Ismat Saira Gillani¹, Muhammad Rizwan Munawar², Muhammad Talha³, Salman Azhar⁴, Yousra Mashkoor⁵, Muhammad Sami uddin⁶ and Usama Zafar⁷, ¹Columbus State University, USA, ²COMSAT University, Pakistan, ³GC University Faisalabad, Pakistan, ⁴Air University, Pakistan, ⁵NED University, Pakistan, ⁶National University of Technology (NUTECH) Islamabad, Pakistan, ⁷GC University Faisalabad, Pakistan

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

Deep Learning, Object detection, Image segmentation, Instance segmentation, Network Architecture.