Advanced Deep Learning Models for Real-Time Image Recognition: Bridging the Gap between Accuracy and Speed

Cutting-edge deep learning algorithms have improved image recognition. This study aims to balance real-time image identification speed and accuracy. Analysis of deep-learning architectures and challenge-specific optimization approaches do this. This work uses deep learning and edge computing to enhance real-time image recognition. The technique suggests using the successful InceptionV3 architecture to train on the CelebA dataset of 202,599 annotated celebrity images. In this work, thorough preprocessing and feature extraction guarantee high-quality input data. Impressively, the InceptionV3 model has an F1 score of 0.96, precision of 0.97, recall of 0.98, and accuracy of 0.96. Edge computing speeds up processing and response times. The algorithm correctly distinguishes faces in every context, according to the results. By merging deep learning models with edge computer networks, real-time image recognition system performs greatly.