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Abstract: self-supervised depth estimators present excellent results that are comparable to the fully supervised approaches to monocular depth estimation, by employing view synthesis between the target and reference images in the training data. ResNet, which has served as a backbone network has some structural deficiencies when applied to downstream fields, because its original purpose was to cope with classification problems. The low-texture area also makes performance suffer greatly. To address these problems, we propose a set of improvements that lead to superior predictions. First, we boost the information flow in the network and improve the ability to learn spatial structures by improving the network structures. Second, we use a binary mask to remove the pixels in low-texture areas between the target and reference images to more accurately reconstruct the image. Finally, we input the target and reference images randomly to expand the data set and pre-train it on ImageNet, so that the model obtains a favorable general feature representation. We demonstrate state-of-the-art performance on an Eigen split of the KITTI driving dataset using stereo pairs.