Grounding-DINO + Segment Anything Model (SAM) vs Mask-RCNN: A comparison

This tutorial explores zero-shot object segmentation using Grounding-DINO and Segment Anything Model (SAM) and compares its performance to a standard Mask-RCNN model. Zero-shot object segmentation enables models to identify and segment objects within images, even if they have never encountered examples of these objects during training. The tutorial delves into what Grounding-DINO and SAM are and how they work together to achieve great segmentation results. Additionally, the tutorial introduces DINO-v2, a groundbreaking self-supervised computer vision model that excels in various tasks, including segmentation. By leveraging zero-shot object segmentation techniques, researchers and developers can create more efficient and versatile models that can adapt to new, unseen objects without the need for retraining or obtaining additional labeled data.