Accurate and efficient ship detection in remote sensing images still remains a challenging task due to the large variations of scales, orientations and distributions. In this paper, we propose an anchor-free ship detector that directly regresses ship localization parameters, offering a simpler pipeline over the previous methods. The detection network is then trained in a multi-task fashion which contains not only the ship center-point maps and oriented bounding boxes but the ship masks. Instead of fixing the weights among the multiple task losses, we adopt a curriculum learning strategy which gradually adapts the loss weights during the training process so that the network can learn the discriminative ship features at the early stage and obtain more localization information while training continues. Experimental results on real dataset demonstrate the effectiveness and efficiency of our proposed method.