Image Recognition and Extraction of Deep Features Using GoogLeNet

Image recognition is a crucial task in computer vision, facilitating object detection and classification across various domains. Convolutional Neural Networks (CNNs) have emerged as state-of-the-art models for this task, with GoogLeNet, a deep network based on the Inception module, demonstrating superior performance in large-scale visual recognition challenges. This paper explores the effectiveness of GoogLeNet in deep feature extraction for image recognition, leveraging Keras with Theano as the backend. Unlike conventional CNN architectures, GoogLeNet introduces multiple convolutional filter sizes within the same layer, enhancing representational power while maintaining computational efficiency. We conduct rigorous experimentation by training and testing GoogLeNet on a benchmark dataset and evaluate its performance against alternative deep learning models such as ResNet and EfficientNet. Our findings indicate that GoogLeNet achieves competitive accuracy while maintaining efficiency, making it suitable for real-time applications. Furthermore, we analyze feature activations and conduct an ablation study to assess the impact of auxiliary classifiers and different inception module configurations. The results highlight the robustness of GoogLeNet in extracting deep semantic features from images. This research provides valuable insights into the trade-offs between network depth, accuracy, and computational complexity, contributing to the advancement of image recognition models in practical applications.