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The deployment of deep learning models for real-time image classification on resource-constrained sensor devices presents significant challenges. These devices face strict limitations in computational power, energy capacity, and memory resources, making it difficult to achieve both high accuracy and low latency. Current approaches either compromise model performance through compression or incur substantial overhead by offloading computation to remote servers. We introduce a novel distributed progressive inference platform that addresses these limitations by dynamically balancing local and remote computation. Our system employs reinforcement learning to make intelligent decisions about when and where to perform inference. Experimental results across multiple standard datasets demonstrate that our approach achieves up to 3% higher accuracy while reducing network traffic and preserving battery life compared to existing methods.