Meta SAM 3 is the third generation of Meta’s Segment Anything family.
Where SAM 1 focused on click-based image segmentation and SAM 2 added video + tracking, SAM 3 goes one step further:

It lets you describe what you want (with text or examples) and then finds, segments, and tracks every matching object in images and videos.

Below is a structured, website-ready overview you can use directly on your Meta SAM 3 pages.

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1. What Is Meta SAM 3?

Meta SAM 3 is a unified vision model that can:

• Detect objects

• Segment them with pixel-accurate masks

• Track them across frames in short videos

using three kinds of prompts:

• Text prompts – short phrases

• Exemplar prompts – example regions/boxes

• Visual prompts – clicks, boxes, and masks (like classic SAM)

Unlike older models, SAM 3 works at the concept level. Instead of “segment this thing I clicked,” you can say:

• “All red cars”

• “Players in blue”

• “Solar panels on roofs”

and the model will find every matching instance in the scene and (for video) follow them over time.

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2. Key Capabilities of Meta SAM 3

2.1 Open-vocabulary text prompts

SAM 3 understands short noun-phrase prompts such as:

• “yellow school bus”

• “striped cat”

• “white delivery vans”

• “trees along the road”

From one phrase and one image (or clip), SAM 3:

• Detects all objects that match the concept

• Produces instance masks for each object

This is called Promptable Concept Segmentation (PCS) – you segment based on meaning, not just clicking.

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2.2 Exemplar-based segmentation

Sometimes a concept is easier to show than to name. SAM 3 supports exemplar prompts:

• Draw a box or provide a mask around one example object

• SAM 3 uses that as a visual template

• It finds all visually similar objects in the image or video

You can also add:

• Positive exemplars → “include things like this”

• Negative exemplars → “exclude things like this”

This is perfect for:

• Custom products or logos

• Brand-specific uniforms

• Objects with subtle visual differences

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2.3 Visual prompts (clicks, boxes, masks) – PVS mode

SAM 3 keeps the classic Promptable Visual Segmentation (PVS) from SAM 1 / SAM 2:

• Point prompts: positive/negative clicks on the image

• Box prompts: rough rectangle around the object

• Mask prompts: refine an existing mask

PVS is ideal when:

• You care about one specific instance

• You want ultra-precise boundaries

• You’re building an interactive editor where users click around

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2.4 Multi-instance & multi-concept segmentation

SAM 3 can handle many instances and multiple concepts:

• One text prompt → all instances of that concept

• Multiple prompts → different concepts in the same scene

Example:

• Prompt 1: “cars”

• Prompt 2: “bikes”

• Prompt 3: “pedestrians”

Now you get structured segmentation of the entire scene, ready for analytics or editing.

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2.5 Video segmentation & tracking

While your focus might be images, SAM 3 also works on short videos:

• Use text or exemplars on a video

• SAM 3 finds all instances in early frames

• Assigns stable IDs, then tracks them forward in time

This combines:

• Detection

• Segmentation

• Tracking

into one unified video system – useful for sports analysis, traffic monitoring, CCTV tools, and smart editing.

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3. How Meta SAM 3 Works (High Level)

Even without code, it helps to know the main building blocks:

1. Vision backbone

   • A large transformer that encodes each image or frame into a dense feature map.

2. Prompt encoders

   • Text encoder for short phrases.

   • Visual encoders for exemplar boxes/masks and click prompts.

3. Concept presence & segmentation heads

   • A presence head predicts if the concept actually exists in the scene.

   • Detection/segmentation heads output:

     • Bounding boxes or regions

     • Pixel-accurate instance masks

     • (For video) consistent IDs over frames

4. Training data (SA-Co)

   • SAM 3 is trained on a huge dataset with:

     • Millions of images and short videos

     • Around a billion segmentation masks

     • Millions of short concept phrases linked to regions

   • Includes “hard negatives” – phrases that should not match – which makes its open-vocabulary behavior much more reliable.

This combination is why SAM 3 is significantly more powerful for concept-level segmentation than previous models.

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4. Meta SAM 3 vs SAM 1 vs SAM 2 (Quick Comparison)

To position SAM 3 in your article, you can include a short comparison:

• Meta SAM 1

  • Images only

  • Visual prompts (clicks/boxes/masks)

  • Great for simple photo segmentation

• Meta SAM 2

  • Images + videos

  • Visual prompts

  • Adds streaming memory and object tracking

• Meta SAM 3

  • Images + videos

  • Text prompts + exemplars + visual prompts

  • Can detect, segment, and track all instances of a concept

  • Integrates with SAM 3D for 2D→3D workflows

Short summary sentence you can reuse:

“SAM 1 segments what you click, SAM 2 tracks what you click, SAM 3 understands what you describe.”

