Optimizing Real-Time Image Classification with Tensorflow: A Performance-Centric Approach

Real-time image classification plays a pivotal role in applications such as autonomous driving, medical imaging, and surveillance, where both high accuracy and low-latency are critical. This study presents a performance-centric optimization framework for real-time image classification using TensorFlow, aiming to balance inference speed and model accuracy. The proposed approach integrates model pruning, quantization, and GPU acceleration to optimize Convolutional Neural Networks (CNNs) for deployment in resource-constrained environments. Experimental results on the ImageNet dataset demonstrate that the optimized model achieves 92% accuracy while reducing inference time to 45 milliseconds per image, outperforming both baseline CNN and MobileNetV2 models. Additionally, a 30% reduction in memory usage is observed, highlighting the efficiency of the optimization techniques. These results underscore the feasibility of deploying deep learning models in real-time applications, offering a viable solution for sectors where rapid decision-making and high classification performance are essential.