One-stage object detectors like SSD and YOLO are able to speed up existing two-stage detectors like Faster R-CNN by removing the object proposal stage and making up for the lost performance in other ways. Nonetheless, the same approach is not easily transferable to instance segmentation task. Current one-stage instance segmentation methods can be simply classified into segmentation-based methods which segment first then do clustering, and proposal-based methods which detect first then predict masks for each instance proposal. Proposal-based methods always enjoy a better mAP; by contrast, segmentation-based methods are generally faster when inferencing. In this work, we first propose a one-stage segmentation-based instance segmentation solution, in which a pull loss and a push loss are used for differentiating instances. We then propose two post-processing methods, which provide a trade-off between accuracy and speed.