Mask R-CNN is a deep learning model designed for object detection and instance segmentation. It builds on the Faster R-CNN framework by adding a branch that predicts segmentation masks for each detected object. This allows the model to not only identify objects in an image but also to outline their precise shapes, making it useful for tasks like image editing and scene understanding. The architecture of Mask R-CNN includes a backbone network, typically a ResNet, for feature extraction, followed by a Region Proposal Network (RPN) to suggest potential object locations. Finally, it uses a mask prediction layer to generate binary masks for each object, enabling detailed analysis of images in various applications, from autonomous driving to medical imaging.