Zero-Shot Image Annotation with Grounding DINO and SAM - A Notebook Tutorial

Recent advancements in AI, specifically through models like Grounding DINO and Segment Anything Model (SAM), have significantly enhanced the process of annotating images for object detection and instance segmentation, making it quicker and more accurate. Grounding DINO excels in zero-shot detection of objects, generating bounding boxes, while SAM converts these into precise instance segmentation masks, streamlining the conversion of object detection datasets into instance segmentation datasets. Although automated annotation is not entirely ready to replace human validation, these models save considerable time and effort in data annotation, especially for common object classes. The blog post includes a Jupyter Notebook tutorial and highlights the potential for even further improvements with the development of a Python library aimed at transferring knowledge from zero-shot models to real-time detectors, promising to revolutionize dataset annotation and accelerate computer vision projects.