IDEA-Research/Grounded-SAM-2

Health v2 · maintenance only

Adoption not part of the score

3708 stars · 430 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Grounded SAM 2 is a foundation-model pipeline that combines open-set detectors (Grounding DINO, Grounding DINO 1.5/1.6, Florence-2, DINO-X) with SAM 2 to detect, segment, and track arbitrary objects in images and videos from text prompts. It provides simple demo implementations and visualization built on the supervision library.

Use cases

• segment any object in an image using a text prompt
• track objects across video frames with text-based grounding
• auto-annotate datasets with masks for training segmentation models
• detect and segment dense small objects in high-resolution 4K images with SAHI
• visualize detection, segmentation, and tracking results on videos
• run zero-shot open-vocabulary segmentation benchmarks

When to choose

• you need text-prompted open-vocabulary detection and segmentation without training a custom model
• you want to segment and track objects in videos with a proven research pipeline
• you need automated mask annotation for dataset labeling
• you want simple reference implementations combining Grounding DINO/Florence-2/DINO-X with SAM 2

When to avoid

• you need a production-ready optimized inference service rather than demo notebooks
• you lack GPU resources, since the models are heavy to run locally
• you only need classic closed-set detection with fixed classes, where a lightweight YOLO-style detector suffices
• you need real-time performance on edge devices

Facets

library · maturity active

computer-vision image-processing machine-learning deep-learning computer-vision image-processing artificial-intelligence deep-learning python cross-platform object-detection image-segmentation video-object-tracking open-vocabulary-detection sam2 grounding-dino florence-2 dino-x zero-shot jupyter-notebooks video gpu

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markdown · JSON · MCP: product_card(name="IDEA-Research/Grounded-SAM-2")