segment-anything
Provides code for running inference with the SegmentAnything Model (SAM), including model checkpoints and example notebooks.
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
Segment-Anything is a tool for image segmentation tasks, offering model checkpoints and example notebooks for generating masks from images.
Good fit when
- When you need to perform image segmentation tasks with high accuracy and flexibility, as Segment-Anything provides a robust model and detailed notebooks for prompt-based and automatic mask generation.
- If you are working with complex images where manual segmentation is impractical, Segment-Anything can automate the process with its automatic mask generator feature.
Avoid when
- If your project strictly requires real-time segmentation, as Segment-Anything, while efficient, may not meet the latency requirements of real-time applications.
- When you are working in a resource-constrained environment, as Segment-Anything requires a significant amount of computational resources, including CUDA support for optimal performance.
- Requirements:
- Min 8 GB RAM; Requires Python 3.8 or higher.; PyTorch 1.7 and torchvision 0.8 or higher are required.; CUDA support is recommended for optimal performance.
Observed Sep 18, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (729d since push)
- As of Sep 18, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 18, 2026
- Security (OSV)
- No lockfile
- As of Sep 18, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/facebookresearch/segment-anythingHow it fits your stack(2)
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
This repository offers tools for using the SegmentAnything Model (SAM) for image segmentation tasks. It includes installation instructions, example notebooks, and scripts for generating masks from images.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Sep 18, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 18, 2026)
The code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. Please follow the instSource link
Tags
README
Installation The code requires , as well as and . Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended. Install Segment Anything: or clone the repository locally...
For agents
This page has a .md twin and JSON over the API.