Computer Vision Instance Segmentation
Instance segmentation is a computer vision technique that allows us to identify objects in an image and segment each pixel of the object from the background. This is a challenging task, but there are several resources available to make it easier. One popular implementation is the Mask R-CNN model, which is built on top of the Faster R-CNN object detection framework. Several libraries like Matterport and Detectron2 provide implementation of this model in Python using TensorFlow and PyTorch backends respectively. The Common Objects in Context (COCO) dataset is a popular benchmark for instance segmentation models. Other resources such as Stanford CS231n lecture notes and OpenCV also provide a comprehensive understanding of instance segmentation.