Meta SAM 2 – Segment Anything for Images and Videos

Meta SAM 2 is the second generation of Meta’s Segment Anything family and the first one truly designed for both images and videos.
Where SAM 1 focused on “click and segment” in still images, SAM 2 adds tracking and streaming, so you can follow objects across frames with simple prompts like clicks and boxes. 

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1. What Is Meta SAM 2?

Meta SAM 2 is a promptable visual segmentation model for:

• Images – like SAM 1

• Videos – short clips and long streams

You give it:

• Visual prompts – points, boxes, or masks

And SAM 2 returns:

• Pixel-accurate masks of the objects you asked for

• Tracks over time when you’re working with video

Meta describes SAM 2 as a “foundation model for image and video segmentation, built to support interactive editing and real-time style applications.

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2. Key Features of Meta SAM 2

2.1 Unified image + video segmentation

Unlike SAM 1 (images only), SAM 2:

• Works on single images and full videos

• Uses one architecture for both, so you don’t need separate models

• Keeps segmentation quality similar to SAM 1 on images, while adding time awareness for video

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2.2 Streaming memory for long videos

A core innovation in SAM 2 is its streaming memory system:

• Frames are processed incrementally (streaming), not all at once.

• The model maintains memory tokens that carry information from previous frames.

• This lets SAM 2 segment and track objects over longer videos with reasonable GPU memory.

This is what makes SAM 2 suitable for:

• Real-time-ish applications (video editing previews, camera feeds)

• Long-form content, not just short clips

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2.3 Visual prompts: points, boxes, and masks

Like the original SAM, Meta SAM 2 is promptable but still visual-only (no text):

You can guide the model with:

• Points

  • Positive clicks on the object you want

  • Negative clicks on background or unwanted regions

• Bounding boxes

  • Rough rectangle around the target object

• Masks

  • Use an existing mask as input for refinement

These prompts can be applied at a single frame, and SAM 2 will:

• Segment that object precisely

• Track it across future frames using its memory and temporal features

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2.4 Object tracking across frames

SAM 2 is built to segment and track:

• Once you prompt an object in one frame, it gets an ID.

• As the video progresses, SAM 2:

  • Predicts that object’s location

  • Updates its mask

  • Keeps the same ID through motion, partial occlusions, and viewpoint changes

This turns simple clicks into full object tracks you can use for:

• Video editing (mask layers over time)

• Sports & analytics overlays

• Training data for tracking models

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2.5 SA-V dataset – large-scale video segmentation

To train SAM 2, Meta introduced SA-V, a huge dataset for video segmentation built with a model-in-the-loop engine:

• Starts with a model like SAM

• Humans correct and refine segmentations

• Loop repeats to create high-quality masks at scale

SA-V gives SAM 2:

• Better understanding of motion and temporal consistency

• Strong performance on multiple public video segmentation benchmarks

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3. Meta SAM 2 vs SAM 1 vs SAM 3

To position SAM 2 within the whole family:

3.1 Compared to SAM 1

• SAM 1 – images only, visual prompts, no tracking

• SAM 2 – images + videos, visual prompts, tracking + streaming memory

If you’re only doing still images, SAM 1 may be enough.
If you work with video, SAM 2 is the natural upgrade.

3.2 Compared to SAM 3

• SAM 2

  • Prompts: visual only (points/boxes/masks)

  • Task: interactive segmentation + tracking

• SAM 3

  • Prompts: text + exemplars + visual

  • Task: Promptable Concept Segmentation (PCS) – find all instances of a concept, plus PVS like SAM 2

So SAM 2 is best if you:

• Don’t need text prompts

• Want a solid, efficient model for visual-only segmentation and tracking

SAM 3 is better if you:

• Want to say things like “all red cars” or “all players”

• Need concept-level segmentation plus video tracking

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4. Real-World Use Cases of Meta SAM 2

4.1 Video editing & post-production

• Track a subject once (person, car, product) with a few clicks.

• Automatically generate masks across frames.

• Apply color grading, blur, or VFX only to the segmented subject or background.

Great for: YouTube creators, short-form video editors, and video tools that want AI masking.

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4.2 Sports and broadcast

• Click players or the ball in a key frame.

• Use SAM 2 to propagate masks through the clip.

• Build heat maps, highlights, and tactical diagrams using tracked positions.

SAM 2’s temporal consistency is particularly useful for sports analytics and overlays.

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4.3 Surveillance and traffic analysis

• Segment and track vehicles, people, bikes in CCTV feeds.

• Count objects, measure dwell time, or detect unusual activity based on motion.

Because it uses visual prompts, SAM 2 fits well in semi-automatic pipelines where operators choose what to track.

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4.4 Data labeling for video datasets

• Label a few frames with clicks/boxes/masks.

• SAM 2 extends those masks across many frames.

• You get high-quality tracks and segmentation masks as training data for other models.

This can dramatically reduce manual labeling time for segmentation & tracking datasets.

