segment-anything
Provides code for running inference with the SegmentAnything Model (SAM).
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Decision brief
An AI tool for segmentation tasks offering pre-trained models and straightforward integration methods.
Good fit when
- When you need precise segmentation in images with varied objects or regions, as SAM provides high-quality mask generation from prompts.
- If your project requires both manual prompt-based and automatic segmentation capabilities, leveraging the SamPredictor and SamAutomaticMaskGenerator classes.
Avoid when
- Avoid using SAM if your project's constraints specifically require real-time performance since running inference demands significant computational resources.
- Do not choose this tool when a lightweight or resource-efficient solution is needed, as it relies on heavyweight pre-trained models that may be unsuitable for devices with limited computing power.
- Requirements:
- Min 8 GB RAM; Requires Python >=3.8, PyTorch >=1.7 with CUDA recommended for faster performance; Optional dependencies such as OpenCV and ONNX may further enhance functionality but are not always necessary for basic use.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
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- Not a fork · Organization account
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Install
git clone https://github.com/facebookresearch/segment-anythingHow it fits your stack(2)
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Evidence and technical details
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Overview
Repository that offers tools to use and run inference on the pre-trained SegmentAnything Model, along with example notebooks.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Aug 1, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 1, 2026)
The code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. Please follow the instSource link
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README
Installation
The code requires python>=3.8, as well as pytorch>=1.7 and torchvision>=0.8. 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:
pip install git+https://github.com/facebookresearch/segment-anything.git
or clone the repository locally and install with
git clone git@github.com:facebookresearch/segment-anything.git
cd segment-anything; pip install -e .
The following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. jupyter is also required to run the example notebooks.
pip install opencv-python pycocotools matplotlib onnxruntime onnx
Getting Started
First download a model checkpoint. Then the model can be used in just a few lines to get masks from a given prompt:
from segment_anything import SamPredictor, sam_model_registry
sam = sam_model_registry["<model_type>"](checkpoint="<path/to/checkpoint>")
predictor = SamPredictor(sam)
predictor.set_image(<your_image>)
masks, _, _ = predictor.predict(<input_prompts>)
or generate masks for an entire image:
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
sam = sam_model_registry["<model_type>"](checkpoint="<path/to/checkpoint>")
mask_generator = SamAutomaticMaskGenerator(sam)
masks = mask_generator.generate(<your_image>)
Additionally, masks can be generated for images from the command line:
python scripts/amg.py --checkpoint <path/to/checkpoint> --model-type <model_type> --input <image_or_folder> --output <path/to/output>
See the examples notebooks on using SAM with prompts and automatically generating masks for more details.
License
The model is licensed under the Apache 2.0 license.
For agents
This page has a .md twin and JSON over the API.