  Depth prediction is fundamental for many useful applications on computer
vision and robotic systems. On mobile phones, the performance of some useful
applications such as augmented reality, autofocus and so on could be enhanced
by accurate depth prediction. In this work, an efficient fully convolutional
network architecture for depth prediction has been proposed, which uses RegNetY
06 as the encoder and split-concatenate shuffle blocks as decoder. At the same
time, an appropriate combination of data augmentation, hyper-parameters and
loss functions to efficiently train the lightweight network has been provided.
Also, an Android application has been developed which can load CNN models to
predict depth map by the monocular images captured from the mobile camera and
evaluate the average latency and frame per second of the models. As a result,
the network achieves 82.7% {\delta}1 accuracy on NYU Depth v2 dataset and at
the same time, have only 62ms latency on ARM A76 CPUs so that it can predict
the depth map from the mobile camera in real-time.
