How to Use Meta SAM 3 Step-by-Step Guide for Image & Video Segmentation

Turn a simple click or short text prompt into a pixel perfect cutout that’s the power of Meta SAM 3. Once you learn how to use it, background removal, object tracking, and smart video masks go from "hours of editing" to "done in seconds."

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How to Use Meta SAM 3 (Segment Anything Model 3): A Practical Guide

Meta SAM 3 is Meta’s newest Segment Anything Model that can find and mask objects in images and videos using both text prompts (like “find all red shoes”) and visual prompts (clicks, boxes, existing masks). It’s built for everything from quick experiments in the browser to full production apps with an API and open-source code.

Below is a structured, step-by-step guide on how to use Meta SAM 3, even if you’re just getting started.

1. What You Can Do With SAM 3

SAM 3 can:

  • Segment objects in images (single shot)

  • Track and segment objects across video frames

  • Use text prompts like “solar panels”, “vehicles”, “red hat”, “box”, etc., to find all matching objects (called Promptable Concept Segmentation / PCS)

  • Use point / box / mask prompts (called Promptable Visual Segmentation / PVS) just like older SAM versions

  • Run fast enough on a good GPU to be practical for tools and apps (around tens of ms per image on strong hardware)

You can use it in three main ways:

  1. No-code: web playgrounds

  2. Low-code: hosted APIs

  3. Full-code: open-source repo on your own machine or server

We’ll go through all three.

2. Try Meta SAM 3 in the Browser (Easiest Way)

2.1 Meta Segment Anything Playground (official)

Meta provides a web demo called Segment Anything Playground where you can instantly try SAM 3: you upload an image or video, then use text or clicks to segment objects.

How it usually works:

  1. Open the Playground

    • Visit Meta’s Segment Anything Playground site in your browser.

  2. Choose media type

    • Pick Image or Video mode.

  3. Upload your file

    • Drag and drop an image/video, or choose one from your device.

  4. Pick a prompt type

    • Text: Type something like “vehicles”, “striped cat”, or “red backpack”.

    • Point: Click directly on an object to mark it as “positive” (you want that).

    • Box: Drag a rectangle around the object you want.

  5. View and refine masks

    • SAM 3 draws mask outlines around the objects it finds.

    • If something is wrong, add more clicks:

      • Positive clicks on areas you want included

      • Negative clicks on areas you want to remove

  6. Export the result

    • Download the image or segmentation masks, depending on what the UI offers (usually PNG masks or overlay images).

This is the best option if you just want to see what SAM 3 can do without installing anything.

2.2 Roboflow Playground (no-code + annotation)

Roboflow also offers a SAM 3 Playground where you can:

  • Upload an image

  • Use text prompts (“solar panels”, “vehicles”, “trailers”, etc.)

  • See all the masks SAM 3 predicts

  • Quickly convert those masks into labeled data for training other models

Steps are similar:

  1. Go to the SAM 3 page on Roboflow.

  2. Upload an image.

  3. Enter a concept (e.g., “vehicles”, “solar panels”, “boxes”).

  4. Inspect/adjust the segmentation results.

  5. Save masks as annotations if you’re building a dataset.

This is especially useful if your goal is to train your own model later and use SAM 3 mainly as a labeling assistant.

3. Use SAM 3 Through an API (Low-Code)

If you want SAM 3 inside your own app but don’t want to manage GPUs and servers yourself, you can call a hosted API.

3.1 Hosted SAM 3 API (example: Roboflow)

Roboflow lets you “fork” a ready-made SAM 3 workflow and instantly deploy an endpoint.

Basic idea:

  1. Create an account on a hosting platform that supports SAM 3 (e.g., Roboflow).

  2. Fork their SAM 3 workflow

    • This spins up an endpoint that runs SAM 3 on their infrastructure.

  3. Get your API key and endpoint URL

  4. Send requests from your app (Python, JavaScript, etc.) that include:

    • Your image or video frame (usually as a URL or base64 data)

    • Your prompt (text like “red car”, or visual prompts like box coordinates)

  5. Receive JSON response with:

    • Masks (as polygons, COCO-style segmentations, or per-pixel masks)

    • Confidence scores

    • Optionally bounding boxes/IDs

A super simplified Python pattern looks like:

 
from inference_sdk import InferenceHTTPClient client = InferenceHTTPClient( api_url="YOUR_API_URL", api_key="YOUR_API_KEY", ) result = client.run_workflow( "sam3-workflow", image="path_or_url_to_image", prompts={"text": ["vehicles"]} # or points, boxes, etc. )

(You’ll get real code snippets on the provider’s docs/dashboard; this is just the basic shape.) 

This is perfect if you’re building:

  • A web app where users upload images and choose a prompt

  • A backend service that automatically segments objects in uploaded photos

  • A pipeline that uses SAM 3 for labeling, then trains other models

4. Run Meta SAM 3 Locally from GitHub (Full-Code)

If you’ve got a machine with a decent GPU and want total control, you can set up SAM 3 from the official Meta GitHub repo.

4.1 What you’ll need

  • A computer with:

    • A modern GPU (16 GB VRAM is recommended for smooth use)

    • Python + PyTorch installed

  • Basic comfort with:

    • Git

    • The command line

    • Virtual environments (optional but helpful)

4.2 Typical setup steps

Exact commands will be in the facebookresearch/sam3 README, but the process is generally:

  1. Clone the repository

    • Use Git to download the SAM 3 repo to your machine.

  2. Install dependencies

    • Create and activate a virtual environment (optional but recommended).

