CNN Architectures for Image Classification: A Comparative Study Using ResNet50V2, ResNet152V2, InceptionV3, Xception, and MobileNetV2

Image processing techniques have been used for picture categorization in several domains over the last year, including education, research, railways, and other sectors. The CNN (Convolutional Neural Network) is often regarded as the most potent method for picture categorization. This study included five renowned image-processing algorithms using the CNN architecture: RestNet50V2, RestNet152V2, Xception, Inceptionv3, and MobileNetV2. We assessed the classification of the Uttaranchal University, Dehradun dataset, which has 20 distinct department photos for classification. After a certain iteration, our primary goal is to achieve the best model accuracy possible using the available hardware. To evaluate performance, we used other measures such as accuracy, recall, and F1-score. The investigation demonstrated the exceptional precision of all five algorithms: RestNet50V2 (98.88), RestNet152V2 (99.10), Xception (99.17), Inceptionv3 (99.2), and MobileNetV2 (93.71). The Xception method was chosen for data training, testing, and validation because of its superior accuracy. Hardware resources, memory capacity, and data diversity are also considered while assessing algorithm pros and cons. This research sheds light on the CNN model's performance and helps companies and universities choose better photo classification algorithms. This research has also advanced machine learning and deep learning algorithms, as well as their practical application in real-world situations.