Comparing the Performance of Pre-trained Deep Learning Models in Object Detection and Recognition
نویسندگان
1 Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, 86400, Malaysia.
2 Faculty of Computer & Artificial Intelligence, Beni-Suef University, Beni-Suef City, 62511, Egypt; College of Computer Information Technology, American University in the Emirates, United Arab Emirates.
3 Department of Computer Science, College of Education, AL-Iraqia University, Baghdad, Iraq.
4 Ph.D., College of Computer Science and Information Technology, University of Anbar, Ramadi, 31001, Iraq
5 Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
doi
10.22059/jitm.2022.88134چکیده
The aim of this study is to evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. This study uses the COCO dataset to evaluate both pre-trained image recognition and object detection, models. The results revealed that Tiny-YoloV3 is considered the best method for real-time applications as it takes less time. Whereas ResNet 50 is required for those applications which require a high probability percentage of prediction, such as medical image classification. In general, the rate of probability varies from 75% to 90% for the large objects in ResNet 50. Whereas in Tiny-YoloV3, the rate varies from 35% to 80% for large objects, besides it extracts more objects, so the rise of execution time is sensible. Whereas small size and high percentage probability makes SqueezeNet suitable for portable applications, while reusing features makes DenseNet suitable for applications for object identification.
