Project Overview
This tool is built on Meta’s Segment Anything Model 2 (SAM2) and provides an interactive image segmentation annotation interface. Running in a Jupyter Notebook environment, researchers can leverage SAM2’s zero-shot segmentation capability to quickly and precisely label target regions in images, making it well-suited for computer vision tasks that require large annotated datasets.Tech Stack
- Environment: Jupyter Notebook
- Core model: Meta SAM2 (Segment Anything Model 2)
- Language: Python
- Domain: Image segmentation, data annotation, computer vision dataset construction
Features
- Integrates SAM2 for zero-shot interactive segmentation
- Notebook-based workflow for step-by-step operation and parameter adjustment
- Rapid mask generation for multiple target regions within a single image
- Annotation results exportable for downstream training pipelines
- Applicable to semantic labeling of custom datasets
Source Code
GitHub Repository
felimet/SAM2_Annotation_Tool