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5. How to Try Meta SAM 2

5.1 Official SAM 2 repository

Meta released SAM 2 at facebookresearch/sam2 on GitHub, with:

• Pretrained weights

• Example notebooks

• Scripts for image and video segmentation

5.2 Segment Anything Playground

Meta’s Segment Anything Playground lets you try SAM 2 in the browser:

• Upload images or short videos

• Click on objects

• Watch masks and tracks update interactively

5.3 Third-party integrations

Libraries and platforms (e.g., Ultralytics, Roboflow, and others) have added SAM 2 support, letting you:

• Run it from simple Python APIs

• Use it in web UIs for annotation and editing

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6. Strengths and Limitations of Meta SAM 2

Strengths

• Handles both images and videos with one model.

• Adds streaming memory so you can work on longer clips.

• Great for interactive video editing and semi-automatic labeling.

• Builds directly on the success of SAM 1’s segmentation quality.

Limitations

• No text prompts – you must use clicks, boxes, or masks.

• Concept-level “find all X in the scene” is better covered by SAM 3.

• Real-time performance still depends heavily on GPU power and resolution; on low-end hardware, it’s mostly offline/batch.

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Meta SAM 2 vs SAM 1 vs SAM 3 – Which Model Should You Use?

2. Meta SAM 2 vs SAM 1

2.1 Core difference

• SAM 1:

  • Built for images only.

  • Great at interactive segmentation with clicks/boxes.

  • No notion of time or tracking.

• SAM 2:

  • Built for images and videos.

  • Keeps SAM 1–style segmentation quality.

  • Adds streaming memory and object tracking over frames.

👉 If you move from static photos to video clips, SAM 2 is the natural upgrade from SAM 1.

2.2 Prompts

Both use visual prompts only:

• Points (positive/negative)

• Bounding boxes

• Masks

Neither SAM 1 nor SAM 2 understands text prompts like “all red cars.” You still interact by clicking or drawing.

2.3 Use cases comparison

• Choose SAM 1 when:

  • You only work with photos.

  • You need fast, lightweight segmentation (e.g., background removal, quick annotation).

  • You don’t care about tracking or video.

• Choose SAM 2 when:

  • You work with video: editing, sports, surveillance, etc.

  • You want your clicks to propagate as masks across frames.

  • You want one model to handle both image and video in your app.

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3. Meta SAM 2 vs SAM 3

Now compare SAM 2 to the newer, “smarter” SAM 3.

3.1 Prompting and concepts

• SAM 2:

  • Visual prompts only (points, boxes, masks).

  • Great if a human is in the loop clicking things.

  • Doesn’t understand language concepts—only what you show.

• SAM 3:

  • Text prompts (“yellow school buses”, “players in blue”).

  • Exemplar prompts (boxes/masks around one example object).

  • Also supports points/boxes/masks for fine control.

  • Can do Promptable Concept Segmentation:

    “Find and track all instances of concept X in this image or video.”

👉 SAM 3 lets you describe what you want; SAM 2 needs you to click what you want.

3.2 Tasks and intelligence level

• SAM 2:

  • Best for: “Track this object I clicked” across a clip.

  • More like a super-powered, promptable roto/track tool.

• SAM 3:

  • Best for: “Find every X and follow them” (all cars, all players, all boxes).

  • Combines detection + segmentation + tracking + language in one model.

If you’re building smart analytics (sports analysis, traffic stats, dataset labeling by category), SAM 3 is usually the better choice.
If you’re building a video editing tool where users click specific things, SAM 2 might be simpler and cheaper.

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4. Performance & Complexity

4.1 Model complexity

• SAM 1 – simplest; image-only; easiest to run.

• SAM 2 – more complex than SAM 1 because of:

  • Streaming memory

  • Video handling

• SAM 3 – most complex; adds:

  • Text encoder

  • Exemplar handling

  • Concept heads for “presence” and multi-instance detection.

In practice:

• SAM 1 → lightest, good for many real-time image tools.

• SAM 2 → medium-heavy, tuned for video workloads.

• SAM 3 → heaviest, best reserved for servers or strong GPUs.

4.2 Typical usage pattern

• SAM 1 → image cutout tools, annotation UIs.

• SAM 2 → video editing plugins, annotation of clips, semi-automatic tracking.

• SAM 3 → high-level analytics (find all objects of type X), dataset mining, open-vocabulary segmentation, and 2D→3D workflows with SAM 3D.

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5. Which SAM Should You Choose?

You can summarize this on your site like:

• Pick SAM 1 if you:

  • Only segment photos

  • Want the simplest and most lightweight option

• Pick SAM 2 if you:

  • Need video + tracking

  • Are happy with visual prompts (clicks/boxes)

  • Are building editing or semi-automatic labeling tools

• Pick SAM 3 if you:

  • Want text and exemplar prompts

  • Need to find all instances of a concept in images/videos

  • Plan to connect segmentation to 3D (via SAM 3D) or advanced analytics