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5. Real-World Use Cases of Meta SAM 3

5.1 Content creation & editing

• Auto-segment subjects in photos or videos using text (“main dancer”, “car”, “sky”).

• Apply filters, color changes, or effects to only the segmented regions.

• Combine SAM 3 with generative models for controlled edits (e.g., restyle the background but keep the subject).

5.2 Sports & broadcast analytics

• “Players in red jerseys” → track them across the match.

• Separate goalkeepers, referees, and teams using text or exemplars.

• Use trajectories for heat maps, coverage zones, or tactic analysis.

5.3 Traffic & surveillance

• Segment and track cars, trucks, bikes, pedestrians from a single prompt.

• Count, measure flow, or detect unusual behavior with tracking data.

5.4 Mapping, aerial, and industrial

• “Solar panels”, “ships”, “buildings”, “containers” → concept prompts on aerial images.

• Use masks for GIS, infrastructure planning, and environment monitoring.

5.5 Dataset creation & 3D pipelines

• Prompt SAM 3 on massive image/video datasets to auto-label objects by concept.

• Feed masks into SAM 3D to get full 3D meshes of selected objects or humans.

• Use outputs to train lighter, task-specific models for real-time deployment.

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6. How to Access and Use Meta SAM 3

You can mention three main paths on your site:

6.1 Playground (no code)

• Meta’s Segment Anything Playground

• Upload images or videos

• Type a text prompt, draw exemplars, or click

• Get masks and tracks visually

Good for: demos, experimentation, and showing screenshots.

6.2 Official repos & libraries

• Use Meta’s SAM 3 code (or integrations in libraries like Ultralytics / Hugging Face)

• Run SAM 3 from Python:

  • Load a model

  • Pass an image + text prompt

  • Receive instance masks as tensors or polygons

Good for: developers building apps, tools, and pipelines.

6.3 Hosted APIs & SaaS tools

• Cloud platforms expose SAM 3 via:

  • Web dashboards

  • REST APIs

  • SDKs in Python / JS

Good for: teams who don’t want to manage GPUs but need SAM 3 at scale.

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7. Licensing & “SAM 3 Credits”

• SAM 3 and SAM 3D are released under the SAM license (Meta’s custom license).

• Meta doesn’t sell an official Meta SAM 3 credit
  credits come from third-party platforms that host SAM 3 and charge per image/second/3D job.

• If you self-host, your “cost” is GPU time + storage, not credits.

On your site you can link from this section to your separate “Meta SAM 3 Credit” and Meta SAM 3 Pricing pages.

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Meta SAM 3 vs SAM 1 and SAM 2 – How Each “Segment Anything” Model Evolved

The Segment Anything family has grown from a simple click-based image tool into a powerful, concept-aware system for images and video. Here’s how Meta SAM 3 compares to SAM 1 and SAM 2.

2. How Prompting Changes from SAM 1 → SAM 2 → SAM 3

SAM 1 – Visual prompts only

• You click, draw a box, or give a rough mask.

• SAM 1 returns one or a few pixel-perfect masks for the object you indicated.

• No text, no video.
  Best for: photo tools, background removal, manual labeling.

SAM 2 – Visual prompts + time

• Same visual prompts as SAM 1 (points/boxes/masks).

• Now works on videos and has a streaming memory.

• You click once in a frame → SAM 2 tracks and segments that object in future frames.
  Best for: video editing, sports clips, CCTV-style tracking when a human can click targets.

SAM 3 – Concepts, not just clicks

• Keeps clicks/boxes/masks, plus adds:

  • Text prompts (e.g., “red cars”, “goalkeepers”, “solar panels”)

  • Exemplar prompts (draw a box around one example → “things like this”)

• Can do Promptable Concept Segmentation (PCS):

  “Find, segment, and track every instance of this concept in the image or clip.”
  Best for: analytics, dataset creation, advanced tools that need “all X” instead of “this one object I clicked.”

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3. Images vs Video

• SAM 1

  • Images only, no tracking.

• SAM 2

  • Images + videos.

  • Strong when you want one object (or a small set) tracked interactively over time.

• SAM 3

  • Images + videos.

  • Strong when you want all objects of a type (all players, all cars, all panels) found and tracked with minimal prompting.

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4. Typical “Best Fit” for Each Model

• Choose SAM 1 if…

  • You just need fast image cut-outs and interactive segmentation.

  • Your use case is simple (thumbnails, product photos, manual labeling).

• Choose SAM 2 if…

  • You work a lot with video.

  • A human can click what matters, and you want the model to follow that object automatically.

• Choose SAM 3 if…

  • You want text + exemplar prompts.

  • You need to segment all instances of a concept in images and clips.

  • You plan to connect segmentation to SAM 3D and 3D workflows.

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