Meta SAM 1 – The Original Segment Anything Model Explained

Meta SAM 1 (often just called “SAM”) is the original Segment Anything Model released by Meta AI in 2023. It was one of the first big “foundation models” for computer vision, built specifically for image segmentation.

Instead of training a new model every time you want to segment something, SAM 1 was designed as a general tool that can:

• Take any image

• Receive a simple prompt (clicks, boxes, or masks)

• Return a pixel-accurate mask for the object you meant

You can think of SAM 1 as the “base level” of the SAM family (SAM 1 , SAM 2 , SAM 3 , SAM 3D).

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1. What is Meta SAM 1?

Meta SAM 1 is a promptable image segmentation model:

• Input:

  • An image

  • A prompt (points, bounding boxes, or rough masks)

• Output:

  • One or more segmentation masks showing the exact shape of the object(s)

Key idea: it should be able to segment “anything” in any image, even if the model never saw labels for that exact object category during training.

This “segment anything” behavior comes from two things:

1. A huge dataset (SA-1B).

2. A flexible architecture that can handle different kinds of prompts.

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2. The SA-1B Dataset – Why SAM 1 is So Strong

To train SAM 1, Meta created a massive dataset called SA-1B (Segment Anything 1 Billion):

• ~11 million images

• ~1.1 billion masks (segmentations)

• Collected and refined with a human + model-in-the-loop system

Because SA-1B is so huge and diverse, SAM 1 learned to:

• Generalize to new objects and scenes

• Work on domains it wasn’t explicitly trained for (e.g., medical, satellite, etc., though for critical tasks people still fine-tune special versions)

This is what makes SAM 1 feel like a foundation model instead of a task-specific network.

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3. How SAM 1 Works (High-Level)

SAM 1 has three main parts:

1. Image encoder

   • A big Vision Transformer (ViT) that turns the image into a feature map.

2. Prompt encoder

   • Encodes points, boxes, and masks into the same feature space:

     • Points: each click is a coordinate with a label (foreground/background).

     • Boxes: coordinates for top-left and bottom-right corners.

     • Masks: downsampled binary masks.

3. Mask decoder

   • Combines the image features + prompt features.

   • Predicts one or more segmentation masks and their quality scores.

The cool part is that SAM is promptable:
change the prompt (other clicks, another box) → get a different object segmented, without retraining the model.

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4. Types of Prompts in SAM 1

SAM 1 is purely visual-prompt based (no text prompts yet):

1. Point prompts

   • Positive points on the object you want.

   • Negative points on things you don’t want.

   • Great for quick interactive editing: click-click-click until the mask looks perfect.

2. Box prompts

   • Draw a rough rectangle around the object.

   • SAM figures out the exact shape inside that box.

3. Mask prompts

   • Use an existing mask as input.

   • SAM refines or corrects it (useful when combining with other models).

You can mix prompts, like start with a box then add a few clicks to fix edges.

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5. What SAM 1 is Good At

5.1 Interactive image editing

Creators and designers use SAM 1 for:

• Background removal:
  Click the subject → get a mask → delete background → add new background.

• Selective effects:
  Apply color grading, blur, or glow to only the segmented region.

• Cut-and-paste compositions:
  Copy segmented objects into other images or designs.

5.2 Data labeling & annotation

AI practitioners use SAM 1 as a labeling assistant:

• Speed up annotation for segmentation datasets.

• Pre-label images, then humans just correct mistakes.

• Generate initial masks for training smaller, task-specific models.

5.3 Research & specialized domains

Even though SAM 1 is general, researchers:

• Adapt it to medical images (MedSAM, etc.).

• Use it on satellite/drone imagery.

• Combine it with detection models (e.g., YOLO) to refine object outlines.

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6. Strengths and Limitations of SAM 1

Strengths

• Works on almost any image type.

• Requires very few prompts (often just a single click).

• Has great edge detail, hugging object boundaries closely.

• Can produce multiple masks per prompt to give choices.

Limitations

• No text prompts – it doesn’t understand “red car”; you must click or draw boxes.

• Only supports images, not videos (video support really appears in SAM 2 and SAM 3).

• Complex scenes with many overlapping objects might need extra clicks to get it right.

• For highly specialized domains, fine-tuning or domain-specific versions perform better.

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7. SAM 1 vs SAM 2 vs SAM 3 (Quick Comparison)

To place SAM 1 in the full SAM family:

• SAM 1

  • Images only

  • Visual prompts (points/boxes/masks)

  • Great for interactive segmentation

• SAM 2

  • Images + videos

  • Visual prompts

  • Adds tracking and streaming memory for video

• SAM 3

  • Images + videos

  • Visual prompts and text/exemplar prompts

  • Can segment and track all instances of a concept (open vocabulary)

So SAM 1 is the simplest and original version, but it kicked off everything that came later.

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8. How to Use Meta SAM 1

8.1 No-code tools

Many web tools integrate the original SAM model:

• Upload an image

• Click on the object

• Download the mask or cut-out

They’re great for people who just want a fast background remover or smart selection tool.

