OpenGVLab/SAM-Med2D

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1134 stars · 110 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official implementation of SAM-Med2D, a fine-tuned Segment Anything Model (SAM) for 2D medical image segmentation, trained on the SA-Med2D-20M dataset (4.6M images, 19.7M masks). Includes training, testing, and inference code plus pretrained weights.

Use cases

• segment medical images like CT and MRI scans
• fine-tune SAM on medical imaging data
• run promptable segmentation with point, bbox, or mask prompts on medical images
• evaluate a medical image segmentation model on large-scale datasets
• download a large medical segmentation dataset

When to choose

• you need promptable segmentation adapted to 2D medical images
• you want the largest curated medical segmentation dataset for training or benchmarking
• you want pretrained weights and train/test code for medical SAM fine-tuning

When to avoid

• you need 3D medical image segmentation (use SAM-Med3D instead)
• you need general-purpose natural image segmentation (original SAM suffices)
• you need a production-ready clinical application rather than research code

Facets

library · maturity active

machine-learning image-processing computer-vision healthcare deep-learning python segment-anything medical-imaging segmentation fine-tuning promptable-segmentation gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="OpenGVLab/SAM-Med2D")