The distribution and appearance of nuclei are essential bio markers for the diagnosis and study of cancer. Despite the importance of nuclear morphology accurate segmentation and classification of nuclei instances is still one of the most challenging tasks due to the wide occurrence of overlapping, cluttered nuclei having blurred boundaries. Existing methods particularly focus on region proposal techniques and feature encoding frameworks, however often fail to precisely identify instances. In this paper we propose a simple yet effective model that precisely recognizes instance boundaries as well as caters to exhaustive class imbalance problems, thus yielding accurate class information for each nucleus. We have also proposed a novel loss function that draws the same nuclei instance pixels function pulls together for learning an object-based clustering bandwidth thus reinforcing the jaccardian index of the nuclei instance.