8.2 Code / GitHub

For developers:

1. Clone Meta’s segment-anything GitHub repo.

2. Download a SAM checkpoint (e.g., vit_h).

3. In Python:

   • Load the model.

   • Pass an image + prompt.

   • Get the mask tensor, convert to PNG, etc.

This lets you build:

• Batch segmentation pipelines

• Plug-ins for editors

• Data-labeling tools

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9. Why Meta SAM 1 Still Matters

Even with SAM 2 and SAM 3 available, SAM 1 is still important because:

• It’s lighter and simpler if you only need image segmentation.

• It’s widely supported in open-source tools and tutorials.

• It introduced the promptable segmentation idea and the SA-1B dataset, which other SAM models build on

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Meta SAM 1 vs SAM 2 vs SAM 3 – Key Differences Explained

2. Meta SAM 1 – The Original “Click and Segment” Model

What it is:

• First Segment Anything Model (2023).

• Designed for image segmentation only.

How you prompt it:

• Point prompts: click on the object (positive) and background (negative).

• Box prompts: draw a rough rectangle around the object.

• Mask prompts: give a rough mask to refine.

Strengths:

• Very fast for interactive image editing.

• Great edge detail around objects.

• Powered by the huge SA-1B dataset (~11M images, 1.1B masks), so it generalizes to many object types.

Limitations vs newer models:

• No video support.

• No text prompts – you can’t say “all red cars”, you must point or box.

• No built-in notion of “all instances of concept X”, only what you prompt visually.

Best when:
You just need high-quality masks in single images with clicks/boxes (background removal tools, photo editors, annotation UIs).

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3. Meta SAM 2 – Segment Anything for Images and Videos

What it adds over SAM 1:

1. Video support

   • Works on image sequences and videos, not just single images.

   • Can track objects across frames using the same visual prompts.

2. Streaming memory

   • Uses a special architecture with memory over time, so it can process frames as a stream and keep track of what it has already segmented.

3. Better interactive workflows

   • Still uses points, boxes, masks as prompts.

   • You can correct masks on one frame, and improvements propagate to nearby frames.

What doesn’t change:

• Still no text prompts.

• Still relies on visual prompts only (like SAM 1).

Best when:

• You want to cut out or track specific objects over time in video:

  • Video editing (track a person, car, or product).

  • Simple sports or CCTV analysis.

  • You’re happy giving clicks/boxes instead of text.

Think of SAM 2 as:

SAM 1 + video + tracking + streaming memory.

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4. Meta SAM 3 – Segment Anything with Concepts

Big jump from SAM 2:

SAM 3 keeps the good parts of SAM 1 and SAM 2, and adds open-vocabulary concept understanding.

4.1 New prompt types

In addition to points/boxes/masks, SAM 3 supports:

• Text prompts

  • Short phrases like “yellow school bus”, “players in blue”, “solar panels”.

  • It finds all instances that match the text in an image or short video.

• Exemplar prompts

  • You draw a box or give a mask around one example.

  • The model segments everything that looks like that example.

  • You can combine positive and negative exemplars to control what’s included.

It still supports click prompts (PVS) for fine control.

4.2 Promptable Concept Segmentation (PCS)

The signature feature of SAM 3 is PCS:

Given an image or video + a concept prompt (text/exemplars), SAM 3 detects, segments, and tracks every instance of that concept.

Examples:

• “All red cars” in a traffic video.

• “Goalkeepers” in a football clip.

• “Shipping containers” in a drone image of a port.

It does detection + segmentation + tracking in one unified model, instead of stitching different models together.

4.3 Data & performance jump

SAM 3 is trained on a newer, huge dataset (often called SA-Co):

• Millions of images and short videos.

• Around a billion masks linked to millions of short concept phrases.

• Contains “hard negatives” (phrases that should not match a mask).

Because of this:

• On concept-segmentation benchmarks, SAM 3 is roughly 2× better than previous open-vocabulary approaches.

• It also improves classic visual segmentation (click/box) compared to earlier SAM versions.

Best when:

• You want to build tools based on meaning, not just clicks:

  • “Find all X in this dataset/video.”

  • Dataset labeling by concept.

  • Smart analytics on sports, traffic, crowds, or aerial imagery.

• You want one model for:

  • Images ✔

  • Videos ✔

  • Text & visual prompts ✔

  • “All instances of this thing” ✔

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5. Side-by-Side: Which SAM Should I Use?

Use SAM 1 if:

• You only care about images, not video.

• You want a lighter, simpler model for interactive cut-outs.

• You’re building basic tools like background removal or quick labeling.

Use SAM 2 if:

• You need video support and tracking,

• But you’re comfortable giving visual prompts (clicks/boxes) instead of text.

• Ideal for: video editors, basic sports/CCTV tools, frame-by-frame labeling automation.

Use SAM 3 if:

• You want open-vocabulary control with text + exemplars.

• You need all instances of a concept in images or short videos.

• You plan to combine segmentation with 3D (via SAM 3D).

• Ideal for: advanced analytics, dataset creation at scale, semantic video tools, AR/VR and 3D pipelines.