In recent years, single-stage instance segmentation methods have made preliminary progress in real-world applications due to their high efficiency, but there are still two drawbacks compared to two-stage counterparts. (1) Low accuracy: the single-stage method does not have multiple rounds of refinement, so its accuracy is some distance away from real-world applications; (2) Low flexibility: most existing single-stage methods are specifically designed models, which are not compatible with object detectors. This paper presents an accurate and flexible framework for single-stage instance segmentation, which contains the following two key designs. (1) To improve the accuracy of instance segmentation, a grid dividing binarization algorithm is proposed, where the bounding box region is firstly divided into several grid cells and then instance segmentation is performed on each grid cell. In this way, the original full-object segmentation task is simplified into the sub-tasks of grid cells, which significantly reduces the complexity of feature representation and further improves the instance segmentation accuracy; (2) To be compatible with object detectors, a plug-and-play module is designed, which can be seamlessly plugged into most existing object detection methods, thus enabling them to perform instance segmentation. The proposed method achieves excellent performance on the public dataset, such as MS COCO. It outperforms most existing single-stage methods and even some two-stage methods.