A comparison between deep-learning models for scene recognition
Zongzhen Liu, Jianlin Zhang, Xiaoming Peng · 2024
Scene recognition has a wide range of applications in autonomous driving, security monitoring, smart home, and so on. Though traditional methods achieved good results in this field, nowadays deep-learning methods are the dominator. In this paper, we conducted an extensive comparison of the performance of five deep-learning models on a common dataset to reveal their strength and weakness. Experimental results show that, of the five deep learning models, ConvNeXt works the best. In addition, all five models outperform a traditional method that used to be the state of the art.