Accelerate Image Annotation with SAM and Grounding DINO - Python Tutorial

Learn to accelerate image annotation using Grounding DINO and Segment Anything Model (SAM) in this Python tutorial. Discover techniques for converting object detection datasets into instance segmentation datasets and explore the potential of these models for automatic dataset annotation for real-time detectors like YOLOv8. Set up the Python environment, load the necessary models, and master single image and full dataset mask auto-annotation. Gain insights into saving labels in Pascal VOC XML format, uploading annotations to Roboflow, and refining them using the Roboflow UI. Convert object detection datasets to instance segmentation and explore the future possibilities of this technology.

Introduction
Python Environment Setup
Load Grounding DINO and Segment Anything Models
Single Image Mask Autoannotation
Full Dataset Mask Autoannotation
Save Labels to Pascal VOC XML
Upload Annotations to Roboflow
Review and Refine Annotations in Roboflow UI
Convert Object Detection to Instance Segmentation Dataset
Conclusions
Announcement