The Meta SAM model (Segment Anything Model) is a family of computer-vision foundation models from Meta AI that can find and cut out any object in images and videos using simple prompts—points, boxes, masks, or even short text phrases. It’s designed to be a “GPT moment” for segmentation: a general model you reuse everywhere instead of training a new one for every task.

Below is a structured overview you can use directly on your site.

---

1. What is the Meta SAM Model?

Segment Anything Model (SAM) is an AI visionmodel that takes an image (or video, for newer versions) plus a prompt and returns pixel-accurate masks of the requested object(s).

Unlike classic segmentation models that are trained for a fixed list of classes, SAM is promptable and works in a zero-shot way: you can ask it to segment things it has never seen labeled explicitly, just by pointing or giving a short phrase.

SAM is backed by massive datasets:

• SA-1B: 11M images and 1.1B masks for the original SAM.

• SA-V: the largest video segmentation dataset for SAM 2.

• SA-Co: the “Segment Anything with Concepts” dataset for SAM 3, linking millions of images to noun-phrase concepts and masks.

---

2. Evolution of the SAM Models

2.1 SAM 1 – Segment Anything in Images

Released in 2023, the original SAM focuses on image segmentation.

Key ideas:

• Input: an image + points, boxes, or masks as prompts.

• Output: high-quality object masks that tightly follow edges.

• Trained on SA-1B, giving strong zero-shot performance across many domains.

It made segmentation feel interactive: click roughly where the object is, and SAM refines it for you.

---

2.2 SAM 2 – Images and Videos

SAM 2 extends SAM from images to videos. It’s a foundation model for promptable visual segmentation in images and videos.

What it adds:

• A transformer architecture with streaming memory, so it can process video frames in real time and remember what it has already seen.

• You still use points, boxes, or masks, but now SAM 2 tracks that object automatically through the whole clip.

• Trained on SA-V, the largest video segmentation dataset to date, giving strong performance on many video tasks.

Result: one model for interactive image segmentation + video object tracking.

---

2.3 SAM 3 – Segment Anything with Concepts

SAM 3 is the next step: instead of only “this object here”, it can segment all instances of a concept you describe with text or examples.

Two core task types:

• Promptable Visual Segmentation (PVS): like SAM/SAM 2 – points, boxes, masks to segment a specific instance.

• Promptable Concept Segmentation (PCS): text phrases (“striped cat”, “yellow taxi”) or exemplar regions to find every matching object in images or videos. 

According to Meta, SAM 3 delivers about a 2× performance gain over prior systems on their SA-Co concept segmentation benchmark while still keeping SAM 2’s interactive strengths.

It’s also what powers new experiences like Instagram Edits and Meta AI “Vibes”, where users will segment people or objects with natural language to apply effects.

---

2.4 SAM 3D – From 2D Segments to 3D

SAM 3D is the 3D companion: it reconstructs full 3D shape, texture, and layout from a single natural image.

It includes:

• SAM 3D Objects – 3D meshes for general objects and scenes. 

• SAM 3D Body – detailed 3D human meshes using the Momentum Human Rig. 

Together with SAM 3, it forms a pipeline: segment with text/visual prompts → send segments to SAM 3D → get ready to use 3D assets. 

---

3. How the SAM Models Work (High Level)

While each generation adds features, they share a few design principles:

3.1 Promptable segmentation

All SAM models are promptable: you don’t retrain them for each task; you prompt them:

• Visual prompts: points, boxes, existing masks.

• Text prompts (SAM 3): short noun phrases describing the concept.

• Exemplar prompts (SAM 3): a box around one example object.

The model encodes the image/video and the prompt together, then decodes masks or 3D outputs.

3.2 Foundation-model scale data

• SAM 1: SA-1B (11M images, 1.1B masks) for broad visual coverage. 

• SAM 2: SA-V, the largest video segmentation dataset, collected with a model-in-the-loop engine that learns from user interactions. 

• SAM 3: SA-Co, pairing millions of images with concept phrases + masks to support open-vocabulary PCS.

• SAM 3D: large 3D+image datasets, mixing synthetic pre-training with real-image alignment. 

This scale is what makes SAM behave more like a general vision foundation model than a narrow tool.

3.3 Architectures built for interaction and streaming

• SAM 1 uses a transformer-based image encoder + mask decoder. 

• SAM 2 adds streaming memory so it can process long videos incrementally. 

• SAM 3 further unifies detection, segmentation, and tracking with concept understanding, so one forward pass can find all instances of a concept. 

• SAM 3D uses diffusion-style generative backbones and 3D parameterizations (like Momentum Human Rig) for mesh prediction. 

