Yolov5, Yolo-x, Yolo-r, Yolov7 Performance Comparison: A Survey
Ismat Saira Gillani, Muhammad Rizwan Munawar, Muhammad Talha, Salman Azhar, Yousra Mashkoor, Muhammad Sami uddin, Usama Zafar
Abstract
YOLOv7 algorithm have taken the object detection domain by the storm as its real-time object detection capabilities out ran all other previous algorithms both in accuracy and speed [1]. YOLOv7 advances the state of the art results in object detection by inferring more quickly and accurately than its contemporaries. In this paper, we are going to present our work of implementing this SOTA deep learning model on a soccer game play video to detect the players and football. As the result, it detected the players, football and their movement in real time. We also analyzed and compared the YOLOv7 results against its previous versions including YOLOv4, YOLOv5 and YOLO-R. The code is available at: https://github.com/RizwanMunawar/YOLO-RX57-FPS-Comparision