Instance segmentation is a common task in computer vision specifically, and computer science in general. Its applications are widely used in areas such as autonomous driving and automotive systems. However, current instance segmentation models are often limited, as they only perform well on fixed training sets. This creates a significant challenge in real-world applications, where the number of classes is strongly dependent on the training data. To address this limitation, we propose the concept of Open World Instance Segmentation (OWIS) with two main objectives: (1) segmenting instances not present in the training set as an “unknown" class, and (2) enabling models to incrementally learn new classes without forgetting previously learned ones, with minimal cost and effort. These objectives are derived from open world object detection task Joseph et al. We also introduce new datasets following a novel protocol for evaluation, along with a strong baseline method called ROWIS (Real-Time Open World Instance Segmentor), which incorporates an advanced energy-based strategy for unknown class identification. Our evaluation, based on the proposed protocol, demonstrates the effectiveness of ROWIS in addressing real-world challenges. his research will encourage further exploration of the OWIS problem and contribute to its practical adoption. Our code was published at https://github.com/4ursmile/ROWIS.