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Mask R-CNN, or Mask Region-based Convolutional Neural Network, is a sophisticated deep learning model designed for computer vision tasks. It extends the capabilities of Faster R-CNN, which is a region-based model that identifies objects within an image and returns bounding boxes with class labels. Mask R-CNN enhances this by adding a third branch to the existing Faster R-CNN architecture, which is specifically tailored for instance segmentation. This additional branch generates segmentation masks for each object detected, not just bounding boxes. This feature is particularly useful in applications requiring precise object delineation, such as detecting and understanding the shape of probe tips in scanning electron microscope images. By producing both bounding boxes and detailed segmentation masks, Mask R-CNN facilitates a deeper understanding and analysis of individual object forms in complex scenes.

Mask R-CNN model for object detection and instance segmentation can be done by using a popular implementation available in TensorFlow's model library. The script below will use a pre-trained Mask R-CNN model to detect objects and their masks in an image.

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