---

4. What Can You Do with Meta SAM Models?

Across the ecosystem, common real-world applications include:

• Photo & video editing – object cutouts, background removal, regional effects, privacy blurring (faces/plates). 

• Content creation – precise masks for compositing, stylization, AR filters.

• Robotics & autonomous systems – robust object and region understanding in dynamic environments. 

• Scientific & mapping work – segmenting wildlife, buildings, roads, crops in large image sets. 

• Data labeling – using SAM/SAM 3 as a fast “auto-labeler” before training smaller, task-specific models (like YOLO) for real-time deployment. 

• 3D asset creation & analysis (SAM 3D) – quick meshes for AR/VR, games, sports analytics, and simulation. 

---

5. How to Try Meta SAM Models Yourself

You’ve got three main paths:

5.1 Browser playgrounds

• Segment Anything Playground (Meta AI demos): upload images or videos, prompt SAM 2 / SAM 3 and sometimes SAM 3D directly in a web UI. 

Great for: quick tests, screenshots, and understanding model behavior.

5.2 Official GitHub repos

Meta’s research repos include code + checkpoints:

• facebookresearch/segment-anything – original SAM. 

• facebookresearch/sam2 – SAM 2 for images + videos. 

• facebook/sam3 (and related repos) – SAM 3 for concept + visual segmentation. 

• facebookresearch/sam-3d-objects and sam-3d-body – SAM 3D models for objects and humans. 

Good for: developers who want full control or to integrate into pipelines.

5.3 Managed APIs and tools

Platforms like Roboflow, FAL.ai, and others expose SAM 1/2/3 and SAM 3D with hosted GPUs plus web playgrounds and REST APIs—useful if you don’t want to manage infrastructure. 

---

6. Licensing and Commercial Use

The original SAM 1 was released under Apache 2.0, a very permissive open-source license. 

Newer models (SAM 2, SAM 3, SAM 3D) are typically released under the SAM License, a custom Meta license for Segment Anything models:

• Code and weights are public, but

• Commercial and large-scale use must follow specific terms (and may have additional restrictions). 

Anyone planning to ship these in a product needs to read the exact license text in the respective repos or Meta pages.

---

7. Why the Meta SAM Model Family Matters

Taken together, the Meta SAM models are pushing computer vision toward:

• General-purpose “segment anything” engines instead of one model per task.

• Natural, prompt-based control, just like language models but for pixels.

• A full stack from 2D segmentation (SAM 1/2/3) to concept-level understanding (PCS in SAM 3) to single-image 3D reconstruction (SAM 3D). 

If you’re building an article hub or comparison site, you can think of “Meta SAM model” not as one model, but as a growing ecosystem:

SAM 1 → SAM 2 → SAM 3 → SAM 3D
from “click to get a mask” all the way to “say what you want, get 3D objects and scenes.”

---

Big-picture overview

---

2. How each one improves on the previous

SAM 1 → SAM 2

• SAM 1: “Click an object in a picture, get a mask.”

• SAM 2 adds:

  • Video support (not just images)

  • A memory system so it can track the same object across frames

  • Still driven only by visual prompts (no text yet)

If you want to cut something out of a video clip and follow it over time, SAM 2 is the jump from SAM 1.

---

SAM 2 → SAM 3

• SAM 2: great for “this object here, please segment & track it”.

• SAM 3 adds big new powers:

  • Text prompts – e.g. “all dogs”, “red cars”, “traffic lights”

  • Exemplar prompts – draw a box around one example; it finds all similar ones

  • Open-vocabulary: it’s not limited to a fixed label list

  • A more unified model that can detect, segment, and track all instances of the concept in one go

If you want “find every player in this match” or “all plants in this greenhouse” with one prompt, SAM 3 is the one.

---

SAM 3 → SAM 3D

• SAM 3: understands what and where things are in 2D (masks & tracks).

• SAM 3D adds:

  • Full 3D shape + texture from a single image

  • Two variants:

    • SAM 3D Objects for general objects and scenes

    • SAM 3D Body for detailed human meshes

  • Works perfectly together with SAM 3:

    • SAM 3: “segment this car/person”

    • SAM 3D: “turn that mask into a 3D model”

Use SAM 3D when you don’t just want a cut-out, you want an actual 3D asset for AR/VR, games, or analysis.

---

3. Which SAM model should you use?

• Only images, just need masks quickly → SAM 1 is simple and light.

• Video editing, tracking objects across frames → SAM 2.

• “Segment anything by concept” in images or videos (text prompts, multiple instances) → SAM 3.

• Need 3D objects or human bodies from photos → SAM 3D (usually combined with SAM 3).