We seek to improve crowd counting as we perceive limits of currently prevalent density map estimation approach on both prediction accuracy and time efficiency. We show that a CNN regressing a global count trained with density map supervision can make more accurate prediction. We introduce multilayer gradient fusion for training a density-aware global count regressor. More specifically, on training stage, a backbone network receives gradients from multiple branches to learn the density information, whereas those branches are to be detached to accelerate inference. By taking advantages of such method, our model improves benchmark results on public datasets and exhibits itself to be a new solution to crowd counting problem in practice. Our code is publicly available at: unmapped: uri https://github.com/GeorgeChenZJ/deepcount.