Optimization of Neural Network for Identifying Scene Images

The fast progress of deep learning has greatly enhanced the accuracy of image classification tasks, especially in scene image recognition. Nevertheless, the challenge of high accuracy coupled with computational efficiency still exists. The optimization of neural network structures is targeted with the aim of boosting the recognition of scene images. Different optimizing methods, such as hyperparameter adjustment, architectural alterations, and regularization schemes, are investigated with the objective of enhancing accuracy as well as speed. Pre-trained convolutional neural networks (CNNs) like VGG, ResNet, and EfficientNet are tested and fine-tuned through transfer learning on scene specific datasets. Model pruning and quantization are also applied to decrease model complexity without impacting performance. Experimental results show that optimized models deliver better accuracy with decreased training time and lower resource utilization. This work demonstrates the efficacy of focused neural network optimization methods to create robust and effective systems for real-world scene image identification tasks.