Segment Anything Model (SAM)

Meta AI's foundation model for promptable image segmentation across any object or scene.

4.8 (5)

レビュー: Daniel Nikulshyn·更新 2026年5月

Foundation Model Research Open Source Computer Vision PyTorch Image Segmentation Zero-Shot

概要

Segment Anything Model (SAM) is an open image segmentation system developed by Meta AI Research. Given an image and a simple prompt such as a point, box, or rough mask, it produces high-quality segmentation masks for virtually any object, without needing task-specific training. SAM was trained on the SA-1B dataset, which contains over a billion masks across 11 million images, giving it strong zero-shot generalization. It can be integrated into computer vision pipelines for tasks like annotation, image editing, medical imaging, robotics, AR/VR, and scientific analysis. The model and dataset are released under permissive terms, making SAM a common building block for researchers and developers who need flexible segmentation without training a custom model from scratch.

主な機能

Promptable segmentation with points and boxes
Automatic mask generation for entire images
Pretrained ViT-based image encoder
Zero-shot transfer to new domains
Open-source code and SA-1B dataset
Integrates with PyTorch and common CV stacks

ユースケース

Accelerated dataset annotation

Use SAM's promptable segmentation to rapidly label objects in image datasets with simple clicks or boxes, cutting manual annotation time for ML training pipelines.

Image editing and compositing

Generate precise object masks for background removal, selective edits, or compositing in creative tools without training a custom segmentation model.

Medical and scientific imaging analysis

Apply zero-shot segmentation to medical scans or scientific imagery to isolate structures of interest, aiding measurement and downstream analysis.

Robotics and AR/VR perception

Integrate SAM into computer vision pipelines for object isolation in robotics manipulation or AR/VR scene understanding using point or box prompts.

メリット & デメリット

メリット

Strong zero-shot segmentation on unseen objects
Flexible prompts: points, boxes, or masks
Open weights and large public dataset
Easy to integrate via official Python library

デメリット

Large model can be heavy for real-time use on CPU
Does not assign semantic class labels
Quality drops on very small or fine structures
Requires prompts or automatic mask generation setup

レビュー

4.8

5件の評価の平均。

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Aaliyah Johnson

Years in this space

I've evaluated a lot of these over the years. What stands out here is pretrained ViT-based image encoder — handled better than most — and strong zero-shot segmentation on unseen objects. Worth the time if this is your use case.

Margaret Whitfield

Years in this space

I've evaluated a lot of these over the years. What stands out here is promptable segmentation with points and boxes — handled better than most — and flexible prompts: points, boxes, or masks. Worth the time if this is your use case.

Hannah Goldberg

Use it every day

Honestly didn't expect to like it this much. Integrates with PyTorch and common CV stacks is exactly what I needed, and easy to integrate via official Python library. I do wish requires prompts or automatic mask generation setup, but I reach for it almost every day now and it just clicks.

Olga Ivanova

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on zero-shot transfer to new domains, and strong zero-shot segmentation on unseen objects caught me off guard. Requires prompts or automatic mask generation setup is why this isn't a perfect score, still, I'd recommend giving it a real trial.

Linda Petersen

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on automatic mask generation for entire images, and open weights and large public dataset caught me off guard. still, I'd recommend giving it a real trial.