DualRes: Production-ready Dynamic Object Detection

Dynamic Neural Networks (DNNs) have emerged as a promising solution to improve the computational efficiency of deep neural networks by adaptively adjusting inference complexity based on input characteristics. Despite their advantages, the deployment of dynamic networks in real-world applications remains challenging because most methods are hard to adapt for practical use cases such as object detection, in combination with the lacking support of inference infrastructure.In this work, we present a dynamic neural network architecture specifically designed for object detection. Using our method, we build a variety of Pareto-optimal models for object detection on COCO for models in the 7-10 GFLOPs range.Additionally, to measure the routing efficacy, we introduce an evaluation metric that facilitates standardized benchmarking across different dynamic network approaches. Finally, we introduce an evaluation of a deployment pipeline utilizing the ONNX format, thus building a DNN that shows speedup in a realistic deployment scenario. Experimental results demonstrate the performance and practical viability of our approach for efficient object detection in resource-constrained scenarios.