We propose sparse-complementary convolution (SC-Conv) to improve model utilization of convolution neural networks (CNNs). The networks with SC-Conv achieve better accuracy than the regular convolution under similar computations and parameters. The proposed SC-Conv is paired with two deterministic sparse kernels, and one of kernels is complementary to the other one at in either spatial or channel domain or both; the deterministic sparsity increases the computational speed theoretically and practically; furthermore, by having the complementary characteristic, SC-Conv retains the same receptive field to the conventional convolution. This insightful but straightforward SC-Conv reuses of modern network architectures (ResNet and DenseNet), and at the same FLOPs and parameters, SC-Conv improves top-1 classification accuracy on ImageNet by 0.6 points for ResNet-101 and keep the same model complexity. Furthermore, SC-Conv also outperforms recent sparse networks by 1.3 points at top-1 accuracy for ImageNet, and after integrating SC-Conv with the sparse network, we further improve another 1.8 points accuracy at similar FLOPs and parameters.