UX-Decoder/Semantic-SAM

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

2854 stars · 145 forks observed · 2026-08-28

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

Official PyTorch implementation of Semantic-SAM, a universal image segmentation model that segments and recognizes anything at any desired granularity, from semantic to instance to part level. It reproduces SAM training on SA-1B and extends it with controllable multi-granularity interactive and automatic mask generation.

Use cases

• segment objects in images at multiple granularity levels
• interactive click-based segmentation with multiple mask options
• auto-generate all masks for an image with controllable granularity
• reproduce SAM training on the SA-1B dataset
• part-level and semantic-aware segmentation
• research on universal image segmentation models

When to choose

• you need segmentation masks at semantic, instance, or part level from a single model
• you want SAM-like segmentation with finer granularity control
• you need training code and checkpoints for SAM-style models
• you are doing research on interactive or universal segmentation

When to avoid

• you need a production-ready product with a polished UI rather than research code
• you lack GPU resources for large vision transformer inference or training
• you need a permissively licensed dependency and cannot accept a missing license
• you only need simple off-the-shelf object detection without segmentation

Facets

library · maturity active

computer-vision image-processing machine-learning deep-learning computer-vision image-processing artificial-intelligence deep-learning python image-segmentation segment-anything sam interactive-segmentation multi-granularity eccv-2024 research-code linux gpu

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