Developed a refined VGG-based image classification architecture for the keras-team/keras-hub repository, focusing on aligning the model with original VGG specifications to enhance robustness and deployment readiness. The work involved correcting pooling strategies, implementing ReLU activation in the classification head, and standardizing dropout and padding configurations to improve model consistency and maintainability. Updated checkpoint conversion tools to support multi-preset compatibility, ensuring reliable deployment and reproducibility across environments. Leveraged Python, Keras, and TensorFlow to deliver these improvements, with clear documentation and signed-off commits that facilitate collaboration. The resulting architecture supports faster production deployment and improved model quality for deep learning workloads.