The pervasiveness of 'Internet-of-Things' in daily life has led to a recent surge in fog computing, encompassing a collaboration of cloud computing and edge intelligence. As a significant field of IoT, real-time detection and classification have a huge demand. Due to the insufficiency of computing power in mobile devices and the increment of network bandwidth, combination of edge devices and cloud servers would be an accessible orientation for real-time tasks. In this work, we present ECNet - an edge-cloud network system dealing with the balance between performance and time cost. We propose to transmit the output ferefature map of an exit point to the cloud with offload controller and quantizer deployed to minimize the transmission cost. ECNet is tested to reach a balance between processing time and accuracy performance with reducing transmission cost down to 25%. We also consider implementing an integrated feature map encoder to further reduce the bandwidth demand and meanwhile minimize the loss of accuracy. Additional achievements could be expected in our future work.