaining its advantages as one stage method. The comparison between yoov2 and fast r-cnn, SSD and other models is shown in Figure 1. This paper will first introduce the improvement strategy of yoolv2, and give the implementation process of tensorflow of yoolv2, and then introduce the training method of yool9000.

paper address : yolo9000: better, faster, stronger

Improvement strategy of yolov2

Although the detection speed of yolov1 is very fast, its detection accuracy is not as good as that of r-cnn system. Yolov1 is not accurate enough in localization and recall. Yolov2 proposes several improvement strategies to improve the positioning accuracy and recall rate of Yolo model, so as to improve map. Yolov2 follows one principle in the improvement: maintain the detection speed, which is also a major advantage of Yolo model. The improvement strategy of yolov2 is shown in Figure 2. It can be seen that most of the improvement methods can significantly improve the map of the model. The improvement strategies are as follows:

for details, please refer to:

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