facebookresearch/segment-anything

Health v2 · maintenance only

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54759 stars · 6355 forks observed · 2026-08-28

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

Segment Anything Model (SAM) from Meta AI is a promptable image segmentation foundation model that produces high-quality object masks from point or box prompts, with strong zero-shot performance. This repository provides inference code, pretrained model checkpoints, and example notebooks for using the model.

Use cases

• segment all objects in an image
• generate object masks from point or box prompts
• zero-shot image segmentation
• extract masks for image editing pipelines
• label segmentation data for training other models
• run SAM inference in a Python notebook

When to choose

• you need high-quality promptable segmentation masks without training a custom model
• you want a well-tested, widely adopted segmentation foundation model with pretrained checkpoints
• you need zero-shot segmentation across diverse visual domains

When to avoid

• you need video segmentation or streaming memory - use SAM 2 instead
• you need real-time inference on CPU-constrained hardware
• you need semantic class labels rather than class-agnostic masks

Facets

library · maturity stable

machine-learning computer-vision image-processing deep-learning computer-vision image-processing artificial-intelligence machine-learning python cross-platform segment-anything image-segmentation foundation-model promptable-segmentation object-masks zero-shot pytorch meta-ai gpu

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