    • Install Python packages listed in requirements.txt or the README (PyTorch, vision libraries, utility packages).

  3. Download model weights

    • Follow the instructions in the repo or on Meta’s SAM 3 page to download official checkpoints (model files).

  4. Run example scripts

    You’ll run commands like:

     
    python demo_image.py --input path/to/image.jpg --prompt "red hat"

    (Exact script names and flags depend on the repo.)

  5. Integrate into your own code

    • Import SAM 3 modules into your project.

    • Feed images/frames and prompts.

    • Get back masks and use them in your own pipeline (editing, analytics, etc.).

5. Choosing Prompt Types in SAM 3

SAM 3 supports two main prompt categories (plus hybrids):

5.1 Promptable Concept Segmentation (PCS) – text & examples

PCS is where SAM 3 gets really powerful:

  • You type natural language like:

    • “red hats”

    • “vehicles”

    • “solar panels”

    • “trailers”

  • SAM 3 highlights every object in the image or video that matches that concept.

You can also use example images or crops as a visual concept (“stuff that looks like this object”).

This is awesome for:

  • Labeling lots of similar things at once (cars, boxes, trees, etc.)

  • GIS / remote-sensing tasks (e.g., “buildings”, “trailers” in aerial imagery)

  • Quickly exploring what’s in an image without hard-coding categories

5.2 Promptable Visual Segmentation (PVS) – clicks, boxes, masks

This is the classic SAM workflow:

  • Point prompts

    • Click on the object.

    • More positive clicks to add regions.

    • Negative clicks to remove wrong parts.

  • Box prompts

    • Drag a rectangle around the object.

    • SAM 3 finds the precise shape inside that box.

  • Mask prompts

    • Feed an existing mask and ask SAM 3 to refine or expand it.

You can mix PCS + PVS too:

  • First, text prompt “vehicles” to grab all vehicles.

  • Then, use point or box prompts to refine a specific car that looks wrong.

6. Using Meta SAM 3 on Video

SAM 3 builds on the video memory system from SAM 2, so you can segment and track objects through time.

6.1 Typical video workflow

  1. Choose a reference frame

    • Often the first frame or a frame where the object is clearly visible.

  2. Prompt SAM 3 on that frame

    • Text: “red backpack”, “cyclist”, “bus”, etc.

    • Or visual: draw a box/click on the object.

  3. Enable tracking / propagation

    • SAM 3 uses a temporal memory to track that object across frames.

  4. Inspect the sequence

    • Scrub through the video.

    • Check for frames where the mask drifts or misses the object.

  5. Correct with extra prompts

    • Add positive/negative points on problem frames.

    • Let the model re-propagate corrections around that region.

  6. Export masks or edited video

    • Use generated masks for:

      • Background removal

      • Visual effects

      • Analytics (tracking positions, measuring movement)

7. Fine-Tuning SAM 3 (Advanced)

You don’t have to fine-tune SAM 3 to use it—it already works well out-of-the-box on lots of images.

But if it struggles on a very specific domain (for example, a special kind of medical scan or industrial camera), you can fine-tune:

  1. Collect a dataset

    • Images from your exact domain.

    • Segmentation masks labeled for the objects you care about.

  2. Choose a training pipeline

    • Meta’s GitHub repo and research paper describe how they fine-tuned using their SA-Co dataset (Segment Anything with Concepts).

  3. Train on GPU(s)

    • Use a script from the repo or your favorite training platform.

    • Adjust learning rate, batch size, and epochs based on your hardware.

  4. Evaluate & iterate

    • Test fine-tuned SAM 3 on a validation set.

    • Compare to the base model to see if it improved your task.

This is mostly for researchers and companies, but it’s how you squeeze maximum performance out of SAM 3 for niche tasks.

8. Licensing and What You’re Allowed to Do

SAM 3 is released under a custom SAM License from Meta (not a simple MIT/Apache license).

High level:

  • You can download and use the model weights.

  • You can build apps and services on top of it, as long as you respect the license terms.

  • The exact legal rules (for example, commercial usage and any restrictions) are listed on Meta’s official SAM 3 page and GitHub.

If you’re building a serious product, always:

  • Read the latest license text on Meta’s site and the GitHub repo.

9. Practical Tips for Getting Good Results

No matter whether you’re using a playground, API, or local setup, these tips help:

  1. Give clear prompts

    • For text: be specific (“yellow construction helmet” > “helmet”).

    • For points: place clicks on distinctive parts of the object, not edges.

  2. Use multiple prompts

    • Add a few positive points across the object.

    • Use negative points to carve out areas that shouldn’t be included.

  3. Pick decent resolution

    • Don’t downscale so heavily that small objects vanish.

    • For video, balance resolution vs. processing cost.

  4. Expect to refine video masks

    • Long clips will always have a few bad frames.

    • Plan to correct key frames and re-run propagation instead of expecting perfection on the first try.

  5. Start in a playground, then move to code

    • Test your idea visually first.

    • Once you like the behavior, switch to API or local code and replicate that prompt pattern there.

10. Summary

Using Meta SAM 3 basically comes down to three levels:

  • No-code:
    Use Meta’s official Segment Anything Playground or Roboflow Playground to upload images/videos, type prompts, click on objects, and export masks.

  • Low-code:
    Call a hosted SAM 3 API from your app, sending images and prompts and receiving segmentation masks as JSON.

  • Full-code:
    Clone the facebookresearch/sam3 GitHub repo, download checkpoints, and run SAM 3 locally in your own Python/PyTorch projects.