Normal estimation from monocular images is one of the most important issues in computer graphics and computer vision research． Short of three-dimensional information，the corresponding normal is predicted from the monocular images，which is of great significance for 3D scene reconstruction，3D model recognition，3D semantic segmentation，etc． In order to find the solution to the problem，this paper adopts a multi-scale convolutional net- work structure to predict an end-to-end output of the image． The network consists of two scales，the first layer uses the DenseNet classification network with the best performance in ImageNet to process the input globally． The second level uses a fully convolutional network to further fine-tune the output obtained from the first level． The experimen- tal results show that the network proposed in this paper can achieve better results in normal prediction of monocular image even without using other pre-processing or post-processing steps．